UiPath Stock

Good morning, everyone, and welcome to UiPath Investor Day. We are excited to have so many of you here with us in Las Vegas for what's shaping up to be our best Fusion yet.

Just a quick reminder, this presentation will include forward-looking statements and non-GAAP financial measures. Please refer to our SEC filings for disclosures and reconciliations. We have a great lineup for you today. We'll take you from the vision and the opportunity ahead to the innovation in our product road map, how we're going to market with our customers and driving value and ultimately, the financial model. We'll bring everyone back together at the end for Q&A, followed by an investor reception. We have a lot to share.

So with that, I will turn it over to Daniel for the company vision and the market opportunity ahead.

Thank you Allise. Hello, everyone. It's really great to have all of you here in Vegas. I'm incredibly excited about what we are going to show this week. I think this is going to be the most consequential event that we ever put here. You remember couple of years ago, we were talking about the ACT II of this company. And now you are going to see this ACT II emotion, customer stories, huge innovation pipeline that we are going to showing. And that's going to reflect really the transformation of our company.

But first, a quick recap of what happened since our IPO. Look, we have built a really solid business. Our revenue grew 3.5x since our IPO. We are crossing this year $2 billion in ARR. In the same time, we considerably increased our profitability and the cash generation. I think I am right to call UiPath like a scale company, and we have transformed our go-to-market operating model. And that gives us a solid foundation to build on our second act to build on the future. I think it's very interesting to see how we got there and what's in front of us. And just to put things in perspective.

If we think of the automation in general, automation of business processes, that was really a big theme from, I think, the introduction of computers. Maybe that was the biggest theme. And what was very interesting that happened like around 10, 12 years ago, this RPA was becoming an emerging trend. And what was the cool thing about RPA? It could have connected disparate systems. In a way, many companies had in-apps automation stories. But RPA, I think, was one of the few successful technologies that can cross the boundaries of silos. So it was -- I think the story of API automation and RPA grew kind of in the same time. And today, I think they still -- both technologies have a strong place in the automation toolkit.

But then what's going on today? It's the advent of AI, I think makes even much more clear to all the enterprises that they can reap enormous benefits by bringing AI and automation together. But that raised really the question, how this is possible. And what this presentation will show you and what Raghu will go into much greater level of details. Orchestration is an essential feature foundation mechanism to deliver AI at scale into enterprise processes. So I'm not talking here about CoWorks, Copilots type that increase personal productivity. I mean how you can bring AI in the context of enterprise processes and really harvest the huge increase in productivity that is basically the promise of AI.

So what we are doing ourselves because I want to make sure that I made clear, orchestration, it's a fundamental trend. That's not necessarily associated with us, UiPath. Orchestration, it's a big trend. You cannot -- I can make a statement that I'm staying behind it. You cannot really deliver AI in enterprise without orchestration and governance to basically offer the guardrails around AI. But how we are doing in our -- in this world, how do we capture this trend? It was kind of a natural expansion to us. Even I said it multiple times from the beginning of the inception of our company. RPA was for us, really a gate to get into the big enterprise game. We got there via RPA, but then we extended. Our goal is always to provide a full platform to offer end-to-end process automation.

And this is what we basically announced if some of you have been here like in around 2019, when we showed our big platform, and we call it like the tapestry that included not only RPA, but included APIs, included process and task mining because discovery, it's a big thing and included the intelligent document processing. And then 2 years ago, we really announced our foray into orchestration. And I think it's important is that we make this distinction. Process orchestration, it's not the same thing as agentic orchestration. When people say agentic orchestration, they mean a swarm of agents that have the same goal, and they can act with a lot of agency to pursue that goal.

When we say process orchestration, we mean literally complex processes that -- and you have an orchestrator that have to coordinate between different type of actors, which are humans, different type of agents, different type of automations. You need to allocate machines to run these automations. You need to allocate credentials. You need to offer security guardrails. You need to offer a very complete governance. This is basically an orchestration platform. And we made a really bold move 2 years ago to build our orchestration on a new workflow engine.

And I think our approach to orchestration is really showing a huge technology leap because as we said a few times, the core engine that offer the durability of our orchestration is a technology, it's an open source technology called Temporal that is here for a while. It's an extremely resilient and powerful technology. It's a big shift in a way. We said our orchestration is like Generation 3 technology of orchestration that is out there. And I think we are the only kind of scaled company that has built an orchestration engine that offer different -- offer business process orchestration on this temporal engine. So that gives us a really good technical advantage.

And you can see nowadays, we have, I think, one of the most complete orchestration and automation platform in the business. And this platform is really required, not necessarily our platform per se, but a platform that offers orchestration and automation is really the foundation to deliver enterprise AI. And it's not only me that is saying this. I have Gartner that just released, I think, a few days ago, their new Magic Quadrant in business orchestration and automation technologies. Yes, look here. It's -- to me, what makes this Magic Quadrant special. If you look at this, besides the usual suspects, you see IBM, Microsoft, SAP, AWS, Salesforce, okay? They participated in this. They competed for this Magic Quadrant, and you can see where they are positioned. And then look where we are positioned into this one. I think it's a huge achievement for us.

And given that we -- our orchestration is relatively new, it even more speaks to the level of sophistication that we brought into our platform. And you will see going forward, this level of sophistication. I think it's also very important to mention that we are not only a leader in this BOAT, but we are a leader in RPA. We are a leader in IDP, and we are a leader in test automation. And all of this contribute to our leadership in BOAT. This BOAT is an overarching technology approach that really offer everything that is needed to do end-to-end process orchestration and automation. But it's also interesting to -- I want to get more clarity why this orchestration and automation is -- it's so powerful and it's fundamental in order to deliver AI.

One of the first argument that comes to mind is if you look at the recent I think, challenges around AI agents that got outside their boundaries and they attack different enterprise systems. I think at this point, it's becoming clear to everybody that this technology is not ready yet. Two, I'm just putting a swarm of agents. I will give them a goal like improve my invoice processing and they will run the business. I don't think any same customer is thinking in doing this. But in the same time, the real question is, how do you benefit of the power? This is also an extremely powerful technology. And nobody, I think, is denying that AI, it's a huge secular trend that will change industries and our way we work.

So to me, our platform facilitates this paradigm that is really what resonates a lot with our customers, where we put AI in the context of the business. We give AI really the understanding of the business, but let them propose, let them draft, let them suggest how we should run and put people in the seat where they decide. And then on -- upon people's decision, our framework, our orchestration can run the automations in a very predictable way. And look, I think when I'm speaking with customers, I bring to them a series of points that I think resonates with everybody. And one of the interesting point here is when you have work that can be described in rules. And this work should be extremely reliable and predictable. Why you are not using software to run that work? I think it's kind of a no-brainer today.

And everybody starts to understand whatever you can put in software, you put in software. It's 100% reliable. I use this word exactness, which is so powerful. A payment, think about you need to have -- you need to make your payment. Even if AI is going to be 99.9% accurate, it's still you need to be 100% accurate. It's not enough to -- it's not enough for regulatory perspective. And the fact that the tool can do something, it doesn't mean that you must use that tool for something. And I think right now, it's also important to think of this -- there is an asymmetry very interestingly into deploying AI in enterprise.

Deploying AI agents, it's actually not less complicated or faster than a year ago. But deploying determinism, exactness, it's way more faster because we are using coding agents in design time to print automations. And we get the best of both worlds. Automations work with exactness and they are tokenless. It's always -- it's impossible to run something that requires token as fast and as predictable with something that is software and runs purely deterministic. So that leads me to more like thinking what's really the problem with the enterprise AI adoption?

But I know you will tell me, AI is vastly adopted. Everybody have CoWorks or Copilot. I guess, this is personal productivity. I mean really AI that works autonomously in the context of enterprise processes. I think there are many pilots, and I think most of them kind of fail to graduate to production. And the real problem of AI in enterprise, it's not that AI is not reasoning well because I have many reasons to believe that AI can reason better than most people. The main difference between an AI model and a human is that AI doesn't learn on the job. Now you tell me AI has memory. Yes, memory is not the same as learning.

AI will need the manual. It's like every process, every business you have will have a full detailed manual of how your business operates. No company in the world has this manual and AI needs this manual. This is really the biggest difference today, and this is the reason that AI can fail completely unpredictable. And look, this is what we call. We call -- this is the missing. It's the process context. I know that many people are talking about enterprise context, but I think we want to be precise. And in our world, we are bringing here to the picture a way to capture the process context, which is the way that the work happens in operations, in business processes.

And now why this information doesn't exist? Why someone cannot create this manual? Look, the problem is that information is in so many places. It's in -- think about, it's in slack, messaging, e-mails, is in different system of records. And it's also in people's mind. And it was kind of -- many people try to capture this information and you use very expensive business consultants. But the problem is this information gets stalled extremely fast because business evolves very fast. Even in the automation world, you need to keep pace with new regulations, new improved operating model. So not only that you need to give a manual to AI, you need to keep this manual up to date for the AI to run really work.

And this is basically one of the biggest innovation that we are going to introduce here. And again, Raghu will talk in details about it. It's what we call the map of work, which is really our enterprise context, around our process context that the AI needs to operate in the context of enterprise processes. My favorite metaphor is to think of this. You have the maps and orchestration and automations are the rails. And that gives someone that has to go from point A to point B needs to have both in order to -- if you go and you have a huge system of rails, but you don't have any map, you don't have any information how to use that system, you are going to be lost. So these things have to -- they have to coexist. So we are introducing a way to capture the information from existing systems and keep it up to date.

And if you think, this is basically complete our platform. We have the foundation that offers security and observability and governance. And we have tremendous investments in task automation. We are a leader in process orchestration as well. And now on the top of it, we build this map of work that gives the AI the keys to your enterprise in order to operate confidently and secure. I want to also make -- this slide is kind of busy, okay? But what I want to try to show you here is this platform that we have built, it's incredible complex. It's not something that you can write codes in 2 afternoons. And this is a platform that is capable of running predictable your enterprise workloads. This is a huge run time that we are talking. I mean any automations that you print in the world runs on this run time with scalability, with failover, with predictability, built in. That's a huge offering.

It's 10 years in the making, thousands and thousands of customers that [indiscernible], it's huge number of bugs that we fix every day on this in order to make. Guys, we have hundreds and maybe more than 1,000 engineers building this platform. We are also built with Claude code. But that's not enough. You need to have the validation of customers. You need to scale it. You need to see it to withstand the storms, when someone attacks it. it's a huge effort that we put it underneath. And this is why right now, we believe we are operating actually in a much larger TAM because this business orchestration and automation is -- it's really the foundation to deliver AI in enterprises.

And again, I'm not saying it's our approach unique and the only one. But I think I can make this prediction that most of the successful AI implementations in the context of enterprise process will have to have a foundation that offers them the orchestration and automation and also the map of work that we showed here. And that give us basically some solid arguments why we can win in this world. And look, number one, we've built, always said our platform is agnostic and it's open and it's secure. But what it means if you feel many workloads happen across multiple systems. And I keep hearing about customers.

Look, I cannot put my -- if I have to orchestrate between Salesforce and Epic, for instance, both have some kind of orchestration engine, but that will require to put the data from one into another or make some compromises. Many people will feel comfortable having an agnostic orchestration and automation engine that connects equally well to the system of records that are out there. And this is what we offer since our RPA era. And this is a platform that can really deliver right now the most complex end-to-end process automation, which I think it's very few companies can really like this Gartner Magic Quadrant proves it's a very rare occurrence in the business to have such a powerful platform.

And also, very importantly, we are not destructive to the enterprise stack. We respect the investments that people have in their enterprise stack, and we integrate there. We are a citizen that work very well in the existing enterprise stack. And having the power to deliver a horizontal platform that can easily scale into different verticals. I think this is a tremendous advantage. And I'll make a very quick remark here. You can go into a particular process, build completely the entire stack without having a horizontal platform that doesn't scale to the next process.

Having a horizontal platform gives you the certainty of the reliability built in, in this run time, and you can print verticals much faster on this one. And that was, in a way, always our model. It was land and expand. So it makes it same easy for an enterprise to adopt in a vertical, and we are coming with a few vertical approaches that you'll see later and then expand to different other verticals. And of course, I mentioned before that coding agents create right now this asymmetry. And in a way, this is one of the best marriage between AI and automation.

AI fixes the 2 biggest flow of automation. Number one was how difficult was to implement actually to discover what are the processes best to implement and discover all the exceptions we are fixing it with our cartographer agent. To print the automation, you need huge investments. To implement, now it's basically almost free. Thanks to AI and also the maintenance of automation because always people say, if any change happens into an upstream system, the automation will break. Yes, that's true. But nowadays, for every exception, then an automation raise, I can bring a coding agents, analyze and fix it on the fly. So basically, we fixed all these 2 flows of automation. And that's helped us deliver really tangible ROI.

But again, I'm talking about in a personal productivity space, AI is great, but many people told me that -- many customers told me that the only measurable outcome was that their employee have more time to walk their dogs. In our world, this is seriously -- it's a measurable outcome because we reduce the time to process invoices. We reduce really the time the work is delivered in a measurable way. And you will also see it later.

So with this, I'm concluding my session. Next will be Raghu and then Ashim, and they will talk -- I'm really happy to have Raghu here on the stage. Please my friend. And I want to -- I think I said it a few times. I think it's great that you see him in person, but this guy who was instrumental to bring orchestration into UiPath and is an amazing leader for us, and he changed really our approach in building software. So, man.

I appreciate the time. Thank you. All right. So I'll share a little bit about our product approach, building on the points and orchestration that Daniel made and really share with you our use case-oriented verticalized selling approach. I think it's a big strategic shift that we've been making over the last year or so that I think we're beginning to realize value for. And then, of course, we'll talk a little bit about AI exactness and how they play together. All right, so here's the agenda. For time, I'll probably just skip this. We'll do orchestration. We'll go into how we use maps to print or build these verticalized use cases. And then we'll talk a little bit about -- we'll launch -- we'll share with you a few product announcements we're going to make tomorrow to the world, but we'll get a sneak peek in a preview today. And then we'll talk about our thesis for why we're in to UiPath, building a little bit on the points that Daniel just made.

All right. So I'll start with a survey we had commissioned a few months ago, which is 2 out of 3 large enterprises with over $1 billion in revenue are using AI agents in some facets of their work. But only about 30% of them are orchestrating those agents. The challenges are on data quality, the challenges are on enterprise access integrations and then, of course, on compliance and change management as well. The largest gaps that these surveyed customers saw was orchestration. The individual tasks, the individual agents may do okay, but when there's a handoff involved between these tasks to solve a larger, more compound and complex process problem, that's when these integrations and gaps begin to surface. And this is where orchestration comes in.

So why orchestration? Daniel explained this a bunch, but maybe I'll use an analogy to explain this even further. Orchestration is the difference between owning a fleet of trucks versus owning a logistics company. The trucks do the work, but it's the logistics company that defines the routes. It's what accomplishes -- it's what determines when to replace a truck when there's a fault. It's what determines when to reroute the trucks when there's like a calamity or a natural flooding occurrence or what have you. Let's start an analogy to how work happens in the enterprise. People perform tasks. Tasks are done by your robots, tasks are accomplished by agents or by humans, by APIs, you have document tasks and so on.

And then orchestration is what ties these tasks together to drive the overall process along. The problem that we see in the enterprise is failures and handoffs. Failures and slowdowns happened during these handoffs because you're missing this orchestration. Remember, I said only 30% of these enterprises have deployed orchestration. And the problem is further compounding because more and more agents and more and more tasks are being built in the enterprise. And what's really missing is this ability to orchestrate these tasks across the set of constituent capabilities to drive a larger process outcome. So orchestration is what eliminates this handoffs that I talked to you about and gives you -- gives our clients a global view for how our process works.

So you can optimize something end-to-end, you accomplish that ROI of a larger process, you reduce costs and reduce cycle times. Maestro -- UiPath Maestro is that layer for us. It's the one place that coordinates and governs all of the tasks, your robots, your agents, humans and everything that goes into building and managing complex process. Think of Maestro as that control plane for the enterprise. Once you build your agents, it's what drives those agents to eliminate those handoffs. One of our largest financial services clients cut their onboarding time for clients from 12 days to 1 day, not by adding more automations, but by simply encapsulating the existing sets of tasks that they had behind orchestration. Hundreds of our customers now have deployed Maestro in production across thousands of implementations.

All right. So I'm going to maybe double down on the point that Daniel made earlier about us, UiPath being the most complete orchestration platform in the world. We offer a fairly comprehensive product suite across 3 distinct product lines, as you see here. And the reason we do this is because the TAMs for each of these product lines are different. The buyer is different and the builder is also different. And we compete with different vendors across each of these stacks. Maestro Case is our approach for dynamic case-driven work. I'll talk a little bit more about that. Our BPM and its way work is a little bit more structured and has less variability. It's a capability we've had for a couple of years now. And then finally, we are launching Maestro Flow to the world, which is a more developer-centered AI native approach to building out complex business orchestrations.

Three different budgets. This helps us expand our addressable market significantly. And Ashim, in his GTM section will cover a little bit more about how we think about our overall TAM. What's common across these 3 layers is we've built a modern durable execution engine on temporal as Daniel described. And then the capabilities are AI-native, meaning coding agents are first -- are used to build and manage and operate and govern these artifacts. So these are modern AI-native products that we've built over the last couple of years.

Let me double-click a little bit on Case. The buyer for Case tends to be case managers. Case tends to be used for more dynamic business processes. And a little bit, I have Mounish talk to you a little bit about what our unique approach to a modern AI native approach to case management is. This is used to very dynamic processes, very complex loan origination and fraud investigation type use cases. Now onwards to business for Maestro BPMN. This is where, as I mentioned earlier, processes which are slightly more structured, have a little bit less variability, tends to be what clients use this for. Many of our clients are BPMN practitioners. So they love the fact that we offer a BPMN-based orchestration system.

And there's the thing that customers call out when we demonstrate this with them is UiPath offers that one product experience, where not only can you define the overall model for how your process works, but implement every single constituent part, all in the same product experience. This is powerful. Your business person defines the BPMN. Your individual developers come in, takes that BPMN and start implementing the tasks. These could be your RPA-based tasks for agents or have humans in the loop. And then most recently, we've announced the launch of Maestro Flow, which is our developer-centered experience, which really targets that pro developer who is gaining a lion's share -- coding agents, especially, is gaining a lion's share of how automation and orchestrations are built in the enterprise today.

Daniel showed this, but it's worth stating again. Our work on this in the last couple of years with Maestro has culminated in analysts, including Gartner, recognizing us as a leader. This last year, we were a challenger. So I think we are the only vendor that's seen this size of shift in the year. Our investments here is going to continue to be at the same pace as we have over the last year, and we will double down on orchestration as a salient differentiator and transition from an RPA-only company to a BOAT company, which includes RPA as a salient considerable partner. Here's a slide which shows a little bit about how our large -- a large percentage of our $1 million-plus clients are using Maestro in production. We've seen this trend accelerating, and we expect this to continue over the coming months.

A few anecdotes here, which represents the transformative value that our clients are seeing with UiPath Maestro. The slide speaks for itself, but massive cycle time reductions, massive cost savings, orders of magnitude. Now the thing I'll highlight, I use these examples only because Maestro was -- Maestro exceeded the customers' transformation expectations with this. And with that, I want to invite Mounish on stage. Mounish is going to show you how Maestro Case, which is really what is used for the most complex dynamic business processes, where traditional -- where traditional case management solutions can be very cumbersome to implement, and Mounish will show us how our unique AI native approach helps address and build these cases in a modern way. So Mounish, take it away.

Thanks, Raghu, and hi, everyone. Let me take one real customer example to showcase it. One of the top insurance companies is using Maestro to completely reimagine their claims process for the agent to get up. It used to take them 24 weeks to process the claim and the reason claims are dynamic, exception heavy, involves multiple stakeholders across departments.

With UiPath Maestro, they're able to close the same claim in just 8 weeks. Let me show you how. So this is Dana, a claims officer. She currently has 41 open claims in her queue, but notice the shift. Most of them are progressing on their own. And the reason is orchestration. Orchestration is what is driving them forward. For Dana, this is a transformational experience. Instead of chasing 41 claims, she's just focusing on 3 that require human judgment. Let's click into one of them. Here, you can see a 360-degree view of the claim, the data from different systems where it is in the overall process and what is spending on her currently. And if she needs more information, she can just ask the conversational agent right here. And the conversational agent will respond with the claims context. Currently, we can see that this claim is in medical evidence and approval stage. But how did it get here? What's under the hood?

Now we switch to Maestro case, our AI-native agent-case management solution. It takes that complex messy process and converts it into a structured observable system. You can see that the claim progress is through stages. And within each of those stages, there are different tasks. RPA for those legacy systems, API for those modern systems, agents, both UiPath agents and third-party agents and human-in-the-loop decisions like Dana's. Now if I zoom out, you can notice that there are no hedges connecting any of the stages. There is no predefined order. And the reason is the case manager that is sitting on the top.

So Mounish, speak a little bit about the salient differentiators that we have, the traditional legacy case management systems. I think the task that we showed where the task can be very complicated. Enterprises run mixed estates, you need RPA to gain access to that system that was deployed in 2005 and then maybe an agentic approach to a more modern system, but also our approach to case agents and how we dynamically trace in that process. Speak to that.

Great question, Raghu. Now our biggest differentiator here is the case manager that you're seeing on the top. It's a specialized AI agent built for long-running processes, and it has only 1 job here, which is to determine what is the next best action for this claim so that it reaches towards resolution. And currently, if I zoom in, it's pending on 1 thing. It is waiting for input from Dana so that it decides what needs to happen next.

Now let me go back, click into the claim and open that particular task. We can see that the claim is currently waiting on new medical information. Let's upload the new medical records that have comment and add a note. Now the interesting part here is the new medical records that have come in are completely different from the ones that we initially submitted. So what this means is the claim needs to take a different path.

Now let's see how the case manager handles it. Now once I submit, let's go back. Now the case manager is always listening for business events. And the document I just uploaded and the comment I just added were exactly those. Now you can see the case manager thinking and deciding what needs to happen next. What it does is it uses the even payload that we just submitted, goes through the entire claims context, what has been completed so far and what is pending, combines deterministic rules with agentic reasoning to figure out the next best action.

Now you can see the different stages that are being fired off. And everything...

So Mounish, is it fair to say that case agent is like the enterprise -- it's like the harness that we put. And it has these stages, these tools, the models and automation is a skill that it uses to make progress.

Exactly Raghu. It's a specialized agent built for long-running processes. And it's all governed and auditable. If I scroll down, I can see the entire execution trail, the different decisions that the case manager has made, the different LLM calls and tool calls that it made to come up with those decisions. And when I click into each of those decisions, I exactly see your reasoning behind each one of them. And this is what makes it unique and differentiated. It is AI native from the ground up. This is applying intelligence at the process layer.

Now if I zoom out, all of this might look very complex. So the question is, how did I build it. Now let me switch to my development environment. This is where I used UiPath coding agents, where I provided my requirements and UiPath coding agents used UiPath skills to generate this entire solution. The case plan that you see, 14 stages, 38 tasks, 400-plus nodes within those stars and 200-plus rules to govern the case agent that we just saw in action.

And not just that, it also created the business app that we were seeing for Dana. So what used to take months of development now happens in days.

So this is where Daniel's point earlier about AI proposes.

Yes.

You have the developer, you're still in charge of the developer's decisions and automation executes. Really, it's playing out. Now, this example, did you -- is this a demo? Or is this literally what you used to build this?

So this is what I literally used to build, and it took about 46 minutes to generate this entire case plan. And then I deployed it in our orchestration software, and then the orchestration is what is executing this durable process.

And how long would it take without a, the case agent that you described and b, UiPath Coding agents?

I think without UiPath Coding agents, it would have taken at least a month for me to come up with the case plan build all the tasks within the case plan and then define the rules that are required to govern the case agent.

That's awesome.

Now to sum up, there are 3 things that we showed. Number one, an AI-native way to build complex orchestrations using UiPath coding agents and AI native way to run them via the case manager agent. And number 3, govern and operate them at scale on Maestro. And this is how one of the top insurance companies is seeing transformation benefits from Maestro. And they're not alone. Customers across verticals and industries are seeing the same. Thank you.

Thank you, Mounish. This is amazing. Yes, we strongly believe that you are at that in 2026, this modern AI native case construct is the way how cases would be built and process will be executed and managed. Thanks, Mounish. All right. Actually, let me go back a slide. So Daniel introduced the concept of the map of work earlier. As he mentioned, process context is scattered around everywhere in the enterprise. The map brings that structure, the meaning so that agents, humans can reason and understand over the process and have the same understanding of how a process actually works in the enterprise. The map is living, meaning as your business changes, as your clients' business changes, the map also changes and keeps there. And the map is 100% owned by our clients. It is governed, meaning changes are audited. It means that they're versioned.

So let's talk a little bit about what the map has. It has structured knowledge. It has the case plans, what Mounish showed a minute ago. It defines how work is structured and how business processes move from the beginning to the end. It has policies and rules. What's allowed? What are the thresholds, who approves who are, that type of thing. And then it has the ontology. And ontology is really a way of saying that there is a shared description for business objects that our clients choose, things like customers, things like their orders, things like their invoices and their relationships. Having this ontology allows every system, every agent to understand the business in the same way.

This is how you take all of the unstructured scraps of information with the Cartographer agent, which I'll talk to you about in a minute, and convert that to something structured that agents and humans can reason together. The next bit -- the next piece in the map is what we call operating knowledge. This is what experienced people in the enterprise know. This is institutional knowledge. These are the people who provide precedents. These are the people who provide worked examples. These are the guidance that people carry. And now when these experts leave, many enterprises lose that knowledge. But with the map of work, we build the systems and capabilities to capture as much of this operating knowledge as possible, and you will see.

And then finally, the decision ledger is where human -- the decisions and judgment that human makes and the rationale and the reason for it are captured. This becomes the gold mine, which our specialized agents can use to drive that continuous improvement for processes and this is a magical thing that the map of work can provide. Not only do you have that shared enterprise context or process context, but you can use that process context as a living, breathing artifact that improves as your process knowledge improves and drives continuous improvements to how your automations are built in the enterprise.

All right. So you might wonder, how does this map get drawn? It sounds like a very complicated thing. And it really is not. I think I'll tell you why UiPath is uniquely poised to solve this very complicated process context problem. So how do we go from this unstructured siloed information? I'll even use the word chaotic artifacts of information into something that is more structured. So there's 2 answers. First, a person. We're defining this new role that we call the Cartographer. Someone who owns the map, someone who curates the map and someone who keeps the map up to date in the enterprise. Now you might wonder, is this a new hire? No, it isn't. Our clients already have business analysts. They know how the business runs. So they already know how to translate business requirements and business lingo to technical teams that do the actual implementation. This is the business analyst role elevated.

And what elevates this business analyst to be a Cartographer is this new product we're launching called the UiPath Cartographer. This is the product. It is really a copilot that guides the Cartographer to pull all that context and give the structure that helps drive a shared understanding of processes. The Cartographer, the human is in charge of defining SLAs, in charge of defining the outcomes. And the UiPath Cartographer, the agent, the copilot assist the cartographer in gleaning that context and helping create this map in a manner that is shared.

Now -- the Cartographer alone cannot possibly map a very complicated business process. What Mounish showed is not something one human being knows about. So the Cartographer -- UiPath Cartographer natively supports collaboration across multiple stakeholders. And this is critical. This is -- we've learned over the last decade how businesses actually manage and map their processes, and we've taken that understanding and brought it into how we've built the UiPath Cartographer. In addition, our customers, business analysts, they think about process reengineering all the time. It's not about just mapping how processes work, but they are the ones redefining how process should work to get that next level ROI. UiPath Cartographer has in-built capabilities to guide the cartographer to help assist with process reengineering. So take these couple of concepts. You drive process reengineering, you map all of the work in one shared artifact and then you drive a collaborative experience with which you glean and gain this enterprise context that helps you drive this transformative value that mapping can help provide.

Now I'll put this all together, so I think you can all reason through the massive opportunity we at UiPath see in communicating with our clients and having shared this vision with them. Step one is cartography is what I just described to you, what Daniel described to you a few minutes ago, is we capture that process context with UiPath Cartographer. Step 2 is you take UiPath what Mounish showed you, you take -- the map is fundamentally written in a way that agents can reason with, which means that even UiPath for coding agents can take the map and build out these automation artifacts. This is transformative. Our clients are finding that both the examples that Mounish showed and a few others from our clients, 40, 50 minutes of work where the agent goes off on the side and just builds these things out is truly transformative, and you'll see a few demonstrations of this over the next couple of days.

And then step 3 is Maestro. Maestro in its various incarnations orchestrates these very complicated processes in a governed, auditable and safe and secure and resilient way. And finally, I think the step 4 is an important step that we're also adding to our product vision. And that is this concept we call the decision ledger. Human decisions, human judgment is captured. Whenever a human does an override to a policy or makes a decision, we capture that in this thing we call a decision ledger. And the inputs in the decision ledger are fed back to the map to drive continuous improvements through a process. We have specialized agents that can reason over the artifacts that are in the decision ledger and assist the Cartographer in curating that information and drive this improvement. So that is the cycle. You capture with the Cartographer, you build with UiPath for coding agents, you run your complex process with UiPath Maestro.

And then finally, you improve the processes with the decision ledger. -- the map of work at the very heart of all this. So you're probably thinking how do customers get started? It's almost never from a blank slate or a blank map. We have hundreds -- I mentioned to you earlier, one of the things we have embarked upon over the last year is this verticalized use case selling. So as it becomes us, we've built about 100 or more of these prebuilt maps that we'll show you in a little bit.

Now where you start depends on your process. There are some processes that run about the same way in every company within an industry. They have very little variability. For those, we've actually completely prebuilt out the map, but we've also built out all of the automation constructs that go with the map and curated business user-specific experiences. But many do not. Many processes have much in common, but there's enough variability that we can't possibly prepackage them. And we support that too, and I'll explain in a little bit. And then there are a few processes that are completely bespoke to a company. That's what maybe makes the company as unique as it may be. So there's kind of 3 places to start with the map.

The first and the best way is what we call the prebuilt maps with UiPath Solutions. Some processes, as I mentioned, look about the same way in every client, and every customer in a particular industry. So we've drawn that entire map, and we've implemented all of it. Every single agent, automation, any automation artifact that is required for us to operate the process, we've built it out. And we built curated experiences for the business user. These are our turnkey products. Now what you see in this slide is a little bit of eyesore, but what we want to tell you is that we built these verticalized processes for these prebuilt maps for a very few industries, healthcare, which is where we have some significant strength in financial services also and utilities and a few cross-industry use cases.

We've chosen these use cases and industries very, very specifically. A, we have the strengths in these industries, like I mentioned. Our teams have done TAM and SAM analysis for where the highest growth and revenue opportunities for UiPath is. So we've used that to select this list. And finally, we had customer demand within our own customer base that we've used to see which ones to see these prebuilt maps with.

Now as I mentioned, these solutions are completely built by UiPath. Our clients manage their business and we take care of the rest. There's only a few configuration artifacts that they have to provide things like their rules, things like their policies for which we provide curated experience and they capture this. Martijn is here on stage, and he'll walk us through how this actually works. But I'm going to maybe leave you with one important point before I hand it off to Martijn we picked a process we call the Source to Pay, specifically because we expect most of you here to be reasonably familiar with it. So you can learn about how the platform makes this prebuilt maps with UiPath solutions possible. We have more complicated processes, especially one on denials, which has, I don't know, 400,000-plus payers, payer policies across 100 payers. The reason we didn't pick that process is because it would be far too complicated. You would probably -- we'd spend more time explaining the process than really the concept of the prebuilt maps.

So with that, I'll hand off to Martijn to use this simple example as a demonstrative use for how we think about prebuilt maps, UiPath solutions and printing these processes. Take it away, Martijn.

Thank you, Raghu. So let me set the stage here. At Cobalt Ridge, the source-to-pay process was a complete mess. maverick spend was out of control as people kept bypassing procurement and we're not making use of the negotiated discounts and contracts. This also led to a host of problems downstream in the accounts payable department. The team was being swarmed with invoices that are littered with exceptions. Now most of these problems originate at the beginning of the process, the request intake. So what about you, Raghu? Do you ever create any purchase requisitions?

Several and not my best days.

I hear you, man. Creating purchase requisitions in a legacy SaaS procurement system can be a painful experience. Let me show you what we do in the UiPath source-to-pay solution instead. So I'm not sure, should I switch? I should be seeing my demo screen. Awesome. Okay. So let me show you how we do this in our source-to-pay solution. We make buying as easy as asking. So as a manager in Cobalt Ridge, what I can do now is ask what I want. I want 5 laptops for developers. Now in the background, the solution is using the business ontology to understand my intent. It knows what I need and who I am. And it's using that to gather this data from our business systems, from our SAP, from our Coupa, from our Workday. But that's not the only thing we're doing. We're also checking my request against the Cobalt Ridge purchasing policy to make sure we only surface the products that I'm allowed to buy in this situation.

This laptop looks great for my use case. I confirm the shipping address and boom, I'm done. It's that easy. From here, Maestro will take over the approval process. It will start reaching out to the users that need to approve on Teams and Slack. So we get a response fast and keep the process moving. So how about that, Raghu? That made the purchase requisition for us.

Yes, yes. So Martijn, this looks almost too simple. And I'm assuming -- I'm guessing the investors have seen this and like many have already seen this, what's going on here. So show us what is turnkey? What is the power of the platform underneath that makes it possible to build something like this in like an afternoon of configuration?

Yes, absolutely. So like any UiPath solution, the Source-to-Pay solution comes with a completely prebuilt map of work. So here, you see that visualized. It comes with all of this out of the box, a case plan for Source-to-Pay, a business ontology describing the concepts, business rules and policies. It's all there from day 1. Now that's not the only thing. It also comes with a complete user experience, agents, dashboards optimized for all of these personas based on years of experience. The only thing the business team now needs to do to make the solution their own is to configure the last mile, their own rules and policies.

I want to see how that's done. Let's take a look. So here, as a process owner in procurement, the only thing I needed to do is to upload my own purchasing policy. It's a 30-page document describing everything about who can buy what at Cobalt Ridge. The solution already understands the principles of source to pay. So use that to translate this written document into a set of deterministic rules. Here's the rule we just triggered for laptops, describing what kind of laptops can be bought for engineers at our company. And when this rule run 100 times, you'll get the same result every single time. Let me see. Okay. So it was really easy to get this set up as a business user.

Now let's move over to the accounts payable team because they were being overwhelmed with incoming invoices before. But now they're back in control. Here as a process owner, I can see all of the invoices that are coming in. I can see what my team is doing, but also what the agents are up to. They're doing a great job actually. 88% of invoices are now being handled completely straight through. Here's one for our laptops, for example. You see the agent picked it up or the system picked it up, I should say. It extracted the data from the document, matched it against our systems, performed all the necessary checks and posted it for payment. So no human action required whatsoever.

Let me -- wait a minute. There's one invoice there that's blocked right now. So the only thing that matters now is how fast we get it moving again. Let's see if we can resolve it. It's an exception of maverick spend. We're missing the purchase order. And also there's no requester listed on the invoice. So I cannot start the approval process automatically now. Luckily, the agent uses business ontology to find out who could be the requester. It's suggesting it might have been Kate. She's in similar requests before in the last 6 months. It's also coming with a set of suggested actions. We could go to the experts, Dana, or go to Kate directly. I'll do that. Again, Maestro will chase Kate on Teams right now to make sure we get this invoice in blocks fast. So that was the exception.

Now let's move over to the decision ledger. You heard Raghu tell you earlier, this is where we're capturing all of the decisions made by the end users. We're using that to optimize the solution over time. So here, I can see all the exception rules, and this is the one we just triggered when an requester is missing. It's not performing well. 42% of the time the people chose the suggested action. For the rest, they didn't. So how to improve? Well, the ledger already provides a suggestion of what we can do. It proposes that if there's one likely requester, I should go -- we should go directly to that requester instead of bothering me. So that's a real improvement, saving me precious time.

So Martijn, what's -- from the business standpoint, what is the ROI that a customer sees with this?

Great question. So Raghu, all of our solutions -- let me stay here. All of our solutions come with a set of prebuilt KPIs that matter for each process. For Source-to-Pay, it's all about reducing maverick spend and increasing our touchless rate. And I can see here that the process is performing much better than it was before we got started with it. We also saved over $300,000 already. So that's an outcome. Now here's what we just saw. The Source-to-Pay solution that Cobalt Ridge rates started in days instead of months. It allowed them to get fast ROI and outcomes that matter. And that's essentially what all the UiPath solutions do.

Over to you, Raghu.

Thank you, Martijn. So as you saw from Martijn, the ability to use the UiPath platform and the concept of a prebuilt map to make wholly built complete products and solutions to customers to solve their process use cases that they really care about. So they did not buy the components. They did not have to hire developers to build these things out. We had prepackaged maps, fully built automations for them to go use. And that's what you saw.

All right. So what do we see here? All right. So on the other end of the spectrum, where a customer may have a very bespoke process that is uniquely just their own -- our customers can build their own maps. I introduced you to the role of a cartographer. The cartographer would build out these maps from scratch, of course, with the cartographer agent doing the heavy lifting. So there's another way for clients to use cartography and the maps.

But there is a middle path, and we are proud to announce Process Atlas. Process Atlas is a collection of maps for a set of processes that are common enough that we can give it some shape, but have too much variability for us to complete those maps. So we give the maps the domain knowledge, which is drawn by experts. And then your cartographer adds that institutional knowledge to complete the map so that it captures what is unique to your implementation. This is a long list of pre-mapped processes that we're going to launch with Process Atlas tomorrow actually. We chose these processes again very deliberately based on our assessment of SAMs and TAMs and where we found the best product market fit for our existing customer base.

All right. With that, I'd like to invite Anvita, our product management leader, to walk you through how customers realize value with Process Atlas and cartographer. You'll see Anvita show how our history and strengths with mapping and building business processes really sets us up to collaboratively build these first-class assets and build out these maps in ways that we believe only UiPath can. And you'll see through the demonstrations how that works.

So Anvita, please take it away.

Perfect. Thank you. Let's ground to yet another complex long-running use case. Large manufacturing equipments, when they break down, need to be covered for insurance, need to be contained for a repair or replacement, and that costs manufacturing companies a settlement amount. Cobalt Ridge, one of the illustrative manufacturing companies, needs to pay about $42 million a year in coverage cost. Here's the problem. Their mean time to resolution, call to close is about 11.4 days and their target for next year is under 4 days. And here's where the problem really lies.

Here's how a single case of warranty resolution gets operated end-to-end. A version of this for every case and thousands of them operated by Cobalt Ridge every single day. There are people across teams that are contributing at different stages of this process and no single person or team within Cobalt Ridge might have seen this entire picture. You just heard about the concept of a cartographer. Me as the cartographer on this engagement is tasked to map how this process runs today, find how a reengineered process should be implemented so that we achieve our business goals of reduced call to close time and reduced coverage costs. Let's see how we do that.

UiPath Cartographer allows my ability to build and maintain this map with an accelerated time to value. Here is where I begin, UiPath Process Atlas, hundreds of complex processes mapped by expert attestations with deep domain knowledge across the core 7 industries we operate in. They're all mapped across business areas and personas. And for my work, as I pick warranty resolution in the aftermarket and service business area, I can see a whole map built about how warranty resolution operates within a manufacturing industry, sample business KPIs followed by the complete case plan. multiple stages from intake to triage and doing the diagnosis and eventually doing restoration and closing the case, along with conditional paths when a case goes sideways and evidence that never arrived, having engineering exceptions to deal with or substitutions of parts that need to be reviewed or product quality escalations need to be managed.

Every stage marked with a human role on who should be taking decision at every single stage, along with standard business ontology of which are the typical systems and business entities that go in managing this process. It's all interlinked and underpinned to SLAs across stages for the entire process. And it all underpins with standard coverage rules and policies that govern the entire process. This becomes my starting point. As the cartographer on this engagement, I built on top of this to complete this map to give it context about how we at Cobalt Ridge will add more information on how we manage our systems, our handoffs, which systems do we track, so we -- I can complete this map and build out my map for warranty resolution.

So Anvita, the 45% number at the top left there, I think it indicates the domain knowledge, the starting point that our experts, our attestations got us to. But there's a sea of red here. How does the cartographer agent help you, the cartographer into accomplishing the -- how do they prioritize it from the set of actions that it can possibly take to help you improve that number to a higher number?

Yes, you're absolutely right. So the 45% coverage got me from the process Atlas skills, the baseline of how a process typically runs in an enterprise setup. A bunch of reds indicating clarifications that I need to provide. And my starting point with UiPath Cartographer is to initiate a conversation providing it all the manual discovery documents that I've had, sets of process interviews, workshop transcripts, sample architecture diagrams, even full sample claims with hours of hand annotated description about how lines that were down for hours were dealt with.

So this is literally the scraps of unstructured information that we're going to provide structure for it.

Essentially. And I can simply prompt the UiPath Cartographer agent to document what I provided it, where the sources contradict, highlight it for me, and I continue to be in charge of approving all of those deviations. One such that it will highlight will be about when there are discrepancies observed on processes that were not observed within the UiPath skills. I continue to spar with the agent to provide it an input on how to continue building this map based off of how we at Cobalt Ridge operate this process. It also highlights inconsistencies what it observes between the context I provided and the skills it already has.

One such where it mentions a couple of stages were missed out in my base map, claims denial and claims withdrawal. And upon my confirmation, it is able to add that into the map of work, so it continues to hydrate it with the context relevant to my enterprise. A few iterations out, we reach at a higher coverage, 45%, moved up to about 68%.

Yes. So a lot more greens, but still some reds. I'm still curious how does the agent help you prioritize from all of these unfinished tasks?

So UiPath Cartographer is a goal-seeking agent. It starts by listing out what the process KPIs are at the moment as a baseline, so it can figure out how the process reengineering solution should work. 11.4 days are call-to-close time, the baseline has been mapped, and this becomes an anchor for UiPath Cartographer to provide reengineering solutions. It built out the entire case plan based on my manual discovery documents, was also able to add the additional stages it observed is missing from my skills, but is relevant for my company. It also looked at mapping all the individual data entities from business -- for business ontology along with the core systems that we at Cobalt Ridge touch in, Helios CRM, AssetVault, systems and more. It brought in all the rules that we at Cobalt Ridge operate across individual stages and steps within this process, along with mapping individual departments and people and personas of who does what and at what stage.

But as Raghu noted, there's still a bunch of reds. At this point, me as the cartographer need input from my business teams. There are a bunch of rules and exceptions that I need inputs from my business teams in the warranty resolution department to confirm how some of these exceptions have been handled. UiPath Cartographer supports that collaboration natively.

Let's see a quick example. Here's one exception on how the UiPath Cartographer mentions that high-value claims over $10,000 need a VP approval, but there are open questions on how these exceptions are really handled. I need input from my business teams to give me insight on that. Ideally, show me how they do some of these exception handling. From within the tool itself, I can initiate a handoff. The agent is able to draft out a sample question with evidence attached and the explicit question that I need, and I can simply create a handoff to my warranty officer, Scott in this case, who can receive a notification on any communication channel for Cobalt Ridge his teams. This provides my -- me to really scale building this map of work through collaborations with SMEs.

A few days out, as I start receiving inputs from business teams, all of those inputs get captured as feedback coming within individual sections that the map continues to build. The agent is able to take all of those inputs so it can add it to the map, redlining the sections that were updated based on the SME input that I received and is able to continue hydrating the map based off of all the inputs with SMEs that keep coming to me over the next few days. Just like that, mapping scales for me. From the 68% coverage, I reach a number which I feel confident about to hand it off to my developer team to go ahead and implement the reengineered process.

What used to take me months of manual discovery, chasing teams, identifying what the exception handling paths are has shrunk to weeks, if not days, with UiPath Cartographer. And this all resides in a living governed asset within the map of work. The case map built out with individual stages capturing how work happens today and a reengineered view of how work should happen, anchoring to the expected benefits that we started with, layering on what the baseline of mean time to resolution of warranty is and what is the FY '27 target. This is how cartographer scales, building process automation opportunities at scale.

That's awesome, Anvita. Thank you so much. As you saw from this demonstration, our strengths and heritage with understanding business processes allows us to use Process Atlas to help photographers like Anvita map out their most complex business processes and not only map it out, but also reengineer them because of, again, our experiences with what the best ideal use cases and processes should be. All right.

So UiPath Cartographer is generally available. We're going to announce that tomorrow on main stage. UiPath Process Atlas, the 100-plus skills that we have to hit the ground running is also going to be announced as generally available tomorrow. UiPath for coding agents is generally available. Now this is what helps our clients automate their processes, but across -- manage it across the whole life cycle from build, deploy, govern and even continuously improve, as you saw in a couple of the demos. And then an important observation that we found is customers of ours who are using UiPath for coding agent are finding about a 59% increase in their developer productivity. And then we're finding that those same customers are finding about 3 to 5x increased deployment velocity. So throughput of the developers is increasing almost by an order of magnitude.

I want to transition to our test section now. So you saw Cobalt Ridge in action. You saw cartography mapping of work and building out automations with coding agents. What this really means is the power of cartography and the power of coding agents really accelerates the time to value for our clients. They can get a lot more done. Lots more automations get built, lots more agents get built, lots more things get orchestrated, lots more lines of business applications are also built. But these systems are all built on critical mission-critical applications like you see here, ServiceNow, Oracle, SAP and what have you. And the resiliency of these applications are fundamental to ensure that all your line of business applications, your automations don't break. Because if they break, obviously, your business comes to a cross.

What we're finding happen is because of the proliferation of these coding agents, the volume of what developers are able to produce has increased manifold, more code, more change, more automations. This is causing test debt. Test teams are struggling to keep up. And the gap is showing in application quality. It's declining, as you can see here, even as developer productivity is improving. And for that reason, at UiPath, we're taking a novel AI-centered, agent-centered, coding agent-centered to providing the power of automation, the power of coding agents, the power of transformative AI value, just as developers have it for building software to also testing software.

And for that, Ingo, I'd like to invite Ingo, our VP of Product, to walk us through Test Dark Factory. Ingo, take it away.

Thank you so much, Raghu.

Clicker is yours for a minute.

Here you go. Raghu, is always spot on, ladies and gentlemen, to put it plainly, software development is accelerating, but our ability to test isn't. So that means we are creating risk faster than we can test. And that's exactly where UiPath Test Cloud comes in. That's our market-leading offering for all things software testing here at UiPath. And today, we want to take you inside one specific capability of Test Cloud, the dark testing factory because the dark testing factory is the solution to fix the challenge Raghu just outlined for us. It is where software testing stops being something you run and starts running for you. And that shift towards more autonomy in software testing has to happen. And you do not need to look too far to see why. Just look at how software testing typically happens today.

First, testing isn't just one task. It's a whole designing, automating and executing test and everything in between. And in many organizations, much of that is still done by hand, entirely manually or at best supported by traditional rule-based automation. And that is the manual grind that typically slows us down in software delivery. And while rule-based automation helps, it only gets you so far because not every task in software testing can be reduced to a fixed deterministic rule. That's where agents come in.

So think of conversational agents like delegate or autopilot and suddenly, more becomes possible. But here's the thing, one agent here, another agent there, these are all disconnected pockets of authentic intelligence. And that's not enough to keep pace with software development. So the real shift happens when you start connecting these islands of intelligence into governed autonomous flows, flows that spin up and tear down and tire test environments that execute test cases across stages and analyze results all autonomously while humans stay in control. And this, ladies and gentlemen, isn't a future vision. That's what our customers are already doing today.

So the lights are already dimming. Our customers are making their testing more and more autonomous or progressively darker, if you like. And every step up in autonomy brings another big jump in testing speed. And that's the journey we've seen our customers take so far. The dark testing factory brings that journey to its next level. You can think of it as autonomous testing on steroids. Here, autonomous testing is no longer a point solution. It becomes an operating system, Raghu, an operating system that allows our customers to turn their testing from something they run into something that runs for them.

Ingo, this is amazing. I share your intuition. How does this work? Let's show that.

Absolutely. Let's see it in action. Here you go. For this, ladies and gentlemen, let's go here into UiPath Test Cloud. That is the place where you design your dark testing factories. And let me show you one we specifically built for SAP S/4HANA here at Cobalt Ridge. And let me start where the work actually begins. So what you're seeing here is the blueprint of the factory. You see how work enters the factory, how it flows through and how work gets done. And remember, you do not run the factory. The factory runs itself.

Now how does it work? Well, the factory is constantly listening to anything that changes your software application that could be a new transport entering SAP or a user story closing a Jira or a commit directly coming from your deployment pipeline. So a change is flowing into the factory. And from there, the factory takes it over. It creates a plan to test the change, breaks that plan into actionable work items and then distributes those across all the tools available to the factory. And Raghu, that's the big picture view of how the dark testing factory operates.

Do you mind double-clicking on one of these, like maybe the risk assessor and actually show what the value that, that provides? And also maybe speak to our investors here on sort of the differentiators here. What was the pre-dark factory cost of developing the risk assessor capability and post dark factory we are doing it.

Absolutely. Let's first dive into the risk assessment and then let's focus on the value add. So let me zoom into one of the tools the factory has available to get its job done. Let's dive into this one, the risk assessor. And as you can see, this tool is not a single agent or a single automation. It's a broader agentic workflow, an agentic loop, if you will. So this specifically is here to identify the business risks identified or associated with an incoming change. It also consolidates those risks with the factory's broader risk model to determine what needs attention first. So here, autonomy is always risk-driven. And here is where it gets interesting. You can even see these agentic workflows at work. So for example, here is the risk assessor currently busy processing an incoming change. You get all the live execution details, and you can see exactly where the factory stands. So you can literally see the factory thinking and working in real time. And importantly, those agentic loops that do not improve themselves.

So at the right moment, it's either an agent or it's a human, as you can see, that jump in for review and approval. And if a person is needed, well, they are looped in right here in the inbox, where they, for example, provide missing knowledge to the factory, they help clarify the exception or they help the factory to make a trade-off. And that is the role of people in the factory. Humans do not run the factory, they teach the factory. And every time humans teach the factory, the factory improves its memory. And this memory doesn't just get better from what people teach it, it also gets better by learning from its own mistakes. And this is what turns the factory into a self-improving system. Raghu, this is what turns the factory into a system that gets a little smarter, a little more autonomous or a little darker over time.

Yes. Ingo, I totally see us bringing the concepts that we've learned with developing software to testing software, testing dark factory. Now tell me, just as a developer of software, the human is in charge. AI proposes but the human actually is the ultimate decision maker. How does that -- how do you manage costs? How do you manage governance, controls, guardrails? How does that work?

Absolutely. That's exactly it. Autonomy without control is not the goal here. That is why the dark testing factory is not only deeply embedded in our platform. It's also deeply embedded into our AI trust layer, where you can, for example, decide how much autonomy you want to grant the factory and all of its components. You can also define cost and budget controls down to the very last level. And you can also, of course, wrap policies around how the factory operates. So that means the factory doesn't just act autonomously, it acts securely too.

And not even that is the goal, ladies and gentlemen. We are not making software testing more autonomous here at UiPath just for the sake of it. We are doing it for a reason. So what this is all about is helping you to answer one question we all have to answer at the end of the day, are we ready to ship the software application? And as you can see, the dark testing factory helps you to answer that question with evidence. No gut feeling, no intuition, facts. And the factory doesn't just help you to answer that question for one single application, but for every application in your entire enterprise. And that, well, gives you the confidence you need to ship fast at scale without leaving quality behind Raghu.

