Pegasystems Stock

Great. Well, thanks, everybody, for joining us this afternoon. I'm Steve Enders, part of the software research team here at Citi. I want to welcome everyone to day 1 of the Citi Global TMT Conference. With us for this session, we have Ken Stillwell from Pega. Ken, I want to thank you so much for being here.

Thanks, Steve. Appreciate the invite again.

Yes, of course. Maybe just to start off, I'm sure people know who Pega is, but maybe we can maybe introduce Pega to investors who maybe are a little bit newer to the story. And maybe what are some of the key problems that Pega is helping customers solve today?

Sure. So Pega has been helping enterprise clients for gosh, 4 or 5 decades. And the types of solutions or some examples of the things that we do to help our clients, is typically referred to as enterprise workflow and specifically workflow where it's deterministic, which is things that either have a certain process that needs to be regulated or there's a requirement for a certain way that something gets done or internally, clients may put those types of controls on their own internal processes. So for example, things around ERP as one example, that might not be legally regulated, but you may have certain internal controls or processes that you need to have repeatable that need to be very predictable that can be proven after the fact that you followed a certain series of steps and stages to be able to execute that work.

And we've always had AI. Pega was -- we bought -- we actually purchased a company in 2010 called Chordiant, which did statistical AI. We integrated that in with our solutions as a Customer Decision Hub of sorts with many people that know Pega have heard that term before, where we helped drive certain statistical probabilistic-based decisions or outcomes. With generative AI, which we've introduced into the platform a few years ago, we're now leveraging Gen AI, both in the design and the run time capabilities, design being heavily when you're conceptualizing and modernizing a specific application and run time where you want to really try to take unnecessary human steps out of that workflow or just otherwise make it be more automated, whether that be kind of the interaction with the customer or in just terms of the back-office transaction flow.

Okay. All right. No, that's helpful. I do want to talk about AI and what you're doing there. I guess before we get into that, I do want to talk a little bit about just the demand environment and the performance that we've seen so far this year. I think for the quarter, we were at 8% constant currency growth on ACV. You were originally talking about 15% growth for the year. How does that maybe performance that you've seen so far this year change your outlook? Or how are you thinking about the ACV environment in the back part of the year?

Sure. So one thing to start with is we only guide, for those that are not aware of this, we guide at the beginning of the year, and we do not reguide. We don't guide quarterly. We guide for a full year. We don't reguide. In that environment, what we've tried to do is to help provide information that would help connect some of the views that we had around where the full year looked like it would land. Many of our sell-side analysts, including yourself, took that information and then kind of reprojected what they thought the year would look like. And I think you guys have done a good job kind of collating around where that was going to be.

And so one, I think -- the reason why we're not going to get to 15% or I would say it's unlikely we're going to get to 15% is we already had a lot of business in the back half of the year. And for us, we don't have a lot of pipeline build and close within a 30-, 60- or 90-day period. Our campaigns tend to progress over a quarter or 2 or 3. So for us to close something in, say, Q3, it likely would have had to been in the pipeline when the quarter started. So we just have like a runway opportunity. The second point is that the ramp that we had in the back half of the year over what last year was a pretty significant ramp already in terms of the growth in net new ACV year-over-year.

We were kind of already targeting something like in the 30s in terms of the percentage increase year-over-year. So we had a pretty aggressive ramp. We don't have a very short pipe building to conversion process. And what we saw in the first half of the year, I think probably we didn't do as good of a job in the back half of 2025 and maybe even into Q1 to try to think about leveling the year more, pulling things forward that we could, building back up pipeline, bookings, et cetera. So for the full year, I think the Street has us somewhere around 10% growth or something. And if I had heard what we said on our call, I probably would have landed somewhere around that number.

Okay. All right. That's good to hear. Maybe we can talk a little bit about the levers that you feel like you can pull into the back part of the year. What do you feel like you can do differently? Or what changes have you maybe kind of put in place to improve the execution and improve the sales performance in the back part?