Thank you, Ingo. Thank you very much. All right. We are at time for the product section, but I'll quickly recap. I think what we showed you is our momentum with orchestration. Hope you saw that, and we can take questions a little bit later. You saw how we are going all in, in this model we call the Cartographer with the cartography agent to build out the map of work, which then is fed to coding agents to truly transform how process context is captured and how automations are built and managed in the enterprise and with the continuous learning loop where you can continuously keep this map up to date, but also your automation is up to date and ever improving. And finally, I think as more software and more code is being built in the enterprise, UiPath Test Cloud enables us to actually rein in the risk that comes with more software being written. So this is our product strategy. This is our vision. We hope to launch it to the world tomorrow, and we're very excited for it.

Thank you for joining us. And then I think we -- I hand it off to Ashim, I think. Or do we have a break. So we have a break now. Thank you.

[Break]

All right, everyone. We're going to get started with the second half of the session. We had technical difficulties in the first half of the session, and we just wanted to let everyone know that the first half will be posted to our Investor Relations website in about 30 minutes. With that, I'll turn it over to Ashim, who's going to run us through our go-to-market strategy.

Thank you, Allise, and thanks for everybody to come here to Las Vegas. I was just thinking about it. It's been almost 10 years for me at UiPath. And when I look around, it's amazing to feel like you're talking to friends who have talked with each other for so long. And as you hear the company's story, I'm just thinking as I was in the break of just reflecting on Raghu's pitch, the sophistication of the platform versus 10 years ago is incredible. I remember meeting Daniel somewhere around 2017. And there was really a box on the page called RPA. And today, when you're looking at cartography, you're looking at agents, you're looking at orchestration, it is incredible about how far we have come.

In some ways, during the 9 years, we've had both an evolution, a period of evolution and a period of revolution. What I would say for UiPath is right now, we are doing both simultaneously. And for the next 25 minutes or so, I want to show you how that's transforming over to go-to-market and to show you what we're doing, how are we approaching our customers to capture the opportunity. The opportunity. So I think Daniel talked about $142 billion TAM. That is one frame to see the massive opportunity that's sitting in front of us. What we see, you can see in the back, you'll meet a lot of our sales leaders here.

Over the last 9 years, we've actually seen an unprecedented level of activity, whether that's POCs, services, driving think orchestration into production, customer inquiries or just the sheer amount of work in enabling our sales team to be talking and responding to the customer requests that we see. That is a little bit of execution in terms of just the incredible way that the teams have become deep with our customers, but it is also because of the confluence of tailwinds that we see before us today. Some of these, I know that we know already, right? Agents. For the last 2 years, I think everybody has seen the swarm of agents coming, have been predicting it. And in some ways, we've overestimated it. But what we have underestimated is today, there is an incredible call for governance and incredible call to be able to orchestrate those agents and enterprise processes.

What I do feel is something that is often left under the covers, but is just emerging is the tokenomics of it all. So I remember about a year ago, Daniel talking about why would you use an agent when deterministic automation can do the same job. Today, 30x higher cost. You hear CFOs coming out and CEOs coming out about how budgets are being exceeded. That is a real call for the RPA portion of our platform, the deterministic pieces of our platform. And so the AI wave not only has a great pull for us in terms of activity of orchestration and agents, but also into the deterministic parts of our platform.

And when you look at what's happening in the world around engineering and what we're able to produce and how fast applications are able to come through, the application spaghetti that went through enterprises, that is only going to proliferate. Not only does that have an orchestration impact for us, an opportunity to be on top of those systems and processes to bring order and efficiency, but it also allows us to do and really grow our application testing business that you see. So when you look at our platform, there are multiple vectors of growth. The one thing that is interesting is that in some ways, the technological moat is becoming shallower with the pace of innovation that is there within the engineering realm. But what is very different is the need for depth and expertise.

So when you look at expertise for us, whether that is in our people or whether that is in our product, UiPath has been investing in this for the last 2 years. And you'll see that throughout this presentation, whether that's cartography or as we go through the go-to-market areas in which we were talking. So when you look at all of this together, what is this activity that we're hearing about? What are they -- what are customers asking? They actually aren't asking about agents. They're not asking about RPA. They're not asking about the technology itself. When they ask about what UiPath does, it's very simple to say that we solve the hardest problems for long, complex workflows that every enterprise is battling with.

That is a battle that has been there since I was an intern at General Electric in a manufacturing plant. It is the same problem that existed when we IPO-ed when we had a broad AI -- a broad story around our platform, and it is the same story that exists today. What is interesting as you look through the pages here is you can see the breadth of what we're able to do, right? So Daniel talked about the openness of our architecture, model agnosticism, the ease of use that we are able to go and put into enterprises, it means that we can conquer a broad set of problems. What is often missed is in the last 2 years, internally, we have been transforming ourselves to be super deep in specific domains and verticals.

This slide, probably if you go back to our last Investor Day, it was just giving you a demographic of our ARR. Today, verticals is the way that we are organizing, the way we are going to market, the way that we are approaching our customers and the way that we are training our teams. So I look -- Eric Bouchard, who you're going to hear in the panel in just a little bit in our customer panel, he has been in the financial services area. I wouldn't call him an automation expert per se, but he's super deep in financial services, right? Joe Tafe runs manufacturing and our Summit business, which is our broad industrial base. He's talking about procure to pay with CFOs. He's not talking about the core technology itself. That happens as you go further into the processes.

So this is not just about a technology area, right? So Raghu talked about the technology components, cartography, getting deep into this, Atlas, very specific processes that we're going. But this is how we are now organized, how we're running and how we are training and recruiting teams. Our Brandon Deer, who is our go-to-market COO, he owns enablement for us. When you look at enablement, enablement is as much about the breadth of our platform, but it is also beginning to drive depth across every function, sales engineering, sales, customer success, services across the board. That depth translates to our customers.

So where we are fortunate sitting here at this moment is all the work that was done by the leaders that preceded us in UiPath. We had an incredible period where we were able to penetrate and move into the deepest customers, and we're still winning them today, which I'll talk about in a second. But when you look at this, we have 9 of the top 10 energy utilities. When you look at financial services, 70% of the top institutions are on UiPath. That has a twofold effect. One, we get a ton of information as we are deepening our expertise by listening to our customers. So when you look at Atlas, that is not our technology team in the back room or in the back office, so to speak, hands on keyboard alone. That is our FTEs deployed in our top accounts. That is our teams and our product managers having access to this customer base to hear it. What it also means is we have an incredible starting point of credibility and a starting point of workflows that we can go and drive against.

When you look at this, though, our strategy still has 3 components. We are still a land-and-expand business. I think a lot of times when you look at companies, they've moved into this area of giving up on the land, but going through expand. And if I'm candid enough, the hardest question that we always get or the most frustrating question is around our customer count. We're not -- we don't look at the total number of customers as the primary metric. We look at the quality of that customer base. So when you look at the quality and you look at our financial services sector, through very intentional deep analytics and through making sure that we are extremely focused, we said federal credit unions above a certain asset value. Those are the type of logos that we want to go through.

And you'll see that translate into Hitesh's section in terms of the economics for the company. But you can see the amount of increase that we are getting on new customers that are starting out with us at $100,000. And you're looking at that because their ASP, even if they don't reach that $100,000 right away, the average ASP around our new logos has doubled. So I can't resist the CFO part of my side. That is efficiency, right? Go-to-market unit economics, the cost of acquisition can go down and every dollar that you're putting in is returning more, both on a dollar value basis as well as a time basis. That is something that is not there sitting in our headquarters.

That is something that is there in every vertical leader that they're thinking about. They're going after and targeting intentionally the ICP, the ideal customer profile. That is where we're channeling our marketing dollars. That is where we're channeling our people. But expansion is still going to be the fuel that will drive the company. And as we think about our ways to approach our customer base, we really started by acknowledging one really important fact, and that is our customer base in itself has expanded. When we first started, I think UiPath roots were a line of businesses. And over time, we kind of -- we invented the center of excellence, so to speak, within many customers during that era.

So when you look at a certain period of a time, the center of excellence became our primary customer that we would sell through, that we would talk through. There are still huge importance to us. We are still well connected. When you go around and meet customers in this -- during this conference, you're going to find many COE leaders. Many of them have broadened their scope, including AI capabilities. But what you're also going to see is as we go into orchestration, we are now moving in and talking to AI architects. As we go into Agentic and governance, our profile of who we are selling to is changing.

As we're deepening with the vertical solutions that Raghu talked about, line of business buyers. We're back into talking to supply chain leaders. We're talking to procurement leaders in the office of the CFO. We're talking to Chief Risk Officers, whether that's with WorkFusion or other products that we have. And when you think about what that means for us, that is an entirely larger market and a new set of budgets that we can go after. So as all things within software, you can go and say, okay, I know the customer, and you can unleash the team. Daniel was very conscious to say, what are the 3 motions that we want to invest in. It gives us focus. It gives us prioritization of where our capital will flow, and it also informs us in terms of what are the types of skill sets we need to bring into the customer.

So we have 3 motions that really are driving our growth that you're going to see some of the impact that, that will have or that has had when you see Hitesh's section. Those are, one, orchestration and automation, really building on the foundational base that we have of customers and centers of excellence and moving up the stack, and I'll go through that in a minute. The second is selling verticals, and the third is application testing, which you saw Ingo's incredible demo that he saw that he gave everybody here just a few minutes ago.

So I want to start with the horizontal. Very specific in terms of a customer journey. You can land with a simple use case that can have deterministic automation. And what's very interesting for us is when you go to public sector in Europe, deterministic is in. They're not -- they don't have high-risk appetites to go through. So landing with deterministic in many regulated industries is not -- is actually a structural advantage to us versus a disadvantage. It is an accelerant in the discussion versus a deterrent.

So when you land with that customer, our biggest thing is land with the right use case. And you saw that with the ASPs and the quality of the new logos, land the right customer, land the right use cases. And then you move up to the stack to say, how do we go into higher-quality workflows that we have. And in some ways, moving from task to process, what Raghu talked about, that is embodied in that motion that has been there throughout for our customers. What is newer is continuing to now scale from the deterministic side to the other parts of our platform. So if I have a workflow of 20 components, now you can go and say, 10 of those are deterministic, 5 of them could be agentic.

When you put it all together, it needs orchestration, and you start moving up to the stack. So when I think about customer metrics, customers greater than $100,000, when you listen to our earnings, they're growing substantially. Customers greater than $1 million. We continue to see double-digit growth across those categories of customer bases. One of the reasons is because we are providing higher and higher value. It's where we're focusing on, moving from task to process, moving from process to impact across everything. And in doing that, 90% of our $1 million-plus customers are pulling multiple elements of that platform together. This motion is actually our second nature. It's very much into our DNA.

What we have -- when you look at this from what the impact is from a customer base, you can look at a key insurance company that we have within our portfolio. They landed and built a multimillion-dollar foundation. That was built on deterministic automation, claims, underwriting. These are things that we were already involved in. As they know our technology, as they know our team, now they're expanding into IXP, they're expanding into Maestro. What is also lost is their deterministic base is also expanding. So this chart is actual data. And what you can see, and I would just point to 2 things that I think are super important.

One, you can see the continued expansion even on the deterministic side. The second point is when you hit into that area where you could get into that enterprise architecture for orchestration and broaden the scope in terms of AI, it has a twofold effect. One is you get a pretty good surge of growth just from pulling in the new products and new capabilities for the outcome that's there. What's lost is look at the curve for the deterministic automation. That's the tokenomics coming into play. Customers know us. They know when to use deterministic automation. And as you get into larger workflows, it pulls in multiple elements of our platform.

The second piece of our motion is verticalization and driving vertical solutions. And I give a lot of credit to the Americas sales team around what they drove over the last 2 years and our international base with Alexandra and Matus, who are also here in the audience. We kind of said, look, for the last 7 years, we know the processes that are driving the highest value in approaching it in a horizontal way. When you see 7 of your banking customers going after loan origination, you can go and say, how do I start selling loan origination right off of the bat. And what that does is it flips the curve in a different way. It packages it from multi-steps into a single step and a single outcome.

And when you do that across the industries and you infuse our company with good expertise, the right analytics, you can actually go and put this together in a way where you get multiple vectors of growth, financial crimes. Organically and inorganically, we approach this, right? And we're getting incredible response from our banks. Here, we are selling to our Chief Risk Officer, Healthcare RCM, Office of the CFO, which Raghu gave you the procure-to-pay example, and you're going to see that actually in the next slide. But what does it bring for people? It brings them a fast time to value. So whether it is the components or a productized solution, they're able to plug it in and get ROI in a much faster way at a much lower TCO.

The second piece is you're able to differentiate because we've invested in industry experts. We have people who've lived their life, both on the product side and on the field side, invested in understanding what a healthcare provider has to go through, what problems are they solving? And we are able to infuse that as we iterate with the customers. And the third piece is it gives us flexibility on pricing. When you're pricing per widget, it's very different than when you price per outcome. When you're able to say you're going to save $100 million, it gives you a very -- a much better discussion when it's something tangible and it's something that you can implement within the next 90, 120 days, and you don't get shifted over to procurement. It's a real line of business leader discussion in which we're facing.

So let's make it real. This is this year, Fortune 500 automotive company. And you can see they were a good deterministic automation company, $100,000-plus customer. They continued to scale. They expanded with their platform. And then what the team went in with is they said, they actually met with the CFO and they said, let's show what we can do around your toughest processes. Procure to pay, no matter how many vendors are out there, you do not hear efficiencies of really transforming procurement for major companies. Manufacturing, retail, that is 80%, 70% of what a finance operations sometimes has to contend with.

We were able to show the solution that was built by our product and engineering team. The CFO, he didn't have to be technical. He didn't have to ask, can you tell me the difference between agentic and deterministic. He went and saw that, that creates, that solves my problem and my need, and it allowed us to price and move that customer from a great customer in our $100,000 club to $1 million-plus customer in terms of where they are today. And that motion is incredibly important to us. That's the second motion.

The third, I would call an adjacency, but it's really hard because it's not parallel play. It's synergistic play when you're talking to the CIO or when you're talking to the COO, CEO or CFO of a company, you're able to go and bring other value drivers for what we have. So with application testing, Ingo showed you the -- like we are, to me, leaps and bounds ahead of what I would consider a lot of legacy applications within that space. So when you sit down and say, here is modern technology to plug into a problem that has been there where there is still a ton of manual testing or inefficient applications, we are landing application testing focusing on specific problems, S/4HANA migrations. We can go and land in that area. It develops a credible landing point for our application testing business. And then from there, we can expand meaningfully into other applications or other areas.

And then lastly, it is reenergizing our GSI partnerships. GSIs, application testing, systems implementations, this is a massive issue that they are dealing with today. As much discussion as there is with AI, we're forgetting how many companies are migrating from on-prem to cloud ERPs. Application testing has a massive tailwind for us to be able to go and take advantage of that. GSIs know that, and that brings into a scale play for us within GSIs. That is another vector of growth for our customer base.

So here, I'm not going to go through the orange and blue, the deterministic side, move them with AI products. AI products pull forward deterministic. That part we talked about earlier. But what you can see is there is another burst of expansion that comes from application testing. So now you're not competing against an automation vendor. We are solving multiple problems for our customer. That is hard to compete with for one-on-one competition, and it deepens our differentiation, and it gives our sales team another avenue of expansion and our customers another avenue of value for where we are.

All of that comes with incredible demands on delivery. And I would say, as much as we have transformed our go-to-market, we are transforming our delivery centers as well. Forward-deployed engineers, I know that's become a term in the industry. What I would say is we are authentically evolving our FTE model in 2 ways. One, bringing forth our services and our engineering teams to really make sure that the first wave of implementations go well. How many implementations have fallen short in the AI realm, falling short on ROI, falling short on expectations of implementation time line or delivery.

Our delivery method is improving every single day, and this is an area that we will continue to invest in. The second piece is our partner ecosystem. I remember 3 years ago, when you walked around, you can see and talk to our partners. And you can feel a little bit of -- you felt a little less energy, you felt a little less connectivity. I'm excited for you guys to meet our partners right now because they are a channel to market, both on the implementation side and within specific segments of where we have. And then professional services is a differentiator for us. Because being a smaller company, that linkage and the ability to move with speed enables us for faster and faster implementations.

So if I go back to those curves that we showed you in terms of when you put forth deterministic to AI, AI to test, the faster that we can get those in production, it gives us the next set of burst around expansion. So we take delivery and the focus on adoption has been a major focus for us for the last 12 months, and we will continue to double down into this area. So that is our strategy.

Our 3 motions: expand horizontally, expand vertically and the vertical depth with vertical depth that we have and frankly, take the opportunity of what is a differentiated application testing platform and deepen our roots with our customers and being able to expand the value that we provide our customers.

So how do we operationalize the strategy? This is a standard pyramid chart. I know for everybody who sit here, sitting in here, I'm sure every single event that you go to, you're going to see this. The difference for us is in that strategic and core business. What you usually hear is large enterprise, enterprise, commercial. For us, what we've done over the last 6 to 12 months is really look at our strategic accounts and say, we're going to change the way we support and deliver them to be very specific, tailored. It's not about a ratio there. It's about the skill sets that you're needed. If you're a healthcare provider and you're going through a massive implementation with an orchestration, we probably need more people to help on the engineering side to ensure the proper governance, et cetera, that is moving into those areas. If you're attacking a case within procurement, we want to make sure that the product team that is associated with there is really working through that area and supporting them in the right way.

So the top level of this pyramid is not about pyramids. It is not about ratios. The top part of this pyramid is driving value and expansion through really customized support. The middle level for enterprise is where we are running programmatic plays across our customer base. So when you listen to our sales leaders and you interact with them, that's where they're putting and where we talk about our ratios of deploying enterprise reps in terms of how we're approaching it. It's where we're going through the 3 motions in a programmatic way, aligning marketing, aligning delivery so we can scale across the thousands and thousands of customers that exist in that space.

And then commercial, it's all about ROI. Commercial is important to us. But how we support it, how we deliver it, channel supported with the right approach and go-to-market and the right digital support. We cannot physically be in all of the locations. So building our digital capability in this area is very critical for us. And you can see that we've expanded the concept of segmentation around our customer base to the global footprint. Three points of excitement for me on this page. The first is we are truly global. We have capabilities in Australia, in Northern Africa, in India, in Korea, in Europe, in Germany, going across and obviously, in the United States and Latin America as well.

So our -- as we talk about operating leverage later, the foundation of where we're playing has been set. What we're doing now very deliberately is saying, where do we want to channel from a prioritization standpoint, our investment? What are the largest markets to go after? So it's not about segmenting your customer alone, it's about segmenting the geographies. That has a specific area of making sure that we can channel the right amount of investment to win in the markets that we need to win in. And then just the last 2 areas, pricing. Our pricing is always evolving in a certain way relative to the market, but we have a foundational set of principles. We price the platform. We price consumables. Our consumables are really around our orchestration and our agentic capabilities, and we price robots and users.

And each -- all 3 of them have tangible value to our customers. And as you look at that, that really has protected us from this question around attrition of the user base because really robots is the lion's share of that robots and user base. And our pricing is moving towards pricing the platform and the consumption of what we're selling to a customer. They have the ability to understand what they are buying from that perspective.

And lastly, our partner ecosystem. It is vibrant. It will continue to improve. We're never satisfied with it. But you can see that we have -- we've kept one of the calls that has been there from our partner ecosystem is to be stable. This is a partner program that we launched 2.5 years ago or 2 years ago. We've kept it very stable amongst our partners. But what's great about it is we are also developing deeper and deeper partners, partners like Genzeon, healthcare providers, deepening with them. That is very important as we're moving and evolving our program. And testing is bringing a new suite of partners into our base, which is super exciting.

And we will continue to invest in technology and go-to-market partnerships. We are more focused. We are deeper and we're more intentional in terms of where we are going. And that element of focus, it's something that we're never satisfied with. If you sat in our meeting 2 days ago, you'd say, hey, we want to be even more focused. We want to be even deeper with these partners. But going across go-to-market, what I hope that you'll see as you listen to our customers is that customer centricity, the focus and the energy around higher-value processes and outcomes is what's really driving our company.

So with that, I'm super excited to bring to the stage Eric Bouchard and an incredible customer panel. I've sat in your seats. I've been on the outside of the software world. When a vendor is saying something, it means something. But when your customers are saying something, you know that, that is true. And I'm super appreciative for the time and the expertise and frankly, the quality and the caliber of customers that we're able to bring on the stage today. So Eric, I turn it over to you. Thanks.

Thanks. Well, first and foremost, thank you all for joining us today. I thought we could start out with just a round of introduction. If you don't mind introducing yourself, your organization, your role, and then we'll jump into some Q&A. Srini?

Good afternoon, everyone. Glad to be here in this panel. Srini Nanduri, I'm the Vice President and Head of Data and AI at PPL. We are an energy utility organization, 100-year-old organization and really looking at the transformation of AI across the board and how do we build the utility of the future.

If you look at it, my team really is looking at AI and automation, how do we help our customer, our field, our grid operations and really helping our employees having those AI tools so we can serve our customers and really serve the community that we serve today.

Jairo?

Jairo Quiros. I'm SVP, Global Business Services. I'm also responsible to drive automation and AI across the enterprise, leading the Center of Excellence for automation. And in my global role, this is what we do day in, day out.

Hi, everyone. I'm Pramod Dibble. I lead AI and automation for USAA's Bank. So USAA, you're probably familiar with them, have an insurance company, property and casualty, life insurance company and a bank. As we start to think about how we deploy AI and automation across all 3 of those lines of business, that's really where my role gets involved. So with a heavy focus on anti-financial crimes, that would be anti-money laundering, bank fraud, customer disputes and escalated complaints.

Good afternoon. Glad to be here. I'm Paru Puttanna with Voya Financial. I'm Senior Vice President, heading the architecture as well as the Agentic AI delivery.

As you guys know, Voya Financial is a Fortune 500 in retirement, employee benefits and investment management. So my focus is going to be across the board, helping the organization in both in the architecture space as well as having the Agentic AI and automation delivered.

Thank you, all. So I thought maybe we'd start out talking a little bit about the journey, your automation journey over the years and more specifically how that's changed in the past 2 years.

So Jairo, maybe I'll start with you and the journey Equifax has been on from its inception, but with a focus on really what's changed in the last couple of years.

Super. So I think at Equifax, we've been committed to improve the way we work. And 10 years ago, actually in 2016, when UiPath was ramping up, we also made partners with you guys.

And since then, we've deployed hundreds of automations across the enterprise. Our role is global. So when you think about the breadth of the type of work that we've done with you guys going from deterministic automation to integrate Gen AI to adoption of intelligent automation and all the flavors that we discussed here today.

When we think about Agentic and AI, for this past year, I think the impressive aspect of how the technology has evolved and the relationship also has evolved as a customer of UiPath is that we strategically have partnered to provide our feedback to help build our requirements within the product.

And I think I speak the name of Equifax, but I think it's a common sense that you guys have been open to integrate the new features into the product. And in this case, particularly over the past couple of years, AI has been an inflection point for us, of course, as many companies. But our focus has been not to really prove the technology works. It's actually thinking about how do we integrate a new operating model, how do we redesign the processes now that we have the technology available in order to do so.

And utilizing capabilities such as Maestro and the orchestration has been key to us for the last year or so as we're designing the future around automation at Equifax.

I appreciate that, Jairo. Paru I thought maybe we'd ask you a similar question. Obviously, past couple of years, you've been very engaged. Maybe talk a little bit about that journey, where Voya was maybe 3 years ago, how that's changed in the past couple of years and the things that we're accomplishing today.

Yes, absolutely. So I think like everyone else, so I think they've jumped on the Gen AI and Agentic AI journey in the past few years following the industry trends. We also learned from some of the initial Gen AI experiences, build versus buy, what is the best way for us to move forward.

And it became very natural of using some of the UiPath capabilities. To name a few, so we are heavily using the platform using the communication mining, Maestro for orchestration, IXP from a document processing perspective. and definitely the unattended and serverless robots. And then recently, we've also started to use the coded apps.

So I think we are touching all the capabilities of the latest and greatest from UiPath. And it's been a great journey from the past couple of years and where we are headed, and we are using UiPath capabilities for our strategic use cases and implementations.

And Paru, maybe staying with you. So you're using the whole platform. What are some of the use cases that you're most excited about? Have you organized some of those things? And to the extent you can touch on it, what sort of value are you getting with some of these workloads in production?

Yes. So we view AI as not necessarily a technology initiative. We are using it as a catalyst to really transform or reimagining how we serve our customers and partners. We also have a cultural shift for our employees, they keep our employees to do the better work. So from the value perspective, some of the use cases, I'll say I'll come to the value next.

So we have multiple use cases which are in production, whether it should be the claims processing, analyzing the fraud and also improving some of the business processes automation.

From the value perspective, I would say, I think the value comes in the way I look at it, whether it's a multiplier of -- you look at the model capability, multiplied it by your workflow, multiplied by trust and multiplied by governance. It's an exponential factor of the human ingenuity. So I think if you -- any one of those factors are 0, your business value is -- you're not going to achieve the business value.

So the way we look at it, how can we reduce the business processes or reducing the time and we'll also improve from the accuracy and serving our quality to the customers and keeping the customer experience and customer obsession in mind, then you automatically your business value is -- you will gain the business value. Yes, I think from the finance perspective, you do have to show from the ROI. I think these are the factors that we are including when we are providing the business value.

And Srini, maybe over to you. I know we've obviously spent a lot of time with Paru and some of our forward-deployment engineers and insurance experts. We've been talking a little bit about some of the opportunities within utilities and specifically meter to cash. Where are you most focused? Where are you guys getting value? What are you most excited about?

Yes. I think as most of us on the panel, like we started our journey with task automation, right? We looked at employees and said, what are the tasks that you're working today? Can I automate those tasks? And we soon found out that the business doesn't work in that way, right? It's a business process that you need to look at from a transformation perspective.

So we step back, we worked with our business and really define what is that end-to-end meter to cash process looks like. This is when you set up a new meter, the billing system that needs to be onboarded and then you finally get the bill, right?

And one of the things we found out as part of the billing process is it's called the high-low issue or we build an Agentic solution on high low, where you get a bill at home, you look at the bill and why is my bill high compared to last month, right? You don't know why -- that's a simple question that you should be able to answer.

But as a company which has a legacy around so many systems, we're still in the process of transforming some of the legacy systems to the cloud, we found out that, that is a pretty complex question for us to answer, where we need to take a question. We need to look at multiple systems. We need to go manually find out why the bill is high by touching the billing system, by accounting system and some of those areas. So there's a lot of steps that needs to be followed to actually make that happen.

And this is where -- I mean, one of the great examples we have, we automated the whole thing. We call it like the Agentic solution around high-low bill analysis, where we have agents actually look at the billing processes. The RPA bots actually go pull that information, provide that information to the agents. And Maestro is really kind of orchestrating that end-to-end process, right? Looking at why is the bill high, is the weather-related issue? Is it something else? And really can provide a much more detailed information back rather than a simple automation that you would have done.

And this also gives an example of how do you look at that end-to-end business process that you can really transform rather than those individual tasks, right? And now the process becomes really intelligent and then you can fully automate the process as we go through. So this is one of the great examples of looking at the end-to-end business process, identifying those tasks and really automating some of those steps as part of the meter to cash process, where the whole process becomes integral.

And Srini, maybe continuing in that vein, I think you said that you guys have mapped 50-plus of these processes within the organization. Are you looking at these? And at what point are you saying these aspects can be deterministic. This is where we need agents. And how are you making those decisions as go through? And then how is that resulting in a build and maybe your use of Maestro orchestration to stitch that together?

Yes, I think that's a great question. And I think the way we have -- we normally start off by not looking at whether that's an agent or an automation bot. We actually take a step back and say, what's the business process that you're trying to automate? Is it something where you need to bring some reasoning into the mix? That's where the agents are really good at.

If it has to go -- and as a regulated organization, we need to make sure there are certain aspects which are highly deterministic, right? When we are pulling information from a billing system, when we are adding details back to the billing system. So there's a lot of SOX and other compliance that we need to go through. So we have to look at it as an end-to-end business process, identify those different tasks that you need to follow, right? As part of the process. Some could be agents, some could be deterministic RPA automation bots and some could be human judgment, too, right?

At some point, if the billing exception, the agent or RPA is not able to solve that problem, the Maestro kicks off the task to a human. So the human actually goes into a workflow, figures out what happens. So there's a human judgment as part of it. And this is where really -- I think the future of automation would be a combination of those 3, as was said before in the previous presentation, is really around orchestration around humans with agents, with RPA bots, right? You need to bring all the 3 things together. And based on that, you can figure out which is the right process for the right task.

So maybe staying in compliance and regulated industries, Pramod. So you're using some of our financial crimes and compliance vertical solutions today. Talk a little bit about how you guys are actually using that at USAA and any ROI or value that you are able to share.

Sure. I'm going to avoid getting my hands slapped by the lawyers, so I'm going to stay a little vague about figures here. Let's just say that we're comfortably in the black on the engagement. We started as a WorkFusion customer. And of course, with the acquisition, now we're part of the broader UiPath family. There are a couple of processes that if you're a bank, you must do.

So if you don't spend your Friday evenings with a glass of wine in the Patriot Act or maybe Bank Secrecy Act, then you might not know that you're required to file a suspicious activity report, which is called SAR.

SAR is you've observed something in a customer's behavior that you need to then refer to the federal government, to the Treasury Department. And this is one of the highest risk compliance processes that exist in banking. So 99% is not good enough. It has to be 100%. If you missed a few, that means you're going to get a consent order. And part of a consent order means you can't sell checking accounts, and that's bad.

So we try to avoid that as much as possible. We're leveraging WorkFusion's solution to perform some of the research that goes into compiling the packet that then goes to FinCEN over to the federal government. In the before times, this process took about a day or 1.5 days to do one, and we do a lot of these because we're a big bank. So that's one space where we're leveraging this. It's a combination of a variety of different modules. One of the advantages of using a vertically integrated solution is I could go out and build this probably.

The issue is it's a complex multistep process, and there are different technology modalities that are baked into each one of those subprocesses. So what's going to happen here in reality is that my business is going to have an idea, and we're going to talk about it for a year, and then we're going to go build code about it for a year, and then we're going to validate it for 6 months. It's the problem that you've solved 2.5 years later doesn't even exist anymore, not really.

So that's where the vertically integrated solutions really speed up your value -- time to value.

Secondary process where we're using this is you're required to review all of your high-risk customers once a year. You risk rank your customers based on what you think they're about to do. And then every year, you look at them to make sure that they didn't do those things. That process takes about half a day, again, in the before times, now substantially less than that.

Jairo, let me come back to you. You have a lot of big technology partners and vendors. Why UiPath, right? Where you're using us? Why us versus others? What capabilities? What's driving you guys to make those decisions?

So I think when we think about technology, I think we don't start there, right? So we think about the problem that we're trying to solve. And thinking about what my peer customers here said, our focus is internal. But it's also business-led, meaning that when you think about the big data issues in the world, the fact that we're a data company, the fact that when we think about how do we manage data, it's got to be secure. It's got to be well governed. So I think when we started our journey, it started because of the same reason we continue to speak to you guys and that is that UiPath provides a very reliable, secure governance around. Even with the Maestro and the orchestration that we're talking about here, I think that's one thing that really stands out is the fact that you have not only a platform, but you have a way to change your operating model internally in the company.

So when you think about what you guys are providing to companies, making sure that we're teaching agents, teaching deterministic automation, teaching human in the loop, that continues to prevail as an advantage. And you mentioned multi-technology. Yes, we are a huge global company, and we will leverage as much technology as we can being a technology company.

But I think the fact that the way you have designed your product, which is open, right, and the way that we can integrate with other technologies and tools that we use allows us for us to be way more effective.

Now the differentiation is there's -- in my opinion, there's no other company that provides that orchestration capability that is also governed and secured in the way that we have the observability, the traceability that we need in order to operate in a regulated environment. And I think we all have that in common, which is we got to respond to somebody else as far as the use and the proper use of the data that we have. So that is one of the elements.

The other element to us is that you guys are also investing in making sure that we are way more effective, meaning from a center of excellence perspective, in terms of the resources and the time that we have in order to execute this automation. So time to market is very important, whether that is an internal finance use case or maybe an external product, data problem that we manage. One example is that through the capabilities that you have offered, we have been able to build automations very quickly. So we go from 6 months to maybe 6 weeks deployments. We take huge data problems that we used to have, and then we create a solution in order to not only reduce the time that it takes but improve the quality.

One example is that we're extracting data from multiple sources that are publicly available in different regions of the world where we didn't have the scale, we didn't have the technology. So it's a highly manual process. And it's also demanding a very high-quality standard. So with these new solutions that we've deployed, we're now extracting more than 400 data points in very huge unstructured documents. And then we're now closing the gap as far as the time that it takes for us to build, package that, build it as a product, sell it out in the marketplace. So huge advantage to us, a lot of the capabilities that you have built.

Maybe Paru, coming back to you, similar question, right? A lot of options, a lot of ability to build versus buy. How are you making that decision? Why UiPath in specific areas within claims, fraud? I'd be curious to understand.

Yes, absolutely. So completely agree with what Jairo was talking about. I think I would say UiPath -- with UiPath, the journey started almost like 9, 10 years ago with the RPA and automation. Having that pretty strong foundation, when we started to look at the -- I mentioned all the capabilities of the platform that we are leveraging, I would say, I think it made sense to start -- when we learned from initial learnings also from the Gen AI and Agentic. so said, okay, we want to take advantage of the Maestro orchestration capabilities and how we can connect to multiple systems because we got to connect with our internal systems as well as some of the external SaaS vendors for us to be able to make the decisions from Agentic and then hand over to the automation.

So by looking at all of those areas, and we said, I think this is where UiPath made more sense for us to be using for some of our use cases. And I think as we are expanding, I think we are seeing more and more opportunities to leverage. I also want to call out that I think that from the past couple of years, it's been the strategic and strong partnership and collaboration between the 2 companies. I think that's the key differentiating factor because I think without that, I don't think we'll be successful in implementing the use cases, and we do need that partnership and collaboration to work these use cases to be successful.

Pramod, you touched on this a little bit around the ability to build versus buy, right? Obviously, with what we're doing with financial crimes prepackaged solution, maybe go a little bit further, right? When you think about sort of the things that are happening around some of the vertical solutions as you guys look into the future, how are you thinking about when to build versus when to buy?

Yes. Thanks. The technology has changed a lot in the last few years. I think that's the worst kept secret in the room. And we will have both approaches in our strategy going forward. We will build and we will also buy. We will build when we need a point solution that's relatively small in scope with strong guardrails around it. It's how you control for token cost. This is how you control for hallucination. And this is also how you deploy small, relatively compartmentalized automations to solve critical business problems.

When you think about buying, it's really about how are you going to be leveraging a suite of technology that is more complex than you can realistically build within a commercially feasible time frame. And that's where the solutions that were -- we've deployed with WorkFusion and now with UiPath, we're able to help us solve some of their more critical problems because they don't just include data compilation or machine learning or AI solutions.

They need to be able to control for all those factors and then orchestrate across and some of my peers have talked about orchestration. It's such an important aspect of what we're talking about. That and data accessibility. Data is always the long pole in the tent for any automation program. So that's how I think of build versus buy. And where I'm really excited to see is how the frontier models continue to evolve over the coming years to see how that balance then recalibrates as the quality of the technology that's widely available changes.

Well, these are my questions. I'd like to throw it out to the audience and see if you guys have any questions that you want to ask the customers up here. I think we got a mic coming over to you guys.

I appreciate all your sharing your experience at this point. I would love to hear -- I know you guys have been a customer for UiPath for a long time. How is your deterministic task automation flow been changing since when you adopted it? And then you guys talked about AI investment. What are the areas of AI investment that you are right now doing? And how is that being funded?

Maybe I'll repeat your question. I think your first question is, I think, how is the deterministic automation changing? I think the third one I heard about the investment in AI. What was your second question?

What are the investment areas of investment and what kind of investment you're doing? And how is that being funded?

Okay. So the deterministic, I would say, definitely, there are multiples of these automations in place for several years because the automation RPA is in this space for a long, long time for both financial, manufacturing, all of these industries. So I think how we are looking at this AI coming in is how can we bridge or combine both together because the way that we are leveraging is you're using for some of the AI for your intelligent decision or your reasoning, all of those things that are Gen AI Agentic and then bringing your deterministic into the -- for execution of the flow as well as I think on connecting to different applications, how am I bringing those automations. So I think that's where there is definitely a lot of opportunities.

And I think we are also going back to some of our deterministic that's already in place, how can we really take a look at those from the AI mindset. So from the investment perspective, so the -- all of the past few years, it's been more of looking at -- like I think I heard Jairo say,but we're looking at the smaller automations or where there is a point automations. Now we're looking at more from the transformation perspective.

I think Srini mentioned this as well. Like how do you look at the big picture? And unless if you do not do those transformations, you will not have those business value from the funding perspective, so that's how we are looking at the -- how can the transformation programs that we can bring in where we can include the AI as part of those transformation, whether it could be a customer experience that you are changing, you're reimagining how you are serving your customers. That's how we are looking at funding for those AI initiatives.

Any other panelists would be curious?

So one thing I would add there is the deterministic, which is building the bot has always existed, right? You take a specific task and you automate it -- what we are also finding is sometimes the rules are pretty complex for you to make a bot or make an automation. And that's where you actually are increasing the surface of how many things you can automate using Agentic AI, right, like with agents actually combining with bots. So we're starting to see the trend where if you look at the architecture of Agentic AI, we have bots with RPA bots, which is going to be doing a lot of deterministic flows, which are pretty straightforward.

But you can actually expand that surface territory and bring in some agents that can combine with it and then you bring humans in the conversation and really do the orchestration end-to-end. And that's where you can really automate the end-to-end processes, right, which was pretty expensive in the past.

And if any of you are willing to share, if not, we don't have to, the funding, is it coming centrally? Is it coming from line of business? Is it a combination of the 2? I think that's likely where the question was coming.

A couple there, please. So I think you asked about the evolution, right? So -- and when you think about the evolution and my colleagues here, they talk about the technology and UiPath the product and the evolution. We started with GUI automation with RPA. Now it's like a whole ecosystem and the deterministic integrates very well with the APIs, with the agents and whatnot. I think the important aspect there also is the way they have envisioned the product, which is when you think about agents and you think about bring your own model approach, for instance, just one example, allows companies like me to integrate with like, for instance, GCP for Gen AI, right?

So you have the agent, you have your own LLM and then you orchestrate the entire thing. But when we started this journey, it was more of self-funding, right? So fighting for processes that you could automate and then self-fund.

I think today, it's way more strategic, is not like point investments, it's more central when it comes to AI. I think many companies were in that path. In my reality, I have to do both, right? So I got to be able to demonstrate that whatever central funded resources they have, they deliver the business outcome back to the units they are serving.

Yes. I wanted to ask, you guys are in regulated industries. And I guess how much was governance, compliance, AI safety, a decision making factor in investing in UiPath versus other functionality, technical reasons, et cetera? Like how important was the governance and compliance angle?

I'll speak to that one. It's extremely important. It's not a secret that USAA had an anti-money laundering consent order in 2022, and it currently has an open bank consent order. So extraordinarily important.

And I want to build on something my colleague just said here where I think what you will see across the industry is more centers of excellence being funded centrally. And that doesn't just solve for the question of where the dollars are coming from. It also solves for the governance piece of this because at one extreme, you can imagine that every data scientist and developer has co-op code available to them and they can ship products on their own, and that would cause chaos. And on the other hand, you can imagine a singular swim lane where all AI projects go through it and that's too much. So it's going to be somewhere in the middle. Like my colleague here, we will have some centralized funding, but we need to justify the investment, right, from a return perspective.

Well, I know we're at time here. Again, thank you all, not just for this, but for the continued partnership. I'm looking forward to what comes. So thank you.

Hitesh, if you wouldn't mind coming up for the fun part.

Thanks so much. All right. What an incredible story. I'm always -- I'm very, very pleased when I hear our customers talk about their journey, especially how they're driving their value and move up the scale, starting with task automation and in a sense state, adopting the entire platform so they can solve the complex problems that they wanted to solve for their companies. So very impressive.

Good afternoon, everyone. I know I have met many of you over the last 2 years. Really thankful to Ashim for bringing me along in the last couple of years. And I'm really looking forward to spending more time with each one of you at our investors reception as well. Over the next 30 minutes, I really want to spend time on 3 things. I want to really highlight all the hard work that has been in the company over the last 5 years in terms of how we have transformed our top line, focus on some of the growth levers, both Ashim highlighted, Raghu highlighted in terms of platform, Ashim highlighted in terms of go-to-market motion, the opportunities that we have in front of us on how we can continue to grow the business.

And then lastly, also talk about from an operating efficiency standpoint, how can we continue to improve our operating margins, improve our free cash flow margins and at the same time, have the discipline of returning cash back to our shareholders. So let's get started with the most important thing, is our customer base.

Over the last 5 years, we have grown our customer base. If you look at it, at the time of our IPO, we were at $925 million, which has now grown on an estimated basis by the end of 2027 to be more than $2 billion. What that really tells us is our customers grow with us. And as you heard throughout the day today, as customers move from task automation to process transformation, it provides a significant opportunity to continue to expand with them.

Now one most important fact here is we don't have concentration in one particular industry or one particular geography. If you look at our entire customer base, it comes across every single industry vertical that's out there. Not only that, you heard from the panel right in front of me here before me, is especially in those industries where the customers are running highly complex processes, regulated enterprises. Those are the industries where I think Raghu also talked about our vertical products, our vertical use cases. These are the ones which is resonating extremely well with our customers. We have more than 50% of our customer base that is in this industry: banking and financial services; manufacturing; public sector, again, high volume of human manual work; and health care.

Each of these industry verticals where the customers are trying to solve those high and complex problems, long-running processes, we are seeing a higher CAGR in each of those industries. Not only that, even apart from those other industries, we continue to see expansion through some of our vertical solutions such as Office of the CFO and others. In addition to this, as we continue to double-click on our customer base, the cohort that is really meaningful for us is our customer base where our ARR is more than $100,000. We have more than 2,600 customers in these cohorts.

If you see as these customers continue to expand, it meaningfully contributes to our ARR base, especially our customers more than $1 million, more than $5 million, they represent a significant portion of our overall ARR base. And our customers greater than $100,000, that is a white space. This is -- these are the customers where we have an opportunity to continue to push our entire business orchestration and automation platform, which will allow us an opportunity to continue to grow further.

Not only that, when you further double-click and look at our top 25 customers, our top 100 customers, this is an interesting fact pattern here is between 2020 to 2027, our average ARR for the top 25 customers has gone up from $2 million to $10 million, 5x. And our top 100 customers during the same duration has gone up 6x. Now this is a small subset of the 2,600 customers that I highlighted. I can just extrapolate the opportunity that we have in front of us in terms of continuing to expand the rest of the 2,500 customers.

This is our land and expand model. Ashim highlighted on this. This is a very important one for us. We have a very good land and expand model. Our platform provides us multiple opportunities to go and land now. What you've seen is in past, we used to land with RPA. Now we have ability to land with our vertical solutions, vertical use cases. We have opportunity to land with testing. Our buyers are different within the customer. In past, we used to just go with COE. You saw in Raghu's slide, now we are able to go with the AI architects. We are able to go with the line of businesses. So opportunities to land has expanded substantially. And then once we land, our ability to go and expand the entire platform and help our customers solve their problems has also grown up significantly.

One of the important metrics is when you look at our top 25 customers, the lifetime value, they have grown 62x from the point we landed. Now imagine the opportunity we have, especially as the platform continues to expand and some of the go-to-market motions that Ashim highlighted, that really helps us continue to expand our relationship with our customers.

Now this is what it was in past. Ashim did highlight on multiple vectors of growth for us. There are 3 things that he spoke about. The first one is taking our existing installed base, especially the customers who were primarily adopting RPA and moving them up to the entire platform as we call it as business orchestration and automation platform. The way we define this is our entire customer base that has not only adopted RPA, but the customers who have also invested in a meaningful fashion in our AI products. Once we move the customers to the business orchestration and automation, we are seeing stronger durability, and I'll cover some of those metrics in more detail.

In addition to this, one of the other drivers that Ashim highlighted and also Raghu showed this morning is several industry vertical use cases. industry solutions. These are resonating extremely well with our customers. Why? This allows us to land the greenfield opportunities. It provides an accelerated time to value. We just heard from a couple of customers here on especially USAA, how it allows them to not only put the technology in production, but also start deriving value. So that's the second area on how we are focused on in driving our growth.

In addition to this, Raghu highlighted earlier today, what you heard is as customers focus more on build, it provides an incremental opportunity for us because the customers need to test more. So there is a natural opportunity in front of us with our DAS testing factory, as Ingo highlighted today, that we have a significant opportunity where we can continue to expand further.

Now every time when we move our customer from RPA to our entire orchestration and automation platform, where we are able to help them solve their process transformation problems, it actually helps us drive our ARR growth. This has helped us grow our ARR base by more than 50%. At the end of Q2, our ARR from our AI -- our AI ARR was more than $250 million. Not only that, this is also helping us improve our dollar-based net revenue retention. So when you combine the ARR growth with our disciplined execution and our ability to solve more complex and long-running processes for our customers, we are clearly seeing it is helping us with our dollar-based net revenue retention.

And if you further double-click on our customer cohort, where the ARR base is more than $100,000, we are already seeing a higher DBNRR of 114%. Not only that, when you look at that cohort and if you further peel the onion and look at the customers that have already moved from RPA to both, we are seeing that DBNRR to be even much higher at 120%. So what it's doing for us? It's doing actually 3 things: one, it is improving the overall durability; two, it is further providing us an opportunity to land much larger sizes with this customer; and three, it is pulling deterministic.

You heard a couple of our customers, including Srini from PPL. This is exactly what is resonating with our customers is every time we move them to a platform where they are able to go and justify an entire process is providing significant value. And for us, this is also helping us from a stronger durability point of view.

Now in addition to this, Ashim highlighted something really interesting is we are really focused on our land and expand, and we are going after new logos. Ashim also gave a bullet point there saying not only that we are landing new logos, especially when you go to our customers with the entire platform, it is allowing us to expand the ASP by 4x. And this is what we have continued to see is when now not only we are focused for new logos, but we are also going after them with a platform so they can start driving much more higher value.

Here's an interesting example. This one is actually very -- it resonates with me extremely well because in my previous role when I was an auditor, I had an opportunity to go and audit chargebacks process. This is one of our Fortune 500 customers who came to us initially as an RPA customer, they landed with deterministic automation. They wanted to solve their accounts payable problem. And then they further expanded with IDP to solve the invoice matching within the chargebacks process and then they clearly realized with the power of the platform, they can go and transform the entire chargebacks process. It's a very complicated process within the pharmaceutical industry. And not only that, we were able to help the customer solve their problem with the help of our FDEs. It allowed us to expand our ARR base by 24x.

As Ashim highlighted, what is more interesting here is it's actually pulling the deterministic automation forward along with the ability to identify the entire process for the customers. And not only that, the beautiful part about this is we are going and solving an entire process, which becomes a use case within the industry that we can go and bring to other customers within the same industry.

So while we are focused on transforming our top line, and we talked about how we have multiple vectors of growth, we are at the same time, focused on transforming our bottom line. And what we have done really over the last 3 years is improved our operating margin by more than 1,500 basis points, 3x. And we have done this while doing 2 things. We've continued to invest in our platform, which is very visible based on what you see. At the same time, we are investing in our go-to-market with our direct sellers.

This all starts with a best-in-class gross margin. And is one of the key levers there is our hybrid platform, our ability to deploy our technology, both on-prem and cloud, provides us an opportunity to drive higher margins and which further helps us continue to be more efficient from an operating expense standpoint.

If you look at our expense profile for R&D, we have continued to invest in our platform expansion. We have leveraged the power of AI to continue to transform ourselves. If you look at our sales and marketing, continuing to invest in sales and marketing globally while remaining very efficient. Not only that, we are strong believers of UiPath on UiPath, us as customer zero. Daniel has tasked the entire company to make sure that we are transforming ourselves on the back of automation. Fortunately, at UiPath, automation mindset is within our DNA. It's from the get-go. Now with our ability to go and transform the entire process, we are further expanding on that and doubling down, which is providing us incremental opportunities to continue to improve our margins.

Some of the additional levers that we have on how we can continue to further transform our global footprint. We have our sellers in each of the geographies where our customers are. Our customer centricity is very important to us. At the same time, we have operational efficiencies based on where our operations are. We are really focused on taking advantage of that to continue to further bring incremental efficiencies within our operating expense profile.

I mentioned earlier about customer zero. As I mentioned, we have hundreds of automations already in production. But not only that, beginning with last year, we also ran an internal agentification program. We have almost 80-plus agents in production right now across the company. And it's not just in finance and accounting. It is across the board. When you look at sales and marketing, R&D, G&A, this actually allows us to become more efficient. At the same time, it provides us insights and great assets that we can bring and share with our customers as well.

While we were focused on non-GAAP operating margins, we are equally focused on GAAP profitability. And so we made a structural change. We became more disciplined when it came to our stock-based compensation. When you look at our stock-based compensation expense ratio as a percentage of revenue, we brought it down from 58% at the end of fiscal 2022 to 12% for the first half of 2027. And again, this is done with a structural conscious discipline and structural change that we have made within the company, one of the core factors that has helped us contribute to our GAAP profitability. We have demonstrated this over the last 4 quarters that we are GAAP profitable. We've guided to GAAP profitability for the rest of the fiscal year. And one of the core reasons is because of our disciplined execution around stock-based compensation. And we are doing this while still continuing to invest in talent and retaining the talent.

Now our operating margin and stock-based compensation behavior has also helped us continue to remain focused on free cash flow margins. Over the last 4 years or for instance, FY '24, we have continued to improve our free cash flow margin. This has allowed us to make sure that we continue to invest at the same time, helped us with our cash position in the balance sheet. And this interesting part is we've done this while executing on our buyback strategy. We've already completed $1.1 billion in buyback. We still have another more than $400 million in authorized buyback from our Board, and we continue to execute on our buyback strategy at the same time, making the investment in platform expansion and our go-to-market, as I mentioned earlier. While doing all these things, we are also focused on keeping our dilution to less than 0%, so it becomes accretive to our EPS.

So while this is all there, of course, we want to make sure that we are focused on our long-term model. As you see, we have significant activity in the business, and we feel very positive about the guidance that I provided at the end of -- during our Q2 earnings call. And while that is there, we are really focused on -- Investor Day is all about long-term margins. So we want to make sure -- long-term guidance. So we want to make sure that we are spending time there.

If you look at where the company was at the time of IPO, we had negative 4% in operating margin. We were committed to transform that. We wanted to make sure that we are converting ourselves into a positive operating margin company, and we were really focused on GAAP profitability. Based on our results that I shared earlier, we've already achieved GAAP profitability, and that momentum is going to continue. All the cost efficiencies and the drivers that I highlighted earlier will help us continue to make sure that we are focused on efficiencies. At the same time, continue to work on our targeted -- what we call targeted operating margin of 30-plus percentage. At the same time, we are continuing to focus on our stock-based compensation and make sure that we are going to bring that within the range of 8% to 10% of our revenue.

While doing all these things, we are really focused on capital allocation strategy. Our framework is relatively simple. Our #1 priority is to make organic investment within the business. When it comes to platform expansion, that's #1 priority. Of course, we are doing it in a disciplined fashion using the power of AI. At the same time, we are focused on customer acquisition and expansion, both. While doing it, we remain opportunistic in terms of our M&A strategy. You've seen WorkFusion in Works. You've seen Peak. We continue to remain opportunistic in terms of acquisitions that allow us to continue to expand our platform, at the same time, focused on bringing in the right talent that can help us expand that agenda forward. In addition to this, we also remain opportunistic to continue to keep an eye on that dilution percentage in terms of buyback and returning the capital back to our investors in form of buybacks.