So a pretty big shift for us, we intended to embark on at the beginning of the year and our sales kickoff. And just to be honest with you, I don't think we fully internalized or pushed hard enough on this change. And the change was, historically, Pega has been more of an account manager type selling motion, which is we have existing organizations. Those organizations spend with us. They look -- we look at expansion opportunities. We don't have a lot of new logo chasing. We don't have a lot of growth even outside some of the buying centers that we've typically done business with. That has not been something that was, I would say, reflexive at Pega.

When we did our sales kickoff in January, we instrumented a number of activity measures that we wanted to measure, things like new contacts, new meetings, more -- kind of think about them as more hunter or outbound type activities. And we -- one could probably argue that we should have probably always had some level of that motion, but we really wanted to intentionally do that in 2026. A few reasons. One, we were starting to see a little bit more interest in new logos. Two, we knew with AI that with AI, there's -- everybody is telling a story with our clients around AI and how they're going to solve all their problems. We had to make sure that we were kind of instinctively going out and getting that message in front of clients.

And it turned out that in Q2, there was a lot of confusion with clients around AI. What's the value prop? Every single week, somebody would come out with some new industry that AI was going to put out of business, right? So there was a lot of talk, a lot of hype, a lot of anxiety, a lot of confusion. And at that exact time, that's when we needed to execute on our hunter mentality. And I just don't -- I don't think we did a good job of executing that. So we really hit hard toward the end of June which quite frankly, was probably too late to actually make a difference in Q2 to make sure that, listen, even if we're talking to the same buyers that we know, even if we're talking to our contacts that we may have known for 25 years, they're getting attacked by all different vendors, whether they're AI natives, whether they're competitors or service providers talking about the promise of what they can do for our clients.

And we need to make sure that we're with them and helping educate them and arming them to understand how we can help them and how our unique value proposition. So I think there's -- that's -- I would call that in the bucket of execution, which is managing activity, managing contacts. And I think we've now -- we instrumented that. We've actually now put that into our sales forecast calls because we think this is not something that's going to be a 1 or 2 quarter. We believe this is going to be critical to make sure that you're constantly building like the motion of getting in front of clients and talking about the value prop.

Now interestingly enough, we have Blueprint that we released a couple of years ago that's been a great tool for actually enabling that conversation with our clients. So the key is Blueprint to activity, getting in front of buyers, whether those be buyers we know, buyers that we don't, new logos to really drive kind of the awareness of how Pega can help. And then the next -- naturally tracking pipeline, build pipeline progression. But the most important first point is really just getting -- not taking anything for granted around our clients' digital transformation journeys, even if we have very, very deep relationships with them.

Okay. And I guess since you started putting this emphasis in place and trying to change the mentality, what have you seen so far from the sales force? Like what are the metrics you're tracking there? How are those kind of trending? And -- just how is that maybe looking through Q3 so far?

It's interesting. We've had -- just as a small start to that -- to answer that question. We've seen some people in our sales team that have said, obviously, thank you for reinforcing that. Of course, we do. We've had others that have kind of paused and said, it's -- are you sure you want me doing that? Like I have one big client, I have 2 buyers. And so what we've realized is that, that was good feedback to understand because we're not saying that every single of our people on our team are going to do hunting and not account. There is a balance of how we distribute the team. So I think we learned a lot.

What I took from that, though, is the people heard -- they were processing it. We had those -- so that was great. Our sales managers, I think, also keeping in mind that this is not just about an account executive. This is about the whole management chain, right? If you have an account exec that's really good at hunting, but a management chain that isn't, that won't work. If you have a management chain that's hunters, but an account exec that isn't -- that won't work. You have to really have -- everybody has to be kind of moving to the same beat. And so I think from that standpoint, like that was a really great like anchoring and awareness to see how much our teams internalize the importance of that, all the way up to our Chief Revenue Officer.

The second thing I've noticed is that the amount of engagement and insight that we have early in the sales campaign, you can see the direct connection where that activity is higher. So I think the -- and that's exactly what we wanted, right? We didn't expect this to be like a silver bullet that when you did something, pipeline would just immediately grow. This is a part of a process of being -- of really being deeply engaged with buyers and influencers. And Pega is the kind of solution that does have -- you do at times have a buying by committee dynamic of it, right? You have a business owner, you have someone in the CIO's office, you have other standard, you have a system integrator, you may have executive sponsors. There's a lot of influencers in that. So I think the thing we've just seen is 2 things. One is the internalizing why we're trying to do it, I think, was -- is a big change, but I think has been internalized by our field teams. And the second is just the activity of actually getting those at bats, right? And by the way, we just released Infinity 26, I think it was GA about 30 days ago. And what better thing to do to use that as a way to get in front of our clients and get them exposed to the AI native solution that we just released.

I do want to touch on that. Before we go into that, though, maybe just last question, tying a bow on the sales execution side of it. It did seem like there had been a big focus on net new logos and trying to drive that and maybe a little bit of shift away from the existing installed base, just with the pipeline build and how long it takes for some of those deals to progress and get through the finish line, just how does that maybe change how you think about the mix of existing versus net new through the rest of the year and maybe into next year and just the pipeline dynamics between those 2 different customer bases?

So it's a great question about where do we think the opportunity is. And I think that I would maybe draw a bigger circle around this and say it's not just new logos, it's all new workflows. It's anything that's new to what we've already done. That could be a new workflow within an existing buyer that you might have had a 20-year relationship with. It could also mean a buyer that's 2 doors down in a different business unit at that same client. And then, of course, net new logos, someone that doesn't know Pega at all. So I think that I would say all of those are important to get that outbound motion. We would expect that what would come from that over time is that new logos would start to creep up a little in terms of the total amount of the impact on growth. although that may be small enough and happen over a period of time that it may not be that noticeable in terms of the ratios. So we're going to be very dependent for our growth to accelerate and continue to accelerate. We're going to have to sell to new use cases that we have not sold to. And that involves in a company like Citi, and it also involves to financial institution that we've never done business with.

Okay. I do want to make this interactive. So if there's questions in the room, we'll make sure to get to those. I do want to ask a little bit about the AI strategy first, and then we'll make sure to get to those. So just on the new Infinity release, and I think that it includes Infinity Studio, what's different that, that enables for you in both from a customer standpoint, like what they're able to do that they weren't able to do before? And maybe how does that kind of augment how you think about the ability to win or right to win within some of these accounts from that?

So Blueprint -- I'll talk about Blueprint and then Infinity Studio.

Sure.

So Blueprint, the purpose of Blueprint was to get into the design and the selling activity where we could help our clients visualize and ideate around ways that they could visualize how to solve kind of a reimagine or a transformation of a legacy application. If we didn't have Blueprint, the way that was done was through a whiteboarding exercise typically. So it involved a lot of, what I would say, custom and unique interactions that were not leading to necessarily as fast as we would like them to. So Blueprint was around a very structured, simple set of drop-downs in a few fields to be able to sketch out what that design experience, that upfront design experience.

But without Infinity Studio, without Infinity 26, that left clients with this concept. Here's a concept of something. I could kind of see what it looks like. How do I make that real? It wasn't as easy for them to make it real. They would go into a development experience that was like the development experience over the last 25 years. So that's where we had to really evolve the development experience, which is Infinity Studio. So think of Blueprint as I'm going to ideate, I'm going to build kind of almost like the wire frame, so to speak, of what this is going to look like. I'm going to load that Blueprint into Infinity 26. And then in Infinity 26, I'm going to have an AI-assisted actually development where I can say, okay, I'm trying to build this customer service app. Here's the vertical that I'm in. Here's what I've done so far. Tell me what I'm missing.

And it will go through a series of things. Have you thought about your integrations, here are some likely integrations. Do you need an MCP connection to be able to have people use AI tools to be able to interact with the design environment for your application, like you could go through, and it would help you -- it would really help you not get lost, right? It would help you not be lost in that journey. And that's really critical because our clients don't want to have to be super users to be able to go through that design experience. And historically, companies like Pega and Pega as well really had levels of higher certification that you were required to get to really be productive in the development environment. And that's -- so we wanted to change those 2 dimensions. One is at design time kind of in that upfront Blueprint and also at design time when you're finishing or building out MLP, one of the minimum lovable product and evolving it, innovating it. We wanted to make sure that, that experience didn't require you to have to have a PhD in Pega.