With that, as I mentioned earlier, we have definitely transformed the company over the last 5 years, expanded the customer base. It is a really large, durable recurring customer base. We've also done it at the same time, improving our operating -- non-GAAP operating margins and really converting the company to be a GAAP-profitable company. I also focused on some of the multiple growth vectors, including what Ashim highlighted, which provides a significant opportunity to continue to drive the company forward. And in addition to this, we are focused on our operating leverage and also strong cash generation. All right.

With that, we'll turn it over to Allise, we are going for Q&A now? All right, thank you. Allise, do you want everyone from the leadership team?

Sanjit Singh from Morgan Stanley. Daniel and team, I thought this was the most well-articulated, well-targeted, most focused presentation about the opportunity since you guys talked about going on an act 2 with Agentic automation. And so I've been trying to figure out the ways to ask this question. But essentially, it's like if you're successful with the boat strategy and all the different motions that you guys are executing on, essentially, do we just have accelerating growth over the next couple of years?

And what I'm sort of implying here is that is there a part of the business, do we have to go through an RPA transition for the business to accelerate? Or just given the customer testimony that you've seen that as both adoption increases, so does the deterministic automation adoption also increases, so we really don't have a headwind to overcome. So I just love your thoughts on if you guys are successful and if you guys execute, does that just mean faster ARR growth over the next couple of years?

Yes, I think this is a fair expectation, and this is why we are all here in all fairness. We want to build a growth company, and we all believe we have a tremendous opportunity in front of us. And regarding your question about RPA moving into both, I think this is a trend that shows, first of all, that RPA is an important technology in your automation toolbox, and it's here to stay. And the numbers that we put on the screen shows that it creates both, including the RPA, it's a more stickier technology. This is a technology that is strategic into the enterprise tech. Some people can say that RPA being RPA alone, being like more of a task-based quick fix until you transform the systems, this is actually about transformation. Many of our clients and partners are using basically both foundation to transform their processes. So that makes it stickier and durable.

I think just one thing I would add is I think people think about RPA as a negative. I think what we hopefully articulated throughout the presentation is that it's a positive. When you see customers adopting our platform, you see them accelerating with deterministic automation as well. Sanjit, I think we've talked about saying RPA is a durable category. It's not a -- our only piece is saying, when you add boat around it, it is durable and has a ton of market in front of us. It is both.

I can add one more thing. I think I spoke in my presentation about 100-plus customers who have Maestro use cases in production. A majority of them have tasks implemented as RPA. And the reason for that is complex business processes run a mixed estate of applications, some built years ago, some more modern. So our ability to be able to have tasks that use RPA as ways of interrogating and gaining information and being that system of action layer alongside agents is a very, very powerful capability that we offer alongside the orchestration layer. Or do you need RPA to build on UiPath? No. But the reality in the enterprise is there is mixed estate. Some applications do tend to be old and dated. And for that, RPA still plays a significant role.

It's Michael Turrin with Wells Fargo Securities. Thank you for hosting. I appreciate all the content and looking forward to the event this week. You've been admittedly talking about orchestration for longer, but we're hearing a number of different software vendors start to talk more about the importance of orchestration and their position within the harness layer. And so I'm just curious your perspective on the points you're hoping to land with customers this week because they're also kind of getting a lot of this new kind of press release type of announcement. What are the key points you'd emphasize? I know there's a core piece of the RPA foundational technology, maybe you're agnostic position. But I'd be curious to hear more on that as well as do you expect this is an either/or discussion for customers, meaning there's one orchestration platform that they choose for all of their Agentic use cases or how you think this evolves from here?

Yes. Let me start. To me, I think the main point that we want to make this week is that in order to deploy AI in an enterprise, and in the context of enterprise processes, you need to put in place this map and rails that we call. You need to have a map of work because otherwise, AI cannot act, cannot really understand your enterprise. And you need to have the rails that creates boundaries for AI in because you cannot let it run a stay. So you need to create what we call controlled agencies, and so that's one thing.

Second thing that I want to make a clear point is coding agents introduce a very interesting asymmetry in how you automate processes. Building AI agents into the context of enterprise processes, it's still difficult. And you need to -- AI has to graduate to trust it. So it has to be -- it has to get a lot of evidence in production in order to give them increased capabilities. But in the same time, with coding agents, you can discover processes, you can print automations much faster and you can maintain them. So this cycle, it's much faster. So -- and it makes sense for every enterprise to automate as much as they can since the entire implementation is basically cheaper.

And another point that we try to make here is we land with vertical solutions. And that makes sense to many of our customers because they address direct clear needs. They don't have the symptom of the blank page that the platform gives them. But it's very powerful to have a solution that is built on a horizontal platform because it really scales to the next use cases. And once you are versed into printing one solution or configure one solution for one particular case, basically, you can apply the same expertise to a slew of processes or sub processes.

If I may add, in terms of the key announcements that we made to strengthen this part, as Ashim was describing in his presentation, as was Hitesh, we're very much about selling vertical use case to our clients. That's a far easier sell for us in selling that outcome as opposed to a set of components. But the products that we're going to launch tomorrow are going to directly assist with verticalized use case selling. First is the Cartographer, which maps out the context, the process context that we talked about. Second is UiPath for Coding Agents that we just announced general availability for. And finally, the orchestration layer that orchestration runs these capabilities and then continuous improvement where the map of work stays up today.

So when we sell these use cases, we sell the whole life cycle of how these processes are managed. And these are the key announcements that we want to make tomorrow, which we believe are massive differentiators over state-of-the-art that exists in the market today.

Bryan Bergin from TD Cowen. I want to kind of follow up on what you were just getting into there. So I wanted to understand on that map of work, how does the economic model change as clients are leveraging more of that prebuilt motion? And is it direct? Or is it more so kind of monetizing the components to operate that workflow so like an accelerant to the implementation? I...

I think we address both questions. Creating a map of work is valuable in itself because it gives you visibility into your processes. I think many of our customers pay expensive business consultants to help them map the processes. So there is clearly a need. Even when you train a new employee, having a map of work, it's much easier to get them through the training processes. But clearly, it's essential in order to print the orchestration and the automation. And I think our biggest play is not necessarily as a stand-alone product. Even we consider it that, I think it has good potential as a stand-alone product. But for us, map of work, the Cartographer and map of work and the map of work that is alive inside the platform and is kept alive by all the exceptions that human decide on eventually go back into the map of work. To me, this is the overall value that we are bringing.

Scott Berg with Needham & Company. Thanks, content was great today. So thanks for the AI disclosure in terms of ARR. It's probably the #1, #2, #3, #4, #5 and #6 questions I've had for the last couple of quarters from different investors.

Built especially for you.

Funny, I had it several times this morning, too. But by my math, that net new ARR coming from your AI functionality over the last trailing 12 months represents about 40% of your net new ARR, at least organic. My number is not yours, plus or minus. But how do we think about that mix going forward as customers are bringing more and more AI functionality in, but at the same time, obviously buying more of the deterministic and other platform features that you obviously offer. Just trying to help understand what the expectation is. Does the AI functionality I don't know, get to 60%, 70%, 80% of what your net new ARR should become over a period of time? Or should this continue to be a balanced mix? And does that change at all with the vertical focused sales?

Yes. As I mentioned, I think we are really focused on moving the customer up the base, right? Our customers are right now -- the primary purpose and intention was how can we take our customer who was RP-only customer and make them a complete business orchestration and automation platform customer. As you heard from some of our customers, they are really trying to solve the process problems. They don't come to us with what technology is going to help them get there. Our main intention here is to help them solve those problems and the technology just becomes a part of it.

Of course, when I mentioned the cohort of the customers, especially when you look at customers more than $100,000, what we have seen clearly is as these customers move from RPA to orchestration and automation, which includes our AI products as well, they are really able to solve the problem, and we have a greater opportunity and from a durability standpoint. So in terms of how you expect, of course, our intention is to continue to focus on that customer base, move them up the platform, and that should drive the overall durability of our ARR base.

Sheldon McMeans from Barclays on behalf of Raimo. I wanted to ask more on the map of work and that context component, I think, is very compelling and these AI agents and models are extremely intelligent, but do not know much about your business and need that. However, you also have other platforms and providers out there that are also trying to build that context. I think about the data platforms like Snowflake and Databricks. And I know you have a tech partnership there as well.

And maybe like how do you see this playing out? Because I would imagine these large customers don't want to build out these context maps in multiple places across their organization. So maybe like is there an opportunity to join forces with like a data platform to -- I know it's slightly adjacent, but to work together there? Or I guess, if it's kind of a winner-take-all approach, then what gives you the right to win there?

I think that's a great question. And it helps us a little bit to explain better the concept of the map of work. So I'll start to give you a bit of a high level what are the layers into the map of work. And then I would like Raghu maybe to go deeper. If you look at our exception of the map of work, right, is basically it's comprised of fundamentally, the bottom layer is what we call business ontology. And business ontology is business entities, their relationships between them, which is one thing.

This is where Snowflake and Databricks kind of play, right? They can -- many enterprises are modeling their data. We are not a data provider company. We always integrate it. Even our data fabric provides virtual entities that can recite actually in Databricks or in system of record. So we virtualize this data layer. So we get -- so to answer directly, we don't compete with this data layer. We basically integrate with the already defined oncology it is there.

But then there is an interesting point. You have actions on entities. This is more difficult to capture into a data layer alone. So this is where we start to shine because we can combine data layer with actions, who modifies, how -- what are the rules, who are the owners of an entity, how an entity -- because it's already, you need to move an entity into the context of a business process. But even further, if you look up the stack is you have workflows that touch all of this, and it's multiple workflows across different departments, and they touches multiple entities. This is basically what an automation company is doing, not a data company. And we combine this in the map of work.

And finally, you have the process orchestration that you will need this information. And this is what I said until now, that's one main component of the map, which is the structured knowledge. Everything here is totally structured, well defined in rules. But then on the top of it, you have the operating knowledge, which basically is the informal knowledge of how you run your company: policies, exceptions, examples, that are maybe in people's head were scattered across the enterprise. When you add these 2 things, you create a map of work. So basically, the data layer and business ontology is one part that is required.

Yes. I think, by the way, it's a great question, one that we spent a whole bunch of time thinking about. As you saw in the other presentation, the and machines. Our intention is to go from the task layer to the process layer. And as Daniel and I and we think about the map of work, we very much think about it as the process context, not enterprise context. So when you bring up a Datadog or Databricks or Snowflake, you're very much in the enterprise context. You have a warehouse with everything. We can interrogate it to answer all kinds of questions.

The problem we are solving is a layer on top of it. It's process context. It's the scraps of information that's living all over the place that humans are using to read and retrieve to take a process along. Because of our elevation from task to process, we also want to elevate the context layer or the process context layer, not a data context layer necessarily. This is where we play hand-in-hand with the Snowflakes of the world, but we add the complementary process context layer, which goes hand-in-hand with the process transformation capabilities that we want to add to our system. So that's our play. It's a process context, not enterprise context.

This is Brian Schwartz from Oppenheimer. Thank you very much for all the content. It was really great. I just wanted to follow up on your AI business and just talk about sustaining pricing power over time. So you have a multi-model approach, you're agnostic, which is absolutely the right approach seeing that number. But what happens in the future if suddenly we get a commoditization in model pricing. And at the same time, your consumption, your outcome-based and token revenue is ramping very high. How do you sustain the pricing power? Is it more use cases? Is it selling more products? Will you be able to raise prices on renewals? Just wanted to get at the ability of sustaining pricing power in a world that model pricing could commoditize very quickly in the future?

Well, I think we need to untangle a bit our revenue. How much of our revenue comes from what we add on the top of tokens? I don't think it's really material. So this is our source of revenue. I mean speaking of -- this is an interesting question about the gross margin. Many of our customers would like to bring their own models. So basically, token is totally irrelevant for our business. For me, I'm a big believer that we will see much bigger commoditization on the models. And that works only to our advantage because you can use Cartographer as base. Cartographer consumes a lot of token. Cartographer agent has to analyze hours and hours of recording. That's an expensive proposition. And our coding agents use a lot of tokens. The more intelligence get commoditized, I think people can consume more of our platform.

Brian, specifically, tokens are not a primary component of our AI monetization. We are monetizing our AI capabilities surrounding the models. So from that standpoint, if you have a use case, and we talked about one of the large automotive manufacturers, they will have their own tokens, their own models that they can plug in and integrate with us. What they're buying is our solution, our orchestration, our governance and other aspects to make that use case come alive. So so long as the value holds of the return, it gives us pricing protection long term. And we've actually seen that upon renewal for many customers.

As we position outcomes, I don't see a scenario where as models get better and cheaper, it's not an effectively nice tailwind for us. If you sell a use case, we're selling an outcome. It doesn't matter how much AI, how much deterministic is used. And if there's a cheaper model that's compliant, governed and it meets the organizations, whatever thresholds, then it's more power to us and more power to the customer, I think. So we benefit when AI models get cheaper, faster and better.

Pat McIlwee with William Blair. So I just wanted to ask one on test. As we think about that as a growth lever, I think it's really interesting and notable that it puts you in front of a different buyer, right, with the CIO or testing buying sensor. But as we think about that, can test evolve over time into more of an independent land motion where you then go in and pull through Maestro or automation workflows? Or is the larger opportunity still just cross-selling it into the existing user base?

It's a great question. It's actually both. So one is we should define different buyers. There's a different user, like a QA team, et cetera. But the CIO, if we're going -- we also work and say, "Hey, you're moving from S/4HANA -- you're moving from ECC to S/4HANA as an example. In that migration, you should also be designing automation directly into your blueprint", right? So in many cases, there's a synergistic customer area, not a synergistic user at the end, but we are seeing also a motion that is beginning to land test directly. And that is an area that we would continue to invest in.

So it gives us a bifurcated like a dual land lane, both a synergistic play as well as a net new buyer and a net new motion that we can capitalize, and that's what's super exciting about it and why we've specifically labeled it as a growth engine.

Yes. And I want to point you to a very interesting fact about the connection between testing and automation and agents. So testing, as we showed here, is one of the area where you can run agents completely autonomously, and it's actually indicated. But when an agent basically test and explore an application, it's a lot of learnings that we get from this that we plug in when an agent will automate the same application, and these learnings are not lost during the testing.

So we can incorporate them basically in the map of work. So people start already when they use an application. Because that's the cycle. You -- as an enterprise that's building a custom application, you build it with coding agents, human review and testing is really a big bottleneck. You use the dark factory that test it automatically, you capture, you create basically a summary of all the interactions, what's successful, what not, how do you use an application. And then you can close the loop, take this summary and put it basically when you use agents to use your application.

It's YC from Citi. Thanks for all the slide on the customer example. It's great to see the multiple from like 16 to 25, especially the one in Hitachi. I think by my estimation, the AI piece kind of jumped 4x at least from the FDE. So I'm trying to ask one around FDE and pricing. To what extent like outcome-based pricing has an impact in that jump in the AI consumption piece? And then like what would that kind of justify your ramp in FDE spending?

There's -- so I think there's 2 flavors of outcome, right? So motion 2 for us is you can sell an outcome of automating procure to pay, right, and generating value for that. From that standpoint of an outcome, we definitely get more value. We have less discounting pressure because we're articulating that value. But in terms of an outcome pressure related to FDEs going and solving a specific problem and pricing is tied to the full outcome, I guess 3x basis. We're actually just at the early stages of those types of transactions. It is an exciting part of where we could go, especially as the platform moves to the C level.

Daniel, I can't count the amount of C-level meetings, Chief Digital Officer for one of the largest hotel chains within Europe or hospitality chains within Europe and globally. Those are areas that we're able to now selectively go after it, and we want to be selective in terms of how we're attacking that area. So it's actually very little of a full outcome-oriented area that's driving it. It is more just the incremental value that we are providing on our solutions.

Radi Sultan from UBS. I wanted to ask like a lot of your customers have large automation backlogs that they're trying to get through. I remember at this conference last year, every slide was, hey, we have 300 automations in our backlog, 200 automations in our backlog. And it seems like UiPath for coding agents is a really meaningful accelerant of converting those automations into production. So I guess my question is, can you see sort of a core acceleration just from that like quicker backlog conversion? I guess maybe just any data points around that converting existing automation backlogs into production faster, how that could impact the business?

Yes. I would say that we are seeing the early signs that the productivity of the developers involved into the automation program might increase like up to 50%, 60%. And it's clear expectation from us that, that will propagate that scale. But this is a thing that we just released in GA. Customers need to update. They need to get into our latest versions in order to use the capabilities. This is something that we continue to roll and focus over the next quarters. But clearly, this is -- we put a lot of focus and effort, and we continue to perfect our coding agents to become better and better at the code generation.

And while it's in the early stages, the other piece is it's not just the time to clear the backlog, the ROI for AI is one of the biggest topics, right, amongst C levels, like where am I getting my ROI? The coding agents also reduces the total cost of ownership. If you had a group now that is able to produce 20 automations a month, 50 automations a month, the average cost per automation is going way down. So we feel it both in economic differentiator for us as well as an accelerant in terms of clearing it.

All right. We're at time for stay. But as a reminder, we have investor reception next door. So thanks, everyone, for joining us, and we hope that this is a productive session for you.

Thanks for attending to the UiPath session at our Citigroup TMT Conference. Day 1, we are pleased to have Ashim Gupta, who used to be a CEO and CFO. He just recently focused more on the COO role. Welcome. Thanks for coming.

Thank you for having me.

Yes. Ashim has been instrumental in driving a lot of the transition change at UiPath that's been go through. He's been here, I think more than 7 years in a row.

9 years.

Okay. So it would be great for you to maybe start a background of your role, how you're transitioning and the background of the company.

Yes. Look, my role -- I joined UiPath in 2018, and it's progressed. I've seen very different parts of the organization. I actually started in customer success because I was a customer of UiPath. It was the highest ROI software, and I really enjoyed it. I took the company public in 2019 as I got into the CFO job in 2021, sorry. And it has been a steady movement up 2 years ago, I took the COO role. And in addition to the finance organization, I led parts of go-to-market, different operational connectivity across the groups. And I'm super happy.

He just walked in perfectly at that time. That's Hitesh Ramani. Hitesh has been my Chief Accounting Officer and then my Deputy CFO for the last 5 years. And I just am thrilled to have him ascend to the CFO position and allowed myself to focus and support the operations of the company in a more focused way.

Interesting, yes. No, perfect timing. I guess maybe we can start with the transition there, like what makes now is the right time for you to focus more slowly as COO and then for Hitesh to take over the CFO role?

Well, Hitesh gave me an interesting stat. I think the other day is the average CFO life span is 2.5 years, and it's been 7-plus years for me in the job. Look, I actually think this actually drives long-term stability from the company. When you were with the company for 9 years and Hitesh for 5, there needs to be continued growth for incredible talent like Hitesh. So I think that is really like one piece. It solidifies our leadership team, and it strengthens us overall. There's things that, frankly, like he's much better at, in my opinion, than me as we kind of move into these next chapters.

The second piece is, look, I think I can use the cliche or the boiler plate line. It is kind of an unprecedented market with a lot of change. And I think that's a market, but it's also UiPath.

We've really transformed from an RPA company, to a business automation and transformation company. And there is so much to do. Our product has expanded its surface area in an incredible way. We are structuring deals across the globe with the demand that we see coming in and really helping Daniel and the team just connect the functions and move through is it's a moment that I think requires more focus.

Interesting. Yes. No, that's definitely provide a lot of opportunity for the employees that make happy to continue to see a growth trajectory. Maybe I'll grab you one more time on the financial stuff while you're the only one on stage right now, you guys just reported a relatively stable quarter that will be characterized in a way. But I think investors look at it a little bit from a different lens, like can you give us like some of the put and takes that your recent quarter you just reported?

Look, I think delivering the sixth quarter where we beat and raised against consensus kind of continues to strengthen the credibility of our overall predictability and operating model as a company. ARR is $1.93 billion. It's up 12.5%. Revenue is up 13% and stock-based compensation is down 42%. We're GAAP profitable for the third straight quarter in a row. We're on track to be get profitable for the year. Of course, when you just kind of turn the path forward in a meaningful way, we're generating $400 million plus of free cash flow.

So I would say a stable quarter and an unstable time is a good quarter. And a lot of people had us continuing to decelerate. When you look at this quarter and you look at kind of end of -- how we ended last year, end of last year, we were 106% net dollar retention rate. Today, we're 109%. So I think when you look at it, like we're really positive about our execution, the trajectory, the breadth of the platform that is getting in there and the customer feedback.

From my standpoint, when you look at the puts and takes of the quarter, I think we had a huge run-up in the software market leading up to this. We're still in good position. And I do think like some of the questions that we get around customer count, I think those are some of the newer investors.

People who know us can see that our $1 million and $100,000-plus customers are really the focus and the engine of the business. $1 million-plus customers are up 21%. $100,000-plus customers are expanding and actually that net dollar expansion rate is increasing. So we actually feel the fundamental underlying parts of the business, both from a growth and an economic perspective are there. And my last point is we're not satisfied at 12.5%.

We've stemmed the stable -- sorry, the deceleration. We are planning to reaccelerate the company. And we think that the platform strategy that we have, the vertical products and test in terms of the motions that we have, those are real good fuels for what the future holds for us and we're really optimistic.

Yes. I mean that sounds like there's a lot of positive in the quarter based on the customer count, the growth in the large customers. I think one of the investor top question is like based on the implied ARR there seems to be a deceleration. I know there's a lot of noise. In the net new ARR this quarter, could you talk more about what is baked into the second half net new ARR guide? And how should investors be looking at it?

Look, I actually think this was a clean quarter to be super clear. I know in first quarter, we had an acquisition and there are some questions. We increased our FX disclosure this quarter where we showed clearly kind of the impact of FX, and it was actually a headwind for us this quarter, and it's a headwind for the rest of the year. So from my standpoint, it was actually a very clean ARR quarter.

As you look forward, to the second half, I think people are comparing all those apples and oranges between actuals and guided. And I don't mind talking about that, right? When you look at it in terms of what that means, that means like in a world where Hitesh is calling the guide now, and as we come together as a leadership team, we're calling to what's in front of us. And we see kind of a proven case of stabilization in front of us which means if we execute, obviously, there is the ability to reaccelerate the business.

We're not -- we're going to look at what's in front of us, and we're going to guide prudently to how we have been and continue that guidance philosophy as the way Hitesh wanted to run things. And at the same time, you look at the second half, it shows continued strength and expansion of our platform across our top accounts.

It shows continued momentum in our core verticals, which is financial services, health care and office of the CFO. And frankly, it continues to assume a variable environment. So those 3 things are what's baked into the second half. And we're really focused on continuing to execute a seventh stable quarter.

I think you touched on a few growth trajectory that you see stabilization and then you expect it to grow. Can you talk about what has the trend been that you're seeing, like either within the consumption because you talk about like deal count with AI, like how has the trend been over the last couple of quarters versus the deceleration in net new ARR that you saw last year?

It's positive. I think when you look at last year, everybody was coping with a ton of AI confusion. What we see is there's still AI confusion in the market, but our customers, that confusion is less. So 18 out of our 20 deals this quarter included broader parts of our platform in our AI areas. Those things are uptime -- multiples of deal value up like 4 to 6x higher than our old typical deal values. That feels really good from our standpoint.

So we see that dynamic of customers understanding that they need both deterministic and probabilistic, that they need a broad platform and that it is not competitive with Frontier models. We are essentially going to solve enterprise-wide processes, which Frontier model will solve a piece of. We are really trying to go and orchestrate and run the long-running workflows of companies and moving from task to process, we see that across our top deal.

Okay. Hopefully, we see a better trajectory that continue to improve in the business. I guess maybe now we can tip a little bit over to like more product questions. Now that you are focus on the COO role, can you tell us what are your key priorities -- also we have an upcoming Investor Day, I'd love to get a little bit of flavor from there, too.

I'll -- like my priority is one is to continue to support Hitesh to have a really smooth and seamless transition. I think he's been the right hand for the last 2 years plus. So I'm not too worried about that, but I think we'll never take that for granted, neither Hitesh nor myself nor Daniel, we want a really smooth transition for the investor community and for the business.

The second piece is really connecting product sales -- product go-to-market and our core functions to drive this reacceleration that we're talking about. And I think as I mentioned it earlier, as we get into a broader set of use cases as we're expanding our business, that requires that level of connectivity. That can be everything from driving the strategic priorities around how do we attach orchestration in all of our installed base.

What's the right strategy to expand and continue to drive test? Like continuing to help with that and connect and maintain the operating rigor, that's really where my focus is going to be. In terms of Investor Day, I kind of say it flippantly, you got to come to Investor Day to see Investor Day. There's no trailer.

But what I can tell you is what I -- if I could imagine you there and you walking out of the room, I really feel like there's going to be an optimistic sense of energy that goes that gets to see a company that was a stand-alone RPA company and how far from that paradigm this company has grown and what the capability of the platform is? What is the level of detail we are in the right execution strategy? And kind of what gives us confidence in a durable -- that we have a durable growth engine here as a company. And I think we're really looking forward to telling that story to everybody.

Yes. I know like we are still -- I think a lot of the research out there is to assume we are in very early stages of agentic. I think UiPath is like going towards agents, agent builders, most this year, like how -- what do you think of the cycle that enterprises is at? How is enterprise readiness? And what is UiPath ready to participate in?

I think we're ready to participate in the broadest transformation that is there. I think it's evidenced by some of the deals and the customers that we're talking about. We had -- we signed one of our largest new logos, LAMs. It had some element of competitive replacement, but what they're asking us to do is competitors of historical competitors cannot tackle the hardest problems that they're trying to solve. They need the breadth of our platform.

And so we are super -- we are already solving those problems. If you look at some of the leading health care institutions that we have as a company, we are in revenue cycle management. We are in prior authorization. We are in claims denials. Like those are multibillion-dollar problems for these customers. And so we are talking more and more at the C level, again, but without ignoring the grassroots, we have a really good balance of it. And I think we're eating the apple from both ends.

We have some really good transformative projects where we're deploying our forward deployed engineers, where we're experimenting with different outcome-based models. And at the same time, we have kind of the grassroots power of our platform that is continuing to generate ROI for our customers.

Got you. I do want to touch on the outcome-based pricing a little bit later. But I guess at what inning that a customer could drive a potential inflection in their spend with UiPath. I think a lot of customers now you have an NRR of like 109 that continue to improve. Is there an inflection point in coming anytime soon?

Yes. So I have the privilege of seeing our entire 10,000-plus customer base. The inflection point is there for many of those customers. When you look at some of the some of the areas and the things that we're tackling. When you look at some of the funding bills that are out there in Congress for some of the -- for many of the agencies, when you look at the major -- the top 10 banks, I see that inflection point.

When you look at our $100,000 to $1 million-plus customers, that net dollar retention rate is not just stronger, but it is uplifting. And so we see that. The question I would ask is -- or that I would say is, when does the entire market inflect? And I would say we're still in the early innings of that, which is exciting because if you take a $1.93 billion ARR company, that's growing 13%, that has strong operating rigor, as that market begins to become clearer, like that has nothing but goodness for us, and we're looking forward to that.

Yes. I think because UiPath has a strong penetration, especially within the Fortune 500 companies. Like how do you view the opportunity between your expansion with current customers versus opportunity of net new logos?

Yes. We are super selective of net new logo. So let me break it down. I think historically, it's been 80-20 in terms of where that growth rate is, the contribution I don't think I would say at this time there's a change to that. Like those are things that ebb and flow every quarter. But the reality is, we have greater than 2,000 customers greater than $100,000. We're approaching 400 customers greater than $1 million. That is an engine that in itself can fuel where we want to go to. It's -- we're not a mid-market company. We are an enterprise company. We were willing -- we're winning our fair share in the mid-market to be clear.

New logos, we want to be super selective on. It's not like in the old days, you'd say, what's the cost of acquisition. There's also a cost of acquisition and a cost of maintenance, right, like that you have that's in that area. So I think when you look at our last 3 earnings scripts, or 5 earnings scripts, you're going to see federal credit unions. There are 5,000 federal -- 4,000 federal credit unions in America. We love that business. We'll go after those types of businesses. There's X thousands of hospitals. We love that business. We're going to go after it. We're not going to go after every bush that seems to wiggle and shake. We kind of let our distributors and our partners do that. And that is gravy to what we really need to drive the -- for the overall company.

Okay. Now I got it. Maybe dive in a little bit deeper on some of the products that -- because orchestration is a brand layer where there's also a lot of competition out there. How do you view UiPath going to tackle orchestration and then there is AI agents and agent builder. Can you kind of break it out which -- like the main pillars between these newer products sets?

Yes. I think orchestration is like saying liquid. So I think there's a lot of competition, but what is the liquid? Is it milk? Is it water? What is that? And so where we -- where people talk about orchestration and all the competition that they say agentic orchestration, managing the swarm of agents is typically where it is.

Where UiPath this plan is business process orchestration. And there's a difference between that. Like we're not just a governance layer for agents. We really look at orchestration as a way to govern and drive the end-to-end workflow for enterprise processes. Invoice to cash, in invoice to cash on any major enterprise, you're going to have robots, agents and humans and you're going to have APIs and different technology and applications that are there. Putting that together, and if you ever get to Google Maestro, just Google, UiPath Maestro, you're going to see visually the process in front of you.

So it is not about an orchestration engine for agents, it is across the entire business process. That for us is actually pretty competitively unique. Very few people have the ability to manage third-party agents, manage third-party systems, has their own tokenless automation, which is our RPA platform and deterministic automation, and the ability to create agents on our platform and have coding agents be able to accelerate the productivity of building on our platform. Those are some of our differentiators in terms of where we are, and we feel really uniquely positioned actually from an orchestration perspective to be able to have both the breadth and depth of that offering for business process orchestration.

Okay. I think there is also like -- because you have UiPath has a core in RPA, robotic whether attended or unattended. But now if you're venturing into a new space where you have other companies, smaller space, in the private or the Frontier labs or the data platform players like Microsoft, where you guys are a big partner with. Can you talk about how you can compete with some of these specific or bigger data platform players as we go forward?

Yes. Our -- like I'll give you 2 points of definition. One is I don't feel like we're competing against the Frontier labs. Frontier Labs can generate code. They can't operate a business process end to end. And so we are that orchestration and governance layer to be able to do it. So I think that's one piece of it.

The second is the breadth and the depth of our platform. So our platform has both deterministic agentic and orchestration capabilities, all 3. If I go to many platforms, they don't have -- some of them may have a genetic capability. Some of them do not. Some of them have RPA capability, some of them have not orchestration, et cetera, nobody really has that end-to-end area.

The third piece is what we're building on top of our horizontal platform is vertical depth. So it is nowadays, it's not enough just to be able to plug in a process. To really automate a process, you need to understand the flow of work and have expertise in those domains. That is why we've invested in WorkFusion, going after financial crimes to be able to solve those problems quickly harnessing the full breadth of our platform, but also infused with expertise that's very hard to replicate, right? Exactly what are the compliance rules? How do you get certified with regulatory bodies for these regulated processes? Those are moats that we have.

And then the last piece from our standpoint is the concept of Switzerland. Like we all joke, is it Claude? Or is -- is it GPT? Claude. Nobody is going to go and say I'm making a 10-year commitment to any one of them. The reality is there's a horse race amongst different models, right, that are out there. So we're not trying to compete with them. We're trying to give choice to our customers and make it have a vendor-agnostic platform to harness their power and automate the processes that we're going after, which are these long-running enterprise workflows.

Got you. I guess just going on like on your customer feedback conversation, right? If you go in, obviously, a lot of customers like Citibank, we have ServiceNow, we have Salesforce across different layers, even legacy workflow. Like what are the top ROI that they want to hear from you based on your customer conversation recently?

Everybody is looking for tangible savings for AI. And what that means is operating costs coming down or quality going up at a much -- at the similar or lower cost, that's there. That's kind of one. Two is they want stability and predictability and governance and trust. And so from an ROI perspective, if you look at some of our top customers, they're still generating $100 million, $200 million return. Some of our smaller enterprise customers. It is -- you can see a 3 to 5x return for many of the processes that we're putting in.

I think that is actually advantageous to UiPath. Whenever it has been a marketing discussion like, hey, we need AI and you have these innovation funds. It's actually harder to compete against the hottest names that are out there into the market. But because it is an ROI discussion, I think that's what's leading also to what we feel in our pipeline and what we feel in terms of the wins that we are getting.

Yes, there's definitely interesting way to kind of look at it because a lot of companies started talking about how you're going to monetize AI as we go towards more outcome-based pricing. I think Daniel and you talk about optionality with outcome-based pricing, like how is the market changing? And how do you think about capturing more bigger piece of this ROI that you're seeing from your customers as we move forward here?

Yes. I think there's -- look, there's different tiers of customer in our mind. Some of the customers are really looking to say, we can transform, give us the software. And I think in those cases, those software subscription will continue to be okay, consumption-based pricing can also come into play.

Then there are CEO and CFO discussions that -- and COO discussions, that we are getting into that are increasing in number. They are saying, we need to get 20% out of finance. We need to get 20% out of operations, cost. In those areas, that is an opportunity for us to use our core platform as a base, but kind of have that motion to be able to build around it. Now we got challenged like do you see progress on the execution for that. The answer is 100%.

Like we are deploying our forward deployed engineers in many processes that we did not have the right to play in 1.5 years, 2 years ago. 3 years ago. We have engagements with some major parts of our customer base that we are attacking that.

In terms of outcome-based pricing, it's -- we say that generically, but there's different ways to think about an outcome. There is a way to think about it of defining MBOs. And if we meet those MBOs, it's there. It's participating and sharing in reward. And I think that depends on the customer. What we don't want to do I think the mistake that companies have made and even the market is over the last year, you always feel like you need to be certain about.

Here is what's going to be there for the next 3 years. I think optionality is super important because the market continues to evolve. So we're not launching new pricing mechanisms every 6 months, but we give ourselves that optionality to capture things, especially for our top-tier customers.

Right. I guess just on like the outcome based opportunity, how do you view that kind of differentiation with it and then versus how you have like main models like the unified pricing model consumption, right? Do you -- what kind of uplift do you expect on the opportunity side?

I think it will be significant as we land those opportunities and deliver against them. I think the hardest thing is getting in and beginning to land and execute, which we are because the moment you get that first piece of credibility you get the second opportunity that's there. So I would say, I think it's a meaningful uplift. We don't publish metrics in terms of what that uplift is. Remember, 18 of our 20 deals, just on a subscription or consumption basis, we've talked about being 3, 4, 5, 6x higher than our average deal size, right? It depends on the quarter.

I think there's a multiple on top of that, that outcome-based pricing can provide for us. And I think it is really about quality for those areas. What I think is very unique to us that I'm seeing, we can move fast. So we don't have a lot of bureaucracy where Daniel can make the call and say, "I'm doing this deal." And I think in other areas, they can't move as fast. They have more defined scopes that they're willing to take on or not take on. We are able to move faster and kind of expand into things and take risk as well.

Okay. The other part is like the pricing model is kind of a little bit noise in there. We have 606 accounting, like licenses because I think a lot of customers still on licenses on-prem deals, like how should we invest to be thinking about just going upfront with like how license is kind of more on-premise deal versus cloud deals, how the company look at it?

Look, I'm going to maintain something that we've said first from the beginning of the IPO through good times, bad times, neutral times, I don't -- revenue is not the defining metric for our company. You'll see quarters where revenue is 8 points higher than ARR growth or trailing 12 months is asymmetric.

Whether it takes on, on-prem, whether they bundle something or whether they go to the cloud. ARR is really the defining metric for us is, like obviously, GAAP being an SEC-regulated metric, it's why we guide to it. It's important for us to be able to model and understand and that's why we've given those assumptions and tried to continue to do so.

But just to give you an example, if somebody decides that they want to run on-prem, a public company -- sorry, a public -- a government entity or a bank or a health care institution. That dollar is -- how it's accounted for is no different in our minds than the dollar that is accounted with 606, whether I take it upfront licenses or not. We're getting the same cash flow. We have the same renewability that we have from that perspective.

And that's kind of how we think about it. So I would tell investors look at ARR, you'll see that very clearly. And when there is some disjoint that happens. We always answer questions and talk about it in terms of some of the drivers that we have. It could be also nothing to do with deployment. It could be just sheer duration of contracts as well.

Right. I think company like UiPath still focus more driving to drive more cloud versus on-prem. Like does some of the more recent security issues that we hear with models out there what Astra or car can do, like does that change the conversation from going more hybrid on-premise that you're seeing from customers going towards cloud?

It ebbs and flows. Like -- what's interesting is I think a differentiator for us is the fact that we have a multi deployment strategy. And I think depending on the environment, people choose how fast they want to go. And anybody here in a bank, I'm assuming many as yourself, banks are not full cloud at this time. And it's been around for 20 years, like there is an entire process and comfort level that needs to get there.

I think that on-prem is increasingly becoming a conversation again in many entities, but people always want a path to the cloud. I don't think somebody is saying my long-term plan is to be on-prem. But I do think they say, we want on-prem, and we want the option to get to the cloud when we're there. So that is why I continue to focus on ARR.

Right. I guess if more customers focus more on, on-prem hybrid, does this change the uplift narrative for UiPath?

No. Actually, it helps us. I mean, if you -- there's not a lot of companies can Salesforce really go on-prem, not that we're competing in Salesforce, right? You have a lot of really cloud-only companies that are out there in terms of what -- where they are and how they've deployed and how they support. The fact that we have a multi deployment strategy is a really good moat for us as we go forward. Then you also have the question of sovereignty, AI models being in the cloud, right? We also have a multi-geographic data center map that allows us to continue to meet different market expectations as well.

Yes. Sounds like we have more 606 accounting issue to come.

I don't really call it 606 accounting issues in all candor, right? I think if you look at the last 4 years, like this quarter, revenue is at 13% growth, ARR is 12.5% growth. I think that it will move. It won't be perfectly together, but I think they generally correlate well over a period of time.

Okay. Yes. I know we have a few minutes left here. I'd love to see if there's any questions from the audience.

When you think about strategic execution, and your shift from RPA to where you are now, how much of it is are you thinking buy versus build in terms of M&A versus internal product development as you go further into orchestration?

Yes. We -- Daniel is an engineer by trade. So there's a lot of build that's there. If you -- I -- like the boiler plate answer is we're going to keep -- we're going to keep our optionality open anytime that we can accelerate our road map. We're not afraid to do in the tuck-in acquisitions as we've demonstrated. If you look at our M&A history and our build history, I think building out the horizontal platform, and we've used M&A as a tool of deepening and doubling down on the vertical proposition is something that is evident with WorkFusion, with Peak, with the different things that we've done.

That is not to say we wouldn't buy a horizontal capability. If it had the right economic equation, it had the right time to value. And as Daniel balances the capability of the team and going across. The reality is Daniel being an engineer, product quality is the most important thing to him. And so I think our -- I think there's going to be a good balance there, but it's going to be more build versus buy. The buy is opportunistic. And I think we buy more than technology, you buy expertise when you're going after these vertical companies.

So last week from Astra, we saw some really significant improvements in AI compute capabilities. So how do you think about the competitive landscape will change for UiPath going forward as the automation plus LLM narrative intact, do you think?

We see a competitive landscape not to be impacted as much by the LLMs and the frontier models. Like as I said, like I think the frontier models continue to generate code. They do not -- if I generate 20 applications within the company, you still need to orchestrate and automate across those applications. So I think what it does do is take the lower end of the automations and the simple automations.

I think those get commoditized and have become commoditized, and we've already acknowledged that and talked about it together. But if you're automating prior authorization within a company, it's a 200-step process that spans across 30, 40 systems. I don't -- I look at using agentic and LLMs to be able to help in that process, it doesn't take the way the need to orchestrate across it.

The second piece is it's honestly become more of a common theme where people are using the cost, the risk like the risk-reward equation between token automation and tokenless automation. RPA is tokenless automation. It is not just UI automation, like our platform has API technology as well, but it is tokenless automation. And I think if you can do the same process with exactness and at a lower cost, what's your motivation to go and take something at a higher cost with higher risk, right?

So I think Frontier models have a place AI models have a place, but so does our platform and so does deterministic. And frankly, remember, we are using agent builder. We are using third-party software -- sorry, third-party models, and we can integrate that into any workflow. People can bring their own models. We are essentially like a software. We are able to channel and harness those things to go after the workflow processes. So from our standpoint, I don't see much of a change from there. that happens, especially in the short term.

Yes. Thank you, Ashim. I think with that, we are up on time here. Thank you so much for participating.

Thanks so much, everybody.

Good day, everyone. My name is Megan, and I will be your conference operator today. At this time, I would like to welcome you to the UiPath Second Quarter 2027 Earnings Conference Call. [Operator Instructions] At this time, I would like to turn the call over to Allise Furlani, Vice President of Investor Relations.

Good afternoon, and thank you for joining us today to review UiPath's second quarter fiscal 2027 financial results, which we announced in our earnings press release issued after the close of the market today. On the call with me are Daniel Dines, Founder and Chief Executive Officer; Ashim Gupta, Chief Operating Officer; and Hitesh Ramani, Chief Financial Officer, to deliver our prepared comments and answer questions. Our earnings press release and financial supplemental materials are posted on the UiPath Investor Relations website. These materials include GAAP to non-GAAP reconciliations. We will be discussing non-GAAP measures on today's call.

This afternoon's call includes forward-looking statements regarding our financial guidance for the third quarter and full fiscal year 2027, and our ability to drive and accelerate future growth and operational efficiency and grow our platform, product offerings and market opportunities. Actual results may differ materially from these expressed in the forward-looking statements due to many factors, and therefore, investors should not place undue reliance on these statements. For a discussion of material risks and uncertainties that could affect our actual results, please refer to our annual report on Form 10-K for the year ended January 31, 2026, and our subsequent reports filed with the SEC. Forward-looking statements made on this call reflect reviews as of today, and we undertake no obligation to update them.

I would like to highlight that this webcast is being accompanied by slides. We will post the slides and I'm happy -- our prepared remarks to our Investor Relations website immediately following the conclusion of this call. In addition, please note all comparisons are year-over-year unless otherwise indicated.

Now I'd like to turn the call over to Daniel.

Thank you, Allise, and thank you for joining us. We delivered another strong quarter with continued execution. ARR grew 12%. Non-GAAP operating margin expanded to 22%, and we delivered our fourth consecutive quarter of GAAP profitability. Over the past 2 years, we've been transforming UiPath for the next phase of our growth. We evolved our platform, our own business orchestration, agentic and software testing; significantly improved our go-to-market execution and operating discipline; and reaccelerated the pace of innovation within the company.

We're a stronger company today and increasingly, customers are looking to UiPath not just to automate individual tasks but to orchestrate complex, long running and exception-heavy business processes and being a critical partner in their AI transformation. We've talked a lot about how AI is changing software. The bigger question now is how enterprises turn AI into real business value. Customers aren't choosing between AI and deterministic automation. They are choosing the best way to achieve an outcome. AI is exceptional at reasoning, but it's probabilistic and can be expensive at scale.

Many enterprise processes don't need reasoning at every step. They need exactness, the same result every time securely, reliably and at the lowest possible cost. That's why we give customers the choice of deterministic or tokenless automation alongside AI. Our approach is simple. Use AI where intelligence creates value and deterministic automation where exactness matters that gives customers the benefits of AI without paying for AI reasoning at every step and ultimately better economics and better ROI at scale. And that's where UiPath is differentiated. We deliver business outcomes by orchestrating end-to-end processes across agents, robots, API systems and people, using the right technology for each step to deliver the best combination of intelligence, reliability and cost.

We are also model agnostic, giving customers the freedom to use the AI models and technologies that are best for their work rather than locking them into a single ecosystem. As AI expands what enterprises can automate, we believe that combination of choice, orchestration and governance becomes even more valuable.

So the opportunity now is to scale what we've built, expanding adoption across our customer base, extending our reach into the business and continuing to translate our innovation into durable growth. And as we scale, strong execution and connectivity across the company become even more important. That's why Ashim will now focus exclusively on his role as Chief Operating Officer.

Ashim has been one of my closest partners and one of the leaders most responsible for the financial and operational discipline we've built over the past several years. As COO, he will focus exclusively on the day-to-day operations of the company, driving greater discipline and consistency across our go-to-market organization, strengthening execution across functions and leading key strategic priorities across the business.

With Ashim focusing fully on the operations of the company, we are making a planned leadership transition in finance with Hitesh Ramani succeeding him as Chief Financial Officer. This is a logical next step and reflects the strength and depth of the leadership team we've built.

Hitesh joined us in 2021 as Chief Accounting Officer and has served as Deputy CFO for the past 2 years, working closely alongside Ashim across the finance organization. He has been a critical partner through every major milestone, including our IPO and has helped build the financial rigor and discipline we have today. Given Hitesh's existing responsibilities and deep knowledge of the business, we expect a very smooth transition and significant continuity across the finance organization. And with Ashim remaining as COO, he and Hitesh will continue to work closely together in their respective roles.

Together, these changes give us greater focus across operations and finance with 2 proven leaders in critical roles as we scale. I'm excited to continue working closely with Ashim and Hitesh, and I am confident in the leadership team we have in place and our ability to execute against the opportunity ahead.

Now turning to our quarterly results. We delivered a strong second quarter, once again beating guidance across the top and bottom line. ARR reached $1.938 billion, up 12% year-over-year, driven by $37 million of net new ARR and revenue of $410 million, up 13% year-over-year. We grew second quarter non-GAAP operating income to $89 million, a 22% margin and up over 400 basis points year-over-year, driven by improved operational efficiency and disciplined execution across the business.

Behind these results, we are seeing the strategy I just described play out with customers. 18 of our top 20 deals this quarter included AI, demonstrating how increasingly central AI has become to our largest customer engagements. Customers are expanding from individual automation use cases into broader end-to-end processes, adopting more of the UiPath platform and in a number of cases, consolidating automation and AI workloads onto UiPath.

And we are seeing this result in larger expansions where AI is attached to the deal. A global insurance provider is a strong example. In a 7-figure expansion, they are modernizing beneficiary claims, expanding their use of IXP, Maestro agents and robots. With UiPath forward deployed engineers supporting implementation, Maestro connects document intake, beneficiary analysis, orchestration, exceptions and human-in-the-loop work into one governed end-to-end process. And because UiPath was already embedded in their ecosystem, they could move quickly on this use case and build on the same foundation as they modernize additional processes across the organization.

In the public sector, the Department of War expanded its partnership with UiPath to support its clean audit initiative across the military services. Building on its deterministic foundation, the department is adding Autopilot, our IDP solutions and test automation to automate critical audit and reconciliation work.

We're also seeing governance and reliability become real competitive differentiators. A leading financial institution chose UiPath over other orchestration providers as its single platform for end-to-end processes. Maestro was the only solution able to orchestrate across their homegrown applications while meeting their governance and compliance requirements at scale. It's already in production on a critical revenue channel process, combining deterministic automation with human-in-the-loop safeguards.

And these aren't isolated examples. Across both new logos and expansions, we are seeing customers standardize on UiPath and consolidate point solutions onto our platform. A leading U.S. regional bank is consolidating its entire automation program onto UiPath, using Test Cloud for conversion testing and agentic processes across fraud and compliance to help manage risk through a significant module; and one of Canada's largest financial services companies, working with Ashling Partners to migrate its entire automation footprint to UiPath and plans to use coding agents to power that migration with the goal of lowering maintenance costs and accelerating time to value. And on the expansion side, Fortune 200 financial services firm is moving all their automation needs onto UiPath in a multimillion-dollar CIO-driven initiative, while expanding their use of Test Cloud to test the investment management software they deploy to customers.

The common thread across these wins is consolidation. As customers think about automation and AI together, we're increasingly seeing them look for one platform that can build, orchestrate, test and govern the entire process. I am excited about the results we are seeing from coding agents, pilots and implementation.

Our initial results from our forward deployed engineers and the coding agents reduce effort by nearly 60%. As we build on this, it has transformational impacts on our customers' time to value and overall TCO.

We are seeing the same potential with customers like a leading U.S. energy company. They're using Cursor with UiPath across the entire automation life cycle from architecture and development through testing, code review and production deployment. The coding agent directly creates UiPath workflows where our platform keeps the development process governed and standardized. So this isn't just about AI writing code faster. It's about making the entire automation life cycle fast. And that's an important part of why we believe AI expands the automation market. It doesn't just create new use cases. It lowers the cost and effort required to build that.

Moreover, to speed up the implementation even further, we announced a new developer friendly workflow automation tool in public preview. It lets developers use coding agents, they already worked with like Claude Code, Codex, Cursor and GitHub Copilot to both orchestrate business processes and automate manual tasks via API and agents. Combining the speed of AI native development with the governance enterprises need, our horizontal platform remains a core strength, giving customers one platform to automate and orchestrate processes across functions, systems and technologies. And increasingly, we are pairing that horizontal strength with vertical and outcome-oriented solutions that bring us directly to line of businesses buyers around specific business outcomes while creating a natural entry point for broader platform adoption.

This quarter, we saw strong traction with customers, including a Fortune Global 500 manufacturer where we are modernizing their accounts payable operations with our office of the CFO invoice solution, automating roughly 700,000 invoices annually. What won them over is exactly what our approach is built to deliver, 96% document processing accuracy in the proof of concept, automated supplier communications, rich operational dashboards and an expected 50% reduction in both invoice handling time and support. And in health care, a leading U.S. health system chose our denials resolution solution to automate medical claim denials with their revenue cycle management process. The solution will help automate appeal creation and submission across inpatient and outpatient operations, allowing them to pursue millions of dollars in claims that previously fell below the threshold for manual review and potentially recover meaningful additional revenue.

WorkFusion extends that's approach further into financial services. The integration is progressing in line with plan, and we are encouraged by the customer response and the pipeline that is building. Its purpose-built agents for financial crimes and compliance give customers a more complete outcome-orientated offering out of the box.

Testing is another area where we continue to expand our reach, particularly through our partner ecosystem. We recently expanded our partnership with Cognizant, which will embed UiPath Test Cloud into its Testing as a Service and many services offering, helping customers move from manual script-based testing towards agentic testing. Cognizant will also help scale Test Cloud onboarding and adoption through its global delivery model.

Before I close, I'm also pleased to welcome Yazdi Bagli to our Board of Directors. Yazdi brings deep technology, operations and enterprise transformation experience from Kaiser Permanente, Walmart and Procter & Gamble, and I'm excited for the perspective he'll bring with UiPath.

And finally, we are looking forward to seeing many of you in Las Vegas next month. We'll kick off with our Investor Day on September 22, where we'll share more on our long-term strategy and product road map, followed by FUSION, our annual user conference, from September 23 through 25. We have a lot to share, and I hope to see many of you there. Please reach out to our Investor Relations team for more information on our Investor Day.

With that, I'll turn the call over to Ashim.

Thank you, Daniel, and good afternoon, everyone. I'm incredibly proud of what our finance team has accomplished, and I also want to congratulate Hitesh, who has been an incredible partner and leader in our organization. Hitesh and I have worked side by side for many years, and there is no one better prepared to lead our finance organization. As I fully focus on my role as Chief Operating Officer, I'm excited to work closely across the company to drive consistent execution and help scale the business. A big part of that is continuing to strengthen our go-to-market execution. We're spending a lot of time with our sales leaders and account segmentation, making sure we have the right resources and strategy against the right opportunities while working across the leadership team to bring greater connectivity to how we take the breadth of our platform to market.