Okay. It seems like what you've been trying to do is accelerate the development life cycle and make it faster to get Pega to production within clients. I guess, where kind of are we on that journey? And when I think of like Pega historically, it was very kind of maybe cumbersome to maybe actually get implemented and took a year-long process. Just how much faster is it now? And what more can you do to make it even easier to get those opportunities over the finish line for customers?

And faster means not only time to value, but also the cost to value, just to add on to what you're saying. So I want to give you an example. We ran through an A/B test, so to speak, of like what would it be like if we did things the way that we've historically done them? And what would we do if we actually took our engineering teams, keep in mind, these are engineering teams and basically said, let's build a demo app. Let's see how long it takes from Blueprint to something that was production ready.

Now admittedly, I'm not saying this is production ready, like we had all the integrations tied out, but it was far enough along in terms of that. And we did that with our engineering team and our own Pega teams internally to be able to -- and in using the traditional way that we would have done it, it would have taken almost 2,000 hours to build an application. Our engineering teams were able to build that application out in 45 hours. So that is an example of how much faster it can be. Now that's our engineering teams. We need to make that so that our clients, so the people that are not. But what you can see is the innate capability that you actually have and how fast that can be. So this is a little bit of a technology and change management challenge. It's not just about technology. But I do think what that highlights is like you can get to a production-ready application in time is actually -- the amount of time the technology should not be the hurdle. It's going to be more the adoption and the change, and that's what we need to focus on.

Okay. That's an interesting point because there's all the talk about forward deployed engineers, all the talk about leveraging resources from one area and moving them to another. Just how do you think about, I guess, using the services as kind of like a go-to-market function versus maybe utilizing that for cost savings or reassigning those folks in other kind of areas?

So I think that the forward deployed engineer type model, whatever you want to call it, I know that we don't refer to it exactly that way, but that model is -- I would characterize it as a necessary evil right now for a lot of companies, right? Because you don't -- things are not inherently as easy as you'd like it to be. I mean for those of you that would go into Claude or OpenAI, and it's -- there's things that are more -- there's things that are easier and there's things that like you have to kind of just like grind your way through trying to figure out you probably have done it 10 different ways that are wrong. And then finally, you figure out like, oh, I realize. That's an enablement challenge.

So I think with a lot of the FTE models, it's like let's just take all that away from a client and let's just do it for you. Okay? That is certainly helpful to demonstrate value and get there faster. That is not a scalable model because those FTEs need to stay on with those clients ongoing, and that's not like you can't monetize that. A client like Citi is not going to want to continue to pay for that, and there's not enough margin to build in to be able to handle that. So I think that is a helpful but insufficient way to solve this. You've got to use some of those professional services and sales engineers to get the momentum started and you've got to have a product that intuitively is -- people understand how to actually build and evolve their workflows.

And I think that whole AI assistant, that agent capability that will sit natively inside something like Infinity 26 for Pega is a critical component of how you do that because it has the whole knowledge base that no other agent has that knowledge base because it knows natively everything that Pega does, where anything -- any other agent would not know that. And quite frankly, every proprietary platform has that advantage of being able to have content that is not publicly available. So I think the key for me is we've got to basically use the kind of the enablers now to be able to get that moving faster. But ultimately, you want these platforms to be built not having to depend on lots of specialists. So I think it's -- right now, I'd call it a necessary evil.

Okay. Maybe this is a good time to ask about the value capture of AI. How you think about a platform like Pega being able to monetize the functionality, gaining the value from AI versus maybe passing on the benefit to the customers or I guess, even other layers of the stack accruing that value like the model layer. Just how do you kind of think about what that looks like?

I think that there are -- there's 3 layers of value right, that I think will synchronize as we become more mature. And those 3 layers are -- there's the layer to the actual AI model providers, what value are they getting? Like how much do they monetize. There's the layer of the Pega level, right, the application, the platform and how much do we monetize? And then there's the client layer, like how much does the client actually monetize us. And I think in order for that -- in order for this to be a sustainable model, there's going to have to be a value that's attributed appropriately to each of those 3 levels.