The same focus extends to how we drive adoption and utilization across our customer base and how we work with our partners. These have been important priorities for us, and we're continuing to strengthen the connection across our field, partners and customers to drive expansion and make it easier for customers to adopt more of the platform. We have a strong leadership team, tremendous innovation across the platform and a significant market opportunity ahead of us. I'm excited about what we can accomplish together.

In a few minutes, Hitesh will take you through our guidance for the third quarter and the remainder of the year, but first, I'll walk through our results for the second quarter. Turning to the quarter. Unless otherwise indicated, I will be discussing results on a non-GAAP basis and all growth rates are year-over-year. I also want to note that since we price and sell in local currency, fluctuation in FX rates impacts results. As we go forward, we will provide the impact of FX for both the incremental impact since our prior guidance and the year-over-year impact.

Second quarter revenue grew to $410 million, an increase of 13%. Normalizing for the year-over-year FX headwind of approximately $8 million, revenue grew 16%. This included an incremental $1 million FX headwind since the time of guidance and our first quarter earnings call. The year-over-year FX headwind was driven by the Japanese yen, the Romanian leu and the Indian rupee.

ARR totaled $1.93 billion, an increase of 12%. This included a $1 million year-over-year FX tailwind and no incremental impact since we guided our first quarter earnings call. Net new ARR was $37 million, up from $31 million in the prior year quarter. The year-over-year FX tailwind was driven by the euro. We ended the quarter with approximately $1.3 billion in cloud ARR, which includes both hybrid and SaaS, and an increase of more than 19%. We ended the quarter with approximately 10,350 customers with attrition continuing to be concentrated among our smallest customers, while customers with more than $30,000 in ARR increased 6% year-over-year. This quarter, we signed one of our largest new logos in company history, a top Canadian bank looking for a platform that could support their evolution to agentic workflows. We demonstrated that with an agentic proof of concept for their third-party demands process, bringing together agents, robots, people and systems, all orchestrated by Maestro with the governance and compliance required at scale.

This win reflects our customer strategy of adding new enterprise customers with significant expansion potential. And this quarter, we also added logos, including Flexsteel, [ Azul ] and Purdue Federal Credit Union. Our strategy is increasingly focused on winning and expanding within the world's largest enterprises, and we're seeing that strategy work. Customers with $100,000 or more in ARR increased 10% to 2,666, while customers with $1 million or more in ARR increased 21% to 387.

Our retention metrics also remained strong. Our dollar-based gross retention remained best in class at 97%, and our dollar-based net retention rate was 109%, a 2 point increase year-to-date, demonstrating stabilization across the business. Adjusting for FX, dollar-based net retention rate was 108%.

Turning back to the quarter. Remaining performance obligations increased to $1.378 billion, up 14%. Normalizing for the FX headwind, which was approximately $19 million, RPO grew 16%. Current RPO increased to $901 million, up 14%.

Turning to expenses. We delivered second quarter overall gross margin of 82%, and software gross margin was 90%. Second quarter operating expenses were $247 million.

GAAP operating income was $32 million, our fourth consecutive quarter of GAAP profitability, up from the prior year GAAP operating loss of $20 million. GAAP operating income included $45 million of stock-based compensation expense compared to $78 million in the prior year, a decrease of 42%. As a percentage of revenue, stock-based compensation was 11%, down over 1,000 basis points from the prior year. Second quarter non-GAAP operating income was $89 million, representing a 22% margin, up over 400 basis points year-over-year and driven by our continued focus on operational efficiency.

Second quarter non-GAAP adjusted free cash flow was $31 million compared to $45 million in the prior year quarter, driven primarily by the timing of tax-related payments. We ended the quarter with a healthy balance sheet of $1.4 billion in cash, cash equivalents and marketable securities and no debt. During the second quarter, we repurchased 2.4 million shares at an average price of $9.63.

And now I would like to hand it over to Hitesh to go through guidance.

Thank you, Ashim, for your partnership and mentorship over the years. I'm excited to step into this role and to build on the strong foundation we have put in place.

Turning to guidance. Our philosophy here is unchanged. We guide to what we see in front of us, and we maintain a prudent outlook. And we are pleased with the team's execution in what continues to be a variable macroeconomic environment.

Before I walk through the specifics of guidance, beginning this quarter, we will provide the impact of FX for both the incremental impact since our prior guidance and the year-over-year impact. As Ashim mentioned earlier, our results reflects movements across several currencies, including the euro, yen, Indian rupee and Romanian leu.

Turning to guidance. For the third fiscal quarter 2027, we expect revenue in the range of $440 million to $445 million. This includes no incremental FX impact since the time of our last guide and a $10 million year-over-year FX headwind. ARR in the range of $1.992 billion to $1.997 billion. This includes a $1 million incremental FX headwind since the time of our last guide and a $4 million year-over-year FX headwind. Non-GAAP operating income of approximately $100 million and we expect third quarter basic share count to be approximately 523 million shares.

For the fiscal full year 2027, we expect revenue in the range of $1.789 billion to $1.794 billion. This includes a $1 million incremental FX headwind since the time of our last guide and $20 million year-over-year FX headwind inclusive of $2 million headwind that was realized in the first half of the year and an expected headwind of $18 million in the second half of the year. ARR in the range of $2.065 billion to $2.070 billion. This includes a $1 million incremental FX headwind since the time of our last guide and a $5 million year-over-year FX tailwind inclusive of $10 million tailwind realized in the first half, partially offset by expected headwinds in the second half of the year. Non-GAAP operating income of approximately $445 million. And finally, we continue to expect the fiscal full year 2027 non-GAAP adjusted free cash flow of approximately $425 million and a non-GAAP gross margin of approximately 84%.

Thank you for joining us today, and we look forward to speaking with many of you during the quarter. With that, I will now turn the call over to the operator. Operator, please poll for questions.

[Operator Instructions] Our first question will come from Sanjit Singh with Morgan Stanley.

Can you hear me?

Loud and clear, Sanjit. Now no.

Sanjit seems to be having some technical difficulties, so we will come back to him and go to our next question. Our next question is going to come from Michael Turrin with Wells Fargo.

This is [ Phil ] on for Michael. I have a quick question on the FTEs. It sounds like with coding agents reducing the FTE implementations quite significantly, how much more deployment capacity are you guys getting per FTE? And does that change any of your hiring plans as customer demand scales?

Yes, we are in kind of proving stage at this point to understand how much incremental value we get from coding agents in conjunction with FTEs. Our initial results are very encouraging, and if -- I believe that we are seeing a positive trajectory. And I think this is not so much about how many FTEs we plan to hire, but it's about how much our customers can accelerate their time to value. And this is an equally important technology for our partners as well as many of our customers do the -- use the implementation services provided by our partners.

We will keep you up to date. This is a very important focus for us going forward, and a big focus of the entire P&E organization is to keep improving the performance of coding agents on our platform.

Next question will come from Bryan Bergin with TD Cowen.

Thanks for the question. And Hitesh, congrats to you on the CFO role. I wanted to just get a sense if you can give us an update on your approach and monetization here on agentic and AI solutions. How is that conversation evolving with clients? And can you also comment on how model costs and tokenomics are influencing kind of the contracting appetite for the broader deals with agentic and deterministic?

Yes, we continue to see an increased appetite from our customers to get the platform that combines, I would say, intelligence with exactness. And our platform is best in the world in process orchestration, in task automation, in document processing. And we are quite agnostic in supporting the best agentic frameworks in the world like LangChain, Claude Agent SDK and Codex harness and some others, and we are model agnostic. And this -- I think this combination, it's extremely appealing to our customers. We provide basically the rails for running the business, while they can choose the flavor of intelligence that they have to deliver.

Okay. And my follow-up, just maybe can you speak to the improvement of net new ARR in 2Q? Obviously, trying just distill how much is coming from AI-related products. Any way you can help kind of break that down between contribution from penetration of new agentic AI offering deployments into your existing clients versus perhaps landing kind of newer clients with the full suite here? It's certainly encouraging to hear the stat on the top 20 largest deals you gave us. But then sticking with net new ARR, just any caveats as we look to the implied second half that you've guided to?

Yes. I'll turn it over to Hitesh for -- to answer on guidance. Look, we're right now reporting ARR product like periodically as we talk about, Bryan, but the stats that you talk about, they're encouraging. And I think there is more encouragement when we listen to our customer calls and our sales team, the executive touch points that we're having. The reality is they are making the deals have a higher ROI, which leads to larger deal values. And what is also encouraging is we're really attacking larger, more complex problems. And I think as the world kind of continues to change, that increases our stickiness. And so it really has a two-fold area, giving us more upfront but making us more strategic within the customer. And we're really pleased with the progress just across the platform and our ability to deliver that. Hitesh, if you want to talk about guidance part.

Yes, sure, Ashim. I mean as I mentioned, our philosophy, as it relates to guidance, has remained unchanged. We guide to what we see in front of us. Also, we take a prudent approach.

With regards to platform, as Ashim mentioned, the platform positioning is resonating extremely well with our customers. I myself met with 3 of our customers this past week, and every single conversation is resonating very well. As we also mentioned, 18 of our top deals -- 20 deals included AI this past quarter. We are making this equation into account as we think about our guidance for not only Q3 but also for Q4.

Your next question will come from Scott Berg with Needham & Company.

Daniel, I wanted to start on go to market and some of the sales successes you seem to be having. You've talked a lot the last couple of quarters about improved execution there, but it seems to be meeting an end market that's also seeing some improved demand. Where do you think you are in that cycle? Are you back now on sales execution kind of level that you want to be, kind of 100%? Or do you still feel like you have a little ways to go to hit your stride properly?

I think it's -- I think we are working right now on the both ends of the spectrum. I think on the product side, we are making the most innovative steps that I think we ever made in our product. And we are ready to announce at our big FUSION event basically our new doctrine about how we are seeing the adoption of AI and orchestration and automation across -- of an enterprise.

And on the sales side, I think given the market dynamics, I think we have started to understand a bit more how our customers think about the AI adoption. I think in a way, among our existing customers, we are seeing reduced confusion, if I can say, about AI. And they understood -- I think it's a better understanding on when it's best to use AI, when it's best to use automation and how they coexist with each other, which I cannot say so much about customers at large. It's more -- when we go after new logos, it might be a bit of a different conversation.

Overall, we are also seeing an increased appetite in the market for outcome-based deals, which it's an interesting area for us. I think at this point, it's just -- they are just scattered and really across the globe, but it might become a much bigger trend. But we are watching closely to understand how we play on these both ends.

Understood. Helpful there. And then, Ashim, as I look at your net revenue retention metrics, they've been incredibly stable the last 6 quarters. But -- and maybe you'll cover this in your Analyst Day coming up. But how do we think about net revenue retention over the interim period here? Do you have a lot more to sell? It sounds like the demand environment is certainly improving a little bit for you all. My guess is customer expansions start to come back versus maybe what we've seen a couple of years ago. But could that number be over -- back above 110% for an extended period of time? Or is this high 100% range, 108% 109%, the right way to think about NRR for the near term?

No, I mean, look, that's what we're going for. And I think the progress we've made has actually been really phenomenal. We ended last year at 106%, so we are up 3 points already as we move to that goal. So I would say it's -- the trajectory is upward in a stable way, which I think is really good versus kind of up and down. And so we feel very good about it.

To your point, we have more products that we are scaling into our customers, as Daniel mentioned. As I mentioned, I think the sales execution continues to improve. And frankly, our focus on consumption is also very critical in that discussion [ and the statement ]. So we actually feel very good about that trajectory.

We'll talk about it more. We obviously don't do long-term forecasting around these key metrics, but the trend is positive. And I would also note the movement upwards and stability is happening at higher and higher scales, which speaks to the expansion on a dollar basis expanding so that's kind of the color that I would give here.

Your next question will come from Sanjit Singh with Morgan Stanley.

Two-parter, maybe one for Ashim. As we look to the federal business in Q3, just their fiscal year is coming up at the end of September, so just thoughts on the federal pipeline opportunity, how that's shaking up.

And then a question for Daniel. I think you and I have been talking about sort of what sort of playbooks and use cases are resonating right now. I think you've called out software testing as something that's particularly resonating. Has there any been other sort of use cases, whether it's sort of industry-specific, cross industry-specific use cases that have started to resonate in Q2?

Yes. So look, I think our federal business is doing a really exceptional job. Joe Perrino is the leader there. I think him and the team has really impressed us and the entire team just with how close they are getting to the customers and the agency is, partnering with incredible partners that are doing transformative work in the Department of War and many of the agencies well beyond it and applying and learning some of the areas that we have in our health care business to some of the health care processes within the government. All of those things are shaping up very nice with the pipeline. And the work that we've done in terms of getting close to understanding and influencing kind of the environment there has been really phenomenal. So we're actually very pleased with the trajectory of the federal business.

Yes. And on the use cases, we are very excited here about our use case sellings and our vertical solutions approaches. So besides test, we are seeing increased demand around the revenue cycle management and of course, on financial crimes where we see good pipeline creation. But also office of the CFO is a place where we are traditionally extremely strong. And also we launched recently our solution in financial services for loan originating.

So overall, this is becoming a big area of focus for us as we believe that the vertical selling, solution selling has the capability of pulling our entire platform, and we have -- traditionally, our business model was a lot on land and expand, and this really help us to continue that motion.

Your next question will come from Jacob Zerbib with William Blair.

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This is Jacob on for Pat McIlwee. You spoke a little bit about less confusion around AI in the market, which is great to see. Can you talk a little bit about how your sales team is adapting to this new environment and particularly as it relates to large new customer lands?

I think we are doing a lot of education in the market of what is basically the seam between where AI is best and where exact execution is best. And we are -- as I said in the previous answer, we're kind of changing our sales approach to be much more use-case-based selling. We have starting this trend in our U.S. business a couple of years ago, and we perfected it here, and. We plan to roll it more across the -- our entire GTM organization.

Your next question will come from Raimo Lenschow with Barclays.

Perfect. Ashim, all the best, first of all, and then 2 questions. Daniel, the one thing that came up -- is coming up here today, and that's probably why you -- to share -- why we have [ to share reaction after our market ] is that it looks like there's a new AI model coming out from one of the big frontier guys that apparently is like so much better in kind of doing jobs, doing kind of workflow. I don't want you to specifically answer that.

But like in your conversations with clients and with customers, like how do you think about that? Obviously, AI is going to get better but you guys are more in the deterministic world. Like how do you think about the workflows you guys are doing versus the workflows you kind of want to share or AI should be doing? I know it's a bit of a fundamental question again, but it's just coming up again, and so it would be good to go through that again. And then I have one follow-up for Ashim.

Look, I had many discussions with our customers across the last few months. I think if you look at AI is getting more powerful with the day obviously, but there is an interesting limitation of AI, which I want to point you to, which is the AI cannot learn on the job. Like when you hire an employee, you expect that -- you don't give them manual. This is how our business run. No company is able to have this manual. And an employee learns by reading some documentation, but learning from other people, being in meetings, talking to customers. It's a continuous learning. So they get transformed by this experience.

That's not true for AI. It's the same model you apply to all enterprises. In every question you ask AI, you basically have to provide the entire modus operandis of your enterprise. So that's -- if you think of this limitation, it's becoming clearly that enterprises will have to create, what I call, a map of work where you will have to describe in a very specific way how the enterprise work. And you will have to also put as much effort as possible into building the framework that gives your rails in how the business operate. In my opinion, everything that can be done by automation and orchestration should be done by that because it's exact. It's reliable. It's tokenless. It costs less.

And then AI is basically surrounding into this enterprise framework. You can -- in a way, you can look at our platform like an enterprise harness that can control and give AI all the information required to run an enterprise. But all the customers I talk to, they want these workflows to sit on their property, not on the model's property. And all this manual that I'm talking is their property. It's not model's company's properties. So to me, that's the -- that's really the best combination into having the enterprise framework that provides orchestration automation and that is the harness around the model. That would provide the best optionality for an enterprise.

Yes. Okay. Okay. Perfect. Yes, it makes sense. And then Ashim, if I think about ARR and revenue -- or the subscription revenue that you're reporting, there is obviously -- there is a relationship, last couple of years, revenue growth kind of run ahead of like what we see on ARR growth. Like how do you think about that relationship and especially going forward as we think about going from here? And all the best.

Thanks, Raimo. And I'm still here, but I appreciate everything, and I'm super excited to partner with Hitesh and Daniel. Look, Raimo, look, from -- remember, like we have the 606 accounting phenomenon that is there. And so as we sell more of our total platform upfront, there is more -- it changes the mix of licenses and kind of the cloud-based software is particularly in some of the bundling of our platform. We'll get into more of that at Investor Day, so to speak. There's still a minor SaaS headwind that hits there, but depending on the mix of the deals and where we're selling more platform, that can result in a mix shift between kind of the subscription service revenue and the license revenue. That's really what it is.

And so when you look at overall ARR, as we point to in net new ARR, we're actually pleased with the acceleration that now -- we're now seeing here, right? And as I just want to emphasize that for everybody between 606 and beyond, last year, we're really kind of going down year-over-year. First half of this year, we were kind of -- like first quarter, we were pretty well stable. And you can see the results there for second quarter in terms of the acceleration, and that really shows you what we feel is the better reflection of the business and its trajectory today.

Your next question will come from Terry Tillman with Truist Securities.

Yes. Can you all hear me okay?

Yes, Terry.

Yes. And Hitesh, congrats to you on this expanded role as CFO. And 2 questions. The first question is just on the 18 of the top 20 deals, including some sort of AI product attached. I am curious though, is it pretty similar in terms of that initial landing or impact? And was outcome-based monetization involved in any of those? And then I had a follow-up for Ashim.

Yes. I mean, again, the 18 of the top 20 deals, which include AI is basically how we are seeing an excitement towards the platform from our customers. That's what we are seeing that we see whenever AI is part of -- or the platform is part of the deal composition, the deal is naturally much larger than what we would have seen otherwise. And so that trajectory is there.

Do you have a question for me?

Absolutely, I did. Yes, I've got the harder one for you, Ashim. I'm kidding. Talking about strengthening execution and leading strategic priorities, I assume you've got a whole slew of things that are more kind of low-hanging fruit, near-term things and then maybe as you all end the year and you continue to evolve products, maybe there are some bigger things into next year. Anything at all you could share early on, on some excitement in areas you see where you could have a quick impact?

Yes. I think we're already having quick impact. Look, I think, especially kind of in terms of getting off to a fast start post sale, I've seen a really remarkable execution and turnaround from our teams. Those turnaround times are now happening pre-deal closure where our teams are moving faster on the delivery area. The second piece is just the coordination between our partners, our services team and our FTE team as we go through complex implementations. I feel like those are areas where, while we can always improve, we're seeing some of the low-hanging fruit getting addressed there.

And I will tell you, I'm just super excited by the delivery and the connectivity that we see with the product team. Raghu Malpani, our CTO, is incredibly field oriented. And so that connection between product and delivery and go to market, I think is something that, as it continues to strengthen, really gives us a right to win as we take on larger and more complex problems for our customers.

Your next question will come from [ Vinod ] with Evercore.

You mentioned improved sales execution. Can you talk about some of the specific factors that are driving the improvement? And then are there any changes to how you're kind of compensating reps to incentivize them to get customers to try out more of your AI products?

Yes. I think the first thing is it's really like the team on the ground. Like we have incredible leaders across our, what I would say, our market units, like U.S. financial services, U.S. health care, public sector, our manufacturing and what we call summit, kind of like our industrial and manufacturing enterprises and really globally. And many of them have been in seat for a good period of time.

And so I think it really starts upfront with their focus, right? It's less about kind of Daniel, myself and top-level leadership but really the expertise that is being deployed on the field and just the message around customer first and trying to continue to cut the bureaucracy that we have over the last 2 years, and we still can do more, to be super clear on that.

So I think that's one. The second piece is I do think like the cross-functional connectivity between product, sales, marketing, like I think that is continuing to strengthen. It's very fast paced. So how do we enable our sales teams faster and more thoroughly with better content? Like those are areas of focus for us that are being driven really by a number of leaders across the company.

And in terms of compensation, we, of course, use sales comp as a tool to drive it. The reality is in a lot of customers, there is a pull towards a broader platform. And frankly, combining probabilistic with deterministic automation really gives -- it is a part of what we have. As we launch new products, we, of course, try to do incentives, whether that's STIPs or uplifts on quota retirements. We do that selectively, and we're really pleased with the results. But we have to continue to do that as the environment and our product portfolio moves.

Next question will come from Sanika Merchant with RBC Capital.

This is Sanika on from Matt Hedberg from RBC. Congrats on the quarter. You've talked about the positive traction you're seeing on your agentic offerings. Can you talk through how you're thinking about pricing for the company's agentic offerings over time, especially as customer adoption of these offerings starts to scale?

We -- I think we are still experiencing with different pricing model on our agentic. We introduced recently a transaction-based pricing that it's all inclusive in our process orchestration of all the necessary calls that one has to do to complete the transaction. I would say that probably we are going more towards outcome-based pricing that would be inclusive of the tokens required to complete the transaction.

Got it. Super helpful. And just as a follow-up, you've talked about ARR acceleration and also talked about reaching the $2 billion ARR milestone. What would you say are the most important factors that could drive you to the higher end of your fiscal year '27 ARR expectations? And are there any puts or takes you would call out that we should keep in mind?

Yes. I mean, again, as I mentioned earlier, we are seeing significant alignment with our customers and the platform story is resonating extremely well with our customers, especially the combination of deterministic and agentic. That is -- which is helping us expand the deal size. And so that is one of the key things, which we are excited about, and that is something which is baked into our guidance as we think about Q3 and Q4.

Your next question will come from Keith Bachman with BMO Capital Markets.

This is [ Jonathan ] on for Keith. Daniel, I wanted to direct this to you. You've talked a lot about governance and orchestration as customers are moving AI initiatives into production. So I wanted to ask, as you're engaging with customers today, where are you seeing the greatest urgency? And do those discussions tend to start with governance and control requirements or with broader orchestration initiatives?

I would say that there is an increased appetite of our customers to get the breadth of our platform. I think in a way, our platform aligns very well with the Gartner Magic Quadrant that is called business orchestration and automation technology. So I don't think necessarily that is -- customers are waking up thinking I want to buy orchestration. But I think definitely, our customers are waking up thinking what is the best platform that can help me get the outcomes, run the processes faster with less human errors and bringing the AI but in a way that preserves my intellectual property. I think this combination of factors is what drives the platform at this point.

This concludes our Q&A session. I'd now like to turn the call back over to management for closing remarks.

Thank you so much for all the questions, and we are looking forward to seeing as many of you during the next few months and especially at our FUSION event in Vegas. Thank you.

Good morning and thank you for joining us for the UiPath session at the Growth Stock Conference. I'm Pat McIlwee, and I'm an analyst here in the software group at William Blair as a part of which I cover UiPath.

I'm required to inform you that a complete list of disclosures and potential conflicts of interest are available at our website, williamblair.com. We're very happy to have the UiPath team back at our conference this year, including COO and CFO, Ashim Gupta, as well as Allise and I think, Jake as well from the IR team here in the audience.

UiPath is one of the leading automation and orchestration software providers for the enterprise, enabling businesses to automate repetitive digital workflows across applications, systems and business functions. The company just reported their first quarter results last Thursday, and their positioning to benefit from the adoption of AI and the enterprise is increasingly clear. So I think it's a great time to be digging into this story. So that's my quick two-liner on the business, and I'll turn it over to Ashim for a better overview of the company.

Awesome. Thank you, everybody. So I'll put the safe harbor up for a second. It's actually really great to be a part of this team and to see everybody here today. I would say we came off of one of -- like, to me, one of the most foundational quarters that we've had. And the reason why I look at it is there's 2 parts of a quarter. It's kind of what you deliver and the foundation about -- that foundation that you're building to deliver kind of the next stage of growth for the company. And for UiPath right now, if you look at where we are, go back to founding of UiPath, 5 years, we took RPA and made it a $1 billion revenue business. In 2019, we branched out from RPA. And I -- if there is kind of a couple of bullets I would impress is like we are not an RPA company. RPA is one part of our platform. But in 2019, we began adding to our AI portfolio, both IDP in terms as a intelligent document processing for structured and unstructured data, process intelligence, communications mining. When you look at where we are today with 18 months ago, launching our agentic -- our business process orchestration, which is Maestro that orchestrates humans, robots and agents. Our AI ARR now is $200 million. That is not switching a metric, changing a definition. That is solid ARR that is coming from real value that we are providing customers with our AI platform, and look forward to talking more about it in our breakout sessions and within our Q&A.

Then you look at fundamentally where we are. $418 million of revenue, that grew 17%. Our ARR growth rate at $1.9 billion is 12%. And when you look at that, that is now stabilized. I think one of the big questions for UiPath was the trajectory. And when you look at last 2 years, we had decelerating ARR and revenue, really some execution missteps, but also some of the change in macroeconomic conditions, et cetera. Right now, if you look at our last couple of quarters, especially solidified in this quarter, we have stabilized the business and the growth rate at greater than 10% as a company. And that is kind of on the backdrop of what continues to be a pretty macroeconomic variable environment. The other big important thing is we're not spending money to get growth. We're not buying growth necessarily. So we are GAAP profitable now. And that is our first, first quarter of GAAP profitability. Again, you look at trajectories, 2 years, we had fourth quarter where we were GAAP profitable. This is the first quarter that we are profitable for our first quarter. And you can just start tracing the trajectory of our stock-based compensation going from greater than 23%, down below 13% or at or below 13%. And so when you look at that together, foundationally, we have a strong AI platform, solid growth at scale, but which we're not fully satisfied with and we'll talk about it further, and a really good profitability equation, both on a GAAP and non-GAAP basis.

So here's our platform. When I say we're not an RPA company, you can see RPA is one box there. I don't want to discount RPA. If I called RPA tokenless automation, or if I call it, deterministic automation. That really resonates in terms of the applicability that it is with the customer. So this isn't basic screen scraping. The deterministic parts of our platform are powering significant enterprise grades automations in highly regulated industries. But look what's now built around it. So we are one of the few platforms that can have human in the loop, build deterministic automations and deploy them on our platform, build and deploy AI-based automations or agenetic automations on our platform. We are not vendor locked in, so people can build -- bring their own models, their own agents to our platform. Our goal is to orchestrate the workflows for regulated industries and complex enterprise processes globally, that ties robots, agents and humans together. So one other misnomer that you may hear, agenetic orchestration is different than business process orchestration. Agentic orchestration is orchestrating agents. Agents are one piece of the equation if you want to drive enterprise automation at scale. These are our advantages, and we'll touch base on them more, but we are a truly unified platform for what I just talked about. What is exciting is we are an incumbent. So we are not trying to go -- if you think about the hundreds and thousands of automations that are there on a deterministic side, one of the ways we were able to grow our ARR base is by looking to the left and to the right and being able to get agenetic processes or agenetic steps that were not previously automatable and then wrapping that around orchestration. And then besides governance, which is something really critical, we'll talk about in Q&A, we have deep vertical expertise, health care, financial services. It's not enough to have software and code. You have to understand the processes and that is an expertise we've been building over the last 5 to 6 years. So these are our customer metrics. You can see our largest customers are growing. We are super proud if there is a metric that is there to look at. It is our customers greater than $100,000 and our greater -- customers greater than $1 million, you can see that customers who know us, these are Global 2000 customers as the majority of them, they are growing fast with our platform. That should show you that our platform is not just relevant today, it is value-added.

And then last is the partner ecosystem. We'll talk about it, but we value this not just because it's a page that is important to all software providers, but it really speaks to the openness of our architecture and how we can partner across every different cloud base that is out there. Every different technology company that is there. We're not married to anyone. So with that, I'm really excited to answer questions. And I thank you for everybody, and I look forward to your questions as well.

Awesome. Thank you, Ashim, for providing that perspective on the UiPath business and the toolkit that you provide to these businesses. With that context, can you just set the table by kind of in simplistic terms, talking about the distinction between deterministic automation and then more probabilistic automation?

So deterministic automation is rules-based automation. So where you need the same answer every single time. Probabilistic or agenetic orchestration is giving software the agency to make decisions. And as it does that, it will make a different decision every time. I'll give 2 quick examples. And for time, I'll try to be brief. Daniel, our founder actually gave me this challenge when I -- when we were talking about it. Go into Claude or OpenAI and ask it to calculate 2 large numbers. It will rely back on a calculator because that is deterministic. But if you say, do not use a calculator, will you be able to produce the same answer every time? It's answer, actually response is, no. Because LLMs or the models that are there, they're probabilistic, they interpret data each time differently. When you look at processes like claims, mortgage, accounts, I shouldn't -- my Chief Accounting Officer and Deputy CFOs in the audience, we don't want to have 2 different calculations for revenue depending on the mood of a model, right? You need deterministic automation. If you have a healthcare claim, you need to be able to look at it. So deterministic automation is super important for enterprise-grade processes because it is low cost, low complexity and the highest level of dependability. Agenetic has its place. There are things that deterministic automation can't do. So when you put the 2 together and then you wrap it with the ability to orchestrate it, you have the chance finally to get to a fully automated enterprise, which we are super excited to be a part of.

That's great. And so you guys obviously provide a platform that encompasses both of those capabilities, deterministic and probabilistic and there's some harmony between the 2, right? But historically, you've been kind of the undisputed leader in RPA market using bots to automate repetitive tasks. So as AI continues to make its way into the enterprise, how do you see those RPA workflows evolving alongside this technology?

They're very synergistic. When you look at our customer base, no one really sees a bit, if -- no one has come to us to say, we are going to take a process that is working that is low cost, that is highly dependable. And we're deciding to move it to a higher cost, lower dependability solution, right? The area -- the only area where we see -- where we have seen that piece of it ever come in is personal productivity. And I think one of the misnomers for UiPath. In 2019, we launched something called a Robot for Everyone, right? That is a very low part of our base. That means if I want to go and download an e-mail or summarize a document, right? Those are things our platform can do today. And it was part of a strategy back in 2019 -- 2018 and '19 in terms of where we are. When you look at our business today, it is regulated industries and it is high complex enterprise grade automation. That is the majority of our revenue. So when you look at from where we stand today, you need deterministic and agenetic to really drive that in that tier of relationships.

Got it. And before you move on, given we have a lot of journalists here, can you just talk about your pricing model, how the digital bots are priced, how your orchestration solution Maestro is priced and how you present the ROI to your customers in your sales motion?

Yes. So one thing that's like a great advantage of UiPath is we're not really, [ seat-based ] pricing is not a major component of our pricing. So let me tell you what it's not to start with. The second piece is we don't have tokenization at this moment. So there's no like large-scale token consumption that is driving a short-term revenue boost in any which way. The way that we price is server-based pricing for our key deterministic parts of our platform like unattended robots. We have what I would call kind of subscription consumption-based pricing, which is you buy a certain amount of units, right? You buy $1 million worth of units. And for every page, you deduct [ X units ], right, as an example, that you process. For every execution that you do on our orchestration, you can retire x units that are there. Those are the 2 primary methods of our pricing.

Got it. Okay. And I think it's clear, we'd kind of be beaten around the bush if we didn't acknowledge there's some fear of disruption associated with some of this technology. But when speaking to your customers and customers of your peers alike, it seems like they're leaning more into your trusted platforms than they are trying to move away from them in this environment and as they execute on their AI strategies. Can you just talk about that dynamic and what your customer conversations look like at this point?

Yes. I mean, look, put yourself in the seat of any kind of company executive. You have this wave of AI that is going with these really loud and important voices, OpenAI, Anthropic, right, Google, et cetera. So I think there's a mindshare that is being taken up right now that we have to acknowledge at the corporate levels, trying to explore what's possible in AI, right? That creates some level of disruption, that creates some level of fear, that creates some level of uncertainty of what is there. The way we -- the way our customers have responded is usually what's happened is as they process, where do they really want to deploy these models? What's the real impact of it? There is a place for those companies, and there is a place for UiPath. And UiPath is actually a great channel for a lot of the models that come in, right? In terms of pure-play competitors, we don't really see. There's not -- if you go and say, which companies have RPA, unstructured document capabilities, processing capabilities, agentic capabilities and business process orchestration, not agenetic orchestration, there are very few companies that do it. So really, the discussion with our companies is really targeted. And so we're selling more and more into lines of businesses with specific outcomes in mind, and that is really helping us. And the second piece is we verticalized. I think one of the things that people have not seen, and I think we can do a better job showing it is, we used to be purely a horizontal platform. Now we have products and capabilities for revenue cycle management and healthcare. We have processes for procure-to-pay, software for procure-to-pay in the office of the CFO. And I think that is also driving further differentiation for us.

Okay. Yes, that's great and very clear. So I think it's a good segue into my next question. Maestro is your control plane, your orchestration plane for agentic processes, which makes a ton of sense, given how embedded you already are in these workflows. How do you see Maestro competing with competing solutions from other platform players, other AI management solutions? And do you ever or at all see frontier model providers as competition or more so as partners?

We see it more as partners. LangChain is an example. We have actually a great integration, great partnership with them, just to give an example of it. From our standpoint, the differentiation is, one is trying to manage AI, and we are trying to manage processes, regardless of the if there's an AI piece of software embedded or an AI automation in there or deterministic automation or a human or 2 or 3 of them. And that is really our key main difference. The other piece is observability. So what I would ask everybody to do is Google Maestro UiPath. When you do it, look at the click on images, right? You don't have to read all of the technical documentation. You can, if you wish. But look at the image, you actually will see the process. And if you click on video, you'll actually go and be able to see the process moving. So transactions move through the process. Imagine a world where you have digital workers, that you can now see as though it is a factory floor. That is for us, that is kind of how we think about Maestro. No, we do not think about the frontier models as competition. We think about them as integration points that we have to continue to drive within -- with our customers and within our partner ecosystem.

Okay. Great. And on pricing, so as you embed AI into these RPA workflows, into your broader platform, you win the orchestration layer, it becomes capable of executing more complicated multi-step processes, right? So what is the typical pricing uplift see when you sell those solutions? And is there a point in the future when those kind of become table stakes?

Two ways I can answer it. One is like our pricing scheme, I think, is dissimilar to what we talked about. We are experimenting with outcome-based pricing that provides significant uplift, but we take some upfront risk as a part of that. There are several customers that we're exploring that with. If you put that aside, if you look at first quarter, 16 of the 20 deals were based on, you had AI components as a significant component as a part of the platform. They were 6x larger than all of our other deals. When you look at process -- when you look at our $1 million-plus customers, the majority of them have large attach rates for AI. So from our standpoint, the more value we generate, the more software gets pulled through, the less discount that comes in, those all go to higher and higher ticket sizes. And where you see it show up is our $1 million-plus customer accounts and our $100,000-plus customer accounts, which are up 11% and 16%, respectively.

Okay. And then kind of to shift gears. You've also talked about how these coding agents can lower the time to value, the implementation time for these workflows, ultimately allowing you to go after more of the long tail of these automation opportunities in the enterprise. So can you talk us through that dynamic and how significant that reduction in time to value can be?

Yes. You're talking from months to weeks or from weeks to days. It is really significant. And from our standpoint, like the best metaphor, our CPTO, Raghu Malpani give is, our goal is that you can 3D print processes, 3D print workflows. And why I think that matters is if you thought about like today, the experience of building an app, on Lovable, on Replit, on some of these platforms, which are incredible. Imagine being able to print a process in the same way. And then as you do that, the question I get is, hey, why don't you do that on those other platforms? Well, think about it this way, like if you've made an app on one of these platforms, you know the difference between an app and a process, right, what it can do. And so from our standpoint, from week -- from months to weeks, from weeks to days, that's the goal. And we've already launched our first set of coded agents. This isn't hyperbole. The results are actually super promising in terms of what we're seeing with our early customers.

Okay. Great. And I think 6 months ago, you launched a forward deployed engineering team. So can you talk about how that ties into that strategy?

Yes. So the piece we didn't talk about for time to value is, I think there's 2 time to value equation. One is how do you make development on your horizontal platform faster. The second is when you go across an industry like health care, most every health care provider that we see is looking for certain solutions that they have been automating, prior authorization, claims denials, revenue cycle management. When you across -- go across the financial services industries, mortgage processing, HELOC filings, et cetera, right? Our verticalization, our FTEs have twofold areas. One is to be working with our customers so we can enhance our product to get greater verticalization into our product as we launch our vertical solutions to be there to perfect them, to add features and functionality that's there. The second is we use our FTEs as we deploy the first wave of agents. Many of our POCs were bolstered by FTEs for 2 purposes: ensuring success; and the second piece is getting the feedback back to our product. That's kind of in the early wave of where we are. As we go forward, we really think FTEs are going to become more and more a tip of the spear type area for our key customers, because this place is going to be very fast moving. The demands are going to be higher, the complexity is going to be higher, and they really serve as that connective tissue between what was a services organization and the product organization.

Got it. Okay. And does that have any margin implications? I know it's early on.

No. Actually, what is -- so investing in FTEs alone, of course, as we add headcount. However, the way our approach has been, we are investing in 4 deployed engineers, key R&D areas like coded agents and vertical solutions and sales capacity. These are kind of 3 key areas that we're investing in. We are divesting in every process that we can agentify ourselves. So when you look at our margin, I feel like this is the area that's there. We're not in cost-cutting mode. We are in a really strategic capital allocation mode. So when you look at whether it's geographic territory, whether you look at management layers, whether you look at centralized organizations, we are constantly driving efficiency into those areas, and we are increasing our capacity and our capability into the areas I just said.

Okay. Got it. And I'm going to jump to one of my last questions actually because it's on the same topic. But you made some really solid progress on your margin on the bottom line over the last few years. And given that progress, you raised your long-term operating margin target to 30%. Can you walk us through what levers you have in mind as you think about making it to those targets? Or if it's more of kind of a North Star over the [indiscernible].

No, I think -- look, I think every quarter, every year, I feel for the last 2 years, we've significantly improved our margin. I personally think free cash flow margin and GAAP profitability are the 2 most important things. The 2 most important things I look at. So if you look at our free cash flow, like last year, we were at $370 million. The year before that was significantly lower. This year, we've kind of given a modeling point of around $425 million in terms of our free cash flow, right? So I don't -- the first thing is it is a North Star, but it's a North Star that is reachable. It's not something that we're just directionally going after. The levers we have are 3. One is, frankly, cut nonsense out, be a highly efficient, be highly focused and prioritize its basic capital allocation, prioritize your -- both your human and your expenditures where you have the highest return, that's the first principle. The second area is as we do that, as we identify processes internally while investing. If we can keep our cost base relatively flattish while growing top line, you naturally get there very quickly. But we are not afraid to invest to be able to grow. We actually feel like right now, we're kind of in F3 of where we are as a company, F2, F3, we are going to invest to make sure that we can take a part -- take a large part of the market that we have in front of us. So I would say long-term goal, 30% where I think 23%, 24% already, 6 points of operating margin between leverage and discipline completely within our grasp. And then the only question is, if we see market opportunity, we'll pace that accordingly.

Okay. And is there a portion of that where you're leveraging AI to drive operational efficiencies as well? And to use the term, I have recently kind of eating your own cooking.

Yes, yes, drinking your own champagne, eating your own cooking, whatever it could be. Eating your own dog food has been like -- has been put away. Hitesh Ramani, who is our Deputy CFO. He's actually in the audience. He's leading that effort across a lot of our back-office functions. We have 70-plus agents, I think, in production already. And we still feel like we're just at the starting point, right? We were able to significantly drive transactional efficiency. We're able to transform functions like marketing, we're able to transform and get productivity out of engineering. So in the past, if you look at our product road map, I would say our product road map is probably 3x the surface area, but it is with the same number of people. So that is coming from coded agents and using the tools even in the engineering shop in terms of the productivity they're getting. So we are kind of -- we're doing that. And within our own platform, we continue to drive automation. We continue to drive agents. And the orchestration and differentiator in terms of doing it now larger at scale, but still being able to have the right controls in place being a public company.

Got it. Yes, that's very interesting. Thank you for the color on that. And something that you and Daniel have talked more about recently that I think is very interesting is the test cloud opportunity as these agentic workflows proliferate. Can you just talk through exactly what that product is for the audience and how large that [indiscernible]?

Yes. So I'll -- test is like -- it's a dark horse for us. I think I purposely also let it be a dark horse for a bit, but you're going to see us kind of like more and more emphasize it. If you just take what application testing is, everybody, I think, should be pretty familiar with it, right? You have upgrades into your software and then you have to have scripts to make sure before you put that software into production, the key transactions are flowing. So about 4 years ago, it was a very logical thing is that if you're automating the actual process, why are we not automating in the scripts that have to be written to ensure that the applications are in good standing as they get updated and moving. So if you think about -- take every S/4HANA implementation that's happening, you have a massive amount of transactional testing that has to be done. A lot of that is manual or a lot of the software in this space is archaic. We actually have the most modern platform now. It has been recognized as a leader by Gartner in terms of the category in which it's in. What's exciting is that it opens up a different buyer base within our company. So we're now selling into true CIO organizations in terms of QA/QC, et cetera. And we've seen -- we haven't disclosed it at this time, but that -- the ARR growth on that has been super exciting. It's kind of operating like a startup within a startup and is a really second throttle of growth for us as a company.

Got it Okay. Yes, very interesting, but we'll leave it at dark horse for now. So we have a few minutes left. Anyone in the audience want to jump in with a question. Otherwise, I can ask a couple more to finish this up. There will be plenty of time to breakout for questions as well.

Okay. So Ashim, you've continued to maintain best-in-class gross retention rates, high 90% range. But your net retention over the last couple of years has dipped a little bit until the most recent quarter, right, when we saw it step up 2 points to 109%. So as we think about everything we've just discussed, can you talk about what's driving that inflection? How you all are driving customer expansions and just walk through the mechanics of those expansions a little bit?

Yes. I think, I would say 50% to 60% of it is just better execution, which Daniel has really driven over the last 2 years with the -- getting the field flatter, getting it close -- getting management closer to the field, driving more disciplined motions. So we have lost touch, I think, 2 years ago with our customer base. And we openly talked to that. I think what you're starting to see now is kind of the rebuilding of both trust and relationships and that intimacy with the customer base that is, I would say, a foundational ingredient of driving more and more software and more and more processes through our platform. The second piece is we have more products to cross-sell. So when you look at Maestro, when you look at IXP, which is advanced unstructured document processing, when you look at WorkFusion, which we acquired, Peak that we acquired, you have more and more parts of our platform that you can sell across our installed base. So our installed base is super powerful. And as we get more products, as we're closer to the cutomer, that is -- that gives us a real chance to drive that leverage. And what we're cross-selling in is both deterministic as well as AI. If you look at leading segments, healthcare. Healthcare, it is all AI all the time, and it pulls forward, pulls through deterministic automation that is needed. But if you look at the public sector within Europe, RPA is the hottest topic. So depending on the market, we're able to continue to drive upsell across our platform. And what's exciting is, I think U.S. healthcare is actually a pretty bleeding edge type of industry, when it comes to technology, and it's [indiscernible]. So now I have a lot of confidence that says in the European market that same dynamic can start coming.

Okay. Great. We've got 1 more minute. So I'll sneak 1 more in. So you talked about the kind of credit base, retire the credits over time as you execute on these workflows. How -- is there any way you'd expect that to change over time as you continue to iterate the platform?

The answer is, I don't know. Just in all candor. I think we have a monthly pricing council. I think the world is evolving very fast. What we don't want to do is like knee-jerk react to change our pricing model to like jump on a bandwagon, so to speak. Our best and most reliable source is our customers themselves. So what customers are really nervous about today is like just unguarded token consumption, token burn. You've seen public companies come out about it. You've seen different posts and different narratives being there. So our advantage point is like we want to give predictability of cost to our customers. I think if you have unpredictability of cost, it hampers a long-term adoption. It may feel short term, but it really hampers long-term adoption. So from our standpoint, predictability is going to be a key pillar, and then we'll see how the industry evolves.

Okay. That's great. Well, thank you so much, Ashim for being here. Thank you all for attending the UiPath presentation. And I believe our breakout is in [indiscernible] upstairs. So we'll see you there in 10 minutes.

Thanks.

good day, everyone. My name is Megan, and I will be your conference operator today. At this time, I would like to welcome you to the UiPath's First Quarter 2027 Earnings Conference Call. [Operator Instructions]

At this time, I would like to turn the call over to Allise Furlani, Vice President of Investor Relations.

Good afternoon, and thank you for joining us today to review UiPath's First Quarter Fiscal 2026 Financial Results, which we announced in our earnings press release issued after the close of the market today. On the call with Daniel Dines, Founder and Chief Executive Officer; and Ashim Gupta, Chief Operating and Financial Officer, to deliver our prepared comments and answer questions.

Our earnings press release and financial supplemental materials are posted on the UiPath Investor Relations website. These materials include GAAP to non-GAAP reconciliations. We will be discussing non-GAAP metrics on today's call. This afternoon's call includes forward-looking statements regarding our financial guidance for the second quarter and full year fiscal 2027 and our ability to drive and accelerate future growth and operational efficiency and grow our platform, product offerings and market opportunity.

Actual results may differ materially from those expressed in the forward-looking statements due to many factors, and therefore, investors should not place undue reliance on these statements. For a discussion of the material risks and uncertainties that could affect our actual results, please refer to our annual report on Form 10-K for the year ended January 31, 2026, and our subsequent reports filed with the SEC.

Forward-looking statements made on this call reflect our views as of today. We undertake no obligation to update them. I would like to highlight that this webcast is being accompanied by slides. We will post the slides and a copy of our prepared remarks to our Investor Relations website and immediately following the concluded on this call. In addition, please note that all comparisons are year-over-year unless could have otherwise indicated.

Now I'd like to hand the call over to Daniel.

Thank you, Allise. Good afternoon, everyone. Thanks for joining us. We delivered a strong start to fiscal 2027 once again exceeding our guidance across all key financial metrics. Before I dive into the results, I want to take a moment to reflect on our progress over the last year, In May of last year, we launched our agenting and business process orchestration products into general availability. One year in, adoption has moved from or experimentation to production deployment. .

We are seeing this play out across 3 areas in particular: installed base expansion, process orchestration adoption and vertical workflows. A great example is one of the largest health care distribution companies in the U.S. One end-to-end workflow, combining UIPath agents and deterministic automation is expected to drive multimillion-dollar annual savings, which led to a 7-figure expansion in the quarter.

One of the world's largest construction companies adopted our purchase-to-pay vertical solution and told us they chose you. Not as software vendor, but as a strategic codevelopment partner for their enterprise AI transformation. And the Fortune 500 energy company placed UiPath, but the center of $70 million cost reduction initiative made possible by our ability to bring deterministic agentic and process orchestration together as a single plus.

Turning to the quarter. First quarter ARR reached $1.901 billion, up 12% year-over-year, driven by $49 million of net new ARR and revenue of $418 million, up 17% year-over-year. We grew first quarter non-GAAP operating income to $92 million a 22% margin, driven by improved operational efficiency and disciplined execution across the business. We delivered first quarter GAAP profitability for the first time in company history.

This quarter's performance is built on the strength of our enterprise automation installed base thousands of customers with deep platform adoption, proven ROI and a track record of expanding with us over time. and it reflects continued momentum with our AI products. In the quarter, 16 out of top 20 deals including AI and expansion deals that included AI were larger than those that did not.

The drivers behind these results are the same core differentiators we outlined last quarter our platform that brings together deterministic and agentive automation with enterprise-grade process orchestration, our installed base slideware, our governance foundation and our ability to combine a horizontal automation platform with deep vertical solutions.

I saw that momentum firsthand across our global events including in India at our annual fusion event and that can developer conference. Across customers, developers and partners. The message was consistent. Enterprises increasingly need the platform that can cover and orchestrate humans, agents, workflows, automations and systems, an area where UiPath offers a structural advantage. At Devon, we launched UiPath for coding agents, enabling developers to connect their holding agent of choice. To create, test, deploy and manage automations across the full life cycle on the UP platform with enterprise-grade governance and reliability built in.

This matters because nearly every customer conversation surfaces the same constraint, and automation backlog that outpaces their capacity to build and maintain Implementation is often harder part, particularly in complex enterprise environment, where upstream system changes can drive maintenance costs over time. By combining coding agents with the governance, orchestration and self-healing capabilities built into our platform, we can dramatically reduce the operational burden and compress deployment time less from quarters 2 weeks.

We expect this to accelerate time to value for our customers. drive deeper adoption and strengthen long-term retention across our customer base. Our internal teams and customers are also seeing great results with coding agents, including 1 of the world's largest consumer electronics companies, which reduced a 4-week project built to 3 hours and one of the world's largest chip manufacturers reduced a 2-month project build to a few days. What stood out most this quarter is how clearly customer priorities before with the focus consistently centered on process orchestration, as one customer put it during that content, models are easy. Orchestration is not that directly reflects what we hear across our customer base.

Customers are no longer asking us simply to deploy more agents or generate more code. They are asking us to transform our entire business functions operate through end-to-end workflows that span departments, connect systems and deliver measurable operational outcomes. And delivering that kind of transformation requires more than individual AI agents. It requires a platform that can orchestrate agents, automation, API systems and people together within secure government enterprise workforce.

A great example is one of the world's largest telecommunications companies with nearly 2,000 processes already automated and all the $30 million in annual cost savings, they are now expanding their that terministicbase further and moving into agent workflow building a pipeline of more than 200 additional deterministic automations and over 20 agenetic use cases. That same process orchestration capability also drove a competitive displacement with the Fortune Global 500 electronics manufacturer where we were the only platform that could take them from task-based automation to enterprise-wide business process orchestration.

Building on a strong deter listing foundation, they are now expanding across manufacturing and supply chain workflows using Maestro to coordinate automation, agent, systems and human decisioning globally. Maestro already excels at structured workflows like invoice approvals and deployment pipelines where the process itself is clearly defined. But increasingly, enterprise work is nonlinear and its dynamic, exception driven and center around decisions that move across teams and systems. This is why at Devon, we launched Maestro case into public purview extending beyond traditional process orchestration into the orchestration of unstructured enterprise work.

The breadth is what makes a the most complete process orchestration and automation platform in the market, and it's already driving broader customer adoption, including Sonic Automotive, an early adopter of our agenetic product. They initially deployed new aircraft to automate vehicle stocking and sales lead all up. They are now standardizing their Agentic automation strategy on the UiPath platform under a broader C-suite initiative and expanding into workflow such month and close and employee onboarding. The key driver of the expansion was Maestro case's ability to orchestrate complex multistage workflows across agents, automations and people.

Beyond process orchestration, documents remain 1 of the biggest sources of friction in enterprise world. And customers are increasingly turning to UIPath XP to automate document-intensive workflows at enterprise scale. In May, we were named the leader in the Forrester wave document mining and analytics platforms Q2 2026. We are seeing that momentum translate directly into largest enterprise deployments and competitive wins A great example is the leading medical technology company that is standardizing on UiPath XP to automate high-volume and structural documents like invoices and purchase orders.

The customer is already realizing approximately $5 million in annual savings and expect that to grow to $10 million as they scale. For industry-specific government workflows continues to grow as enterprises increasingly adopt purpose-built AI solution tailored to their business. What differentiates UiPath is our ability to combine them with deep domain-specific solutions with the same process orchestration, automation and governance platform. This quarter, we expanded our portfolio across financial services, retail and manufacturing and the office of the CFO.

We are already seeing momentum in health care in a 7-figure new logo win, a leading Latin American health care provider, selected our vertical solutions to support revenue cycle management, medical record summarization and claim denier management and expect $12 million in cumulative benefits. Customers are also realizing meaningful operational benefits from these vertical solutions, a leading health care technology company produced clinical summary review times by 90% using our medical record monetization solution.

We are seeing similar momentum in financial services a digital bank is now automating 61% of sanctions heat reviews with our transaction screening alert review solution, processing roughly 14,000 or less per month. and is accelerating software creation, but is also accelerating the need to validate it as coal volume grows, so does the testing bore, Independent research for have consistently recognized UiPath as a leader in this space, and we believe that validation reflects the real and growing structural abandon.

This cloud is at the center of that, helping customers move testing from a downstream bottleneck to a continuous intelligent function embedded across the delivery life cycle. One example of this quarter is the leading U.S. utility provider that adopted UiPath Test Cloud for gentic testing to streamline customer platform support launch. The solution is expected to significantly reduce manual testing while generating nearly $3 million in savings. During the quarter, We continue to deepen our partnerships across both go-to-market and technical integrations.

This included our expanded collaborations with Deloitte. Embedding UiPath, Test Cloud into the ascendelivery platform, bringing agentic testing capabilities to Deloitte's global client base. We are seeing similar momentum with Accenture, a life sciences customer, we highlighted last quarter, worked with Accenture to deploy global genic sales entry solution and has now came based across 70 countries. Building on that success, they signed a seve-finger expansion and are now partnering with us to design an office of the CIO intake solution built on our process orchestration platform.

On the technical side, we continue to broaden our reach across key enterprise ecosystem. With Microsoft, we integrated UiPath security this to help automate trade detection and response with Salesforce, we launched a new agent exchange offering that extend my extra process orchestration across sales force and back office systems with Google Cloud, we brought our ISP solution to their marketplace. And with data bricks, we connected their data intelligence platform directly with IPO process orchestration to help enterprises move from data insights to automated action we then govern workflows.

In summary, this quarter reflected disciplined execution across the business, continued AI adoption and growing momentum across our platform. No other vendor can bring together deterministic automation, agent AI, document intelligence and business process orchestration on a single platform. And that complexness is what customers are standardizing on. We believe we are uniquely positioned for this next space of enterprise AI adoption and our strong start to fiscal 2027 reinforces both the durability of our business and the scale of the opportunity ahead.

Before I turn it over to Asim, I want to take a moment to acknowledge the loss of our dear friend and board member, -- so massage. Soma was the long-time investor in UC and we joined our Board just 8 months ago. its impact on UiPath was immediate and profound. He was the mentor drastic adviser and someone I deeply admire both professionally and personally. I will miss him greatly -- and I know our entire Board and leadership team share that feeling. Our hearts are with his spending.

With that, I'll turn the call over to Ashim.

Thank you, Daniel, and good afternoon, everyone. Before turning to the financials, I'd like to provide a quick operational update. We continue to make meaningful progress across the key priorities we outlined last year. Our partner ecosystem is becoming more deeply integrated with both our go-to-market motion and customer adoption efforts, helping us scale larger enterprise deployments across industries. As Daniel mentioned, partners like Deloitte and Accenture are increasingly instrumental, not just in selling, but in helping customers operationalize and scale AI-driven work. and we are seeing that play out across financial services, health care and other key verticals.

At the same time, our internal focus on customer adoption remains a central operating prior. We continue to invest in our services organization and industry expertise to help customers accelerate deployment and expand platform usage. A key part of that effort is our forward deployed engineering program, which we launched 6 months ago, are proving to be an effective bridge between product innovation and customer deployment, shaping vertical workflows directly in customer environments and accelerating time to value. In addition to adoption, our go-to-market teams are executing with discipline and customer interest. AI is now part of virtually every strategic customer conversation.

And those discussions are increasingly expanded into platform, orchestration and vertical solutions. The deal data Daniel mentioned reflects that. AI was included in 16 of our top 20 deals and expansion deals that include AI were 6x larger than those that did not. Finally, on operational efficiency, AI is changing how we run the business internally. We are seeing increased operating leverage across the organization while continuing to invest deliberately in R&D, vertical solutions and customer-facing functions.

Turning to the quarter. Unless otherwise indicated, I will be discussing results on a non-GAAP basis, and all growth rates are year-over-year. I also want to note that since we price and sell in local currency, fluctuations in FX rates impact results. Since the time of our last earnings call through the end of the first quarter, rates remained largely stable and resulted in an incremental tailwind to our first quarter ARR and revenue results of less than $1 million. First quarter revenue grew to $418 million, an increase of 17%. Normalizing for the year-over-year FX tailwind of approximately $7 million, revenue grew 15%.

ARR totaled $1.901 billion, an increase of 11%. This included a $9 million year-over-year FX tailwind. Net new ARR was $49 million. Normalized for foreign exchange and the impact of M&A, net new ARR improved on a year-over-year basis. Our dollar-based gross retention -- gross retention rates remain best-in-class 97% and our dollar-based net retention rate was 109%, underscoring the durability of our customer base as they embrace our genetic automation solutions. Adjusting for FX, dollar-based net retention rate was 108%, demonstrating stabilization across our business.

We ended the quarter with approximately 10,550 customers. Attrition continues to be concentrated amongst our smallest customers, while customers generating more than $30,000 in ARR grew 7% year-over-year. That dynamic is also reflected in our cohort performance. customers with $100,000 or more in ARR increased 11% to 2,624 and customers with $1 million or more in ARR, increased 18% to 374. Our customer strategy has continued to focus on deepening our presence within the world's most complex enterprises, where we see the greatest opportunity for long-term expansion.

Consistent with that strategy, we continue to add new enterprise customers with significant long-term expansion potential, including new logos like Candela Medical, Tire Rack, Shoprite Holdings and a global semiconductor company. who is replacing a legacy RPA vendor with UiPath as their strategic automation plan. Our cross-system integration and end-to-end process orchestration capabilities, given them a scalable foundation they need to migrate their existing automation program beyond task-based automation into broader agentive workflows.

Remaining performance obligations increased to $1.413 billion, up 15%. Normalizing for the FX headwind, which was approximately $9 million, RPO grew 16%. Current RPO increased to $908 million, up 17%. Turning to expenses. We delivered first quarter overall gross margin of 83% and software gross margin was 90%. First quarter operating expenses were $256 million. For the first time in company history, we delivered a GAAP profitable first quarter with GAAP operating income of $28 million, up from the prior year GAAP operating loss of $16 million.

GAAP operating income included $53 million of stock-based compensation expense. First quarter non-GAAP operating income was $92 million, representing a 22% margin, up over 250 basis points year-over-year and driven by our continued focus on operational efficiency. First quarter non-GAAP adjusted free cash flow was $130 million. We ended the quarter with a healthy balance sheet of $1.4 billion in cash, cash equivalents and marketable securities and no debt. During the first quarter, we repurchased 20 million shares at an average price of $11.47. Since April 30, under our 10b5-1 plan, we have repurchased an additional 2 million shares at an average price of $9.63 through May 27, 2026.

Now turning to guidance. We are pleased with the team's execution and what continues to be a variable macroeconomic environment. We continue to maintain a prudent outlook and guide to what we see in front of us. Since we provided guidance on our last call, the euro has remained largely state while other currencies such as INR and Romanian Lane have experienced volatility. As a result, for the second quarter and full year we expect a nominal incremental FX headwind to ARR and revenue.

Despite the incremental FX headwind, we are raising guidance for the progress we've made on our operating priorities. Turning to the specifics of our guide. For the second fiscal quarter 2027, we expect revenue in the range of $395 million to $400 million. ARR in the range of $1.929 billion to $1.934 million, non-GAAP operating income of approximately $75 million, and we expect second quarter basic share count to be approximately 518 million shares. For the fiscal full year 2027, we expect revenue in the range of $1.776 billion to $1.71 billion.

ARR in the range of $2.058 billion to $2.063 billion, non-GAAP operating income of approximately $430 million. And finally, we continue to expect fiscal year 2027 non-GAAP adjusted free cash flow of approximately $425 million and non-GAAP gross margin of approximately 84%.

Thank you for joining us today, and we look forward to speaking to with many of you during the quarter. With that, I will now turn the call over to the operator.

Operator, please vote for questions.

[Operator Instructions] Our first question will come from Bryan Bergin with TD Cowen.

Asim, maybe just to start on the overall demand environment. any interesting changes in the underlying demand trends and pipeline conversion, anything as it relates to deal timing, sales cycles, things like that, just as this conflict has been extended.

No. We actually feel like the environment has stayed relatively stable versus what we saw in the first quarter. Sorry, when we got into the first quarter earlier this year, Brian, I think we actually feel very positive about the momentum in the business, the health of our pipeline and the conversion rates and the predictability.

The customer conversations are going really well. A lot of the pilots are beginning to now starting to convert, which we feel really positive about. So overall, we're actually very positive overall on our pipeline and the environment remains variable as it has been, it feels like a new normal is the way we think.

Okay. And then on AI product AR levels, any sizing you can update us there? And how the pricing conversation across those solutions is evolving?

Yes. We'll disclose the product IRR periodically here. We feel really good about the momentum. I think we pointed to it in terms of 16 of the top 20 deals for the quarter involved AI. I think Gentex and our AI products in general have really good, strong momentum. And our vertical solutions are also starting to really get traction both from customer interest and pipeline, particularly in health care and financial services.

And then lastly, I think test, which is our Augentic testing solutions that has really good traction as well. We look forward to update the numbers here in the coming periods. But right now, we feel really good momentum. And I think the deal traction kind of speaks to the overall trajectory for the [indiscernible].

Your next question will come from Scott Berg with Needham.

Hi, everyone. Nice quarter. Daniel, you spoke extensively about orchestration, and it's a key topic that comes up in our work on the space consistently over the last probably year -- when you think about Mitro and the deals that you have out there, is there any reason why Maestro is a part of basically every deal that has AI? Or is there some combination that would suggest that, that's not going to be a part of every deal going forward.

I don't think my store can be part of every deal. The way we are looking at our business, it's -- we have an entire platform that can address the whole spectrum of past can process orchestration. Maestro is a solution that comes into play when customers are doing process orchestration and automation and end-to-end process orchestration and automation. But we have customers out there that are happy to start with the task automation product.

And thus automation can also be deterministic and cabinet. I would say that RPA and API automation plays into deterministic task automation, while we have agents that can be applied to task level. Maestro comes into place when you need more complex orchestration of work that involves humans, task automations, enterprise workflows systems and agents.

So it's naturally more for our more involved customers. Maestro helps us lending bigger deals. -- makes our installed base stickier to the customers, but I cannot say it can be deployed in every single year.

Got it. Helpful. And then Ashima follow-up to the last questions that were out there. I think what we're all trying to understand is the impact of, obviously, some of your AI modules on the business and the bookings and what the general trajectory is. I understand that you don't want to necessarily report that AI metric every quarter.

But if I ask a question a slightly different way is if I think about those 16 deals in the top 20 that had an AI component of them., How significant are those transactions is coming from some of the AI functionality. I think we're all trying to understand is it still traditional RPA heavy in those transactions or if we're seeing a bigger impact from some of the AI function...

No, we're seeing a bigger impact I think the way I look at it is I kind of would divide it into 3 areas, like our top customers and our CoCdeals, the majority of our transactions have a significant AI, if not a majority, AI component. Scott that's driving it. They're not piecemeal where it's kind of like 1 or 2 SKUs that get moved in or small quantities. They are materially what we are selling, right, to our customers. I think there is a mid-tier of customers where you see actually a continued demand in traditional RPA and deterministic automation.

And those are companies that are not -- that either are -- have embraced in genic and AI in a major way, and they are actually pulling forward more deterministic automations as they weigh both the cost and the trust and governance. -- that agents versus deterministic automations give you. And then really, the kind of some of the drag that we talked about is really from the low end of the market, smaller customers and personal productivity. That's kind of the way I would divide up the quarter.

So we're actually really pleased with the pull that we're getting on the Agentix side and its contribution to our growth.

Your next question will come from Sanjit Singh with Morgan Stanley.

This is Abhishek from early on for Santen. -- just to hear a little more on the beat kind of dig into -- given Q1 revenue upside was strong, but the beat was largely driven by license revenue and the ARR was relatively in line. So can you kind of help us understand the quality of that revenue be -- is there anything unusual in license timing or customer behavior that we should be aware of? And then how should we think about the relationship between license performance and ARR trajectory for the rest of the year?

Yes. I mean, I would say 2 things. One is we feel really good about the quality of that revenue, both in terms of the products as well as the deal quality and structures. I would say it's -- our quarters have been very clean. And we feel very good about the overall deal quality and construction. Remember, revenue is a quarterly performance metric when you're looking at the growth rates, and we are on ASC 606. -- versus ARR, which is a 12-month metric, right?

So if I break down the question, you look at revenue growth at 17%. When you look at a trailing 12-month period, the revenue growth rate -- so actually, which makes me feel very good about 15% growth on a trailing 12-month basis. And it's relatively in line with the ARR growth -- in terms of ARR versus revenue need, it's really just the mix of deals with 606 timing. And the license revenue being a factor in that is a side actually of really good quality revenue overall.

And then as a follow-up, anything you can share in terms of the mix between consumption-based revenue and proceed?

We don't -- consumption-based revenue is a very small part of what we do. We still have -- the subscription really dominates our pricing model. and per seat pricing as well, that is not the majority of what we do. We are really selling executions as well as kind of our typical server-based pricing that we have for unintended robots in particular. I just really emphasize again, personal productivity is a very small part of our portfolio, simple task-based automation. So what we sell is the larger complex use cases now -- and that really mix us higher towards both server-based and subscription-based pricing. .

Your next question will come from Samik Merchant with RBC.

Guys, this is Sonic Mojin on format Hedberg RBC -- could you talk a little bit about the broader competitive environment for orchestration and any changes or trends you're seeing -- and there's also been a lot of developments around frontier model capabilities. Could you talk to how you see these developments impacting the broader competitive landscape and the company specifically?.

Yes, sure. I would like to start by saying that we have a really unique platform in the market. So -- and it's based on 3 major viewers. We have a very modern process orchestration technology that is built on a very innovative workflow engine capabilities. We have proven a 10 years deployment of scales of automations in a secure and governed environment with some of the largest companies in the world. And we have a unique ability to connect to both modern API-based systems and legacy systems. This 3 pillars make our platform quite unique in the market. In terms of the new development that we have seen I think we all recognized the huge impact of the coding agents of the entire ecosystem.

And I want to point to you to an interesting phenomenon that it's -- it's something that we spot with our customers and within our own IPP operations. It's becoming increasingly easier to build deterministic automation. You are using coding agents to build deterministic automations and deploy very much scale. It's becoming really easier to address the long tail of opportunities of work. And it was not economically feasible before coding agents to get to this level of automation. And building automation, it's really creating the substrate for deploying genetic AI later on. I would point to why coding agents are so successful nowadays because they really combined model, the strength of the models with the strength of deterministic automations.

[indiscernible], it's so good because there is this deterministic harness around it. So cloud generates cost but then it uses a compiler, which is a deterministic piece of technology to compile the core, and then it's using testing, which are another deterministic piece to validate the code that is generated. So I think it's becoming more clear to everyone that the combination of deterministic automations and models are what makes the real deployments in production. And I would say that in this regard, we do have tremendous advantage -- our platform is already enabled for coding agents, and we showed that our decon in India, we show that we can reduce significantly the implementation times. Think for a second weeks, 2 hours, that really means a lot when you go and deploy automation to the long tale of possible work.

Thanks. Appreciate the color there. And as a quick follow-up, so you've talked a lot about sort of profitability. And last quarter, you also updated new long-term non-GAAP operating margin target to 30%. And keeping in mind the fact that we remain a priority for the company, what are some keys to margin expansion in fiscal year 2017? And is there any seasonality you would point out on that?

Look, I think from a cost seasonality, nothing except for -- obviously, there are later parts of the year, we have sales compensation. There's just normal SaaS seasonality from an expense standpoint. Otherwise, I think we're pretty -- there is no real seasonality to mention. From my standpoint, I think we're looking at as growth is our first priority. So we are investing in FTEs. We are investing in test. We're investing in vertical solutions. We are investing in coding agents as evidenced by the speed of the launch by which we're moving through things. And so from our standpoint, that investment is our first priority.

At the same time, we updated our long-term models because we are able to find increasing levels of efficiency both through continued discipline and scrutiny and then also from implementing both our platform as well as broader AI tools within the company. And so I would say we're a best first mindset. -- and a waste nothing mindset. And that combination, I think, gives us the ability to both grow and drive the strategic initiatives while expanding operating margins.

Your next question will come from Pat McElwee with William Blair.

Daniels. My first question, I thought the AI summit you put on earlier this year was very helpful in envisioning how customers can evolve from your traditional RPA workflows towards more agentic-enabled workflows. And specifically how they can choose their own autonomy level and then kind of use a feedback loop to evolve the level of automation in that process. over time.

So I know it's early on, but for your existing customers, where are they in that autonomy, evolution right now are a lot of them content with the value they're getting from current RPA workflows and leveraging AI within newer workflows? Or are they really racing towards these agentic solutions to maximize the ROI they're getting from the platform, both existing and new workflows alike.

Yes. I would like to point out that despite the technology being very new, it is hailed by our customers with a lot of enthusiasm. Even when we were in like close review, we got a lot of requirements from the customers. They -- some of the customers even went to find only some of the skills that we publish and use them with the coding agents. And Also, I would like to point out to the fact that basically holding agents saw 2 of the biggest hurdles in deployment of automation. .

Number one was always the implementation leading time to when -- until an enterprise would get value from automation. So -- that's been already proven internally by our own forward-deployed engineers and externally by a few advanced customers that can be shrinked in many cases, from weeks to hours, which is very significant. The second way that coding agents unlocks a bottleneck of automation is in maintenance. One of the apparent flows of automations was always the fact that they are project and they break.

If there is an upstream modification in an enterprise system that automations are not if they might break and that will require human interaction and many days of reviewing and understanding. Now we offer both a healing agent and the diagnosed agent. So the heating agent can do a lot of the work during run time during execution.

And in many cases, the hearing region can fix the execution in itself and the processes are unaffected. When there is an exception, we help tremendously developers with these diagnostic agents to gather all the context around automation, and they can publish a fix in much faster than before. So yes, I would conclude that for us, this is a really big unlock. And we see the potential for a huge acceleration of customer adoption.

Right. Okay. And to kind of continue on it, it sounds like AI agents are largely extending, not replacing deterministic automation within your platform. But as we think about that, I wanted to ask, is there any sort of dynamic where you're seeing customers leverage Gentek AI to somewhat cannibalize some of the traditional bot monetization? Or is it largely building incremental automation and therefore, incremental monetization on top of those workflows?

Yes. I would like to say that perhaps this is 1 of the biggest confusion that AI brings into the table. The idea that nondeterministic probabilistic technology can replace deterministic automation. This is not true. It's not true from the capability perspective, and it's not true from a economical standpoint. And let me elaborate a bit on both. A probabilistic technology is not architectural meant to follow a dozen of steps and sometimes hundreds of steps in the same order in the same sequence. Every step will have a probability.

When you multiply these probabilities you will end up with something that is not reliable end of the day. And there are many regulated industries that cannot tolerate anything that is not 100% reliable. They will prefer an automation to pay as an exception rather than produce an unexpected result. So what that [indiscernible] bought cannot be replaced by nondeterministic agents. Again, this is the architecture that is proven over and over again by all the AI agents that allow them, the most sophisticated agents like lot codes were open AI codex are built on the foundation of deterministic tools that they quote. So it's a hardness around the model and deterministic tools. This is exactly how they work.

This is exactly what we are proposing to our customers, yes, reduce your investment in your existing deterministic automation and surround it with process orchestration, which is also deterministic that are orchestrate models, agents in the context of determinism. That's really the only way to deploy effectively into an enterprise context. And now to the second point about the economical aspect, even if in certain cases, a major can replicate some steps that are [indiscernible]. Why would you do something that is costly and it's going to consume tokens at every step in the process rather than generate a script that works. It costs nothing in order to run.

So to my previous answer, this is the best combination between AI and deterministic. AI creates automation, sometimes maybe even on the slide. I will run those automations. It's very cheap to run, very that we stick reliable or deal and only when this scripts break, you can invoke again a high to fix the screens, but that's basically the right model to run agentiKI and automation into enterprise context.

Your next question will come from Raimo Lenschow with Barclays.

Perfect. Daniel, could you stay on that subject because that's obviously where a lot of the investor questions are coming around. So how do you -- how does the world work then going forward? If you do the deterministic part, and you have all the experience in the words, so you should do that, who is doing then the probabilistic part. What are you seeing there in terms of where customers thinking and how they think about you in that context? And then I had 1 follow-up.

Well, I think the answer varies in we are model agnostic in terms of how we see the old -- we provide deterministic orchestration and we can infuse that monistic at orchestration at any steps with a GenDKI.ThataKI used behind the same frontier blood models can use open weight models. We have to bring your own model policy. So we will accommodate every spectrum of requirements from our customers. But again, I think what's important to note that even on the frontier lab model, the offering, it's a combination between deterministic and the model itself, which is purely cognitive.

We extend in a way that model into the enterprise work itself. And when you go and I think very important distinction to understand the enterprise work is to think of who initiates an agent or process automation. It's a big difference if it's initiated by a person and the agent work on a personal desktop versus an automation is triggered by an event or buy an enterprise workforce where you will need to have a different degree of auditability and reliability. And again, this is where we really shine. We have this 10 years of experience in running a large-scale unattended automation that work on event figures. And we are involving agent KI and models into these workflows that can run unattended.

Yes. Okay. Makes sense. That's very clear. And then in the 1 other question I get from investors a lot at the moment is you're doing really well on the revenue side. ARR is very steady. But at some point, they kind of need to kind of start lining up. So revenue at the moment keeps growing faster than ARR. How do we need to think about that dynamic? Because in theory, you would think that they should line up or we think..

Yes. I think, Rama, the first piece is, again, like when you look at it on a trailing 12-month basis, the revenue growth rate is 15% versus the ARR growth rate of 12% that you see. The second piece is within the revenue growth rate, there's obviously the license revenue growth rate and then there's services, et cetera. You can see we actually had good services revenue as FTEs, et cetera, are in demand from our customers. So that's a second piece. -- that is there.

Over time, this has moved in different directions. There's been times where with 606, revenue has trailed as certain duration and mix has moved the growth rate and where it exceeded -- but when you look at it like on an overall average over a longer period of time, it's together.

I don't really see any major disconnect at this moment that is driven by a business-specific area. It's really just a mix of 606 impacts on the business. And again, I would emphasize to look at it on a trailing 12-month basis, versus looking at it where it is just in a particular quarter because ARR is obviously an annual metric.

Your next question will come from Michael Turn with Wells Fargo.

Reappreciate you taking the questions I'll just ask 2 upfront, and you can take them on to sequence you like. I guess the first is just in terms of public sector. as we roll into midyear, maybe just remind us how you're thinking about public sector this year. Any updates in terms of progress or deal progression from that vertical specifically and maybe option just on the net retention rate, just what you're seeing currently in the uptick there and how to think about the trend line obviously, without guidance, but just thinking through the drivers there.

Yes, I can answer both questions. I'll start with the net alternate. I'm actually super excited with the Nutella retention rate and the progress we've made on it. As you can see, we have a 2-point increase quarter-over-quarter. That's 1 of the first times we've had an increase as we've stabilized net new ARR and beginning to point the trajectory up towards that reacceleration Mark. One, when you normalize for foreign exchange and the impact of M&A, -- that is 1 point, but it's still a reacceleration of net dollar retention. I we're actually very encouraged by. And as I said, as we start to stabilize net new ARR, the next step is reacceleration.

So we're moving into that territory. And I think that's really great progress by the teams and speaks to the strategy and the operational execution that we -- that you see. In terms of Poly, I actually was at the public sector, a fusion event that we had -- the energy was very strong. I think public sector in terms of disruption of budgets, et cetera, we feel pretty -- we feel like there is good stability.

Obviously, as funding moves with different defense initiatives and awards, et cetera, that are there. We stay on top of it. But within many agencies, we actually have a very good presence, strong relationships with really good use cases, whether that is audit compliance within the government, which we have a very strong set of solutions and partners that we're working with or other transactional areas we actually feel like our relationships are very good. In terms of what's in for -- as we talk about , we continue to guide what's in front of us there, which is we're pretty measured and prudent. We know what projects are generally funded then we're looking to execute against that.

Your next question will come from Radi Sultan with UBS.

First for Daniel, just on the UI Path for coating agents, you mentioned this will be targeted at the full software development life cycle. But I guess -- are there like 1 or 2 areas where you see the biggest pain points where you can add the most value. And then you also mentioned this could strengthen retention. Maybe just how you imagine monetizing or bundling these agents into the broader suite.?

So we plan to bring agents across the entire development life cycle. We are starting with an agent that helps us planning for an automation. So you can have a business analyst that came with the help of the agent interview different subject matter experts, gather all the information, create a process documentation document -- and then we will have like a solution architect agent that will take this design document and convert it into code. And we will have different agents for different types of course.

We have an agent that can create enterprise user interface. We have an agent that will create RPA, another agent that can create API of course, an agent that will create process orchestration based on milestone -- this can be deployed and tested, again, it's fully agented. Once they are in production, we have agents that monitor the entire execution and can fix proactively the errors that are coming. And once there is an exception, we have also agents that help our developers to diagnose faster, the exceptional fixed them faster.

So the entire life cycle, there is no single point in the life cycle that is not touched by pages. In fact, we believe that the entire offering surface of our platform is basically a genetic first. Humans, we think, are mostly going to do validation. They will inject goals to the agents, and they will do the validation and supervision of the work. But most of the work itself is going to be created by agents.

Got it. Got it. Maybe just 1 follow-up for Asim. Last quarter, we talked about core RPA still growing and becoming increasingly strategic to the AI product offering. Can you just talk through how you think about how pricing should evolve for the RPA and terministic automation side of the business, given that it's becoming increasingly more strategic to customer AI initiatives to kind of capture that incremental value?

Yes. Look, I think that there's a lot of discussions around outcome-based pricing that are real and active more than they ever have been before. So I think like that is 1 tier of pricing to our top customers. that I think it's a real evolution. We see real lined path. And we see people, especially with their fears of -- about getting ROI with AI. -- really looking for that.

The second piece I would say is I think where we're looking through is we also see like use case or process-based pricing. -- where people are looking for restricted use cases so they can solve problems and have -- be able to use different parts of the platform that enable them to do so. Those are 2 evolutions that are there. in terms of where we are with the Determine side and overall.

This now concludes the Q&A session. I'd like to turn the call back over to management for closing remarks.

Thank you so much for the questions. And as usual, we would like to speak directly who many of you over the next few days. Thank you so much.

Hi, everyone. Thanks for joining us today. I'm Allise Furlani UiPath's Investor Relations team, and I'd like to welcome you to our virtual fireside chat and product strategy overview. We'll begin with the fireside chat featuring Daniel Dines, UiPath's and Chief Executive Officer; and Raghu Malpani, Chief Product and Technology Officer. We'll then take a deeper look at our product strategy and road map, followed by a customer example to bring these concepts to life. We'll conclude with time for questions. [Operator Instructions]. Before we begin, I'll cover a few housekeeping items. Today's event is being recorded and will be posted to our Investor Relations website following the session.

I would also like to point you to our safe harbor statement and remind you that today's discussion may contain forward-looking statements. Actual results may differ materially from these statements as a result of various factors, including those found in our SEC filings. We may disclose information related to development and plans for future products, features or enhancements, which are subject to change at our discretion without notice.

All statements are made only as of today, and UiPath undertakes no obligation to update any forward-looking statements and makes no assurances and assumes no responsibility to introduce future products, features or enhancements described today. Additionally, we would like to note that this is a product webinar and we will not be taking any financial questions.

With that, I would like to hand it over to Daniel to begin. Daniel?

Thank you so much, Allise, and hello, everyone. Thank you for joining us. Today, I have the pleasure to introduce Raghu to you. I heard many people in my life. And -- but in very few cases, I had really this type of positive vibe that I had when I met Raghu first time. He strike me as a clear thinker, no nonsense, no politics type of guy that we really needed to run our engineering organization, and he didn't disappoint. Raghu was basically the artisan behind our big push into Maestro into adopting the new modern workflow engine that is changing the entire company.

So, Raghu, what brought you to UiPath, basically?

Yes, Daniel, thanks for the kind words. I appreciate it, man. Look, as many know here, I worked at Microsoft and Facebook prior to coming here, worked at Microsoft a few times, left it a couple of times. Worked on Azure -- worked on what became Azure in my first rodeo there. And then the last time I left, I left the office organization to join UiPath, to join you and the team here about 2 years ago.

After spending close to 20 years in the industry, I wanted a place which met some expectations for what is it that I work on? Who is it that I work with? And how is it that we work together? So Dan, you and I had many conversations before I joined. In fact, we met, I think, 2 or 3 times in person. And then it was -- it is clear that the transition from RPA to Agentic automation to business orchestration was the need of the hour. The ACT I and ACT II transition that you've spoken about for a couple of years now made this to me a compelling -- a resoundingly compelling opportunity, actually. And where my passion and my interest aligned with helping drive a significant transformation, working with people I enjoy working with.

But more importantly, I think -- and we don't talk about it enough in the industry is what also drove me here is who I work with and how we work together. It was clear that Daniel's way of leading and the culture inside of UiPath best matched my ideal workplace. It's a uniquely customer-centered organization where there is humility all around with everyone you work with regardless of levels and titles. Being plainspoken and direct, being humble and decisive, being strategic and hands-on at all levels, where everyone generally steers in the same directions was an important part of how I wanted to spend the next decade of my career. So working in a [indiscernible] environment, making meaningful impact. And I found this company, UiPath to meet the cultural needs for me to thrive doing the kind of work that I was passionate about.

And I'm so glad to be here, Daniel, actually, it meets all of my expectations. As we all know, the world of software is changing so rapidly. The winners and losers are to be decided, to be quite honest. But the technological moat that we have and we are building, the customer base we have and we are growing. And most importantly, the culture of fast decision-making and the velocity and the customer centricity, I think we have the ingredients to build a great company and strengthen the great company that we already have. And so I'm excited to be here, and it's been a fun couple of years and I'm looking forward to many more, Daniel.

That's great having you here. And you passed the best test of our engineering leads in Romania, that was part of the hiring process. So I was impressed that you are the only one or maybe two people that got a good review from that site.

Yes, yes. It was both hard and easy to pass the test. It was hard the first 5 minutes, but I knew who I was speaking with and the connection was deep and immediate, Daniel. So I think I understood in those first few minutes talking to some of those folks, what was expected and the cultural alignment was clear, clear from the get-go. So I'm...

And you were talking about the [ code ] in the interviews.

Yes.

Myself, I have asked you some kind of hypothetical coding stuff, so good. So I think for -- it was really a good lesson how to get someone culturally fit.

Yes, it's super important, Daniel. And I think the leadership team that we have at UiPath now between you and your direct reports. I feel like we are -- we have that team where we can speak freely, we can spar openly, challenge each other and really push the boundaries for what is possible. So I feel like we're in a good spot for all the reasons that we just discussed.

Daniel, I want to ask you a couple of questions that I'm sure is top of mind of many investors that are here. There's is a narrative that AI could simplify or even eliminate large parts of the software stack. In that world, what moats do you think we as UiPath have? And how does our role become you think, even more important? Do you want to take a stab at that and then I can add my 2 sides?

Yes. Yes, of course. Look, as we all know, this is not a new narrative for us for UiPath. So I think we've been on this AI kill list, the first, I think, around 2023. And then we've been on and off that lists sometimes we've been on the benefactors of AI, sometimes on the kill list. So I would like to debunk maybe a few things about AI versus RPA and versus UiPath.

So look, I think it's important to distinguish between using AI during implementation time, during design time, during process requirements definition and using AI during execution. Because I think first of, the main important question is AI capable of executing a task as RPA is doing. Well, the answer -- it's very nuanced. In some cases, it's capable in many cases to run a complex multi-step task, growing multiple application completely autonomous, it is not today. So the most interesting incarnation of that we are seeing have seen, like Claude Code, so they are meant for doing ad hoc tasks in the presence of the humans. So humans is ultimately what decides if the payment transaction is processed or not.

Our business is really about running complex processes, workflows in an unattended autonomous fashion. And I think this is also even philosophically, it's not the territory of AI. AI creates a code that is running on an infrastructure, it's running on the framework. But it's not meant to replace that code even if it can. In a very simple example, AI can multiply 2 numbers right now. Look, sometimes, there can be errors. If I really put it to test and give it very big numbers, AI might not succeed. And anyway, if I have to multiply 2 numbers in a loop a million times, AI will struggle, and it's not going to have 100% accuracy on that one. Nobody ever is thinking that AI should multiply 2 numbers, everybody really understands that AI will call a tool that will multiply these 2 numbers. AI is not enough to understand, this is a request to multiply 2 numbers. Therefore, I'm going to call a tool. We think it weights hard to translate it into automation. Obviously, AI can understand when it's the right context to run an automation and it's going to run that automation.

How do you create that automation? This is where we shine. And we offer amazing platform that makes it very compelling for most of the enterprise to build their automations to run on our cloud. And also as part of this, it's not as simple as you create one automation, you run it and you are done. This is -- if you look at the landscape of enterprise processes, it cannot -- it's so much complexity there. When we speak of a process like procure to pay or order to cash, we might have hundreds of sub workflows involved that have to be clearly orchestrated by rules, by policies, by human judgment. And so you'll have always multiple actors. It might be a dozen of people involved in a process.

It's not -- people are not going to let just the black box AI do my order to cash and they people and AI have to reason over a system. So right now, what I'm the most excited is actually the emergence of the coding agents. Because this is basically -- it's the best of both worlds. Coding agents will help our customers and our partners to build automation. And to build automation at a larger scale that we've seen before. This is basically our biggest road map change that we had in the last few months. So we pivoted the entire company into enable our platform to be used primarily. We even see the primary person to use our platform is going to be the coding agents.

And we will support where coding agents agnostic. Of course, we work with Claude Code, we work with Codex, and we will work with all the best coding agents out there. But our ambition is to have our platform enabled since the inception of an idea of automation to creating a process specification, interviewing multiple stakeholders, understanding the process, the nitty-gritty of the process, creating the solution architecture, creating all the artifacts, including RPA, including Maestro, including document understanding to debugging, testing, deploying in production, monitoring in production, fixing all the exceptions that happens in production.

So AI will be the main factor in interacting. So it's going to be a very heavy use of AI, but who runs the pieces, the artifacts that run on our platform are good, are very reliable, work a million time in the same time, you can reason the same input is going to produce the same out. It's governed, it's secure, it's auditable. So in a way for us, I now I see that really coding agents, it's an amazing accelerator.

Yes. Thanks, Daniel. So how about -- it might be a good segue for us to talk a little bit about our product strategy, Daniel. Talk to our investors on how -- like you mentioned, how we're going up that value chain from tasks to more complex processes and then how coding agents make -- is a massive force multiplier for a platform. So let's just jump right into the product road map presentation. I'm going to share my slides.

So let's talk a little bit about what Daniel spoke at a very level for the vision of where we want to take our products over the coming months. We'll talk a little bit about our product strategy and how we believe we capitalize on the opportunities that are ahead of us in the coming year.

All right. So let's first maybe look at the reality in the enterprise today. There was a survey done with a few CIOs was last month, where you'll see on the left that integrations is the #1 barrier for transformation, agentic transformation and automation transformation. Nearly 50% of the CIOs say that connecting AI agents to existing systems like their databases, their CRMs is in our challenge, and it's compounded by all kinds of data quality issues. And on the right, you see the emergence of -- this surprised me, too, the emergence of shadow AI where about 20-odd percent of deployments are already unauthorized. Now this makes it clear that enterprises need a unified platform that solves both sides, the deep integration into all types of systems and governance built in.

Now of course, UiPath solves for it, but it also clearly gives us signal that the best way to solve the problem is to really go up that value chain to take away this complexity from the CIO, the COE and even the business users and provide them more turnkey and end-to-end solutions.

Now let's take that enterprise reality and transition a bit to talking about how we are going to take our platforms and our products forward. We want to introduce to you our agentic business orchestrator platform. We'll read this from bottom to the top, you see -- we started where UiPath found its first product market fit and defined category leadership, as you know, at the task layer with UI automation and pretty all-encompassing enterprise connectivity. This is the foundation that let us reach into any application and any interface. It's our biggest and deepest moat, as you all know. And it cannot be overstated because the most complex enterprise processes have a mix of legacy and modern applications, and it's critical to have robust RPA support and API support to meet that diversity that we need.

Now then, as you may know, in the last 12 to 18 months, we've moved up that value chain to also include process orchestration in our product mix with Maestro coordinating robots, humans and AI agents, to orchestrate the most complex business processes end-to-end. We've built some of the deepest technological moat here with support for long-running workflow, that run for hours, days, weeks, even months spanning multiple agents, multiple systems and humans, work that gets interrupted, work that can fail, work that needs to recover and still complete with full visibility into where everything stands at our throughput, auditability and security, some things that enterprise customers really care about.

And then further up that chain is what we call agentic case management, where we embrace the dynamicism that exists in business processes and the countless variations that they take. These processes are chaotic to say the very least. And our newest innovation, what we call our case manager agent can autonomously triage and resolve these complex and dynamic processes. This is our newest offering, launching in May, and I'll talk a little bit about it in an upcoming slide. And then we are now at the top of the value chain here on vertical solutions, where we offer industry-specific offerings that deliver and outcomes to customers that implement the highest value business processes end-to-end. And also, we'll cover this in a little bit. Now that's really the value chain story. We're going from tasks to also include processes with our orchestration layer to cases and vertical solutions. All these outcomes delivered on our secure and governed platform.

Now I'm going to spend a little bit more time talking about the modern agent native stack. As Daniel described a bit earlier, and you've probably heard that the coding agents are taking the software world by storm, and they are totally revolutionizing. Our software is built and managed. Now at UiPath, we are going all in and making our platform fully accessible by coding agents. This includes the full life cycle of automations. From building, operating, managing and obviously, of course, governing automations as well. So on this slide, on the left, you see our platform stack, which I just talked to you about.

On the right is the key unlock. This is coding agents natively embedded in the platform. They accelerate every phase of the automation life cycle. They will trust this context, reading process documents and really capturing the requirements for a real business workflow. They author and deploy, meaning they go from natural language to production-ready agentic workflows with guardrails. And then they diagnose and repair proactively analyzing logs, errors, proposing fixes even and redeploying them in a closed loop. And then critically, they support governance and operations, managing machines and making sure that SLA, business SLAs are met and adhere to. The key here is that coating agents compress the time to value dramatically. Every developer, every operator on the platform becomes more productive. What does that mean? That means that more automations get built secured and governed. It means that we really can expand the value inside our existing accounts and accelerate new customer onboarding as we add new accounts to our list.

Now there is a segue that I'll present here, which is our developer base expands also. Today, as you know, we target what we call automation developers. But coding agents allow us to target pro developers in the enterprise as well as those that are less technically proficient. Now does this mean that our bets and investments on low code experiences go away. But we don't think so. It actually becomes more important because it gives people, especially those that have less technical proficiency, the confidence to visually verify and inspect that their natural language intent matches the actual automations were done. So we believe that the low code experience becomes even more critical, at least in the short, medium term in terms of how people express and manage their intentions.

All right. So we've introduced the business orchestration platform. Now I'll spend a few minutes taking a deeper look at the orchestration layer, the case layer and the vertical solutions player. Now this slide describes the very core foundation of our orchestration platform, the orchestration layer. On the left, you see a real orchestration flow, this isn't, as you can see, a linear automation. Even though it's simplified to fit in this slide, you see that it's a reasonably complicated process with branching logic with human checkpoints, multiple agents and handoffs, all coordinated by Maestro. On the right, I'll talk to you a little bit about what are the sets of salient capabilities we've built in here that makes this enterprise grade.

First and probably most importantly, it's an integrated and unified platform where you can model your most complex end-to-end processes, but also implement every single constituent offer. To be clear, Maestro is endpoint agnostic. What we mean by that is you can bring your own systems of record, you can bring your own agents built outside of UiPath, it doesn't matter. The problem we solve this orchestrating these complex processes, planning systems, agents, build on multiple platforms. Somewhat still needs to move the process along and execute it optimally. Someone still needs to provide you the visibility on how the process is doing. And that's the core of Maestro. It's like that control tower that gives you that visibility, that drives the process along to completion.

Second, this is our technical moat, which is the durable execution. When an agent fails, when a system goes down, especially for these complex processes and they happen all the time. The orchestration layer needs to pick up exactly where it left off to ensure that we provide mission-critical reliability built on this engine we call the event source engine. That's really at the gut of this new wave of products that UiPath is building, Maestro, case management and of course, all of the vertical solutions as well. And then, obviously, business users stay in control, humans are in the loop at the right moments for triaging escalations, approvals and so on. Every step of Maestro is audited and governed end-to-end, so that -- as I mentioned earlier, there's concerns from the CIO is about governance, and this directly addresses that governance concerns that they raised.

And last, but critically probably equally important to all the rest is Maestro has or will soon have native integration for coding agents. So these orchestrations don't take months to build. Developers can use coding agents to also test and deploy them quickly closing the loop on time-to-value story that we just covered. All right. Now let's get real about what a business process actually looks like. This is an actual insurance claims process and really just one variation of it. Look at the complexity. A claim comes in, an AI agent runs evaluations it branches, maybe the confidence is low, it escalates to a human, the adjuster may correct the data. It loops back and there's probably some legal and compliance coverage or reviews that need to happen in parallel. Maybe some parts of the process needs to be reevaluated and so on.

Notice the nodes here, there are AI agents participating in this process. There are some deterministic automations, APIs and RPA automations of the mix. The nodes that are dark red, the red-shaped, the red-colored nodes are the humans in the loop. A single process leads to all these 3 components, agent, humans, APIs and robots continuously. It's not about really automating a single step. It's really about orchestrating all of these constituents across all of the variations and exceptions and making sure that the work actually completes. This is why naive or simpler workflow tools breakdown, they can handle the happy backlog. But the exceptions, the handoffs between AI and humans, the parallel branches, that's where you need a true orchestration platform, and that's exactly what we've built.

Now what you saw in the previous slide was the reality, right, the messy and tangle process. The goal really isn't to create that perfectly linear flow. We know it does not work. The goal is to orchestrate everything that needs to happen. When and as it needs to happen with Maestro's agentic case management capability. At the core of this is our newest technological moat, what we call the case manager agent. This is a foundational investment that makes our push into these complex processes possible. What does it do? It maintains and manages state in context and progression of work across all of the stages that you've seen here.

Think of it like a brain that knows where every case stands and moves it along. As you can see here, the process has broken up into these 3 stages, the intake step, where claim comes in, an agent processor, an AI agent extracts the document, the case manager agent decides the path dynamically and not using a fixed flow chart. They do with the second and third stages. This is the same complex process, same variation, but now it's orchestrated and governed and observable end-to-end because the case manager agent holds it all together built on top of our orchestration layer. And remember, even this is coding agent power, like I mentioned earlier.

Moving on to -- and this is a quick glance at how this manifests in our product, we'll see a demo of it in a minute. Now moving further along the value chain to providing solutions, as I mentioned earlier. This matters because many enterprises do not want to buy AI in the abstract, they really want to buy outcomes. They want fast cycle times. They want measurable ROI. Our solutions are explicitly designed to make value visible in days, not weeks and quarters. And this is not a separate strategy from the platform. It is the platform. Every vertical solution is powered by the same underlying platform that I just shared with you, the same governance layers, the same AI trust layers, the same data and integration layers run underneath. It's key to note that we are not pivoting to solutions. We are expanding our addressable market upwards from selling infrastructure to IT, but also selling outcomes to businesses now.

It'd be fair free to ask what makes us uniquely biased to succeed with this? And our answer is a simple. Our core thesis is actually pretty simple. AI, as you can all -- as you all know, we'll reengineered every major business process. And we believe we are uniquely positioned to lead because we combine deterministic and agentic logic in one credible platform with strong governance supporting complex and regulated areas and systems. And what we're building are solutions, not point products. The architecture, as you can see in this picture includes what businesses care about. Domain expertise is built in, specialized agents that understand bespoke industry-specific logic and bespoke business-specific logic, workflows pre-configured for use cases and ROI dashboards that speak the business users' language correctly. And we're also disciplined about how we will scale. Instead of attempting massive transformation, we start with high-value subprocesses where we can prove impact quickly, build trust and then broaden from there.

And then here, we are populating the previous picture with a few vertical solutions that we are investing in. On the left, you see by industry where you'll see our investments in financial services, health care and life sciences. These are our customers that we have -- these are industries where we have a proven strong customer base, and we understand these industries deeply. On the right, you will see the departmental level use cases, QA testing account, accounting and procurement, Test Cloud is our beachhead into the QA department already a leader in the Gartner in Forrester quadrants, and we continue to see a significant momentum there.

Now the key point is each of these solutions is built on the platform, as I mentioned earlier, this really means that every new solution we ship makes the platform stronger. And then the platform becomes stronger with every new solution. It's that compounding model that makes the solutions and the platform stronger over time. Now I'd like to make a lot of this real to you. I'd like to invite Mark Rubinstein, our Director of Product Management, who is walking through a real example of this vertical solution that he's up leading. Mark, why don't you explain to us the product that you built via demo and then explain to our folks here. So take it away.

All right. My name is Mark Rubinstein. I'm helping lead our vertical solutions team on financial services. And over the last several months, we've spent time with dozens of lenders sitting alongside loan officers, processors, underwriters, QA analysts watching how the process actually works for loan origination. And on the front end, before underwriting, loan officers and processors manually collate dozens of documents. They're hunting for gaps that could stall underwriting and they're repeatedly circling back to the borrower for more information.

And on the back end of the process after underwriting, QA analysts work through hundreds of business rule checks. They're manually leaping through dozens of documents to check and catch compliance or data entry issues before closing. And ultimately, the result is that there's no single source of truth, cycle times drag until one of their competitors end up closing faster and winning the business and cost spike every time volume surges and risk keeps accumulating with every file that relies on humans to catch it. And similar to what Raghu showed earlier, the process is nowhere near as linear as it looks, as I showed on the last slide, it's extremely dynamic. It's exception heavy and fragmented. There's loan origination in one system, core banking in another, documents in another, checklist in another, there's no orchestration layer that's connecting them, humans, these separate teams of humans are the glue that hold it all together. And that's exactly why there's errors, delays and high costs.

And this is what their day-to-day actually looks like. It's multiple systems that open simultaneously. They've got documents scattered across tabs. Data is being manually cross-reference. I can just feel their pain looking at this slide. And this is the environment that our solution must work within. And these aren't just operational headaches. They show up directly in the numbers, with 42 days on average to close a conventional mortgage, nearly $11,000 to originate a single loan and 2/3 of that cost is labor, and 47%, almost half of critical defects that are found for these loans are directly tied to manual verification and calculation. This is the cost that the process has that really hasn't fundamentally changed for a wide gamut of our customers.

So this is where our UiPath solution for loan origination comes in. It has 2 purpose-built modules. We have loans set up on the front end of the process between application and underwriting. It automatically reviews loan data and documents, identifies gaps, recommends remediation and it helps expedite borrower follow-up. And then we have the QA/QC module that sits after underwriting and after closing as well, that ensures that documents are clean. Business rules are applied consistently, escalations can be handled efficiently and the lender is audit ready, and I'll demo this module in a bit. And both are connected directly to existing loan origination systems, content management systems, core banking systems, there's no rip and replace needed, which is very important.

And together, they're designed to cut setup time in half, cut QA/QC review from hours down to minutes, so that they can lead to faster time to close, lower ever overhead loan and fewer defects. And all of this is coordinated using UiPath Maestro, automations that pulling loan data and documents from their systems of record. We have agents that extract relevant fields and execute hundreds of checks. And then there's people that can operate this solution in a single workflow, which again, I'll show in a bit. None of this was built on assumptions, by the way. It was all co-designed with a set of real customers deployed in real production environments. We started with regional banks and credit unions, some of which are shown on the slide here so that we could move fast, we could learn, we can iterate quickly. But we're seeing the same challenges at significantly larger global banks and we're working to onboard more of these customers and expand.

So without further ado, let me switch over to our QA/QC demo, so I can show you a little bit about how this works. So in this case, I'm the Head of lending, I'm responsible for loan quality, and I care about reducing the number of bad loans that were originated due to error and staying in regulatory compliance, all while reducing our overhead. And you can see right here, I can view all of the KPIs, metrics that I care about, things like processing time is decreasing. Our defect rate is decreasing, our loan volume is increasing over time with fewer errors. And most tactically up here, I can see all of the top issues that were found during QA review, so that we, as a processing team can improve and catch these issues further upstream.

Now let me switch over to an individual loan where I, as an individual QA analyst would be doing my work. This solution, again, it aggregates all of the data, it stitches together our existing loan origination, core banking and content management systems into one unified view. These systems that were never really designed to talk to each other. And on top, I can see a summary of the loan. So if I come right in, I can see where the loan is at. I can see exactly what my QA agents rather have already done on my behalf. And below are a series of checklists that I have to work in. And before these were all reviewed manually, they were tracked in Excel spreadsheets with dozens of documents and windows open on multiple monitors to triage hundreds of different business rules per loan. And these checklists are now suddenly smart. All of them are processed automatically using UiPath agents, deterministic workflows and intelligent document extraction, allowing me to focus just on the issues that need remediation.

Now let me go into one of these checklists where I see my review is needed. So instead of needing to manually steer and compare between these 2 documents on separate monitors and scroll through them to hunt for what I need. The solution automatically extracts the key data points so I can confirm that they're accurate. So I see right here, the first rule that I need to check is that the name on this document matches what's on this ID. You can see that the agent automatically found that as a match. And I, as a human reviewer can confirm it for auditing purposes. There's a series of rules here that are designed for this specific document.

I see right here that an agent found an issue with one of the rules. And I can see, if I zoom in a little bit closer here that. These dates are expected to be within 30 days of each other, and the agent found that they were in fact not. So clearly, someone entered the wrong date on the credit approval memo. So I, again, as a human can mark this as a no, not matching. But let's just say that I disagree with the agents finding. Maybe you got something wrong, I can easily override that and include a note to indicate why the agent was wrong. And this is both used for auditing purposes, but it also helps the agents learn and improve over time so that it can get more and more accurate.

All right. Let me go back real quick. And I just want to show 2 actions that I can take now as a QA analyst. So one, very often when this is done, I need to escalate back to the processing team so that they can remediate the issues. And before that required me to collate notes on another screen, write an e-mail and send it to them. But I can do all this automatically. I can see right here that the issues have been all summarized for me. These agents know everything about this loan, and I can easily send an e-mail right here. The other action that I typically do is that I generate a report that can be used post closing for auditing purposes. And before this is all done manually typed in a word document, for example. But given, again, the solution has all the necessary context, I can automatically generate this report, which before I again, had to do manually.

So everything you saw here was something that used to take me hours, but can now be done in minutes. And the solution augments and accelerates my entire team of QA analysts. This was built fully as a UiPath process app. On top of Maestro case management, which Raghu talked about a little bit earlier. Everything from application submission to closing, all of the automations, escalation paths, agents are all defined and orchestrated within. That is our QA/QC module for UiPath solution for loan origination. Combined with our loan setup module, they're designed to accelerate time to closing, reduce operating expenses per loan and mitigate bad loan risk, all while working with bank's existing stacks.

Thank you. Raghu, I'll pass it back over to you.