There are things that you can do like at the AI model level, you're going to see model selection, harnessing and managing the tokens. You're going to see that become -- we're doing that right now on behalf of our clients, but you're going to see that be table stakes, right? People are not -- they're going to want to know what it costs to run a model. They're going to want to know with 100% certainty that you always pick the right model. And that's where it becomes challenging when there's so many different models. So you almost need an intermediary in there to be able to manage the models and how do you manage that at companies at large banks. I know that Citi cares a lot about creating this AI gateway where you're basically -- you are managing all of that by actually sending all transactions through your own gateway.

I think that will become increasingly popular. And then I think that there's going to be this value attributing between the client and the platform provider, someone like Pega and someone like the client, where we're going to have to give for what may have been the same cost of ownership in the past, a lower cost of ownership. The way that, that -- the biggest place that's going to show its face, though, is going to be the time to go live, the cost to manage the operating system, the cost to upgrade, like we want to eliminate as much as we can those costs and make these systems be able to build in an AI native way that are upgradable, sustainable that you can take evolution and innovation and evolution and manage security vulnerability risk that threats that come out. We really want to try to get that into a continuous flow as opposed to something that has these episodic updates that take tens of millions of dollars to go through.

I mean you've also focused on driving predictable costs. Like that was a big point of focus, it feels like at the Investor Day was -- in your conference, we're just emphasizing making it predictable for the clients, just how critical is that for the customers and the types of use cases that you're focusing on? And I guess what does that also mean for balancing frontier model usage versus the open weight models or the open source models? Just how do you kind of think about what that means as well?

Well, I think that -- listen, it doesn't -- we all expected that the cost of executing AI was going to come down precipitously. We all kind of said, Oh, well, it's going to be -- the efficiencies are going to play out. The reality is they haven't. Why haven't they? They have, if you look at static use. But what's happened is the models have become more powerful. You have availability and capacity challenges, a lot of investment. So what's happened is for the same use case that you may have done in OpenAI 1.5 and now you're in Sonnet 4.7, the cost is exponential in terms of the -- like the amount of actual token use. You've got input, output tokens, reasoning tokens, right? Unestimable. You can't actually tell what it's going to take.

The problem is not -- that is a natural evolution, something that's not as powerful or something that's more powerful. The problem is you don't want to use the same model for everything. So I think there is a way that you can still welcome and embrace the most powerful models for the most critical things that you need to do. But for many of the AI use cases, you don't need the most powerful model, right? If you're trying to do an automated call wrap up in a -- for a customer service representative based on listening to a call and looking at all the notes and the transactions and put a 2 paragraph summary together, you don't need the most powerful frontier model to do that, right?

But if you're trying to measure the arc of a missile that actually gets launched in a combat sequence to be able to insert -- okay, you're probably going to want the most performance. So that's one piece. The other piece is availability. If you look at AWS Bedrock, for example, they've got a number of models in there that they're constantly shifting usage depending on availability, performance, accessibility. They're actually -- so that is the world we're moving into, which is right model, right time, supply/demand pressuring the whole system to get efficient on that. And I think that, that's -- that was not like -- I don't think any of us would have realized that like we're conditioned that when Microsoft comes out with the 2006 version, there is no place for Windows 97, right?

Like that's like -- but in this world, there is a place, right, because there's a different cost frontier for that -- for those different use cases. So I think that's like a real aha moment for us. And we then use that behind the scenes to help manage token costs on Pega side. And also, I think clients are recognizing that a lot, which is why each large client is picking their own models and negotiating their own deals and trying to manage the token usage to be the most efficient.

Okay. Makes sense. Any questions in the audience? Okay. Maybe I do want to ask maybe what this means for, I guess, the cost structure moving forward? Like if you're absorbing the cost here, how much of it is you absorbing it, taking a bit of a margin hit versus being able to route efficiently to the right model for the right use case.