Yes. Mark just showed us what this -- what our overall platform and product look in practice. Now let's bring it home, like why buy UiPath? We opened the session with the reality that CIOs face that about 50% of them struggling with integrations and data quality issues all kinds of governance challenges and shadow AI. These days aren't just AI problems. They're also orchestration problem. And orchestration is exactly where we're building our moat. We are the category leader in task automation that proven at scale and in the most complex and regulated industries.

We're adding coding agent support, lock stock and barrel all throughout our stack to take developers from natural language to production-ready workflows. Obviously, we're building agentic case orchestration and case management with the case manager agent that I talked to you about earlier that coordinates work across people, humans and robots and drive processes, the most complex business processes along. We're delivering out-of-the-box vertical solutions. Just an example of which Mark just showed. And then all of this is built on our enterprise grid governance and trust layers. Now each of these moats reinforces the other. No one else we believe, has this combination, the depth of automation and the breadth of orchestration and the discipline to deliver these as outcomes, not just tools. And that's why we believe we are uniquely positioned to drive a lot of value to our customers upcoming.

Now I want to switch to helping bring this to life with the real customer example. We recently sat down with Jason Paris, the CEO of One New Zealand, one of the country's leading telecommunications providers who is driving a pretty significant agenda to leverage AI as a competitive edge across the business. His organization, we believe, is a good example of what's possible when you combine agents, deterministic automation and orchestration within a single platform. And UiPath is at the core of their transformation strategy.

I think it took 5 weeks to bring their order to cash process into production. It reduced their cycle time, the processing times from multiple days, 4, 5 days to 5, 10 minutes. And they're not scaling their overall B2B operations with expected tens of millions in savings. What stands out is this isn't a one-off use case. It's the platform that they're bedding on for their long-term transformation with orchestration at the center. Let's hear directly from Jason. Jake, do you want to take it away?

Sorry, Jake and Allise can you take it away, please? Appreciate you taking the time to share your story. Can you walk us through your transformation goals and how you see One New Zealand evolves as AI transforms our industry?

Raghu, thanks for having me and also thanks for the partnership that you give us. We've been deploying variations of artificial intelligence for over a decade now, thousands of RPAs in our organization using large language models, generative AI and now Agentic AI. Our goal is to be the most AI-enabled telecommunications company on the planet. And the only way that we can do that is with pace. We're a small market, the bottom of the South Pacific.

And so when we're working with partners like yourselves, the thing that hopefully attracts you to us is the pace with which we will experiment and that we will deploy the technology. We have a secondary kind of mission, which is AI first, but human where it matters. So it's also important to state that AI is going to transform our entire organization but it's not going to stop human-to-human interaction being really, really important. In fact, what we're finding is that it gives us more time to make those human moments even more important. And the way that we can do that is by using a partnership with you to automate its scale. And so there's pretty much -- not a single part of our organization currently, which is not being process mapped, rewired, automated and having agentic tools laid on top of that.

Yes. It's amazing that you've been able to take your employee base along JP, as you've incorporated AI into your technology stack into the way work gets done in One New Zealand. Now I'd be curious to understand what are the key parts of your AI transformation strategy? And then how does a platform like UiPath specifically UiPath Maestro fit into that strategy? And I'd be curious also to learn a little bit about specific impact or ROI that you achieved with the platform?

Yes, that's a great question because I think everyone is deploying artificial intelligence very few being able to bank the cash. That's not the case with our partnership, which is why we are scaling our partnership with UiPath. So as I mentioned before, there's not a part of our organization that we are not trying to process map, automate and rewire using advanced artificial intelligence tools. And that -- an important part of that ecosystem is our partnership with UiPath. Your Maestro tool, we see as an orchestrator over the top of our AI and systems and people. The thing we love about it is that we're a legacy business. We've been around for 20, 30 years. We've grown through acquisitions of different types of businesses. We've got multiple stacks, mobile billing platforms.

And so what we haven't needed to do is a major re-platform or replacement to partner with UiPath. And so the ability for you to map and then automate and orchestrate legacy technology without having to replace it has been awesome. I'll just give you one example. So customer -- a business customer wants to replace the handset either because it's broken or they need to refresh it. That's a path that goes across mobile parts of our businesses using multiple technologies, multiple processes, including external technology and external support. Currently, we are used to be about 4 to 5 days to make that process happen end-to-end. And then it's not acceptable when your mobile phone is your life remote. If you want to refresh it or you need to get it replaced, you need that replaced within a day, not within days. And so what we've been able to do with UiPath is exactly what I've just said before, proceeds may automate, have an orchestration layer over our existing processes, no change to existing processes, and we've changed that 4 to 5 days to 5 to 10 minutes.

How incredible is that where you can use this technology with your existing technology, your existing processes, your existing workflows and move from 5 days to 5 to 10 minutes. So the ROI on that, of course, is extremely strong, and that's why we're scaling this across the organization.

Yes. JP, I mean, your commentary here really resonates. I think the most complex enterprises, such as yourselves, is a combination of modern and legacy technology stacks and our orchestration layer, as you found out and as you know, incorporates the most modern technologies as well as the legacy technologies and brings it all together. So you don't have to rip out what is working for you. You don't have to forcibly modernize what is there for you and so on. So it's great that, that our orchestration platform has worked for you in the way in the way that it has. I also learned, JP, that you went from a proof of concept to production grade deployment in just a handful of weeks, like 4 or 5 weeks. Can you talk a little bit about what enabled that level of speed for you?

Yes. Well, again, we think speed is an advantage for us, not just to attract partners like UiPath to work with us, but also as a differentiator of market. And to be fair, we did have our prior proof of concepts. As I mentioned before, we've deployed thousands of robotic processes across the organization. In fact, we would estimate that we've made about 20% more people in our organization, we've currently got if we didn't have robotic process automation in place. That's a significant competitive advantage and cash advantage just there. The proof of concept that we had with UiPath gave us a huge amount of confidence. It's an integrated platform, AI plus RPA plus orchestration. And again, because that proof of concept worked well, that meant that we wanted to scale quickly.

So I think we built our very agentic agent within about 12 hours, and then it took us a few weeks to deploy it because we've to clean the data up, make sure that it was operating appropriately. And so -- that's why we had so much confidence to scale so quickly. I would say that's not just the mix of the technology, though, Raghu. It's also the subject matter experts, the capability that we've had sitting side by side. We have a kind of 2-in a box model. I think as you'll be aware, we've got our own experts sitting beside your experts working on the issue. So it's something that we've really benefited from and getting UiPath's expertise to upskill and reskill our own people within the organization at the same time.

And then, of course, you want to make sure that you test it end-to-end for scalability. And so again, the proof of concept did that well. 5 weeks, we can see the value and now it's being scaled across the organization.

Yes. The partnership has been incredible across our teams for sure. Now as you -- JP, as you scale this technology across your organization, where do you see the biggest opportunities for you next? And how central is UiPath to that longer-term AI transformation strategy for you?

There is genuinely no part of our organization that is not going to be transformed through this technology. And so your biggest decision is where do you prioritize first. And so we're prioritizing where really the biggest layers of volume and complexity and cost set. So areas like provisioning, finance, risk, fraud and also even really big complex programs in IT like SAP upgrade. So -- but that's just our first bucket of priorities. Genuinely, I can't see any part of our business that's not going to benefit from this -- from Maestro and from the technology you're providing us.

Yes. And we're looking forward to supporting and partnering with you through this transformation that I know lock stock and barrel you're going through at One New Zealand. That's fascinating. Now you've -- I know you've evaluated a number of leading AI and automation platforms at One New Zealand. What ultimately led you to choose UiPath? And then what gives you confidence that UiPath can support that mission-critical execution of your most complex mission-critical automation, it's not just experimentation.

Yes. Well, I think the first part of it is like when you're looking and you're looking at what the technology is available to you, you want to make sure the technology partner is agnostic. And so you need to make sure that it would work across a lease environment with a lot of complex technology. So that was the first tick that the UiPath received. Then also, you want to make sure that it avoids kind of multi-tool complexity. So again, it's an integrated tool that works in combination, not just as an orchestration layer, but across AI and RPA, which makes sense.

And then when you start to test it, you want to make sure that it's got compatibility and you can actually deploy it within your organization, just tick. And then when we did the proof of concept, we could see that not only did it work, but it could scale and you can scale it quickly and you can, as we talked about before, get a cash return on it. So a pretty simple checklist that anyone should be going through as the platform agnostic and can work within your existing environment? Can it be an orchestration layer, which works both with advanced artificial intelligence but also robotic process automation. And then can it scale across the organization and deliver the money step, right, cash that you can either bank or reinvest in other parts of our businesses. So all of those have been ticks for us, and that's why we chose you and we're delighted that we did.

Great. It's always valuable to hear directly from our customers. With that, we'll open it up for Q&A. Unfortunately, we only have time for one question. So I'll combine a couple of themes that we've been seeing come through. Daniel as customers begin to deploy more agents, what are you seeing in practice around the need for orchestration? And more broadly, how do you think about adoption of Agentic solutions and UiPath's right to win in this space as it becomes more crowded?

Yes. Allise, I would like first to give a quick explanation of what's the difference between agent-to-agent, orchestration and process orchestration because I think there is a bit of a confusion in the market. I think when people speak right now about orchestration, I think they implicitly pursue some kind of purely agent-to-agent orchestration, like having a swarm of the agents, we give them a goal. And the agents will communicate to each other, create the planning, will split the task something maybe more akin to like open grow is happening.

When we speak about orchestration, we speak about process orchestration. So it means that in order to achieve an enterprise goal that is being compliant with all the regulation, the regulations in place and understanding the complexity and the many actors involved. You need a bit of a different approach. And typical way to an enterprise solve process orchestration is to have like a process view process description. Many people would use something like this business process modeling notation for showing depicting the process, the workflows involved with the caveat there can be, as I said in the beginning, hundreds of sub workflows there. And each sub workflow can have many steps. Some steps can be purely deterministic and they can be sold by RPA or API, automation, some steps will be agenetic, some steps definitely will require humans in the loop to supervise as you've seen in these demos.

It's kind of clear from all the customers I talked to. In my case, it's almost no exception that the preferred method of bringing AI into the context of an enterprise process is basically injecting AI steps in a deterministic orchestration and workflow engine. In this way, the AI is limited more to understanding a specific task work, understanding the work at the specific stage in the process.

So yes, I would say that the advent of agentic makes even more compelling for enterprises to have a platform that offers a built-in process orchestration. It's much more -- it's much easier and more compelling proposition to have in the context of the same platform, the nondeterministic agent code, the deterministic code and the humans and enterprise workflows that organize that basically manage all the interaction between these actors. You can apply the same on same governance, the same security model, you will have the same audit trails, same observability, more the same analytics across the entire process, end-to-end. This is very valuable for enterprises to be capable of understanding every single interaction that happens in order to deliver go across of an end-to-end process.

Great. Thanks, Daniel. Unfortunately, that brings us to the bottom of the hour. So Daniel, I'll turn it back to you for closing remarks.

Thank you so much, everyone, for staying with us. I hope that this session gives you more clarity of what we are doing. If I have to summarize everything, we are extremely focused on bringing coding agents into the picture. I believe this is going to be a big accelerator into the adoption of our platform.

And I want to finish saying if somehow under the hood, we built this amazing and only platform in the market. We are the only platform that is built right now on the top of a new model workflow engine that is really very good for -- to be used by coding agents. And on the top of this engine, we built business friendly way to describe a process using BPM. And then, we have our proven scalable engine that was capable of delivering for many years, automation escape. And we are talking about hundreds and thousands of automations that run in parallel, concurrent run at a big scale, you need to orchestrate them, to manage them, to have to feed them with data to understand analytics. That's not something that you can build overnight in -- and it requires a lot of deep engineering architecture of thoughts.

And then the third important pillar, we have the task automation capabilities. Basically, we have the capability to integrate with every system out there. The legacy system and model system will continue to coexist for the foreseeable future. And it's so powerful to have all of these components in a platform that offer integrated security and governance. So with this, again, thank you so much for staying with us. We would like to connect in the next couple of weeks with as many as you possible. Thank you.

Greetings, and welcome to UiPath's Fourth Quarter and Full Year 2026 Earnings Conference Call. [Operator Instructions] Please note, this conference is being recorded.

I will now turn the conference over to Allise Furlani Head of Investor Relations. Thank you. You may begin.

Good afternoon, and thank you for joining us today to review UIPath's fourth quarter and full year fiscal 2026 financial results which we announced in our earnings press release issued after the market closed today.

On the call with me are Daniel Dines, Founder and Chief Executive Officer; and Ashim Gupta, Chief Operating and Financial Officer, to deliver our prepared comments and answer questions. Our earnings press release and financial supplemental materials are posted on the UiPath Investor Relations website. These materials include GAAP to non-GAAP reconciliations. We will be discussing non-GAAP metrics on today's call. This afternoon's call includes forward-looking statements regarding our financial guidance for the first quarter and full year fiscal 2027 and our ability to drive and accelerate future growth and operational efficiency and grow our platform, product offerings and market opportunity.

Actual results may differ materially from those expressed in the forward-looking statements due to many factors, and therefore, investors should not place undue reliance on these statements. For a discussion of the material risks and uncertainties that could affect actual results please refer to our annual report on Form 10-K for the year ended January 31, 2025, in our subsequent reports filed with the SEC, including our annual report on Form 10-K for the year ended January 31, 2026, to be filed with the SEC.

Forward-looking statements made on this call reflect our views as of today. We undertake no obligation to update them. I would like to highlight that this webcast is being accompanied by slides. We will post the slides and a copy of our prepared remarks to our Investor Relations website immediately following the conclusion of this call. In addition, please note that all comparisons are year-over-year unless otherwise indicated.

Now I would like to hand the call over to Daniel.

Thank you, Allise. Good afternoon, everyone, and thanks for joining us. I want to start by thanking the people who made this year possible. Our employees, we executed with discipline and purpose. Our customers who trust us with their most critical workflows and our partners who have made a genuine bet on our platform. This is a team effort, and I feel that every day. We delivered another strong quarter, beating the high end of our guidance across all metrics and closing out a year of disciplined execution.

Fourth quarter ARR reached $1.853 billion, up 11% year-over-year, driven by $70 million of net new ARR and the revenue of $481 million, up 14% year-over-year. Alongside that growth, we've achieved full year GAAP profitability for the first time in our company's history. We grew fourth quarter non-GAAP operating income to $150 million, a 31% margin, a reflection of the operational progress we made throughout the year driving meaningful efficiency while continuing to invest in growth. And in Q4, we posted our strongest sequential net additions of customers with $1 million or more in ARR in 2 years with deals over $1 million, up over 50% year-over-year, a reflection of both improved sales execution and deepening enterprise platform adoption.

I have never been more energized. What we are seeing now goes beyond a single quarter, we are at an inflection point in how software is built. Advances in AI are dramatically reducing the time and cost required to create software. And that has led to understandable questions in the market about how value will be created going forward. Historically, moments like this don't eliminate software, they shift where value is captured. Enterprises don't simply pay for quote they pay for trust, for operability and for government, the ability to run complex systems reliably, securely and with full accountability as the cost of building software falls the value of platform that can safely govern, orchestrate and scale that software rises.

And there is a second dynamic that I find even more exciting. When building becomes cheaper, more gets built, more processes get automated, more edge cases get addressed and more systems become autonomous. That expansion does not shrink the need for enterprise orchestration. It increases it. And this is precisely the environment UiPath is designed to operate.

We entered this new genetic era with 4 advantages. First, a unified platform combining deterministic automation, agentic automation and enterprise-grade orchestration with governance, security and scalability built in. This is the full stack, it is what wins new logos and drives expansion across our base; second, a powerful installed-based flywheel, thousands of enterprises run mission-critical workflows on UiPath today. And within those workflows, there are opportunities for agents to be deployed and the overall process to be orchestrated.

Third, 2 decades of enterprise trust and governance, deployment experience that AI plus automation is expected to deliver accountability, ability, observability and reliability at scale; and fourth, the vertical expertise with enterprise-wide reach, regulatory depth in the industries where the stakes are highest paired with the horizontal ability to orchestrate across the entire enterprise.

Let me spend a few minutes on each. [indiscernible] our unified Agentic automation platform. As AI makes intelligence more accessible, what matters is execution. Enterprises are getting answers to complex questions faster than ever before, and yet they still struggle to reliably execute complex cross-system processes with accountability and compliance built in. The goal now is to pay the insight they are getting with the actions and execution that our platform enables financial reporting claims processing, regulatory compliance. This cannot be improvised. They must be institutionalized.

Enterprise automation requires 2 modes, determinist for precision audibility and agentic for reasoning and adaptability. Most vendors offer 1 or the other, UiPath purpose built to integrate both under a single control play, allowing enterprises to move from experimentation to scale production grade deployment. Most people think orchestration means agent to agent coordination. Real enterprise orchestration brings together agentic automation, deterministic automation and humans because that is how work actually gets done. We offer that and the full execution layer underneath it, governing how our transaction moves from start to finish and ensuring that it completes reliably every single time. This is what Maestro is built to do at enterprise scale.

What makes Maestro uniquely powerful is its architecture. It is built on temporal, the most modern workflow technology featuring durable execution and trusted by the most demanding technology companies in the world. Workflows are defined in a way, AI agents can generate and modify it directly while remaining fully transparent to business stakeholders and auditors in a world where agents are increasingly the ones creating and maintaining workflows that distinction matters enormously. The customer results make this concrete, a U.S.-based semiconductor company fail to deploy an agentic workflow with another vendor after more than a year of trying with UiPath, they were successful in under 2 weeks leading to a 7-figure expansion across agent builder, Maestro and Test Cloud.

Today, they run over 3,000 automations and have sales more than 2 million hours and 1 New Zealand who went from proof of concept to production grade pilot in 5 weeks reduce 4- to 5-day order-to-cash process to 10 minutes, and they are now scaling this across their B2B sales operations. With UiPath, they expect at in cost savings this year as they plan to further leverage the platform to support their broader transformation programs.

Driver 2, the fly well inside our installed base. The most important story this quarter is the economic shift underway inside our installed base. Customers are not experimenting with the. They are expanding their operating model on our platform. AI product ARR which includes genetic, IBP and Maestro reached nearly $200 million this quarter with strong growth fueled by Agentic. But the number I keep coming back to is this. The number of customers above 100,000 in ARR, who have bought AI products grew 25% year-over-year, and they spend nearly 3x as much as those who have not.

Additionally, of our top 20 deals this quarter included AI products. All of this is clear evidence that Agentic automation is becoming central to our largest customers' road maps. Importantly, this AI growth is layering on top of a core unattended automation business that continues to grow. We are not seeing agents replacing deterministic unattended automation in production, we are seeing customers extending their processes with AI. A major U.S. airline illustrates this well, building on their deterministic Foundation, they are now deploying agent billers, communications, mining and Maestro to automate, procure-to-pay and supplier workflows a propane for how customers move from task automation to end-to-end process orchestration and how the journey drives platform-wide expansion. This is the fly with, every workflow automated, a new surface area for agents. Every agent deployed drives more automation, deeper integration and broader platform adoption. Testing is another area where we see a significant and underappreciated expansion opportunity as the genetic workflows and applications roll.

Traditional QA simply cannot keep up. Forrester named UiPath, a leader in the Forrester wafer autonomous testing platforms in Q4 2025, with Test Cloud receiving the highest possible scores in 7 criteria, including vision, road map and automation creation, orchestration and execution. A global technology company is a strong example standardizing the entire automation program on UiPath expanding into test Cloud and planning to implement UiPath agents and Maestro to automate supply chain workflows.

Turning to driver 3, governance. Building an agent is becoming easier, making it enterprise grade is not enterprise-grade agents require deterministic execution with traceability in handling and audit trays that satisfy external regulators. We see this play out in how customers choose us. An American Credit Union selected UiPath as we were one of the only solutions to meet their strict banking, security and governance requirements. And a European automobile manufacturer chose UiPath as the foundation of their Agent strategy, selecting Maestro because we could deliver enterprise-grade governance air handling and human in the loop [indiscernible] at the level there compliance standards demand.

In both cases, governance was not a consideration. It was the deciding factor, and that brings us to drive 4 vertical depth. It's not just about governance, it's about knowing the domain deeply enough to manage and operate it at scale for real impact. That is why vertical death matters more in the Agentic [indiscernible] not less. As building becomes easier, differentiation shifts to domain-specific workflow intelligence, especially in industries where the cost of getting it wrong is existential. Advising February, we launched Agent AI solutions purpose-built for health care, targeting revenue cycle management, medical record [indiscernible], claim denial resolution and prior authorization.

In line with that strategy, we acquired WorkFusion in February, bringing purpose-built agents for financial crime compliance with deep anti-money laundering and know your customer exported directly into our platform, expanding our reach into the highest takes compliance workflows inside global banks. Health care and financial services are 2 examples of a broader strategy. We pair vertical depth with the horizontal reach to orchestrate across every function of a global enterprise, a combination that either horizontal or vertical platform alone can match. And great platforms don't scale along.

Our partners are building practices, joint solutions and go-to-market motions around our platform. Our expanded partnership with Deloitte is a strong example. Together, we launched Agentic-ERP embedding AI agents into mission-critical finance and operations workflows a Fortune 20 oil and gas company that is migrating to SAP S/4HANA is already scaling through the partnership, expanding test cloud coverage from 10% to roughly 50% on their SAP environment while building new Agentic use cases across the migration.

Accenture tells a similar story. Together, we deployed the global agentic sales order entry solution for a strategic life sciences customer, reducing processing time by 1/3 unlocking automation for orders previously too complex to handle and orchestrating autonomous agents transforming the orders while navigating 150,000 exceptions. [indiscernible], I want to give you a preview of what's coming next on our product road map. Over the last few months, the world has changed. The boundaries of what is possible have shifted faster than most people expected. We have spent years building a unified platform for exactly this moment. And what it can now unlock with the next generation of coding agents, it's something I'm generally excited about.

Our platform is evolving into 1 where coding agents can participate across the entire automation life cycle. Agents will work with subject matter experts to discover processes and identify exceptions. They will work with business analysts to generate process definitions. Since developers in building automation, deploy those automations into production and help manage them at [indiscernible] The first capability of that vision ships in the next couple of months and it targets a problem I hear in nearly every customer conversation. Their automation backlog is growing faster than their ability to build. The ROI exists the executive...

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Hello, and thank you for your patience. I will now hand the call over to Daniel Dines.

Hello, everyone. Thank you for coming back after our outage with the service provider for our Investor Relations conference calls. We are ready to take questions. I hope that you guys get the chance to listen to the end of our reading. And also, we have published online the entire transcript of our earnings calls.

So thank you again and apologize for the delay. We are ready to take questions.

[Operator Instructions] And our first question comes from the line of Bryan Bergin with TD Cowen.

First one I have is just as it relates to net new ARR. And as you build the 2027 outlook -- just how are you thinking about net new ARR expansion potential here on an FX-neutral basis? Sorry if I missed what you said on FX contribution assumptions as it relates to 1Q and the full year. But just trying to unpack that looking ahead, and then my follow-up is going to be on margins.

So an op income margin, I appreciate the update on the 30% target. Just want to dig in on how you're thinking about the potential kind of the moving parts of that as it relates to gross margin and OpEx components moving forward?

Yes. So Brian, great to hear from you. When you think about the IRR contribution, I think our guidance kind of says that there's really no significant or material FX contribution from that versus our prior guidance. So as you look at it, really, FX is a minimal impact from where our previous estimates were.

The second piece of it is, from a margin standpoint, you look at the moving pieces and definitely across the board, there is opportunity to identify and to use the technology advances across every function. That includes engineering, G&A as well as sales and marketing, which gives us really the ability to continue to reinvest in growth as needed. But we're going to look at a balanced way in terms of what makes sense for the company. And you can see our commitment to operating margin expansion over the last two years.

And then just back to the IRR, I want to just give a little bit of color. When you look at our base, we have a sizable Japan business. So we have headwind from the yen, and tailwind to the euro, and they basically net out to be an immaterial impact for the full year. So we're really pleased with the progress. As Daniel commented and I did in the script, we really feel positive about the expansion that we're seeing within our customers and our ability to stabilize our net new ARR, and that's kind of reflected in both our performance as well as our guidance.

And our next question comes from the line of Sanjit Singh with Morgan Stanley.

Daniel, thank you for the disclosure on the ARR traction. I'm sorry, the AI traction with respect to ARR, the $200 million that was great to see. In terms of the composition of that, could you give us any details on sort of the split between IDP and what you're seeing on the Asian side. And to the extent you can't sort of disclose that, I'd just love to hear about the underlying momentum with the agentic side of the house, including Maestro as you go into next year?

Sandeep, we have really a great momentum on diffusion of the AI within our platform. We have not provided clear ratios between different components of what we put into the AI. And I will let Ashim to comment further.

So like when you look at the way we price, we actually allow pretty good fungibility between our AI and Agentic products actually, both in some of our old pricing as well as our new pricing. So we don't really materially split it out.

Of course, IDP hasn't been in the market for a longer period of time for like the last 2.5 to 3 years. So IDP definitely has a good portion of the IRR. But genetic is a significant portion, and we see that in the platform. You can see that in the deals and the commentary that we're giving and selling as a part that we talk about in our script. So from that standpoint, we can't really split it apart -- but we also see them as complementary because remember, IDP also includes XP, which is not like simple document processing. It really uses advanced technology to be able to parse different documents using different models and that is part and parcel of the way we price.

Yes, that's great. That's great context. And then just a follow-up on the guide, time on 2 aspects. One, in terms of work Fusion, how should I think about that contribution I sort of calculate what the guidance implies from a net ARR basis. I think there are some reports out there that they are around $25 million ARR toward the end of last year. So I just want to sort of stand to check that. And then from this time last year, there was some concerns around do as you guys are pretty cautious on the federal business, just for sort of underlying assumptions about Fed going into next year, maybe the first half of this year given some of the headwinds you saw this time last year?

Yes. So the first thing is the $25 million is not accurate. That's the first thing I can say categorically. The second piece is, they also had a different method of accounting. So when we brought it back, even the numbers that have been out there also do not account for it. It is actually below our materiality threshold, Sanjiv. So that gives you an indication. We really look at this like a tuck-in acquisition in terms of where it is. And from that standpoint, you can also just see kind of the strength overall within our guidance, and we've been transparent that, that includes the WorkFusion contribution, but it is immaterial, and we don't break it out.

And then just on the Fed piece?

Yes, sorry. On the federal government, we're actually seeing a really good traction there. I would say just like the environment, I would say the federal government is a dynamic economy. But I would say our team has done an incredible job connected at really high levels within the organization. And I'll let Daniel comment on some of his discussions and his views of it. But within certain agencies, we feel very well strong position. And then there are some agencies, of course, that are going through their changes. But overall, we're actually very bullish about the way our teams are executing and the opportunity that exists there.

Yes. And we are seeing an increased appetite for more long-term projects, strategic projects, especially in the department of world.

And our next question comes from the line of Michael Turrin with Wells Fargo Securities.

Just to start, maybe a higher level one. You had some commentary, but just in terms of budgets and what you're seeing around categories like automation, in AI, it would be great to get just a top-down view there and also how you're positioned to capture that in the market where there's just an increasing number of vendors also positioning agentic solutions, which may be newer to market, but might also insert some noise into those conversations.

Okay. I think we are really well positioned to help customers with the diffusions of AI within their enterprise workflows. We are -- we have -- we built Maestro, which is essentially a process orchestration technologies that -- and at its core, is a new powerful workflow engines. That's -- that gives us a very interesting advantage in the market right now.

So we all know about the impact of the coding agents. I would say that this will translate for us. And I'm extremely bullish about it. into a much faster adoption curve for our customers. We aim to use coding agents to enable our platform for coding agents that will accelerate dramatically the time to value for our customers. And that, of course, includes creation of AI agents, deployment of agents in the context of enterprise workflows. I would like also to stress how important is the combination between deterministic automation and Agent automation into the context of the same platform that can orchestrate both what I would say, humans, agentic and deterministic automation.

Ashim, just you gave some texture. I know the commentary and the guidance on the call was pretty similar to entering fiscal '27, '26, but it sounded like in some of the prior answer that maybe public sector is trending a bit better. So just any more context you'd give us around how you're characterizing the current environment the visibility you have into the model for the forward year at this point and just how you're thinking about the contribution from the AI product portfolio as that scales in fiscal '27?

Yes. I mean we really continue to characterize it as variable. And I'll double-click just again for anybody who's new in terms of what -- but I think we do see pockets of strength and we see pockets of pressure or fluctuations that happen from a macroeconomic standpoint. And at the same time, those tend to move around quite a bit. Like right now, our bullishness in terms of public sector feels really good.

Last time on this year, if you remember, we kind of felt a lot of uncertainty in that area. We're seeing strength in areas like financial services and health care, international markets like Australia. And then there's -- obviously, the Middle East conflict is there, so there's uncertainty there. So we really characterize it as variable. As I commented in the script, we continue to kind of maintain a very consistent guidance philosophy. We look at our pipeline. We have really deep inspection. We get a lot of signal from the field. Daniel has spent a lot of time with customers over the last 3 months, 4 months. We have -- we've been very in touch with kind of the field in terms of hearing. And then the other piece is, we obviously have a very strong now statistical and forecasting models between our finance and our ops team, and we triangulate the 3 of them. So we talked about kind of putting the appropriate prudence in the -- for guidance, accounting for the variability in macroeconomic environment, and we've done so. And at the same time, when you look at our guidance, I do think it also reflects kind of stabilization of net new ARR and what the potential is yielding in terms of the traction our teams are making in the Agentic market and how we're positioned. So that's how I would characterize it.

And our next question comes from the line of Kirk Materne with Evercore ISI.

This is Chirag on for Kirk. You highlighted multiple industry partnerships, right, Veeva like with Veeva and certain vertical solutions like health care and financial crime, would you highlight health care and finance as the 2 verticals that are showing the strongest willingness to spend right now on a genetic AI initiatives? Or -- are there others that you would flag? And when you think about genetic automation at scale, what does success look like in terms of repeatable playbook and sales cycle impact here?

I think you got it very right. It's the health care. And I think we nominate it within the health care in particularly, I would say, parts of revenue cycle management, denials, prior authorization. It's a very important type of processes for us. Financial industry has been since the beginning of the company, our stronghold, and we strengthened it with the acquisition of World Fusion with our big foray into financial crimes. And I would add also the public sector is an important vertical for us that we are eyeing.

And our next question comes from the line of Terry Tillman with Truth Securities.

I have two. So first on Maestro is my impression, it's vendor agnostic from an gentex standpoint. Are you all seeing situations where it's involved in managing agents from system or record companies or AI-native businesses? Or is it mostly like a control planning for your own agents? And then I have a follow-up.

Yes. I think Maestro, it's kind of agnostic in terms of what kind of agents it can manage. Of course, for our own agents, that are built with agent builder, we have very tight integrations. But we have also brought agents built with open source frameworks like the land graph type of agencies, first-class citizens in our platforms. And in terms of using -- utilizing agents built on system of record applications.

Of course, we facilitate using them in our platform. I would not say we manage them. It's more or less like you can call an API that is provided by that platform. But I want to be specific, the all agents that are built with open source framework can be deployed and executed in the contract security and governance that our platform provides.

Yes. That's a good clarification on the API side. Thank you, Daniel. And I guess, Ashim, the SaaS shift, that was an important call-out, 1% impact to growth as we look into FY '27. I'm also curious though, is there also starting to be this impact of timing dynamic or around consumption or scaling volumes related to the actual agenetic solutions that we need to kind of appreciate that's not going to show up in revenue yet.

No. I mean, remember, we do -- we still price on kind of a bundle, meaning on a subscription, consumable-type hybrid model, meaning we sell kind of use it or lose it units that are there. So we're not on a consumption basis of accounting, so to speak. We're still on an ARR basis of our accounting.

So I would say there's -- it's not about any trailing or any delayed impact that you would see there. At the same time, I think our [ Gentex ] solutions are scaling and our customers are adopting more and more as we talked about in the script and sales are moving very well for us. And that obviously is what's contributing a little bit to ourselves.

And our next question comes from the line of Radi Sultan with UBS.

Daniel, in your prepared remarks, you mentioned this growing backlog of automations you're seeing at customers. I just wanted to double click on that, like how big is that tailwind of AI unlocking more automatable workflows. And you mentioned the AI product there, but just how material is that sort of pull through to the core automation business as well? I just love to get your thoughts there.

Yes, that's an acute observation. Because of the huge interest in AI, it's actually driving renewed interest in automation. I think in most cases that we are seeing, people expect that the use of AI will result in some sort of automation. And it's becoming more clear that AI and Agnetic AI and the terminating automation are very complementary.

So basically, any AI initiatives surfaces more opportunities for deterministic automation, especially in our case for unattended deterministic automations.

Got it. And then just a follow-up for Ashim. Just as you think about the ARR and revenue guide for the year and we think about sort of what the biggest drivers are you guys really extended the product portfolio over the past 12 to 18 months. And just as we think about AI product, test cloud vertical solutions, sort of core RPA. Like how should we think about sort of what the biggest drivers are of that sort of growth next year as you kind of think about the guide?

Yes. I think if you just look at some of the metrics that we disclosed, right, 90% of our $1 million-plus customers haven't incorporated AI products, right? I think that is a great -- to me, kind of a great tell of the success of the AI products and the ability for us to expand. And we've also talked about the number of customers that still have room to adopt those AI products that are there.

So from our standpoint, AI and agentic is going to lead the way. But at the same time, as Daniel talks about, they're not a separate stream. They actually are very synergistic. As people pull forward AI and agentic products from us, it actually also pulls through the rest of the platform, whether that is IDP IXP unattended robots, et cetera. And we see that. We are very purposeful in discussing that we are seeing growth rate within kind of the core RPA business and we look at that as very synergistic as we go forward. The other thing to highlight is we're super excited about our test automation business. And that is still in its infancy, but we really see that having good traction in the market, and that can also be -- that is also a growth driver for us as we enter this year.

Our next question comes from the line of Scott Berg with Needham & Company.

I've got 2. Daniel, we've been doing some more question partners here. It's become very evident and clear that your partner strategy seems to be resonating really well right now across several different or your vertical strategy, excuse me, is working well across several verticals. But my question is, as you look -- are you able to lean into that strategy even more so given the success you're having there lately? Or do you feel like you're already at kind of a maximum effort.

On the contrary, I think we are at the beginning of our vertical strategy. We are doubling down our focus on investments into this year. So if I can summarize our product strategy, I think there are 3 major pillars that we are seeing right now. So we focus on adopting coding agents all across our platform.

So every single artifact is building on our platform will be built primarily by coding agents. Second its process orchestration that really drives everything Agentic AI and deterministic workflows. And third, it's vertical solutions. And we have seen clearly more of a move into customers that have a higher demand of kind of an outcome-based vision by use case-based type of solutions that they want to adopt.

Got it. Very helpful there. And then Ashim, I was hoping you can drill down in the quarter a little bit I know there's a $14 million tailwind around FX for ARR. But what was your assumption of that number going in the quarter? Get a lot of questions to try to kind of back into the math in terms of how much incremental impact they might have been versus your expectations 90 days ago?

Yes, it was honestly right. It was just right in line with that. As I talked about, like I think the yen you could see has an inverse correlation to the euro and the net for both of those tended to be zero. We see that both as we look into the current year as we've seen FX rates move as well as the current assumption that we see there. So from both our guidance standpoint and our results, we really see an immaterial impact to that. The driver for our beat in the quarter was really just sales execution. And we're -- we feel very strong about the customer response as we've seen about the traction that we're getting within our AI products. FX did not have a material impact versus our guidance.

Our next question comes from Kingsley Crane with Canaccord Genuity.

And I think the idea of AI on top of deterministic automations, is really resonating. Just on this idea of Agentic really being about pulling through to the whole platform. Just trying to get a sense of how that ends up playing out from a deal timing perspective? Like -- is the customer typically renewing at a much higher rate? Is it happening where they'll adopt AI and then through the life cycle of their contract they'll realize that they need more automation? Just trying to get more color on that.

I think it's all of the above. Honestly, like we've seen the customers renew just at renewal, expand into AI products. We have very good examples of that, both within -- across every vertical and every geography. There's also areas that they're still working through their POCs, but it's bolstered their renewal and their confidence given our road map. And the POCs are moving well, so they would expand just a little bit as they continue to kind of dip their toe in the water.

So from our standpoint, it's not one single motion. It really depends on the customer or the circumstance. But what is encouraging to us is the success that our proof of concepts the feedback that we're getting from customers that as Daniel talked about governance matters and the full extent of our platform is a difference maker for us.

Great. And then just a quick follow-up. That #1 OS fold ranking for screen agent definitely impressive, and that's still holding up -- just curious like how specifically Screen Agent is driving more automation growth within customers. And just a reminder on the unit economics that's affected by running OPIS versus running HiQ, things like that.

Yes. I think we are still in the early innings of deployment of the screenplay agent. We are seeing really good use cases from our customers. They -- the powerful use of this screen play agent is that it is used in the context of autonomous workflows.

So basically, the best we combine like using the terminating UI automation technologies. And in the places where it extremely difficult to define in rules how to use the screen when the screens are -- have a high degree of variability.

Our customers are using the screenplay agent. So that basically extended our plus 4 in a few use cases that we couldn't basically touch before. But again, I think it's still early to comment on how does it help with the platform adoption.

And our next question comes from the line of Arsenije Matovic with Wolf Research.

I just kind of wanted to go back and expand kind of on the ARR guidance methodology in terms of that conservatism. Like what does that mean? And I understand we're not going to be talking about inorganic from WorkFusion, $20 million, whatever it is. Even if you strip out that number growing at the 65% rate the CEO talked about. Is there a way that it still looks a little bit less conservative in that guide? And if there is a little bit less conservative a dynamic where it's just, hey, larger renewal cohorts and also more confidence in that execution tailwind that you started to see exiting the year?

Yes. So one is I just want to correct, Like, I don't think we should -- the metrics that we talked about, as I said, we bring it on at a different ARR methodology. So I really want to caution everybody to use kind of those -- those assumptions. It's immaterial for a reason as we've done that test. The second piece is, while the business was growing at 65%, remember, we also have overlapping customers, et cetera. We really view this as a technology tuck-in that can drive utilization and stickiness across our Agentic and AI platform. And of course, we do see potential there for the upsell, but we also have to go through an integration period with the company. And that is all baked into our guidance from that standpoint. We look at it as our core business continues to be very strong, and we are stabilizing net new ARR. And with AI and Agentic, we do feel bullishness about the overall business. But given the macroeconomic environment continuing to be variable, we do layer the appropriate prudence that is there.

Got it. And then just in response to an earlier question, I didn't really kind of get the in line with the constant currency. Can we just clarify what was the constant currency ARR growth rate implied in the guide for revenue and for ARR growth because the communications are out the year on tailwinds and incremental headwinds has kind of laid up a weird kind of analysis to figure out what the actual core constant currency growth was?

Yes. From our standpoint, we gave the $14 million, which we assumed -- which we -- for the guidance that was there, but the growth rate remains 11% for us. It is largely a material year-over-year.

And our next question comes from the line of Siti Panigrahi with Mizuho Securities.

This is Phil on for Siti. So you guys raised the long-term non-GAAP operating margin target to 30%, which is a meaningful step up. Can you walk us through what gives you confidence in that number? And what is the time frame of achieving that target?

Yes. So right now, we're in and around 23% north of that. We've shown really good progress and scalability over the last couple of years, in particular. The first thing is we just continue to operate with really good discipline. And so we constantly are moving investments to higher return areas.

And so when you're able to do that, it obviously creates a scalability of expansion. The second is we believe in the productivity that is being unlocked right now with Agentic and that identification within our own business is something that is very exciting for us and our teams to unlock further steps of productivity. And that includes all areas within the company. We can be more productive, expand and support our broader road map, really with similar technology spend just because of the advances that are there or R&D spend.

The same goes with our G&A function as well as our sales and marketing function. So we're really seeing that scalability just even with the technology advances as well. In terms of time frame, it's a long-term margin target. We -- as it implies, that's kind of within a 3-year time frame from our standpoint in and around it. And at the same time, like we don't take -- we're not waiting for 3 years. We're going to continue to execute and drive productivity as we see fit.

And our next question comes from the line of Koji Ikeda with Bank of America.

I'm going to ask one on dollar-based net revenue retention. So it's down 1 point to 106% when adjusting for FX. And so looking into fiscal '27, what are the main drivers we should be thinking about, whether that's product, geography, vertical or maybe something else in there that can drive expansion in that metric? And how should we be thinking about the dollar-based net revenue retention assumptions that are embedded in the guide? Is that flat, up or down from the 106%?

Yes. I think when you look at overall net new ARR stabilizing, like we don't really see a difference in the mix shift between net new logos as well as expansion. We see them both as areas that will continue. We've kind of operated in this 80/20 70-30 split. So that gives you, I think, enough data to be able to see that net new ARR stabilizes over this period of time from where we are. In terms of what gives us confidence or kind of how we see that expansion, again, as we spoke about earlier, it is really around our AI and Agentic products. And then with that, really pulling through the overall platform, including deterministic automation, continuing to expand across our customer base.

Our next question comes from the line of James Kisner with Water Tower Research.

I guess first, just -- from the foundational model perspective, I mean has the entropic supply chain the designation -- have you seen any kind of ripple from that at all? Is there any kind of exposure at all any change in behaviors out there -- and then just on the Work Fusion acquisition, does that portend potentially future acquisitions and other verticals for identicabilities?

Yes. In relation to Antelope -- our strategy was from the beginning to be model agnostic. And we -- 1 of the features that many of our customers have requested this to give them the capabilities of choosing what model and even bring their own model to be used by our platform. So we do offer entropic models but they are optional and not mandatory. And from this perspective, there is zero impact on our working relationship with public agencies in the U.S. of our Work Fusion.

Yes, it's -- we are always looking into the market, especially for tuck-in acquisition that gives us the talent technology and expertise in a particular vertical.

And with that, ladies and gentlemen, that does conclude the question-and-answer session. I would now like to turn the floor back to management for any closing remarks.

Well, thank you so much for listening to this call. And once again, I would like to apologize for the outage that we experienced, and I'm looking forward to meeting many of you in the coming days. Thank you.

Thank you. And with that, ladies and gentlemen, this does conclude today's teleconference. We thank you for your participation, and you may now disconnect at this time, and have a wonderful rest of your day.

Thanks, everyone, for joining us today. My name is Scott Berg. I lead the enterprise software and SaaS research efforts here at Needham. Thanks for joining us for our 20th Annual Growth Conference here. Today, with us right now, we have UiPath. We have the company's CFO and COO, Ashim Gupta. Thanks for joining us so much, Ashim. Appreciate the time. Yes, got lots of stuff to talk about here. But I guess, for those that are less familiar, how about an overview of UiPath?

That's awesome. So UiPath, founded by Daniel Dines, founded in Romania. As he would tell you, 8 people sitting in an apartment, started kind of with really humble beginnings, never really thought it would grow to where it has been. Around 2015, product market fit. And really, it found its roots in core RPA, so robotic process automation and really through -- really years of research of improving computer models, which allowed it to scale. So it was third -- it kind of entered the market third or fourth back then, and it scaled pretty dramatically. And really, when you look at every quadrant in that category, UiPath was #1 and really had robust global growth from 2015 to where we are today.

We're $1.5 billion -- $1.8 billion plus of ARR. And when you look at it, really global, 50% of our revenue is international, 50% is domestic across every single industry. And really at the core of what UiPath does is we emulate what people do, and to automate and transform processes. The core, as I mentioned, was RPA, but recently, the last 3 to 5 years, we really have scaled in AI and multiple vectors of AI. Advanced intelligent document processing, process intelligence. Those are just 2 examples. And then, of course, 18 months ago, at our Ford, we launched the Agentic vision. It wasn't something that we did in reaction. We didn't repackage. This is something that Daniel really had a vision of starting in 2022 when he coined it semantic automation, really bringing natural language into the forefront about how you develop and how you approach automation and transformation.

And today, we look at agentic automation as really -- and process orchestration as really new tips of the spear that give us really a great view in terms of further driving growth across the world. And then just financially, $1.5 billion of cash sitting in the bank, no debt, really strong performance on our buyback. We bought $800 million plus of stock over the last 2 years back from the Street, returning it to shareholders. And frankly, we're really happy with the progress on operating margins as well, getting to 20% plus, both on free cash flow and operating margins.

All right. So let's talk about products. This has been a fascinating space from my seat to actually kind of watch this evolve over the last couple of years is, there's been a significant amount of investor chatter about how generative AI or automation in general will overshadow or replace what some investors might think is an existing RPA and RPA automation, right? But you had me join you at your partner conference in this fall on stage. It was interesting. Ashim got to interview me instead of the other way around on a couple of things. But I got the chance to speak to several hundred partners. I appreciate all the partners that LinkedIn me for connections. That was great. But you had me discuss what we, from our viewpoint, see in the agentic automation space.

And what was really reassuring is all the partners I spoke with that had a similar view. I've been on the side of I think you all are going to win in this space. The market obviously has not seen that the last couple of years in general, right? But I always thought this was going to be something that will evolve over time. And whether it's orchestration across processes or departments, et cetera, there's lots of opportunity here. But the partners were really reverberating that. I guess product and maybe understanding in budgetary timing is maybe what was missing in this investor viewpoint. But the market's understanding is, I think, starting to improve, stock is working a little bit better, maybe budgets are starting to unlock a little bit. But what's the benefit really of offering this entire expanded solution and platform together? Because I don't think really investors understand how you're marrying all this into a single solution.

Yes, it's a great question. And we were super fortunate to have you, Scott, at our conference. I just want everybody to understand like -- start with the outcome. What is every company trying to do? They're trying to become more efficient. And to become more efficient, you got to transform the entire process, right? The more parts of a process you can automate, the more efficient you get. It's very simple. Break down any process that you know, right? Invoice to cash, procure to pay within your own sphere, right, even processing and analyzing quarterly reports of -- for an enterprise. There is a set of tasks that are -- there are a set of steps that are hugely repetitive, deterministic in nature, rules-based, right? There are tasks that require a reasoning power, right? Like, hey, which are the first 3 companies I should work through? Which companies have the largest variances that have the most potential and you can have a reasoning model kind of work through that.

So when you combine deterministic and agentic automation capabilities, that allows for the most breadth of a process to be transformed. And so when you approach a CIO of a health care company and talk about claims processing, whether you approach the government of the United States and talk about getting more efficient in managing operational data of the Air Force or whether you go to an oil and gas customer talking about procure to pay and all of the kind of the operations that they're doing, we now have a broad platform that processes -- that helps automate the processing of documents, deterministic or rules-based steps and probabilistic steps.

And then you elevate one level higher for process orchestration, which is part of our solution now or part of our platform. Now you have a process and you want to say, how do I observe that process? How do I manage that process? And process orchestration is not about giving a few governance details. It's about creating an entire framework of governance and observability around your processes that gives insights and data, which is hugely valuable to an enterprise, especially in the advent of agentic.

Yes. The orchestration parts like that whole framework is, I think, what investors seem to get a lot -- or forget a lot kind of through that process. Within this framework, you all have announced a string of partnerships with NVIDIA, Google, Microsoft, Snowflake, OpenAI. I think they're all kind of known in this agentic space right now, last time I checked. But what is the advantage of an open ecosystem that you're developing with these partners? And how do these partnerships really enhance what your product is doing today?

Yes. So the second one is going to be bespoke depending, but let me just talk about the first one. The first one is we believe -- I think Daniel is very philosophical. He would tell you he's always believed in freedom as a pillar -- cornerstone, whether it's organization or product. So in our minds, no customer wants to be locked into a finite set of vendors. And if you think about it, 5 -- 10 years ago, there is a little bit of risk to doing that. Now if you're a customer, there's a huge amount of risk. Who knows what's going to happen in the Gemini, GPT, Claude kind of race that is out there. So I think the first piece is having openness of architecture really is about putting the customer ahead of your own philosophies, letting customers have the most amount of choice that's there.

I also think in the world of security, that's super important. We met with one of the top 5 banks, their CTO. They're only allowing certain LLMs to come in and be used across their environment. What if you only partner with one and the other one is left out, right? So I think having choice really matters and that horizontal openness of architecture is hugely important. In terms of the partnerships that you mentioned, they're all very different. If you look at NVIDIA, if you look at Anthropic, if you look at OpenAI, really having -- giving access to the latest models and being able to integrate their models and give our customers fast access to those things as they're developing agents, as they're building automations is hugely valuable.

And some of that is from the customers' own requests, right, because they're forming their own partnerships, their own convictions. So it again brings those choice. When you look at Snowflake, Snowflake and integrating kind of being able to integrate their data know-how allows us to create a data fabric. So if you think about it, we're a zero copy company. So we don't go in and just the invoice data of a customer, and they want control over that. So for us to be able to integrate with Snowflake, where they can spin up for particular processes, specific data sets and store them, but under their own control, that is a great partnership with Snowflake that is there. So each of them has a purpose, but going across the platform is super important and across the ecosystem to give customers choice.

I'm glad you think Needham is a top 5 investment bank. We weren't the customer. I'm just kidding about that. Excellent. So I guess as generative AI drives more complicated use cases, how do you think about the verticalization of the product or verticalization in general, both on the product side and on the partner side because that's an area of emphasis recently.

Yes, it's a great question. The first thing is I think vertical -- I think time to value is maybe the most underestimated part of the ROI equation for customers, right? So we talk about ROI, but how fast they get that ROI is super important. Super important in the selling process from us to a customer, super important for their internal selling to their stakeholders. And frankly, in between purchase of software and realization of ROI is the most -- is the largest period of risk for any software company that exists. So verticalization accelerates time to value. Let me give an example for verticalization, being able to use AI and agentic capabilities to optimize pricing and inventory and being able to automate the scheduling of production planning that happens.

UiPath has acquired that capability from PEAK. Another example is revenue cycle management. Many people have used our horizontal platform to automate revenue cycle management or claims denials within health care. Most every health care company does that in some shape or form with UiPath, like in our customer base. Imagine instead of delivering them the LEGO blocks, we can deliver them a solution that they can plug and play faster that accelerates that time to value. And so when you add that up, I think the time to value is there, but it also expands TAM. Because when you're looking at it, horizontal technology is good, but a lot of times when people are looking for those solutions and say, "Hey, I really want an Ariba, right, to give you an example or I want something that processes procure-to-pay. They think Coupa and procure-to-pay and Ariba, right, just as 2 examples of it.

Tomorrow, if you're talking about how to automate procure-to-pay, we can do a procure-to-pay automation that integrates with the hundreds of platforms that are out there. The last point I would just make about this, Scott, is verticalization is very synergistic to horizontal. So if I build a really strong horizontal base, I can verticalize and build large towers across that foundation, compared to if I put a bunch of towers together and try to become a horizontal leader. So what we really feel is our horizontal strength allows us to productize the large scalable vertical use cases that our customers are asking for.

Okay. How much -- as your customers are going down this journey right now, how much of their use case of the agentic theme has a human in the loop in it, do you think? Is that still a significant component of how they're deploying today? And as they get more comfortable maybe removing that human, does that, I guess, maybe improve or add to your opportunity with these customers?

It's an awesome question. Within UiPath, and Hitesh Ramani, our Chief Accounting Officer and Deputy CFO, is sitting in the audience, we -- there needs to be a proof period, especially for high regulated processes, and you want that human-in-the-loop assurance or that throughput. So it also depends on like where the agent is proving. So if I look at the quality of an agent, if I have an agent that has a 70% -- we score all of our agents, which is a good differentiator in my mind for UiPath is the way we provide scoring of an agent, how accurate they are, kind of the degree of variation and the outcomes, et cetera.

If you have an agent that's scoring 70%, that's still productivity, but it's not enough to be unattended productivity. So we do look at human in the loop to be there. In cases where -- if I'm doing an SEC filing, I don't think there's ever a day where I'm going to let an agent go and put an SEC filing in if there's a 1% chance for an inaccuracy. So I probably will have unattended or human in the loop or some level of control there all the time. At the same time, if I am approving purchase orders against budgets for my engineering team to purchase monitors, I can picture that if I'm 99% accurate, that's good enough, right, depending on the level of risk, both for the enterprise and as well as for the company.

So in our minds, I think, to answer your question directly, there are some processes that I don't think will ever be fully unattended. And I think there's going to be a lot of processes that evolve to be unattended. The more unattended a process, theoretically, the higher the ROI, right? But to do that, I also think we have to continue to be able to improve and integrate and become deeper into those processes. And I think verticalization helps because you start specializing into the systems, the data, the flows, the capabilities of a company. And the more expertise we can bring in our platform, the higher that agent score becomes. So for really complicated processes, we can get to that score where there's more reliability. And I think that is part of our R&D road map.

I think the human in the loop parts, like it's kind of a fascinating item to all this because I think customers want fully agentic processes. But to your point, it's just not realistic because you have to guard against that 0.5% or the 1% issue sometimes at times. But the more you can get there, obviously, the better for what they do. So let's talk a little bit about changes in your go-to-market in the last quarter.

The company just reported its first quarter of net new ARR growth on a year-over-year basis in 2 years. I guess you all have spoken about a lot of go-to-market changes last year with the annual kind of coming back into the role. How much of the improvement there and then your guidance for the fourth quarter, which I think caught a lot of us by surprise on the positive side, how much of that is driven by some of the go-to-market changes or maybe just a change in your customer demand patterns?

I would say it's 80%, 90% execution. I think just the way that we're executing, the stability of the company, the clarity by which we're operating, the principle of kind of no need for large middle management layers, I feel like that's really been a lot around the performance of and -- I would say, predictability and performance of this past year. So entering the year, there was a ton of volatility. I was just commenting to you like last year sitting in this room, we had political administration changes. And if we left that room, I remember there was such a bullishness and then we go in and there's tons of volatility that happened throughout the year, right?

And I think if we look at kind of how UiPath navigated and how we communicated there, I think that's because we are close to our customer, close to our field. And that makes a really big difference from where we are. And I would say the execution is around there. Like being -- we're talking right now about my last 2 weeks, I'd say 40% of my meetings are about first and second quarter, right, meaning getting ahead of that, starting to understand it, starting to look at your top renewals, look at your top accounts. And when you do that, looking at your day 2 implementations. When you're there, it's not all pretty. It's not all pretty, but the longer time that you have, the more that you can affect change. So I think we're just a more disciplined, more stable company, and that's really driving to me 90% of what you're seeing right now.

One of the things that's been interesting for me to track over the last year is the consistency in your messaging around that. I host a bus tour, software bus tour in New York every June, and you all, of course, were on it, both you and Daniel were on it. And that consistency around the changes and improvements and what you're seeing really in the last 6 to 9 months, the messaging has been consistent, which is great. I think the natural follow-up question though on where the benefit is come from is now that you've had the improvements, is it repeatable going forward? Because 2 years ago, it looked like some changes were coming that people -- or that the company seemed to enjoy and it wasn't repeatable. Is this repeatable that it's not just a 1- or 2-quarter benefit.

So I'll say the popular answer -- or the obvious answer, and then I'll try to double-click so it's more interesting for everybody. The answer is, of course, it's repeatable from where our conviction is. It's substantive. It is substantive in the rhythms, the organization, the metrics that we are seeing, right? The more interesting question is it's only repeatable if we continue to not be satisfied with where we are. So if I'm sitting down here in 6 months or 9 months or Daniel is sitting here, I hope it is also not articulating that we're just looking at renewals 6 months ahead, but our account planning is moving to be deeper and longer. Just to give you an example, I think there's opportunity in every company, right? The problem you get into is where you get satisfied with your first step, and the environment changes.

And what was strong in one environment is not strong in another environment. And so for us, I can tell you me personally, I live in a constant state of demand more. This isn't good enough because tomorrow, we just don't know what's good enough. And I think that is what we look at every single day, right? So I love that we're going ahead on our renewal planning that we're there. I'm super happy right now about the level of project management that we're putting into some of our installations and our services installations that are there. I'm super happy about Daniel being on tour with certain customers that have had bad experiences in the last 2 years, not just the ones that are good that we have deals in front of us right now. And I think those are the things that give confidence for me as long as we're not content that we can continue to repeat. Repeatability means improvement. It doesn't mean staying the same.

Okay. As you look at the third quarter sales and maybe what you had thought fourth quarter was going to look like, but really, we're not -- we're late enough in the quarter. We're not talking about the quarter. But as you talk about maybe the next quarter or 2 going forward is what is the composition of some of that kind of deal flow look like? Are you seeing any changes around customer interest in new products? Have you had any impact, I would say, benefit from maybe less downsell coming off of some of the 0 interest rate cohorts like some of my other companies, have kind of gotten through that headwind. I don't know if that's a benefit there. Or is there anything that's kind of like different, I guess, as you think about deal composition today than maybe a year or 2 ago?

I mean Agentic is definitely a part of the deals, right, in terms of the discussion. I don't -- it's not at a point where as we've said like it's meaningfully impacting the revenue number per se. But I give this example. So in our third quarter earnings call, when the CIO for one of our top customers says, hey, with Agentic, we love the vision and with process orchestration, you're a part of our road map for the next 6 years, 7 years, right? That gives them confidence to start upselling even on what they're doing today. There are deals that are out there that are -- some deals, some of our pilots and POCs, yes, they're starting to come into our pipeline. We're excited about that, right, in terms of where that could be.

So I look at it, I don't really see core RPA deals anymore alone. I think RPA is a great entry point for the company, but I look at our growth coming from the entire breadth of our platform and including for products that we don't talk a lot about, test automation. We're super excited. If you go out and you look at the Everest Peak matrix, we are now a leader in test automation. So application testing. Like those are things that are under the radar that are very synergistic. And if you go to a CIO and you can say, wait, I can transform my processes and I can automate manual testing using contemporary software, not kind of legacy software.

They're super excited about that. So there's multiple vectors, I think, of our platform that are speaking to and manifesting itself into our pipeline. Your question around downsell and 0 interest rate environments, I think the world is super volatile. So I think it depends on the quarter in terms of composition of what you see in terms of those things. Like that is why it's super important for us to just continue to deliver and drive adoption every single day rather than worrying about cohorts of past customers.

No, I just asked the question because as I talked about the message in the last 6 months or so, 9 months maybe, your level of confidence around the sales improvement and what's happening. It's just -- it's been unique. It's been different than what we've seen in the last couple of years. So obviously great to see. I guess how much of the -- you kind of already talked about this is on the current conversation is agentic versus RPA. Do you have any of those conversations anymore? I know you're not focused on it, but is it really all agentic? Or is there a few stragglers out there?

No. I mean, agentic definitely is like the hot buzzword. So there's no -- from a marketing entrance standpoint, say agentic and a door will open, right? I think that's kind of -- that is the truth of it. The part that's hard for me to articulate to an investor, and I've used this example, and I'll try it again, is one of our customers, they wanted to automate their operational flow of financial reconciliations, okay? They didn't ask us at the end of -- we did a workshop. At the end of it, there was 100 ideas that came out. They didn't care if it was a robot or an agent. They didn't say, so how many robots did we have? How many agents did we discover? It's just how many process steps of the process can we automate.

And that happened to be 50-50. So what's interesting is like you kind of lead with transformation, you can lead with the agentic line, but it's kind of the tip of the spear that pulls the full platform through on our best engagements with customers, right? And that's really what we're seeing from a customer response. And I mean, like if you go and ask -- if you go to any CFO and you say, like agent versus robot, they're just going to say ROI. And I think our job is to show how the combination of that maximizes ROI. But agentic is definitely the thing that gets response.

Okay. So if I look back at the third quarter results, one of the verticals, I think that surprised -- certainly surprised us, even though it was in our preview because we saw the data was your federal business, had a really good quarter, especially in a segment that's seen lumpiness all year for a variety of reasons, right? General disruptions, whether it's DOGE or government shutdown, obviously, clearly saw plenty of challenges there over the last 12 months. But how has that opportunity kind of changed for you in general? Is some of those items that have been implemented the last year, that thought process, is that what maybe drove a slightly better quarter and better opportunities there? Or is there maybe a more complex answer than that?

I think it's more complex. Like -- so I want to dispel a few doubts. There was nothing that, hey, you had a slow second quarter, so we got your third quarter strength was a catch-up in federal. That's not true, to be super clear. the federal business, I think, is -- it's more complex because there are areas that I was in the Pentagon. And I actually -- as I was coming in, one of the undersecretaries like gave me an introduction to the DOGE team member that was there. And their view is for that branch of government, DOGE was kind of like, no, I'm just kind of an oversight now. Like I don't really have a budget. I don't have a team because we're like, hey, we should get together and talk through where your efficiencies. And he was pointing me back to the undersecretary, just as an example.

There are other branches of government where some of the DOGE members have become staples into that area. So the way they scrutinize the deal looks differently, right? So when you add it all together, it's kind of like a normal functioning business on average, right? Just like the customer, you end up areas where you got a really tough procurement person. You got an area where you got a super exciting initiative that is business-driven, that is kind of budgets are available and we're moving. And then you got areas where there's harder problems that you have to go to solve. So that's why we've talked about the federal as kind of a new normal. We haven't talked about it as a onetime kicker to the company. I just -- just like the overall economy, we talk about it as variable.

I consider the federal government as variable. I think we're going to have different branches in different points and different times that are there. One last plug is the Social Security administration, they had a great renewal and even additional software that goes there. Why I mentioned that is that was one of the biggest things last year around February, March with a tweet that came out that said, we're using 40% of Salesforce, 50% of UiPath, whatever it could be, don't quote those numbers. Those are metaphorical or examples. And they start digging into it. And they say, no, we're getting a ton of value for it. We got a big discount. We would rather get more usage than try to restructure the contract. So it really depends on kind of the moment and the agency.

Probably 2 more questions for me and then happy to take any questions from the audience. As we think about from a model perspective, this last year had a 200 basis point headwind from the shift to the cloud on the gross margin line. How do you see kind of cloud adoption growing with the new agentic functionality? And should we expect some similar types of headwinds around margins in the next couple of years? And I know you're not guiding to next year, just kind of...

Yes. Look, I think we're going to be doing cloud. Like cloud is going to continue to move. So I think SaaS headwinds, I think they'll continue to exist. There are no -- I feel like we've been pretty consistent from what we've talked about. Especially with agentic, you got to kind of -- like it's even a bigger drive to cloud in our mind. So I think SaaS headwinds, I think, will continue to exist. And 606 revenue recognition versus kind of full ratable, like those are all parts of the discussion depending on the technology and pricing, et cetera, that will be a part of every software company, but particularly ours as we go forward.

In terms of margin, I look at margin -- gross margins, we performed very well. We look out at the agentic and the AI era. At this moment, we don't see anything that like is something that we're super worried about. I think we're going to have to continue to monitor and we'll update our models accordingly for that. But overall operating margins, we feel good about. We feel like we can invest in engineering and sales and continue to drive productivity and everything that is not engineering and sales. And that, on average, we feel like gives us continued operating leverage that we can realize across the bottom line. Some of that is our own internal identification that we are driving, and it's super exciting in terms of the productivity that, that can yield for us as well.

Yes. Your operating margin leverage this last year, at least with current guidance in the fourth quarter is, we'll call it, 600 basis points and number, I believe, at least. And I thought that was a super interesting year because you're above 20% now. We expect it to be over 20% with the fourth quarter results. And growth looks like it's really stabilized after a couple of rough years. And natural question I get is how do you get the leverage from this level and you feel pretty confident about.

Yes. I think G&A -- so R&D, I think you're going to see us continue to invest. I think sales and marketing is too big of the line, right, to say we're going to invest in sales and marketing. We're going to invest in customer growth sales, meaning things that drive growth. Things that drive processes, we're not in -- like that's areas of pockets of efficiency. And then the last thing I would just point out is like we're super proud of is GAAP profitability. Third quarter was the first third quarter in the history of the company for GAAP profitable. We talked about kind of being on track to be GAAP profitable overall. And now we have like -- I think we are looking at capital allocation, not just on a non-GAAP basis, but inclusive of stock-based compensation, et cetera. For us, that's overall capital efficiency. And that positive GAAP profitability to me is also scalable as we continue to grow.

Fantastic. With that, we do have a few minutes left. Happy to take any questions from the audience, if there are any.

I'm curious how you are seeing the global system integrator market and some of the BPO players look to leverage UiPath technology to accelerate some of the roadmaps of their process transformation.

Yes, it's a great question. The GSI framework, I think every GSI obviously is working on their strategy, right? Our strategy has been let's double down on a few of them. I think the mistake we've done in the past is just bandwidth. It's not that we like one, we don't like one, one likes us, another one doesn't like us. It's really about bandwidth about return on our investment and theirs of where can we get like real growth that comes back to it. So the concept of saying you just got to give money across 7 GSIs, 8 GSIs, like that's just dilutive to the company versus really doubling down on a few.

If I look at Deloitte, I think Deloitte has become a really great GSI partner, an example of a great GSI partner for us. We are embedded in sales motions together. The S/4HANA upgrade is a massive initiative for them. We are deep within them to drive higher clean core percentages for customer outcomes, inclusive of including test automation in a lot of their S/4HANA migrations. There's a real 360 relationship and a core strategy that's we see ROI too, right? We're excited about other GSIs as well. I won't go through all of them for time. But I think we're going to be really particular about going deep in a few and potentially having to deprioritize others to get that.

In terms of the BPO market, same discussion. I think there are certain companies that we've developed a great relationship with. And there's real opportunity there that they see, particularly with Maestro. If you think about complex, where do you need observability? where do you need of automation? And where do you need to be able to govern agents, robots and humans? What's more complicated than a BPO shop that's doing that across hundreds or thousands of customers. And that is a big area of partnership for us and look forward to giving some announcements on that front.

Maybe one follow-up on that. On the Maestro platform, here's how your competitive landscape changing as [indiscernible] market and how much of differentiation [indiscernible] capabilities with RPA coupled with the [indiscernible] agentic platform.

So the first is I think people -- I think we contributed to this to be clear, but I think that it's important. Agentic orchestration and process orchestration are 2 different things. Agentic orchestration is focused on the agents and being able to trigger them, govern them, et cetera, independently. Process orchestration is stringing the end-to-end process together and being able to govern deterministic, probabilistic and human in that area. So when Scott was asking about human in the loop, agentic orchestration is not around human-in-the-loop capabilities, right? So in our minds, we are contemporizing kind of a -- we're leading in a process observability market, and we're contemporizing what was BPMN to a certain extent, and that gives agentic orchestration capabilities as well. That's how we think about it.

Snowflake just announced that acquisition of Observe. Is that likely to be a comparative....

No, I think yes, I think data observability is -- so and I always say this to Elise, like I should have a plaque of Maestro behind us. And I really encourage everybody. There's a fireside chat that's on our Investor Relations site from a couple of years ago -- from a couple of months ago. We can post it or we can put a little blog, Elise or I can send it back. I want you guys to just think about any process. And I said -- and think about a whiteboard. And I said, go map out the -- you would put a process map, right?

Put boxes on the thing saying, extract document, upload document here, if the document, if this matches, go here, et cetera. That's different than what Snowflake is doing. I'm saying in process map, that's what we're doing. Now imagine that, that process map is not static. It's live. And you can see the software, the robot go and grab 100 documents. You can monitor how many of that got uploaded. You can see how many is sitting in Ashim Gupta or Scott Berg's queue to review in human in the loop, right? And you can see that in the end-to-end process. That is kind of what we talk about when we're saying process observability.

Probably time for one last one.

Your questions were that good.

I guess. Well, with that, we'll leave 2 minutes for everyone to fight the elevators. Ashim, I want to thank you for the time.

Thank you so much, Scott.

So let's start -- like to get everyone on the same page, just Ashim, you had like really healthy Q3 results last week. Shares were up a lot. So everyone is happy probably in the organization, that's nice to see as well. But to get everyone on the same page, can you talk about the highlights you saw last quarter?

Yes. I think -- so our third quarter really showed the -- to me, the confluence of really strong execution and the beginnings of what Agentic, the combination of Agentic and deterministic automation, can mean in the marketplace. So revenue of $411 million, up 16%, strong operating income, strong free cash flow continuing for the company. And it was actually our first -- our first third quarter of GAAP profitability and has put us on track to be GAAP profitable for the entire year. When you look at the top line, I always go first to our large customers and our customers greater than $100,000, more than 2,500 customers now, that's growing double digits year-over-year.

And customers greater than $1 million, 330-plus customers, that's growing double digits as well year-over-year. So when you kind of step back and you look as to why, Raimo, I think very good progress around proof of concepts and POCs and pilots within Agentic. But without even a direct impact of material Agentic revenue, people are looking at our platform, the combination of deterministic automation, probabilistic or Agentic automation and process orchestration. And just the feedback has been so positive that we are a staple in people's enterprise architecture. And we'll continue to expand with them over the coming years.

And then the -- just to stay high level a little bit longer, like federal was a big discussion all year, DOGE at the beginning of the year, and there was a lot of kind of noise on -- for our industry, then the shutdown as well, like, how does it play out for you guys?

It's a dynamic environment in the public sector. I actually was just at the Pentagon on Monday. And with customer meetings, like they're going through budgets and not unlike a lot of corporations, like you go in and people are pulling their hair out in terms of challenges that they're facing and areas that are there and different dynamics. With all of that said, our teams are executing incredibly well. Like we are connected to keep people in the government under secretaries of the Department of the Defense or different functional leadership there.

We closed several key deals, the Social Security Administration, a key deal that was -- that closed there, the Coast Guard. So we're relevant across a lot of major industry -- major sectors of the government. I think it's going to continue to be dynamic. And I think it's a very good long-term opportunity for us. Our goal is to stay connected, continue to drive value and execute on the projects that we're getting today. There is a lot of momentum that we can continue to capitalize on in the coming years.

And to you like for the quarter itself, did the shutdown impact you? So I mean, in the quarter negatively, it could kind of drag into the next quarter. But like was there any impact?

No. I mean the shutdown was -- it was topics of discussion, but it wasn't anything that impacted transactions both positively or negatively for us. I'll just kind of give 2 reasons as to why. The first is a lot of our software is in mission-critical areas that are protected from areas like the shutdown that is there. And then the second is the Department of Defense is a really big customer for us, and they are also somewhat shielded from a lot of the different areas.

That being said, having walked through the floors of the Pentagon, like a lot of people were impacted, so we're very empathetic I think a lot when I say mission-critical areas, but our software was protected from that standpoint.

Yes. And then last question on this kind of more bigger picture subject is like beyond federal now, like what do you -- if you talk to customers in other regions, other industries, like how is end-demand kind of playing out at the moment? What are you hearing there?

The best thing I can say is variable. I mean like what we're seeing in our -- maybe I'll do it from 2 perspectives. What I see kind of from our -- hear from our field and what I hear directly from our customers in our customer visits. Directly from customers, they kind of have -- their budgets are really getting scrutinized for high ROI items. So you listen to kind of like there's not a lot of excess fat for experimentation and innovation. They're in the budgets. And people are preparing themselves for different outcomes of the economy because I don't think there's a consensus view about what the economic outlook looks like for next year.

At the same time, that is also spurring really renewed interest on meaningful ROI and productivity projects. So industrial customers as they're getting their cost out targets and we're very -- we're close to them. I think that presents opportunities. I think there's other customers where there's opportunity and they're saying, listen, we at the same time, may not take as large of a bite at the apple as what we've been doing. So I think it's a dynamic environment.

Then I look at our field, I look at our activity. We were really pleased in the third quarter with kind of commercial momentum in the commercial enterprises, Americas, particularly in health care and financial services. I think we did see pockets of strength in international, like Australia and New Zealand. But then there are markets that are a little bit more challenged internationally.

Yes. Okay. Perfect. And then Shifting gear a little bit, and I'm happy you are here as the CFO because I wanted to talk about AI. And I love Daniel to bit, but his explanation is almost getting too technical for me. Like how would you describe UiPath's role in this new AI now?

So a minister from really basic building blocks. AI is just unequivocally a net -- is a meaningfully net opportunity for UiPath's growth in the coming years. And I think there's a direct and an indirect basis. And I'm going to unpack it in 3 steps.

The first step is I think sometimes we forget what does UiPath do. When I ask people that they'll tell me what we did in 2017, which is RPA.

Yes.

Our -- what UiPath is an AI-powered platform that helps businesses automate and transform processes. So our goal is to drive outcomes that yields productivity for companies by emulating what humans do, to automate processes and reduce the cost of transactions and process ownership within a company.

Now dissect what is a process. A process is a series of 20 steps. Within those 20 steps, maybe 30% of them or 40% of them can be done on rule-based, task-based deterministic type steps. Another 40% or 50% of them can be done, but they need reasoning power. It's probabilistic in what needs to happen.

You could deny this claim, you may not decline this claim. It's not a rules based. If it is above this value denied. It is -- there's multiple factors that require reasoning and probabilistic waiting to go after that.

Three years ago, we automated those first 30%, and we scaled the company from next to nothing to $1 billion plus in 4 years with those steps. What is amazing is now we can go and automate those next series of steps and continue to drive that automation.

What is missed -- so there's a direct impact of AI? And what is AI? The ability to reason like a human to complete process transactions, if I keep it nontechnical right?

That's why you're here.

And then the second piece of it is, though, for UiPath, three years ago, you may have looked at this 20 step process and said, "I can automate 6 steps of the process, and it's worth it." But there are also times you'd say, "If I automate 6 steps of the process, it's not worth it. I still don't get the productivity. But if I automate 12 steps of the process or 15 steps of the process, that is really worth it." So what agenetic is also doing is it's coming back around and saying, you can automate those. So it's actually pulling back through deterministic opportunities as well. So that's the second piece.

The third piece is, AI has now created another element into the architecture of business processes that we all have to contend with. If you thought about years ago, you deal with infrastructure applications, right? And then you deal with integrations. And I would put RPA in kind of an integrated type technology that stitches together systems, et cetera. Now AI has another area. So what is becoming super important is process orchestration. And I want to differentiate between process orchestration and Agentic orchestration.

Agentic orchestration, many people do, it's governance frameworks around agents. UiPath does that as well. But process orchestration means now you have a 15 -- a 20-step process. Realistically, it's a 200-step process. You have humans, robots and agents working together, how are you going to monitor it? How are you going to govern that? How are you going to visualize it?

That is what Maestro is, a product that we GA-ed earlier this year that allows companies to do that. So AI has now unlocked a whole set of processes and opportunities for UiPath and for our customers. And what's super important is it revitalizes demand for deterministic as much as it presents an opportunity for a new revenue stream in Agentic. Does that make sense?

Yes, totally. The one question I have on that one, Agentic is where everyone wants to go. And that sounds like super cool, new, exciting -- but the one thing that I hear from the field is where customers are struggling is that Agentic, the outcomes are not as predictable as deterministic. And so in your conversation, where are customers on that journey of understanding Agentic is different and kind of having the right guard rails, et cetera, to kind of still come to outcomes that they actually kind of want?

It's the early innings, but it's the early innings with progress. So to use kind of a football metaphor or a soccer metaphor, you're not in the first -- you're not right at kickoff where players still haven't moved, but you're not near half time, right? You're not near kind of the midpoint of the game. And the reason for that, I think, is a couple of areas. One is everybody is now contending as I said, another entity into process architecture, et cetera. So there's security. There's governance. There's things that have to be unlocked with it.

The second is just understanding where do they want to deploy it. Here's what I'm excited about at UiPath. We have 900 companies that are using UiPath and building agents upon UiPath, right? 750 plus companies that are doing that. So they're in the mode of experimentation. Some of those experiments have moved to pilots. Some of those pilots have moved to production, and a few of them have also converted to orders. So in my mind, kind of the spear is moving forward with customers that have like a very progressive culture, a SaaS execution culture, they are converting, and we're seeing great examples.

Reputable health care customer. They're using our Agentic platform with Deterministic to clear 140,000 provider claims that are in backlog. That's real progress and a real goal that is coming forth. A cybersecurity firm is using both deterministic automation and what we call IXP, applying different models to the right documents, to get to the next level of productivity around something as -- a process that's been forever like procure to pay. But there are many customers who are still in the early stages, setting up their guardrails that are there.

And I think that's healthy personally. I think that sometimes when you get the euphoric adoption, it doesn't last. But I think we're in an area of sustainable kind of a really sustainable demand trend, which is super exciting for us.

And for you as the CFO, how do you think about monetization like more -- I don't want to number I'm more thinking about the vehicles of monetization in terms of is it like a SKU model? Is it a consumption model? How do you think about that?

All of the above. I think -- and I know that drives people crazy. So I'll break it down a little bit. Our pricing model has always had -- first of all, Agentic is a form of AI. So I really look at our platform right now. It's kind of the core server-based RPA, API type offering that we did between 2017 and 2021, or in 2019, we already started with AI with things like document processing. We expanded into autopilot, semantically being able to develop faster.

And we're monetizing those things through AI units and Agentic units. I think -- sorry, AI units. Now Agentic is another way that we can create a consumable in a subscription type way that's kind of a use it or lose it in terms of where it is, but then also put safeguards for COGS or for cost of goods sold on a consumption layer, if the people go crazy and want to run 1 billion agents all at once. My -- when I look at it is that is our pricing scheme. Our monetization scheme is twofold. One, lead with Agentic because it will pull forward the rest of the platform, for existing customers. For new customers, get them on to deterministic because once they automate those first 3 steps, they're going to look to the right and say, "Oh, there's an Agentic opportunity."

So that is our monetization that we can go after. And then the third piece is focus on ROI. I think the more you focus on ROI, people are less worried about how am I pricing this but more is the net price of the bill of material justified against the ROI that they're getting. And that's kind of been our philosophy so far.

And just wanted to ask, and I'm not kind of trying to get the question for my sessions that I had later today. But like if I listen to the software industry at the moment, like that message of I want to be the platform, I'm going to help you, et cetera, you're going to get from quite a few players. Like how do you fit? Is it going to be competitive? Are you all working together, but for different outcomes, like how do you think about that going forward?

With different entities and different customers?

Yes. And different software vendors as well.

Yes. I think it's a great question. So let me tell you where I think we're uniquely positioned. And then I'll tell you where I think -- how we work with others that's there. Technologically, we are uniquely positioned because we are, to me, the only platform that has RPA, API, Agentic and process orchestration, underscored with AI products like document understanding, advanced document understanding like IXP, test automation, et cetera.

So I think we have a really broad platform from there. Where I look at where we can play with -- where I look at automation, I look at 4 categories: personal productivity, in-app automation, cross-application, complex enterprise-grade automation and verticalization. Okay?

Within kind of citizen development personal productivity, we'll cooperate with anybody. We want -- that's not our bread and butter. We can do it, but we're not really kind of moving into that area cross application, same thing. We will integrate and we will create connectors with the partnership with SAP to help people automate more even within SAP that is there, but that's not a core area for us.

The third area around cross application, I think I'm going to double-click on. We want to own that area. So we want to compete. And verticalization, we want to compete. We're going to pick verticals that we are uniquely positioned to go after. So we're not going after ITSM, that's ServiceNow. But revenue cycle management, claims denials. Those are areas that we can create vertical solutions and really scale.

How do we then, just to go back to the cross application area? I think on deterministic, we are the technology. On Agentic, people want to be able to build and deploy agents on our area, and we will integrate with every model provider that is there. We created our partnership with OpenAI, created partnerships with Anthropic, with Gemini. We have partnerships in place. So models can be used by anybody.

The question then is how do you kind of integrate with the rest of the ecosystem we actually love -- we don't mind if people go and build agents on other platforms, where we want to win is in process orchestration, to orchestrate UiPath robust, UiPath agents, third-party agents and humans across. And in that area, honestly, there's -- we are very uniquely positioned to win in that area.

Yes. Okay. And then the -- yes, okay, makes sense. I'll leave it there. The other thing I wanted to discuss with you is like you guys from a more organizational perspective, have been on a journey, especially last year, there was a lot of disruption. It looks like it's getting better this year. But like maybe for the audience, it's good to understand like where you guys came from, what course did and then the changes you took to kind of try to solve it.

So Daniel came back into the CEO realm in May of last year, May, June of last year. I think when we look at it, it was very simple. I think we went through an era where we scaled so fast. I think we got an enamored -- UiPath got enamored by putting big company structure and big company processes across. And I don't think that's a wrong thing to do for the right company. We were just not the right company for that, for those processes. And so I think what happened well in that time frame is connecting and selling to the C level.

What happened incorrectly is we created too much intermediary, both internal structure and strategically between ourselves and the users and the people driving process transformation on the ground within our customers. And we lost a little bit of that innovation. So what did we do? We have really kind of gone, I wouldn't say nuclear, but we were pretty aggressive in removing central organizations.

If your hands are on a keyboard, generating code, if your hands are with -- are shaking -- or leading a customer towards transformation, we are investing heavily. Go to our website, we're hiring, we're investing. If you're in the middle of those two, there's some necessity there. But we really took a very strong restructuring mode to remove that. And that's created twofold effects. We're closer to our deals. We are closer to adoption, and we are farther ahead. We walk into a quarter, not talking about the deals this quarter, we're talking about the deals next quarter.

We're talking about the renewals 6 quarters from now. And I think that operational rigor that's been brought to the company is a reason why you see net new ARR actually stabilized, which I didn't mention in the first question. It's net positive now. And so UiPath, kind of, one of the themes where we're not the same company as 2 or 3 years ago. Not just product-wise, but execution-wise, and you can start seeing that in the metrics as they begin to inflect.

And the -- if you think about it, the -- on the one hand, you kind of removed a little bit the guys to kind of create like an empire -- sorry, if I say it like that. On the other hand, your momentum up market actually continues to grow better. That almost like -- it sounds like -- I remember when you did the changes last year, it was like we're going back to our roots, et cetera. But now the momentum is coming up, like how do you explain that?

I'm still learning in the business world around things, I would say. But the one rule that I've found is if you focus on your technology and you focus on your customer, like only good things happen. And I think that has been our premise. So in my mind, it's not a question about an empire or a person or a set of activities about it's just how much of your energy and your resources are going towards innovation and helping your customers win. And I think the more that you do there, the ROI is kind of like very natural.

So from our standpoint, that signal with the field, there's an example for a customer, big Agentic use case. And there were a couple of hundred thousand dollars of a pilot. They went and they ran their first set of COGS, it came in at like $150 million estimate. I think 3 years ago, that would have been lost in the system. One day later, Chief Product Officer engaged. Our 4 deployed engineers and our services team are moving into that customer. We realize that they can do things through batch coring and from there, those COGS have come down to a point where that POC is now considered a wild success.

And now it's a question of obviously getting it into production and getting the order, but that's just a very practical example to say when you can move up and down from your customer to your team and solve their problems, you unlock demand. Demand is there. It just needs to be unchained.

Yes. And then just as we track the progress there, like the 1 thing we all want to know is like, okay, what's the metrics, what are we looking at? Is it -- as you said, net new ARR, is that still kind of the one you look at?

Yes. Well, net new ARR to me is the primary metric. ARR is the primary metric that drives the company. I think net new ARR is a good metric and the trajectory of what that looks like. I think the second too or is the momentum we have with our large customers. Customers between $100,000 and $1 million, their net dollar expansion rate is 113%. For me, that matters.

Now why? Because when you look at kind of the lower end of the market, those are customers that we bought that we kind of -- that bought from UiPath me 4 years ago, but they don't have high propensity to buy. I'm not firing customers, but they're not going to expand at the same level.

And then when you look at customers greater than $1 million, I'm super happy with the expansion of customers in that low million dollar but we have customers that are 8 figures. Those 8-figure customers with 2- or 3-year contracts, they're not going to be expanding 20% per year, right, in that way. So I would look at that $100,000 to $1 million cohort. That's a really good sign for us. And the continued momentum to push customers into the $100,000 category and the $1 million-plus category.

And then the -- is there like the one thing and then there was always kind of difficult for you is like you have 606. There's cloud and cloud mix, it kind of would make it kind of slightly easier. But how do you think -- do you think there is something that you can solve or you -- we basically -- I'm just asking for myself actually because...

It's a fair question. I think it's on us over the next period of time, like I think refreshing the models for everybody would help. That's something that's been on our mind. But ultimately, 606 revenue is done -- it's impacted by duration and deployment and volume, right? I think volume -- I think ARR is really just impacted by volume, right? And through there, there's obviously different idiosyncrasies that exist in every metric. So my view is I look at -- I still think revenue, like revenue grew 16% this quarter, right?

I think when you look at it, it's looking to guide in low double digits is where consensus has us here for the year. ARR is at 11%. So I think you have to look at -- I think if you look at revenue on a quarter it's less relevant than looking at revenue for a year, and it's less relevant than looking at it on a trailing 12-month basis, right? So I think the more aperture you give to, if you look at revenue, you have to give it in its appropriate aperture, and it correlates very well with ARR. I think the myth around ARR and revenue, there are dynamics that change it. But when I look at it today, I think we've done a very good job when you look on a trailing 12-month basis of it correlating pretty well with the AR movements that you see.

Yes, yes. Okay. So no change, but like no change like in terms of what you do, but it's more like focus us more, ARR is the kind of the metric.

Exactly.

Okay. Last couple of minutes, profitability. If you think about it, there's a lot of stuff that we want to do around AI. The world is evolving everything quickly. At the same time, like we need to think about profitable growth on rule of 40, like where are you on that journey?

Yes. So one is I want to be unequivocal, like we are investing for growth. There is no doubt that should be there. I think the misnomer is that in order to invest for growth, you have to not be profitable, and that's not the case. You can eat if you exercise, right, so to speak. And you can get the right foods and enough of them if it's the right food and still be healthy.

So I look at it this way. I think field sales capacity, making sure we're funding our POCs and our pilots and the success of those early areas of Agentic. We are all in, and we are aggressively investing in. I think the innovation side in R&D, we're investing in. The areas, we still have areas to leverage even within G&A and sales and marketing to fund those investments where we can fund growth but continue to drive operating leverage. You can see that in our metrics.

First year of GAAP profitability, that's where we're on track for this year. I think one of the interesting things that people aren't looking at is open our stock-based compensation percentage of revenue, how it's come down and look at the trend that you've seen there. That's with the same number of people, but just being more thoughtful about how we deploy it, right?

Second is cash allocation. I think we're really very mindful about what are the top customers we want to win. It's not going to say, all markets are all equal, all at the same time. So I'm actually really pleased with growth being our priority. Profitability being about discipline. And when you look at the metrics, stabilizing net new ARR, first year of net new ARR growing year-over-year, a little bit of FX in there, but overall, that trend is there. But it's also the first quarter of GAAP profitability, and third quarter of GAAP profitability. I think those 2 things are proof that you can do both simultaneously.

Okay. Perfect. And then last question for me on the last minute, like capital allocation. What should we view...

Yes. I think UiPath is in a really good, unique position for it. So we have $1.5 billion on our balance sheet. We've already shown that we will execute responsibly for stock buybacks, we're at $800 million of a buyback. The average price of our buyback was in the low $12. So just think about that relative to a stock that is $16, $17, I think we're $18 plus today. So we're very responsible on it. It's not just a lever for the marketing point. We really want to generate shareholder returns in everything that we do.

And at the same time, we've got -- we're generating free cash flow. So we can continue to buy back stock opportunistically. We will always evaluate that. We can deploy and tuck-in M&A, which we've also done with the acquisition of Peak this year, and we can continue to look for opportunities to do so, where we can continue to have cash and have a big wallet for the right opportunity economically.

Yes. Okay. Perfect. 10 seconds, like I'm German, so I need to give some time. That was great closing statement as well. Good to see you again.

Thanks so much.

Thank you.

Thank you.

Greetings, and welcome to the UiPath Third Quarter 2026 Earnings Conference Call. [Operator Instructions] Please note that this call is being recorded.

I will now turn the conference over to your host, Allise Furlani, Vice President of Investor Relations. Thank you. You may begin.

Good afternoon, and thank you for joining us today to review UiPath's third quarter fiscal 2026 financial results which we announced in our earnings press release issued after the close of the market today. On the call with me are Daniel Dines, Founder and Chief Executive Officer; and Ashim Gupta, Chief Operating and Financial Officer, to deliver our prepared comments and answer questions.

Our earnings press release and financial supplemental materials are posted on the UiPath's Investor Relations website. These materials include GAAP to non-GAAP reconciliations. We will be discussing non-GAAP metrics on today's call. This afternoon's call includes forward-looking statements regarding our financial guidance for the fourth quarter fiscal year 2026 and our ability to drive and accelerate future growth and operational efficiency and grow our platform product offerings and market opportunity. Actual results may differ materially from those expressed in the forward-looking statements due to many factors, and therefore, investors should not place undue reliance on these statements.

For a discussion of the material risks and uncertainties that could affect our actual results, please refer to our annual report on Form 10-K for the year ended January 31, 2025, and our subsequent reports filed with the SEC. Forward-looking statements made on this call reflect our views as of today. We undertake no obligation to update them.

I would like to highlight that this webcast is being accompanied by slides. We will post the slides and a copy of our prepared comments to our Investor Relations website immediately following the conclusion of this call. In addition, please note that all comparisons are year-over-year, unless otherwise indicated.

Now I would like to hand the call over to Daniel.

Thank you, Allise. Good afternoon, everyone. Thanks for joining us. We are pleased with our performance in the quarter, which was driven by team's focus, consistent execution and progress across our strategic priorities. Our automation strategy combining the reliability of deterministic automation with the intelligence and adaptability of agentic AI continues to align with what customers want most: trusted enterprise-grade automation that delivers tangible ROI fast.

We've always believed that our approach to automation, agents and orchestration creates a durable competitive edge. This quarter's results reinforce the value of our platform and the improved execution from our teams. We beat the high end of our guidance across all metrics levering third quarter ARR of $1.782 billion, up 11%. Further reinforcing my conviction that our business is stabilizing and being driven by $59 million in net new ARR. Revenue was $411 million, an increase of 16%.

Our disciplined approach to operational efficiency continues to strengthen profitability, resulting in our first GAAP profitable third quarter while increasing non-GAAP operating income to $88 million or a 21% margin, and we are on track to be GAAP profitable for the full year 2026 for the first time.

The momentum we are seeing in the market isn't just in our results. It's in how customers and partners are engaging with UiPath. You could see that momentum in action at FUSION where thousands of customers, partners and developers joined us in Las Vegas to see how agentic AI is delivering measurable ROI today. We showcased advancements across the UiPath platform, including new integrations with partners like OpenAI, Microsoft, NVIDIA, Google and Snowflake and real customer storage where we had leading enterprises across key verticals, sharing how they are transforming mission-critical processes with Agentic Automation.

Customers tell us they get the most impact from a unified platform, bringing together deterministic automation, agentic intelligence and Process Orchestration. And we are seeing that play out that enterprises move quickly from pilots to production. Over 950 companies are developing agents, and there have been more than 365,000 processes orchestrated with Maestro across our platform.

A powerful example is one of the world's largest investment management firms which shows UiPath for Maestro's vendor-agnostic architecture and ability to connect systems. They've already demonstrated measurable impact through multiple agentic POCs integrating with ServiceNow, Confluence and specialized LLMs to orchestrate end-to-end workflows and delivering a 95% reduction in time to value and tens of millions in projected savings.

With more than [ 260 ] automations, they expanded this quarter to increase their adoption of Agentic Automation. And together with Ashling, has identified over 40 high-value use cases expected to generate more than $200 million in savings over the next 3 years.

These stories demonstrate how our innovation is meeting customers where they are and helping them scale. And it's that customer energy that inspire many of the new capabilities we announced at FUSION. One of the most exciting is UiPath ScreenPlay, where we are combining traditional RPA with the power of LLMs build more reliable automation. ScreenPlay, understands intent, build multistep plans and execute them autonomously, giving developers and business users a faster way to automate complex UI tasks.

Our ScreenPlay technology is one of the best position in the computer use benchmark World OS (sic) [ OSWorld ]. We are also helping customers move from pilot to production faster through continued enhancement to Agent Builder, including a new visual canvas for debugging and optimization and reusable templates that make automation easier to scale, that focus on usability is driving real customer success.

A great example is USI Insurance Services which selected UiPath because of our multi-agent orchestration capabilities. Working with Lydonia, they are automating a complex workflow where UiPath agents and robots process incoming requests and generate output, all orchestrated by Maestro. USI expects over $32 million in savings over the next 3 years.

As more customers scale their Agentic Automation programs, we're expanding that what agents can do, especially in document-heavy processes. This quarter, we introduced agentic capabilities to our Intelligent Extraction and Processing, our IXP product which delivers specialized extraction and validation agents that handle complex nondeterministic scenarios and reduce manual review. And with UiPath's autopilot for IXP, customers can automatically generate document template saving hours of setup time per project.

A great example is Corewell Health, which plans to leverage IXP to automate the processing of referral information into Epic, in addition to improving efficiency and accuracy, they are on track to redirect $1.5 million of labor savings this year and expect over $3 million next year.

Something like this show how our innovation is helping customers turn manual document-heavy work into intelligent automating processes. And these strong results continue to earn us third-party recognition. We were named a leader in the inaugural Gartner Magic Quadrant for intelligent document processing which we believe highlights our capabilities for data and information extraction to help enterprises unlock value from their documents in the age of AI and Agentic Automation.

We are also recently recognized as a leader in the Gartner Magic Quadrant for AI augmented software testing tools, which, in our view, validates our vision for Agentic Testing and the results our customers achieved with UiPath Test cloud. One example is [ Energie Energy ], which adopted UiPath test cloud to address testing challenges across SAP and its digital apps. With Agentic Testing and autonomous self-healing, they expect 30% better coverage, 1.5x faster cycles and almost $2.9 million in savings over 3 years.

And lastly, we are proud to be recognized by Everest Group as both a leader and star performer in the 2025 Intelligent Process Automation Platform and full suite IPAP peak metrics assessment, this recognition underscores the strength of our end-to-end platform, our innovation in Agentic Automation and AI integration and the tangible business impact we are delivering for customers.

These recognitions highlight the strength of our unified platform, which connects every layer of automation from discovery to orchestration within a single governed system. They also reinforce what we hear directly from customers. The power of our platform comes from how it works together.

And at the center of that platform is Maestro, our orchestration engine. Maestro builds beyond managing automation it serves as the control plane for how work is orchestrated across the enterprise, powering through end-to-end automation at scale. A powerful example is the leading U.S. managed care provider that is leveraging UiPath agents, robots and Maestro to tackle a backlog of more than 140,000 provided appeals. They automated the entire workflow using agents to classify forms, robots to handle processing and Maestro to orchestrate the process. The result is a streamlined operation targeting 80% autonomy in year 1.

As Maestro becomes more deeply embedded in customer operations, we are continuing to enhance its capabilities with new features like case management and process apps, Case management helps customers model and manage now-running processes, while process apps enable customers to build tailored end user experiences with real-time visibility and actionable insights to drive proactive operational improvement.

In order to realize the value of agentic, customers need a strong foundation in deterministic. And this quarter, we announced the general availability of API workflows, which delivers API-centric automations that complement RPA and agents. We believe this strengthens our position as one of the industry's most comprehensive automation platform. We are continuing to expand our cloud footprint in important markets like Switzerland.

And at GITEX, we announced the launch of Automation Cloud in the UAE. This expansion gives customers the ability to run automation, AI agents and orchestration in a secure, locally hosted environment that meets regional data residency and governance requirement. The power of our platform really comes to life when it's applied to industry-specific challenges. We are focused on building vertical solutions that help customers accelerate our constant ROI.

The capabilities we gained through our Peak acquisition earlier this year, are extending these vertical capabilities. By combining their industry-leading pricing and inventory optimization technology with Maestro and our broader platform capabilities with creating an agentic merchandising, pricing and inventory solution with Debenhams Group. We are taking this joint agentic solution to market with other leading retailers and manufacturers expanding the reach of our platform across key verticals.

These industry solutions are just one way we're helping customers realize faster value. We're also enabling them through deep collaborations with global technology leaders. At FUSION, this collaboration came to life through new integrations, including with Microsoft Azure AI Foundry, enabling UiPath agents to work seamlessly with Azure agents and models. Using model context protocol, UiPath, expands integrations with Microsoft Copilot and empowers organizations with the trust and governance needed to run agents at scale. We announced a collaboration with OpenAI to deliver a ChatGPT connector bringing OpenAI's Frontier Model directly into enterprise workflows, simplifying AI agent development and accelerating time to value.

We also launched a new conversational agent with Google's Gemini models enabling natural voice-driven automation without complex coding. Additionally, we introduced a new integration with NVIDIA, allowing organizations to enhance high trust workflows like fraud detection and health care with AI models deployed through NVIDIA NIM Microservices. And finally, we partner with Snowflake to combine Agentic Automation with Snowflake Cortex AI, helping businesses to data insights into fast autonomous action.

While our technology partnerships expand what's possible with our platform, our go-to-market partners make that innovation real for customers. During the quarter, we expanded our collaboration with Deloitte to help organizations accelerate how they build, test and release software. By combining the genetic testing capabilities of UiPath Test Cloud with Deloitte Ascend, we are transforming the testing life cycle with Agentic AI to automate repetitive tasks, we take changes and execute tests autonomously. And with autopilot for testers and Agent Builder embedded in Ascend, Deloitte teams can leverage over 1,500 prebuilt testing bots and AI agent.

Lastly, we are encouraged by our federal sector performance this quarter, where efficiency mandates are creating a long-term tailwind for automation. Highlights this quarter include expansions with the U.S. Coast Guard to modernize core systems and improve mission readiness through automation and AI. The Department of Veterans Affairs which is automating disability claims and enhancing contact center service for veterans and the social security administration which is migrated to the cloud to expand to our IDP solutions to help accelerate benefits processing.

Our unified platform and innovation continue to strengthen these partnerships and underscore the significant opportunity ahead in the public sector, even as the federal purchasing environment remains dynamic with pockets of strength.

Looking ahead, our continued innovation is expanding what's possible for customers and delivering measurable results for the power of deterministic and Agentic Automation. What continues to set UiPath apart is our unified end-to-end platform architecture, delivering one connected experience from discovery to deployment with Maestro orchestrating work across systems and our built-in governance capabilities, ensuring control, compliance and trust, I am pleased with the progress we are making in improving the execution of our teams and the pace of innovation across product organization is delivering for customers. We feel well positioned as we close out the year and continue executing on our vision for the future.

With that, I'll turn the call over to Ashim.

Thank you, Daniel, and good afternoon, everyone. Before turning to the financials, I'd like to provide a quick operational update. This quarter reflects the meaningful progress we've made in sharpening execution across the strategic priorities Daniel outlined earlier this year, including strengthening customer relationships, accelerating innovation and driving operational rigor across the organization.

Through these areas of strategic focus, we have improved performance of our sales team. Deepened areas of strategic focus, we have improved performance of our sales team, deepen engagement and strengthened alignment with our customers' priorities. We're partnering earlier co-developing solutions and scaling automation faster. Our broad installed base gives us unique visibility into enterprise workflows helping customers connect people, robots and AI agents to deliver measurable outcomes at scale.

Our pace of innovation continues to accelerate, supported by deeper ecosystems, integrations and advancements in our Agentic Automation platform. Combined with disciplined execution, these efforts contributed to another solid -- another quarter of solid top line and bottom line performance, including our first GAAP profitable third quarter and putting us on track for our first GAAP profitable year.

And we're seeing customers lean in a great proof point of our end-to-end platform is a leading cybersecurity company that expanded to our agentic products. With support from our Forward Deployed Engineers, they are leveraging UiPath agents, robots, IXP and Maestro to create seamless end-to-end workflow. IXP extracts data from purchase orders across 700 vendors, robots retrieve quote details from SAP, and agent supply confidence scores before passing the data to sales order creation. Maestro orchestrates the process, ensuring speed, accuracy and control which is expected to help them improve their accuracy rate from 50% to 90%.

Turning to the quarter. Unless otherwise indicated, I will be discussing results on a non-GAAP basis, and all growth rates are year-over-year. I also want to note that since we price and sell in local currency, fluctuations in FX rates impact results. Rates have remained largely stable since the time of our last earnings call through the end of the quarter. And as a result, there is no incremental FX impact to our third quarter results.

Third quarter revenue grew to $411 million, an increase of 16%. Normalizing for the year-over-year FX tailwind of approximately $5 million, revenue grew 14%. ARR totaled $1.782 billion, an increase of 11%. This included a $6 million year-over-year FX tailwind. Net new ARR was $59 million.

We ended the quarter with approximately 10,860 customers. We continue to be successful in signing new enterprise logos that align with our strategy of targeting long-term customers with a propensity to invest, including new logos like Nuffield Health, Resurs Holding and an Australian brick manufacturer, which selected UiPath due to the breadth of our Agentic Automation platform, and they will be leveraging UiPath robots, agents, IXP, Maestro and process mining to automate sales order processing.

As with prior quarters, the vast majority of customer attrition continues to be on the lower end. Customers with $100,000 or more in ARR increased to 2,506 and continue to have a strong dollar-based net retention rates. While customers with $1 million or more in ARR increased to 333. Dollar-based gross retention remained best-in-class at 98%, and our dollar-based net retention rate was 107%, underscoring the durability of our customer base as they embrace our Agentic Automation solutions. Adjusting for FX, dollar-based net retention rate was 107%.

Remaining performance obligations increased to $1.265 billion, up 12%. Normalizing for the FX tailwind, which was approximately $20 million, RPO grew 10%. Current RPO increased to $840 million, up 17%.

Turning to expenses. We delivered third quarter overall gross margin of 85%, and software gross margin was 91%. Third quarter operating expenses were $261 million. We delivered our first GAAP profitable third quarter, with GAAP operating income of $13 million, up from the prior year GAAP operating loss of $43 million.

GAAP operating income included $71 million of stock-based compensation expense. Third quarter non-GAAP operating income was $88 million, representing a 21% margin, up more than 700 basis points year-over-year and driven by our continued focus on operational efficiency.

Third quarter non-GAAP net income was $85 million, which excludes a nonrecurring noncash tax benefit of $184 million from the release of a valuation allowance on certain deferred tax assets. Third quarter non-GAAP adjusted free cash flow was $28 million. We ended the quarter with a healthy balance sheet of $1.5 billion in cash, cash equivalents and marketable securities and no debt. Now turning to guidance.

We are pleased with the team's execution in what continues to be a variable macroeconomic environment. We continue to maintain a prudent outlook and guide to what we see in front of us. As Daniel mentioned, we are pleased with the progress of our public sector team. As a reminder, while we are encouraged by early traction with our agentic capabilities, adoption is still in its early phases, and we don't expect a material top line contribution in fiscal 2026. Lastly, since the end of the quarter, the Japanese yen has depreciated against the U.S. dollar, creating a headwind to fourth quarter guidance.

Turning to the specifics of our guide. Despite the incremental FX headwind from the yen, we are raising guidance for the progress we've made on our operating priorities and the strength we are seeing in the business. For the fourth fiscal quarter 2026, we expect revenue in the range of $462 million to $467 million. This range reflects an approximately $3 million headwind driven by FX rate movements since we provided guidance on our second quarter earnings call. ARR in the range of $1.844 billion to $1.849 billion. This range reflects an approximately $3 million headwind driven by FX rate movements since we provided guidance on our second quarter earnings call.

Non-GAAP operating income of approximately $140 million. And we expect fourth quarter basic share count to be approximately 536 million shares. And finally, we continue to expect fiscal year 2026 non-GAAP adjusted free cash flow of approximately $370 million and non-GAAP gross margin of approximately 85%. Thank you for joining us today, and we look forward to speaking with many of you during the quarter.

With that, I will now turn the call over to the operator. Operator, please poll for questions.

[Operator Instructions] Your first question comes from Bryan Bergin with TD Cowen.

Maybe just starting off here on some of the agentic solution traction. Daniel, I heard you mentioned, I think, 950-plus clients. Just first, is that comparable to the, I think, the 450 or so last quarter using Agent Builder? Or is that a broader kind of view across your agentic solutions? Just trying to get a sense of kind of that quantitative traction, if you could share that. And for the cases where you are seeing scaling past the proof of concepts, is it more about the underlying clients and their capabilities or more so about the specific types of use cases that they're pursuing?

Thanks, Bryan. Yes, we are seeing really good momentum across our agentic offering. And this creates a pull-through across the entirety of our platform. One -- we are seeing some kind of consistent buying patterns emerging from POC to pilots and to some use cases into production. I would say that it's more the highest ROI use cases are very customer specific. I don't see necessarily a single one across multiple industries or different departments. But overall, it's pretty encouraging to see the movement from -- again, from pilots into production for some of them.

Okay. And then my follow-up is on the federal business. So you had positive commentary here, probably a surprise for some given the shutdown. Just curious, was there any shutdown impact just worth calling out here in October and into November?

No, there is no direct impact from the shutdown. You got to remember, Bryan, a lot of our projects are just funded through the bills. So -- and many of them are considered -- are in critical operations like in areas like the Department of Defense, et cetera. So we had no major impact from the shutdown.

Your next question comes from Jake Roberge with William Blair.

Nice quarter. Congrats on the results. I know there's some FX impact but your Q4 guide implies that net new ARR could start growing again on a constant currency basis. Can you talk about the driver of that return to growth and just how we should think about the sustainability of net new ARR growth moving forward?

Yes. Look, I think the entire business is positive. We are really pleased with how our team executes. We see consistent execution across the board. I would like to nominate especially our teams in Americas, where we are really seeing signs of great traction, especially in the agentic. And yes, I would say it's overall, it's -- things are improving and stabilizing.

Yes. I would just echo what Daniel said as well. I think there is no magic to it. We talked about the improved execution. The focus -- the launch of the new products, we think, is going to continue to help us both, definitely indirectly in pulling through our platform and increasing stickiness and getting us deeper in customer conversations. But then as we go through time more directly. And we look at it kind of just as the business stabilizes, there's a lot of good news, both the consistency of the leadership, the talent that has been brought in as well. So those are factors that contributed. There's not one single magic button or one silver bullet that we're counting on for that.

Okay. That's helpful. And then I know you're not expecting a material contribution from AI solutions this year. But for customers like that cybersecurity company that you called out in the script that are starting to put these agents and Maestro processes into production. Can you help us understand what type of pricing uplift or monetization that you're seeing once we actually get these go-lives in production?

Yes. It's not about the pricing uplift. I think the first thing to note is we talked about the cybersecurity, Jake, is it's pulling through the entire platform. IXP, additional robots, Process Orchestration. So I think that is an important part of this. It's not agentic in isolation. It is agentic paired with the rest of our platform that really is driving value for our customers. So I see this kind of the monetization, not as a pricing uplift, but increasing stickiness, giving more conviction to deepen our platform into the architecture of our customers, as customers really like the road map and are starting to experiment with agentic and find tangible ROIs through the full breadth of our platform.

Your next question comes from Mike Turrin with Wells Fargo.

This is Austin Williams on for Michael Turrin. I just wanted to go back to U.S. Federal. I'm curious how results in 3Q came in versus your expectations at the beginning of the year related to DOGE? And then maybe just as a follow-up. Anything you can add on the OpenAI collaboration, what exactly that could drive for UiPath?

I would say that the federal business continues to be a dynamic environment for us with pockets of strength. We are really encouraged by the progress in 3Q, I think it's returning to a new normal. The deals that we mentioned in the scripts are really solid, team is executing well with focus on efficiency positioning as well. The projects are long term and strategic, not short term. We continue to be prudent in our guidance estimations about the sector.

On the OpenAI, yes, we use GPT5 across the board in our platform. We highlighted especially the use in one of the most innovative parts of our platform, the product that I mentioned in the script called ScreenPlay, which is basically our own version of computer use or operator. But the key thing here is that we can combine the reliability of UI Automation with the power of adaptability of LLM driven computer use. And I think to my knowledge, we are the only company that can succeed delivering autonomous UiPath using LLMs, using this approach.

And your next question comes from Matthew Hedberg with RBC Capital Markets.

This is Mike Richards on for Matt. Kind of building on that last question, there is a ton of excitement around all the partnerships you guys announced, and I think it validates your positioning and orchestration. So I was wondering, even beyond OpenAI, if you could just give some more details around the partnership just in terms of, is there a joint go-to-market element to any of them? I know it's early, but have you seen any pipeline build as a result of these partnerships? Just any more details so a lot of excitement around it.

I would say that at this point, our -- the partnership that we announced at FUSION are clearly into the technology-enabling partnerships. And they were driven largely by our customer needs. We always -- we praise ourselves for having an open platform that can really be flexible and customizable to customer needs. So we believe so, if we look at kind of our partnership, we believe that the foundation of delivering reliable in enterprise have different layers. So it has the data layer, ontology layer, so we -- this is where our partnership with Snowflake is shining.

We offer ourselves the automation, the RPA and API and agentic layer. But of course, with Open AI and Google we use the Frontier LLMs in order to power the agentic. We use NVIDIA for security and governance in regulated industry. So I think our goal is to create a set of partnership that offer a very solid foundation to deliver reliable AI into a secure and governed manner.

Super helpful. And then if I could just do a follow-up. I think before we talked about in the early days of the orchestration opportunity for you guys, it's been mostly agents created on UiPath. I was just wondering, have you seen more of a shift to third-party agents yet? Or is it still -- you guys are mostly orchestrating agents built within UiPath?

We are seeing many customers interested in building coded agents where we have partnered with companies like LangChain, CrewAI and LlamaIndex. And we are seeing right now a mix of agents that are hosted and managed by our platform that are both low code and coded agents. I think at this point, it's kind of too early to see us managing external agents that are built completely outside of our platform.

Your next question comes from Sanjit Singh with Morgan Stanley.

This is [ Ryan Lance ] on for Sanjit. Just with regards to the channel, you mentioned the expansion of your partnership with Deloitte. So can you just provide some additional details around how much incremental pipeline is now partner sourced relative to a year ago? And maybe just how these partnerships can help drive additional kind of AI-related product deployments next year?

Yes. One is, I think the quantum has definitely increased, but more than the quantity, it's the quality. So when you look at the S/4HANA migrations that are happening and partners like Deloitte and their presence within many of these customers, we're now involved in those conversations. And I think that, that is helping pull us through and being in the conversation about larger scale transformation processes. I think that is -- Deloitte has obviously done a really exceptional job and that partnership has been meaningful for us. But I think that's a motion that we're also seeing across our teams with various partners that are there. So the quality for me is much higher quality pipeline than just a superficial quantum or a quantity number that I would tell you about.

Okay. Great. And then just one follow-up. You guys have driven some pretty meaningful OpEx leverage throughout this year. And I'm just curious if you could provide any additional details around how you're thinking about OpEx investment next year to kind of support this AI product rollout and just broader monetization path.

Yes. Of course, I'm not going to provide anything specific regarding next year at this time. But I think putting this year in context, we'll give you a sense of the strategy that Daniel and the leadership team here are employing. So I think the first thing is we've got an OpEx leverage by not austerity but by discipline and really being super focused on where we're prioritizing. So we are actually hiring in our engineering segments. We are expanding sales capacity. As we talked about earlier this year, and last year, really, our focus has been continuing to drive efficiency across our processes, being selective about overlay functions in terms of where we're investing.

And I think that area allows us to get operating leverage while still being able to invest in key areas. So as we're looking at our platform, whether it's Forwards Deployed Engineers, whether it is more hands and legs at our customer sites or just core engineering capabilities. Those are areas that are in our investment zone. When you look at our line item, we're getting leverage across every area because we're really going to all of the cost structures that exist outside of those 3 and really seeing what are the areas of efficiency and prioritization. And that has not just given us OpEx leverage, it's also enhanced our focus and has contributed to us being better in our execution cadence as well.

And your next question comes from Scott Berg with Needham & Company.

Nice quarter. When I was at the conference a couple of months ago, some of the partners I spoke with really talked about a high level of pilots and proof of concepts that your customers are going through right now with the different types of AI functionality? And I know it's not a big part of the expectations around bookings for this year but I guess, as you've seen some of them convert to actual sales or production, are there any key drivers or levers that you've learned through some of these that you can help maybe use some of these other deal cycles you're currently going through?

Yes. We see these patterns emerging. It's -- I would say that the landscape is extremely scattered right now. We have deployed our teams in various use cases across various industries. We see some particular partners emerging in health care like, for instance, in revenue cycle management, prior authorization, claims management, in financial services, financial crimes, it's -- but again, I think it's a bit too early to mention one particular use case where we see like great replicable potential.

Understood. And then, Ashim, as you look at the fourth quarter guidance, someone else had already mentioned that the implied ARR number suggests that your net new ARR is up again on a year-over-year basis, help us kind of think through maybe the construction of that. Is that just purely a result of the improved execution that you all have been working on this year? Or is there some aspect of maybe deals that were maybe slipped from Q2 and Q3 that kind of moved in to the expectation? And I know that AI, some of the functionality there is not a big driver this year. But are there some expectations around maybe some bookings improvements in that category that's helping kind of drive what your initial guidance here is?

Yes. So let me be super clear on it. There's nothing in terms of like slip deals from third quarter or anything that is timing oriented, Scott, in any material or significant way. There's always normal course deals that move back and forth, right, deals that we're in your fourth quarter pipeline that closed early in deals that closely. That's just normal course and nothing out of the usual that I would talk about.

When you look at the year-over-year growth implied within our guidance, I think there's 3 main factors for me. The first is execution. We are seeing improved execution across the board from our sales team. We -- it is supported by good customer activity, maybe not 1 or 2 deals, but really broad-based activities of POCs and pilots and renewals that are coming in with more conviction about our long-term place in the architecture that leads to natural upsells, et cetera. There is macroeconomic environment, as I talked about in the second quarter earnings call around foreign exchange, that will play a role in that year-over-year impact that is there.

And then the third one is I just think momentum. I think that we've had a good stable base now in terms of if you look at our sales stability 6 months, 9 months after kind of the restructuring that we had completed at the beginning of the year, as well as just a little bit of the normalization around areas like the public sector, which we've said is kind of back to a new normal. No catch up, but at least at this time, a new normal.

We feel like we're still prudent on our overall assumptions on the macroeconomic environment. But it's a confluence of things that to me are at the opposite end of when net new ARR was declining, poor execution, foreign exchange having a headwind and frankly, just losing -- having too much inconsistency in our strategy as well as our organizational structure. So I'd say all 3 are contributing to that, to the stabilization of net new ARR you're seeing.

Your next question comes from Alex Zukin with Wolfe Research.

This is Arsenije on for Alex Zukin. And I guess congrats on the results and also just kind of wanted to expand on what the downtick in NRR was? Was it just weakness at the low end? Or was there anything else? And then you kind of unpack what's driving that stronger new business, but is there anything that you can kind of give us in terms of Q4 applications on that new business strength given the 3 factors you just talked about?

Yes. So look, I think there is -- I think when you look at it, definitely at the lower end of the segment. We've talked about that. It has a little bit of pressure on the net new ARR. To give you a supplemental metric, our net dollar expansion rate for customers between $100,000 and $1 million was 113%. So that can quantifiably show kind of the lower cohort having more pressure. And I think you get a little bit of the law of large numbers as well. But as we stabilize net new ARR, obviously, the rest of the metrics begin to stabilize as well. And you can see that pattern starting both with our third quarter results and, of course, kind of the guidance that we've provided here.

Got it. And then just to kind of follow up on kind of the same thing. You doubled the number of customers developing agents quarter-over-quarter and Maestro process instances. What's the uplift you've seen in those customers? And is it kind of fair to assume that into next year as that revenue ramps, it can help sustain that NRR and offset some of that low-end weakness that we've seen?

Yes. We will continue to contend, but we're in the early innings of agentic. I think what the momentum around customer activity, it has both the direct and indirect. I think the indirect definitely has started to help us and will continue to help us. customer pulling through other parts of our platform, investing in us as a longer-term part of their enterprise architecture.

In terms of direct scalable, direct monetization place from agentic. We're -- at this we'll update everybody as we kind of get into next year around assumptions there. But for the immediate short term, there's nothing material as we cited in the script.

Your next question comes from Kingsley Crane with Canaccord Genuity.

I think it's interesting if we think about the Code Red moments over the past couple of weeks, it's clear that competition among model providers is healthy, of course, that makes integration, orchestration even more valuable. Have you seen a shift in perception in the past quarter? And then from an integration activity perspective, how is heterogeneity trending?

I don't think we necessarily see yet a shift. I think you aim what -- Google Gemini release. We were using Gemini in our IXP business for quite a few quarters somehow. I think we -- as a platform, we continue to assess all the frontier LLMs, and we use, frankly, a mixture of them. And for instance, in our IXP business, we use GPT5 to understand the better the structure and intent documents, while we can use Gemini for specific extraction like multiple tables, indicated tables. This is just an example. But across the platform, we always monitor and use the best of breed LLMs.

Great. And then just a quick follow-up, Ashim, you mentioned in your prepared remarks that you're partnering earlier, your co-developing solutions earlier. That's been a big focus for this year. Just to what extent did this directly translate into improved results in Q3? And then do you think that this could be more of a future predictor of success next year?

I think a leading indicator, yes. I think any time you're closer to customers, you are innovating with them, you're solving problems. I think it helps. And I would say what our conviction is, is that ROI is going to be where choices are made. And so as we're co-innovating, as we're teaming up with partners around bigger problems, I think we have a chance to have meaningful impact around ROIs, which we feel we'll continue to do that.

I think it also shows the relevance of UiPath. If I can -- like in terms of the validation of the agentic framework, it's not marchitecture, it's real product that has real impact that both partners and customers can innovate around. And I think that is super important and a really great validation point for our product team, our sales team and all of our customer teams combined.

To answer your question around third quarter, as we've cited in the script as well in the prepared remarks, there's no meaningful impact from agentic in our near-term results. We feel like this is continued disciplined execution. What agentic continues to do is the closer you are to customers the more you are a part of their long-term road map and architecture, the more that they're willing to invest and pull through the existing parts of your platform today, and have higher level impacts within their org and that indirect impact we're definitely feeling the momentum and we're excited about it.

Your next question comes from Kirk Materne with Evercore.

This is Chirag on for Kirk. Congratulations on the strong quarter in traction. Daniel, you've talked briefly about prebuilt Agent solutions, highlighted strong modernization use cases within certain categories in the past, which verticals do you see adopting these prebuilt agentic solutions the fastest? And are you moving more aggressively into industry-specific packaged automation at all?

Yes. Our verticalized approach is getting a lot of traction within UiPath. We put a lot of focus and effort. We actually did recently rework of our product and engineering teams to better address the creation of vertical solutions. In terms of the industries, we focus on health care, financial -- and financial services primarily with again, with an accent on revenue cycle management in health care and in financial services, I would say, financial crimes, can be one of the -- know your customers, anti-money laundering type of risk cases we are seeing the most interest from our customers.

Okay. Maybe just one more. What are your thoughts on the balance between deterministic automation and agentic or LLM-based automation? And do you see this balance shifting materially at any point over the next few years or few quarters?

Look, our thesis is that they are very complementary, and they address different steps in the business process. And in many processes as long as task or workflow is well defined. People would use a deterministic automation. There is zero need to use an LLM-based. You use an LLM in order to create a deterministic automation was or to maintain it to make it -- to improve it over time, but you don't use an LLM to drive it. But of course, there are so many areas where you cannot have -- rules are either too complicated or process is too complex, you sift through tons of documents where or it involves conversational aspect of the process.

So in all these areas, LLMs are an amazing complement to the deterministic automation. And I also want to mention that the orchestration piece that combined the AI-based and deterministic with humans in the loop is an essential piece that is required in order to deliver a solution that is secure, governed for the enterprise. We continue -- we have continued saying that orchestration is the key. We announced our effort a year ago. And I'm pleased to see that across the industry, people are talking right now and realize that orchestration is a key technology in order to deliver a reliable AI.

Congrats on this quarter.

Your next question comes from Terry Tillman with Truist Securities.

This is Dominique on for Terry. So it was mentioned previously that one of the biggest customer hurdles with agentic consumption pricing is spend predictability. So have early deployments given you enough usage patterns to help customers forecast more reliably. And also just curious as to what else you all are doing to help customers get over that hurdle?

Look, we are constantly evaluating how our customers are adopting AI. And we aim to have a pricing model that really reflects the adoption of -- consumption of AI. We constantly monitor industry trends. And I would say the entire industry is dynamic at this point and is trying to figure out what's the best pricing. We are pretty flexible in our approach. We can price by components. We also are pretty flexible on understanding an outcome-based pricing can be for our customers. But again, I think we are in the early innings of really understanding consumption patterns across agentic AI at scale, I mean.

Got it. And then just as a follow-up, has the typical cycle time frame from an agentic PLC to a production deployment shortened versus earlier in the year? If so, could you just highlight what specific efforts are driving that compression? Or what do you all plan to do to accelerate it?

Yes. I think we, as a company, understand a bit better how various use cases. We are building internally solutions accelerators that can help us replicate experience across industries and verticals. And my estimation is this trend will continue to accelerate within next year. I strongly believe that the key to unlock huge scale AI consumption in enterprise is the prepackaged solutions where will -- we can meaningfully accelerate the time to value. This is why, as I mentioned before, we put a lot of emphasis in building these solutions.

Thank you. And there are no further questions at this time. So I'll now hand it back to management for closing remarks.

Thank you so much for all the questions. I would like to wish you happy holidays. And as usual, we like to hear from as many as you throughout the quarter. Thank you.

Thanks. This concludes today's call. All parties may disconnect.

Greetings, and welcome to the UiPath Second Quarter 2026 Earnings Conference Call. [Operator Instructions] Please note, this conference is being recorded. I will now turn the conference over to your host, Allise Furlani, Vice President of Investor Relations. Thank you. You may begin.

Good afternoon, and thank you for joining us today to review UiPath's second quarter fiscal 2026 financial results, which we announced in our earnings press release issued after the close of the market today. On the call with me are Daniel Dines, Founder and Chief Executive Officer; and Ashim Gupta, Chief Operating and Financial Officer, to deliver our prepared comments and answer questions.

Our earnings release and financial supplemental materials are posted on the UiPath Investor Relations website. These materials include GAAP to non-GAAP reconciliations. We will be discussing non-GAAP metrics on today's call. This afternoon's call includes forward-looking statements regarding our financial guidance for the third quarter and full fiscal year 2026 and our ability to drive and accelerate future growth and operational efficiency and grow our platform, product offerings and market opportunity. Actual results may differ materially from those expressed in the forward-looking statements due to many factors, and therefore, investors should not place undue reliance on these statements.

For a discussion of the material risks and uncertainties that could affect our actual results, please refer to our annual report on Form 10-K for the year ended January 31, 2025, and our subsequent reports filed with the SEC. Forward-looking statements made on this call reflect our views as of today. We undertake no obligation to update them.

I would like to highlight that this webcast is being accompanied by slides. We will post the slides and a copy of our prepared comments to our Investor Relations website immediately following the conclusion of this call. In addition, please note that all comparisons are year-over-year unless otherwise indicated. Now I would like to hand the call over to Daniel.

Thank you, Allise. Good afternoon, everyone. Thanks for joining us. I want to begin by thanking our partners and customers for their continued trust. I also want to thank the entire UiPath team for their progress on improving execution, consistency and discipline. And the progress is evident in our platform's innovation, customer outcomes and financial results. Together, we are shaping the future of enterprise transformation.

In every conversation with customers and partners, the message is clear. Automation and AI are stronger together. Our deterministic foundation, enterprise-grade RPA and API automation capabilities deliver the trust, scale and reliability mission-critical processes demand. On top of that, our leading AI capabilities, Intelligent Document Processing, or IDP, and our Agentic AI offerings that can reason, plan and act bring adaptability, intelligence and speed.

In addition, what brings it all together is orchestration. With UiPath Maestro, we unify agents, robots and people across systems with governance and transparency so outcomes are measurable and repeatable. This combination is delivering tangible ROI, fueling increasing commercial momentum and positioning UiPath to lead in this new era of automation.

It has also translated into strong second quarter results, where we exceeded the high end of our guidance across all key financial metrics. Second quarter ARR grew 11% to $1.723 billion, driven by $31 million in net new ARR. Second quarter revenue was $362 million, an increase of 14% from the prior year period. Importantly, our AI and Agentic solutions are helping us win deals and increase deal sizes faster than traditional automation engagements and now represent a growing share of commercial activity.

We continue to deliver growth while driving operational efficiencies across the organization. Non-GAAP operating income increased to $62 million, representing a 17% margin and an improvement of more than 1,500 basis points year-over-year, reflecting the operating leverage and discipline we are achieving in the business while capturing the benefits of agentifying UiPath from within.

This momentum reflects the work we've completed to elevate execution. Over the past year, we rebuilt our go-to-market for scale, stabilizing our structure, adding sales capacity and specialists and instituting value-based plays with tighter pipeline inspection. We are seeing the impact: higher-quality pipeline, more predictable forecasting, better customer adoption and implementation and larger multi-solution opportunities, especially where Agentic and IDP land on top of our RPA and API foundation.

You can see this execution showing up in the field. We continue to acquire high-quality new logos. For new customers, deterministic automation remains a high ROI, low barrier entry point. That's why over 95% of new logos this quarter included our core automation capabilities. For our large installed base, our new AI and agentic capabilities are translating into real momentum. Since launching our Agentic platform, customers executed almost 1 million agent runs. Maestro orchestrated over 170,000 process instances, and over 450 customers are actively developing agents.

Now let me share a few examples of how customers are using these capabilities in practice. One example is Voya Financial. After automating more than 100 processes, they expanded this quarter to adopt our Agentic capabilities. With Agent Builder, they are targeting over 40 high-impact use cases in accidental claims, and Maestro is orchestrating the workflows.

Another great example is an international mobility service provider, which shows how quickly customers can scale Agentic Automation across their business. This quarter, they purchased our Agentic products with the vision to deploy AI agents in more than 2,000 branches. They will begin with complaints management, using Maestro as the orchestration layer and Autopilot to summarize and draft responses. From there, they plan to extend into other departments to deliver cost savings, higher customer satisfaction and greater efficiency.

Our Agentic product are not only deepening engagement within our installed base. They are also helping us win major new customers. A standout example is a 7-figure deal with a Fortune 15 global technology company. In a competitive win, they chose UiPath to power their SAP transformation and reinvent employee operations across functions like supply chain and manufacturing. They chose UiPath for our broad platform, including application testing, process intelligence and Agentic Automation and our proven ROI in complex enterprise environments.

What I consistently hear in conversations with our customers is the need for transparency and control. That's why we built Maestro to unify AI, automation, and human decision-making so outputs can be trusted with RPA playing a critical role alongside Agentic Automation. A great example is a leading U.S. health care provider that is piloting our Agentic products to streamline their accounts payable process. Agents monitor the mailbox, extract metadata, retrieve status updates and draft replies, while robots process attachments, send mails and create tickets.

At the same time, UiPath Maestro orchestrates the workflow, involving humans only for exceptions and final reviews, reducing manual effort by 60% and cutting processing time by up to 75%. Another strong example is a Fortune 500 American manufacturer, consumer and professional products who expanded to our Agentic products with a successful proof of concept in procurement operations. What was once a manual purchase order review is now handled by agents before a robot updates SAP. With UiPath Maestro orchestrating they cut latency and manual work while ensuring full policy compliance.

And we believe it's results like this that contributed to UiPath being recognized for the seventh consecutive year as a leader in the 2025 Gartner Magic Quadrant for Robotic Process Automation for both ability to execute and completeness of vision. As customers continue to scale their Agentic Automation programs, we are focused on helping them accelerate adoption. This quarter, we introduced 2 initiatives to support the journey. A team of deployed engineers to codevelop solutions and guide complex strategic implementations and UiPath Playground, a frictionless, no sign-up environment where customers and prospects can test, prebuild, verticalize agentic solutions and see the differentiated value of our platform.

Customers choose UiPath for our unified end-to-end platform that brings together API UI and AI-powered automation. This quarter, we are extending that advantage with API Workflows, our new foray into simplifying deployment and using APIs in our platform, giving customers greater control over how automation securely interacts with their business systems and data. This capability is essential to scaling agents securely and giving them a deterministic foundation to interact with enterprise systems.

Our focus is to continue building an open agentic ecosystem. While low code has made agent building accessible, growing complexity is driving demand for pro-code flexibility. That's why we launched UiPath Coded Agents. Now developers can fully customize and deploy secure and auditable agents built around their data and systems. A great example is Cato Networks, a cybersecurity company specializing in secure access service edge, who has deployed UiPath Coded Agents in production for IT ticket classification and analysis and is now piloting a master IT support agent expected to resolve up to 30% of tickets.

Following our initial integrations with LangChain and LangGraph, we have continued to expand support for other open source frameworks in the market, including LlamaIndex. This makes it even easier for developers to build in Llama and deploy directly into UiPath, leveraging our orchestration, security and monitoring for mission-critical processes. As enterprise automation evolves into agentic automation, the effectiveness of AI agents and automation depends on the quality of the data they can access.

To address this, we recently introduced UiPath Data Fabric, a unified data layer that powers agents, apps and workflows across the enterprise. By giving agents on automation access to trusted context-rich data, Data Fabric eliminates delays and ensure seamless integration across systems. Complementing this, our IXP capabilities help customers unlock unstructured information and turn complex documents into actionable data. This quarter, we launched IXP to general availability. And customers like Coronis Ajuba Solutions are already seeing an impact, automating data extraction from over 20 million pages and reducing errors by 35%.

Building on this success, Coronis plans to adopt Agent Builder and Maestro to scale denial processing across payers. UiPath's human-in-the-loop experience and flexibility to integrate custom LLMs are key drivers to support their enterprise-wide Agentic Automation vision.

The excitement around our agentic capabilities extends beyond customers with partners embracing them to create joint solutions that deliver greater value and faster transformation. A great proof point is one of our top GSIs. After leveraging our agentic products to automate order-to-cash collections, they highlighted UiPath Maestro as one of the most robust agentic orchestrators on the market. They are now committed to building over 20 agentic solutions across core finance process and claims and contract management and are already deploying them with joint customers like a global fintech company where initial POCs in corporate finance are focused on contract validation and revenue recognition.

Cognizant is another strong example of how our partners are embracing our Agentic products to co-create next-generation solutions. They are leveraging a variety of our solutions such as Maestro, Agent Builder, RPA, and IDP to build an intelligent claims processing suite. With this solution, we will jointly work with customers to reduce human involvement, streamline the intake process and de-silo processing across the entire claims value chain.

We also deepened our collaboration with Deloitte as they launched Agentic Global Business Services, a pioneering solution combining Deloitte's advancements in agentic AI with the UiPath platform to move enterprises from task automation to intelligent orchestration. Alongside our GSI partnerships, we continue to strengthen alliances with leading technology platforms. We are proud to expand our long-standing relationship with Microsoft who has reinforced UiPath as their preferred enterprise Agentic Automation platform for their customers. This strengthened alliance brings the power of Microsoft's industry-leading cloud and AI to customers through cutting-edge agentic products on the UiPath platform, like the UiPath Autopilot plug-in for Copilot and Teams and the bidirectional connector for Copilot Studio, which is growing in adoption and usage.

Lastly, in the U.S. public sector, with budgets now largely finalized, buying patterns are returning to a more normalized state and agencies are turning to automation and AI for mission-critical initiatives. The United States Navy is a great example with over 200 automations already deployed. The Navy expanded their IDP initiatives to streamline quarterly account reviews as part of its effort to achieve a clean audit opinion by 2028. As we look ahead, we have deep relationships across agencies, a strong team on the ground and we are well positioned.

Before I turn it over to Ashim, I'd like to invite you to UiPath FUSION, our flagship user conference taking place September 29 to October 2 in Las Vegas. FUSION brings together customers, partners and innovators from around the world, and we're excited to showcase how Agentic Automation is transforming enterprises and share more about our product vision and customer success. The future isn't about choosing between agents and automation. It's about combining them. Together, they are stronger and we are delivering best-in-class innovation in both categories to lead this next era of enterprise transformation for our customers and partners. Please reach out to our Investor Relations team for more information. With that, I'll turn the call over to Ashim.

Thank you, Daniel, and good afternoon, everyone. Before turning to the financials, I would like to reiterate the progress we've made on our operating priorities. First, on operational rigor and efficiency, we completed our restructuring and remain focused on driving productivity and disciplined execution. As a part of this, we've established a stronger cadence around predictability and key operating decisions, bringing product and field teams closer together to drive growth.

Second, on customer adoption, our teams, field organizations and partners are working hand-in-hand to deepen customer centricity and expand usage across our installed base. This includes strong momentum from partners leaning into our Agentic platform, such as Ashling Partners, calling it the natural evolution of RPA, and TQA underscoring how UiPath enabled their teams to help customers reimagine their operations and is driving record demand from both existing and new customers who want to lead their industries with game-changing automation strategies. These collaborations are critical to accelerating adoption at scale and moving customers from pilots to production deployments.

Lastly, while we continue to drive efficiencies across the business, we remain focused on investing in innovation as we look to capture the significant and expanding opportunity that automation and AI brings to the enterprise.

Turning to the quarter. Unless otherwise indicated, I will be discussing results on a non-GAAP basis and all growth rates are year-over-year. I also want to note that since we price and sell in local currency, fluctuations in FX rates impact results. Second quarter revenue grew to $362 million, an increase of 14%. Normalizing for the year-over-year FX tailwind of approximately $9 million, revenue grew 12%. ARR totaled $1.723 billion, an increase of 11%, driven by net new ARR of $31 million. Normalizing for the year-over-year FX tailwind of approximately $5 million, ARR grew 11%.

With the launch of our Agentic Automation platform, we continue to see customers moving to the cloud. We ended the quarter with more than $1.08 billion in cloud ARR, which includes both hybrid and SaaS, an increase of more than 25%. A great example is KLM Royal Dutch Airlines. After saving over 200,000 hours in 2024 with UiPath automation, they are migrating to the cloud, exploring Agentic Automation initiatives and implementing UiPath Test Cloud for SAP.

We ended the quarter with approximately 10,820 customers. As with prior quarters, the vast majority of customer attrition continues to be on the lower end. We continue to be successful in signing new enterprise logos that align with our strategy of targeting long-term customers with a propensity to invest, including new logos like Henry Schein, a Fortune 500 global health care solutions provider, who selected UiPath due to the breadth of our Agentic Automation platform capabilities and other notable logos in key sectors like the Watches of Switzerland Group, Community Financial Credit Union and the Vita Coco Company.

In addition to key customer adds in the quarter, our agentic capabilities and go-to-market improvements have resulted in deepening relationships in strategic cohorts. Customers with $100,000 or more in ARR increased to 2,432, while customers with $1 million or more in ARR increased to 320. Dollar-based gross retention remained best-in-class at 98%, and our dollar-based net retention rate remained at 108%, underscoring the durability of our customer base as they embrace our Agentic Automation solutions. Adjusting for foreign exchange, dollar-based net retention rate was 108%.

Remaining performance obligations increased to $1.209 billion, up 12%. Normalizing for the FX tailwind, which was an approximately $19 million tailwind, RPO grew 10%. Current RPO increased to $789 million, up 15%.

Turning to expenses. We delivered second quarter overall gross margins of 84% and software gross margin was 90%. Second quarter operating expenses were $243 million, a reduction of 6% from the prior year. Second quarter GAAP operating loss improved $83 million versus the prior year to $20 million and included $78 million of stock-based compensation expense. Our continued growth and disciplined expense management for cloud, operating expenses and stock-based compensation positions us well to achieve GAAP profitability in the near term.

Second quarter non-GAAP operating income was $62 million, representing a 17% margin, up more than 1,500 basis points year-over-year and driven by our continued focus on operational efficiencies. Second quarter non-GAAP adjusted free cash flow was $45 million. We ended the quarter with a healthy balance sheet of $1.5 billion in cash, cash equivalents and marketable securities and no debt. Our disciplined buyback activity reflects both confidence in our long-term opportunity and our ongoing commitment to return capital to shareholders. During the second quarter, we repurchased 8.3 million shares of our Class A common stock at an average price of $12.10.

Now turning to guidance. We are pleased with the team's execution in a variable macroeconomic environment, which is consistent with what we experienced over the last several quarters. And as Daniel mentioned, we are pleased with the progress of our public sector team. With this, we continue to maintain a prudent outlook and guide to what we see in front of us. Lastly, as a reminder, while we are encouraged by the early traction with our newly launched Agentic capabilities, adoption is still in its early phases, and as such, we don't expect a material top line contribution in fiscal 2026. Lastly, as a reminder, FX impact is recognized at the time of renewal and contract signing and is driven by European currencies and the yen.

Turning to the specifics of our guide. We are raising guidance for the progress we've made on our operating priorities and the incremental FX tailwind since we provided guidance on our first quarter earnings call. For the third fiscal quarter 2026, we expect revenue in the range of $390 million to $395 million. This range reflects an approximately $2 million tailwind, driven by FX rate movements since we provided guidance on our first quarter earnings call. ARR in the range of $1.771 billion to $1.776 billion. This range reflects an approximately $2 million tailwind driven by FX rate movements since we provided guidance on our first quarter earnings call.

Non-GAAP operating income of approximately $70 million. And we expect third quarter basic share count to be approximately 532 million shares. For the full fiscal year 2026, we expect revenue in the range of $1.571 billion to $1.576 billion. This range reflects an approximately $7 million tailwind driven by FX rate movements since we provided guidance on our first quarter earnings call. ARR in the range of $1.834 billion to $1.839 billion. This range reflects an approximately $7 million tailwind driven by FX rate movements since we provided guidance on our first quarter earnings call. Non-GAAP operating income of approximately $340 million.

And finally, we continue to expect fiscal year 2026 non-GAAP adjusted free cash flow of approximately $370 million and non-GAAP gross margin to be approximately 85%. Thank you for joining us today and we look forward to speaking with many of you during the quarter. With that, I will now turn the call over to the operator. Operator, please poll for questions.

[Operator Instructions] And our first question comes from Bryan Bergin with TD Cowen.

Wanted to ask as it relates to the client demand progression on your agentic solutions. Can you just talk about how the pacing is progressing in those accounts where they have moved from kind of POC and pilots to production? I don't know if I missed it but the mix of the clients that are developing agents here. And as you look at these accounts, I think you mentioned you're seeing agentic solutions increase deal sizes at a faster pace than the traditional RPA work in the past. Any context you could provide around that?

Yes. Bryan, let me start with the product overall, the Agentic product, and I will let Ashim comment more on the increased deal sizes. We launched our Agentic Orchestration and Agent Builder in May this year. And the progress that we are seeing is very encouraging. Having like 450 customers actively working with our technology, building agents with the intention to deploy in production is really a meaningful -- it has a meaningful impact.

I would say another good momentum for us is that most of those deals actually uncover even more opportunities for automation, scoring really on our strength, which is combining the orchestration, RPA plus API and the agent. And so I would say that the results so far are very encouraging. We've seen some significant deals that were driven by -- again, by the combination between agentic and automation.

Yes. And then, Bryan, I think when you look at what Daniel talked about, because people see the value in the Agentic platform, that is actually new product that we are monetizing, so it naturally increases the deal sizes for there. But what's interesting is the fact that it reinforces often the need for deterministic automation with our RPA and AI capabilities. So it has a twofold effect as they're going through their process transformations in our POCs as well as our pilots. That's really what's affecting our deal sizes.

Okay. All right, understood. Just a follow-up on DBNR, stable here 1Q to 2Q. Do you expect that to sustain as you go through the second half?

When you look at DBNR implied in our guidance, obviously, you can calculate and see what the normal ratio is. I would say we're stabilizing. That's what we feel. I won't make a commentary at this point to out quarters too far. But we feel, as we talked about in our guidance, we're assuming -- we continue to have a prudent outlook on the macroeconomic environment. We do see the government returning back to kind of normal buying behavior, which in my mind is positive. But with the macroeconomic condition, et cetera, we're still embedding the guidance as we did in the script.

Your next question comes from Jake Roberge with William Blair.

Great to hear about the nice start with some of your new Agentic solutions. There's obviously a lot of people talking about Agent Orchestration. When you're talking with customers about Maestro, what's the key pitch that gets you in the door? And who are you seeing most in those types of deals?

So our key pitch is being really agnostic. I think most of the customers right now are concerted into being completely on 1 side of a business platform. Because if you look at agentic and orchestration, most of the processes actually spend multiple business systems. So you will have to make a choice if you choose an orchestration or an agentic solution that is provided that one of the business systems.

How you are going to move data between business system? Where you are going to ultimately store your data? And many of our customers are really reluctant to choose for orchestration, 1 major business system, and they prefer an agnostic approach. Also, our Maestro is very tightly integrated with our automation platform. So it makes it extremely easy to combine agents and people-in-the-loop and actions that are provided by our robots.

And I think our platform is really the breadth of our platform in the terms of the offering of Orchestration, Automation and Agentic is one of the best in the market today, so this together makes it a very compelling offering for our customers. And we are seeing really interesting wins against major orchestration platform providers.

Okay, that's helpful. And then now that we're a year or so into some of the go-to-market changes that you all have made, how would you assess kind of the overall health of the go-to-market motion? I know things are always evolving, but do you feel like things are largely stable at this point in that motion?

Yes, I would say stable is a good word. I think we made really solid progress on making our entire go-to-market much closer to the customer. As we said, like in the past year, one of our major strategy was to be much more customer-centric and to break the silos. And I think our go-to-market, it's also working much closer with the product right now. And this is important in an era of where the iterations are largely driven by customer interactions. So we are pleased of how our go-to-market is structured and is functioning right now.

And your next question comes from Michael Turrin with Wells Fargo.

This is Austin Williams on for Michael. I just wanted to double-click on the U.S. federal business. And just any other color that you can add on how that business performed and just how you're navigating the uncertainty there.

We don't break out our segments, as you know. But I would say our public sector had just a really good quarter in terms of the momentum of selling with Agentic. We are encouraged by the progress in the public sector. The budget finalization, we see signs of stabilization there. The teams are really executing well so we feel we're well positioned for the second half. We just had a recent win with the Veterans Affairs and the Coast Guard. So we're really happy with both the wins in the quarter and the feedback we're getting from customers, including large customers like the Navy and the IRS as well.

Got it, helpful. And then just 1 follow-up on the big sequential step-up in subscription revenue this quarter. Was there anything specific to call out like related to that line? Is there anything onetime in nature that impacted that?

Yes. Going back to last quarter, we had kind of a leap year impact that was there that just kind of caught up in that, and so that was the kind of the movement that you saw. And now this quarter, it's back to stable.

And your next question comes from Raimo Lenschow with Barclays.

First, a quick number question for Ashim and then 1 for Daniel. The -- on the number side, if I look at your ARR guidance for the full year and FX and total revenue, given FX, so thank you for that, that's really helpful. You raised by a touch more than you beat. Can you speak to what's driving the confidence?

Yes. I think we're continuing to maintain a consistent philosophy. So when you look at the field sentiment, we look at what's in front of us in terms of the pipeline and just the momentum we are seeing with both the improvements that Daniel talked about on the go-to-market side as well as Agentic, Raimo, and that we wanted to reflect and really put that into the numbers for everybody to see.

Yes. Okay, perfect. That's really encouraging. And then, Daniel, one for you is like if you think about talking with customers at the moment, is there still kind of the market is still twofold -- like in 2 camps that you have some projects where people really understand RPA and process automation, et cetera, want to do that? Or is it all now like modeled together with agents, AI, et cetera? So in other words, is there like a core part of the business that can still continue to be sized? And then others is just kind of newer and hence, you have like you need to think about that? Or how is the market kind of behaving at the moment when you talk with customers?

Raimo, I think you can see the entire spectrum among our customers. There are -- in all fairness, there are customers that believe that AI agents will do everything. So they think very far-fetched in terms of swarm of agents that talk together. In the same time, I think I would say that majority of our customers are starting to realize that their automation programs are actually quite important to power their agentic initiatives.

I think it's becoming more clear in the market that the combination of orchestration, automation and agentic is really essential into delivering basically AI into predictable manner into enterprises. And as I said before, I think all the agentic exercises that we are seeing happening with our customers uncover more and more automation opportunities. They come -- typically, I can say they can come up with like 100 ideas that they call it agentic. And then when we look deeply, we discussed that 50 of them are better suited for automation.

Your next question comes from Matthew Hedberg with RBC Capital Markets.

This is Mike Richards on for Matt. I was kind of just wondering, now that we're more than a quarter in here, what the reception has been to the pricing of the Agentic portfolio? What have customers been saying? What are some of the learnings that you guys have had? Have you evolved that pricing as more customers have adopted the solution?

Yes. I think we are monetizing Agentic through a consumption-based model, which basically align very well with customer interest. I think one of the issues that is not UiPath-specific is the predictability of the pricing. I think everyone is trying to understand better how can you make a business case. We are working with quite a few of our customers to understand better this aspect of the business. But overall, I think the reaction to our Agentic pricing is positive and well understood.

Got it. And then just thinking through the go-to-market motion, you talked about bringing on specialized sellers. Is that specific for the Agentic solutions? Any incentives for the go-to-market motion to go after the Agentic opportunity? Or are you guys still sort of evolving that motion?

I would not say that we bring specialty sellers for our Agentic motion. We -- Agentic, our idea about agentic go-to-market is twofold. One is the horizontal agentic as part of our automation platform, basically completing our automation platform. So everyone in our go-to-market is equipped to deliver on this horizontal Agentic Orchestration and Agent Builder and the combination between Orchestration, Agentic and humans-in-the-loop.

At the same time, we have initiatives around vertical agents. Like we mentioned in the past, Peak is a great example where we have specialty sellers that deliver -- that are tasked with a dedicated task to basically inform customers about what that type of vertical solutions. And this motion is going to continue.

And your next question comes from Sanjit Singh with Morgan Stanley.

Ashim, I wanted to start with you. When I look at the guidance and I take a peek out to what that implies for Q4, if I sort of take the high end, it kind of implies a return to positive net new ARR growth, which I think for a lot of investors is kind of what we've been waiting for in terms of at least in terms of inflection from UiPath. I know you don't want to look out beyond the next quarter. But if you can maybe talk to the stability of the go-to-market organization and maybe the leadership that you've put in place. Are you confident enough that you've seen enough execution on the ground for multiple quarters in a row where that starts to become more likely in terms of seeing net start to inflect some flip from negative to positive?

Yes. I think -- let me answer it just also by looking in the context of third quarter. One is, I do want to acknowledge, like foreign exchange had some of that lift in there, which we acknowledged in our guidance, Sanjit, so I want to be -- make sure that we are transparent on that. But even when you look at third quarter versus second quarter, we're narrowing the year-over-year gap both operationally and then, obviously, some of the macroeconomic factors like foreign exchange helps that for us. So that's kind of 1 piece.

So you see the progression through the year, which frankly is kind of what we talked about at the beginning of the year, right, in terms of the federal government stabilizing, being really disrupted in the first half, stabilizing here in 3Q and 4Q as well as just the changes in momentum that we've seen in the go-to-market organization.

Specifically, am I seeing those signals within the go-to-market organization? The answer is yes. I think the commercial activity that we're seeing in pilots and POCs, it does 2 things. It reinforces -- it gives us opportunities to upsell, whether that's now or in the future, but it reinforces the importance of our platform and the architecture and the transformation journeys of a lot of our customers. And as Daniel said, we're really pleased with the execution that we're seeing in the ground.

Field, our field is empowered. They are customer-centric. We've reduced that bureaucracy. And we've really also closed the feedback loop between the field, product and management where we're able to react and really integrate with customers better. So all of that, combined with a consistent strategy of evaluating our pipeline, our data, et cetera, I feel very comfortable in terms of the guidance that we provided.

That's great color, Ashim. Daniel, for you, I had the opportunity to talk to some of the third-party industry analysts who we all sort of know, and they tell me 2 things. The first thing is that the real estate that UiPath is trying to occupy is where there's most value to be had and where there's value to be created. And the second thing they tell me that there's a lot of players trying to occupy some of that positioning where it comes not just being Agent Builder, but also being sort of the management layer, the orchestration layer.

And then the third thing they tell me is that this is really difficult to pull off. And so when I think about some of these 450 customers that are taking the leap with you guys on the Agentic, are there like initial processes that you are targeting, whether it's quote to cash or something around inventory or supply chain in terms of just trying to build that flywheel, build that trust, get those early successes? Do you have the sales and go-to-market organization sort of prioritizing specific processes that cut through various systems of records?

Yes, that's a great question. I think that we are both strategic and opportunistic in our approach. Indeed, we prioritize a few processes in health care, revenue cycle management, in particular, financial services, I would say, procure to pay, order to cash are areas of much interest to us. In claims management as well, we are seeing quite good movement.

But we are interested at this point to learn a lot about the blueprints of large-scale agentic deployment. I agree with you that the market is very complex at this point. And it's a great value to be extracted from this market. I think that we have some unique advantage because we are incumbent in this type of markets for a long time. As a reminder, we are in the business of automating manual processes since our inception. And most of the agentic initiatives are actually completing what we have started.

You just go beyond what RPA and automation was capable and just automate the steps of the process that couldn't automate before. But as an incumbent, having already robots that work as an action, it's a natural extension for us to add orchestration to provide better end-to-end process automation and agents that interact with our robots in order to get access to enterprise systems.

I think it's a really palpable advantage that we are seeing. So this is why with the product only a few months into production, we are seeing a lot of interest from our customers.

[Operator Instructions] Your next question comes from Brad Sills with Bank of America.

I wanted to ask about the partnership that you announced here with Deloitte. Is this indicative of just a greater focus on the SI channel as you embark on this agentic journey with company? Or is this more kind of indicative of a 3-way partnership with SAP and Deloitte? So just curious, really 2 parts to that question. One, what's the state of the SI channel? Is this indicative of a greater focus there as you get into agentic? And then the second part would be, what does this mean for your partnership with SAP?

I'd say this is indicative of both. Our 3-part partnership between us, SAP and Deloitte is bearing fruit and progressing really well. And at the same time, we are traditionally focused on extending our relationship with GSI. I think the major GSIs that we are talking to are basically in the process of understanding the market, making the bets on their platforms of choice for orchestration and agentic. And I'm happy to tell you that we are in many of these discussions, and we are seen as one of the major platforms they want to bet on.

Your next question comes from Scott Berg with Needham & Company.

This is Ian Black on for Scott Berg. Great quarter. Does the new Agentic portfolio enable you to implement RPA in additional workflows? Or is the focus on adding agentic capabilities to existing workflows right now?

We can extend our RPA and broader automation capabilities with Agentic. And as I said before, it's a renewed interest on our customers to do the exercise on identifying what processes are better suited for the overall initiative of bringing agentic and automation together. And we are seeing these initiatives surfacing more opportunities that we were seeing in the past years.

And your next question comes from Keith Bachman with BMO Capital Markets.

Ashim, I'll direct this to you. It looks like if I look at your ARR and the DBNR, it looks like you're still roughly 70% of your year-over-year growth is driven by existing customers and, call it, 30% for round numbers, from new logos. But your existing customers' growth is still down year-over-year pretty meaningfully. What causes that to flatten out as we look out over the next couple of quarters that your growth rate with existing customers improves? And the related -- my second question, a related question. You've clearly said on the call tonight that the agentic capabilities won't contribute to ARR this year but can it contribute next year in FY '27?

Yes. So let me take them 1 by 1. I'll actually just do the second 1 first. I want to be clear, like we said meaningfully contribute this year. We do see ARR agentic monetization happening and we are really pleased by that. And like I said when I answered a previous question, I think it also solidifies our position even in the renewal process in terms of the value of our form.

The second piece is, obviously, we believe with the momentum we have here, we see agentic to continue to contribute more and more. But I won't give specific guidance in terms of when and where and next year at this time. The question you asked about customer growth, for me, I'm assuming you're talking about the customer count. Remember that...

No. Sorry, the ARR from existing customers versus new logos, so really focusing on ARR from existing customers has been down pretty meaningfully over the last 3, 4 quarters. And I'm just wondering what caused that or when does that turn around.

Yes. I think we talked about this in terms of both the combination of the macroeconomic environment and government for the first half. And as you look at kind of our third and fourth quarter here, you can see that starting to come back from our vantage point, which is embedded within our guidance. And that's what we commented back all the way in March and it's played out very similarly.

Our existing customer base, again, if you look at the core metrics of customers greater than $100,000 and customers greater than $1 million, you actually see continued momentum in those cohorts of customers. And for us, that is very encouraging just as we navigate the macro environment and the stabilization of the U.S. public sector.

Your next question comes from Terry Tillman with Truist Securities.

This is Dominique Manansala on for Terry. So a variable macro environment just mentioned again this quarter. Just curious as to what patterns across geographies or verticals really persisted here in the second quarter. And are there any particular industries or regions where you're seeing more tailwind or areas of relative strength that you could be into? And maybe if you could just double-click on how your assumptions about the macro are really informing your prudent outlook for the rest of the year.

Yes. So when you look at pockets of strength, actually, the financial sector for us in the U.S. in health care, we really see that as pockets of strength right now in terms of customer demand and buying. The public sector, we're starting to see that momentum, as I talked about, both in the script and as in the commentary here. And the energy we're seeing from the public sector has been really encouraging.

That's been consistent across geos. And within Europe, I think some of our manufacturing customers also exhibit strength. When we talk about variable, it really is variable. I think it looks month-to-month, you kind of hear different things as companies are responding to different areas, whether that be tariffs or interest rates or geopolitical items. So variable really just -- it moves across the quarter and the year in a variable way. And I think that is felt not just by us but just if you open the news, you kind of see that in total.

And your next question comes from Devin Au with KeyBanc Capital Markets.

This is Devin on for Jason Celino today. Just 1 quick clarification question on the guidance, Ashim. Encouraging to hear that U.S. fed business has normalized. But does the ARR guidance raise still bake in prudence in that business or are you expecting incremental contributions from that segment in the second half, just given the more stabilized operating environment?

Sorry, can you repeat which segment when you said incremental?

Yes. Yes, the U.S. public sector, are you expecting, I guess, incremental contributions from that in the second half just because of the more stabilized environment?

Yes. So we see that normalizing, as Daniel mentioned, in terms of a more predictable buying behavior. I want to emphasize, I guide to what's in front of us. So as we comment, we do continue to bake in prudence. But at the same time, we're -- what's in front of us is a good amount of energy, and we obviously evaluate that with the tangible pipeline we're seeing from the U.S. -- the federal government at this time. So the answer is yes to both sides. We are baking in prudence, but at the same time, we do see more contribution from the U.S. public sector.

Thank you. And there are no further questions at this time. I'll hand it back to management for closing remarks.

Thank you, everybody, for the questions. And as usual, we are looking forward to meeting as many of you during the quarter. Thank you so much.

This concludes today's call. All parties may disconnect. Have a good day.