So we give predictability to our clients. Predictability for AI does not mean free AI. It means predictable AI. So we actually tell you for a transaction, you will have a fixed cost on what that transaction will be. And it's our job, Pega, to manage the token usage on the back end. And so what we are doing is we're taking away that ownership or that risk. We know what the -- we know best. There's nobody that knows best on which model to use at each step in the workflow at run time than Pega does. So we actually understand like what are you doing? Why would you use which model? How do we want to manage routing to the right accessibility?

So we take that on. Is there a risk that what we charge as an uplift for a transaction might not be enough to cover what -- yes, that is a risk. Do we feel confident that, that will not be a risk that represents itself 90% or even 5% of the time? Yes, we feel confident there. But I just don't think that's something that a client is able to do because they would have to do that application by application. And so we feel like we're better suited to do that. So that's the way we're managing the cost of the client, which is fixing it and managing the cost to us by thinking about model choice, model selection, model availability kind of like -- and also, there's things you can do with the model. Like, for example, imagine if ChatGPT on your phone, when you asked it a question and it gave you an answer and you asked the question again, I don't know if any of you realize this, it goes and re-reasons again to give you that answer. What if it just knew that you asked that question an hour before and repeated the question that it gave you. Imagine like those are simple things that you can actually do in the build of an application.

Okay. In the last minute here, maybe we can talk about free cash flow, everyone's favorite topic. I think you've talked about maybe for the year coming in a little bit below the original $575 million guide. Maybe what is that kind of -- or what's the line of thinking around it now? Or like what's the right ballpark? And then as you think about the medium-term outlook you gave, the $700 million plus in a few years, just what are kind of the puts and takes of the ability to kind of reach that number given the bit of a, I guess, downtick here?

So yes. So free cash flow is critical for all businesses, certainly for us because it gives us all kinds of flexibility buying back shares like it just -- the capital allocation value is huge. So very important to us. So 2 parts to that. One is our ACV being lower, obviously, we're not going to have a lot of time within the year to make up on any actual impact that has to free cash flow. So there's some part of the free cash flow that's just given that there's only so much you could do within a year, we are very confident that we will address any shortfalls we have there from a free cash flow standpoint so that 2027 and 2028 are on track to what we said.

But -- so I would view '26 as kind of a year that we will make some adjustments to correct any shortfall that we have. But there's one thing that is very unique to '26 that I've not talked about a lot, and I think it's worth mentioning here, which is we settled our shareholder suit. We settled our derivative suit. We actually -- we had these lawsuits that came out of out of the Appian verdict. And we are down really to the Appian retrial that happens in the first quarter of 2027. So our legal costs for this year are unusually high because it's just a lot of stuff coming in. A lot of that just relates to settlements or conclusions to that. So Steve, that is -- I did not add that back to our free cash flow when we guided.

So we'll be very clear as we show how much of any cash flow deviation is related to that because that's not really structural in the business. That's very much episodic. So I think there's -- we have to just -- our cash flow that we modeled, for example, was, I think, $30 million for the -- of legal costs for the whole year, and we might be $70 million to $90 million for the year. So that's like -- that's not a small number that manages it and we'll -- that will not repeat, obviously, when we go into 2027 and '28. So that's -- those are the 2 components that we'll bridge back.

Okay. Perfect. I think we're out of time, so we'll leave it there. But Ken, thank you so much for joining us today. So thank you. Thanks, everybody.

Good morning. It's Blair Abernethy, software analyst here at Rosenblatt. Thanks for joining us. With us for this session is Pegasystems. We have Don Schuerman, who has been a long time CTO of Pega. Welcome, Don.

Nice to be here.

We've put out some prepared questions that I'll walk us through, but if anyone in the audience has questions, they can feed them to me through their -- the button in the upper right corner of the screen.

Let me just start, Don, for just to set some context for the discussion for some people on the call that might not be as familiar with Pegasystems. Maybe just give a brief overview of your business, sort of, the core end markets that Pega addresses? Just a little bit about your role.

Certainly. So Pega is in the workflow and decision space. So we drive, what I would call, mission-critical workflows and decisions for pretty global firms across industries like financial services, federal and regional governments, insurance, health care, et cetera. So an example of some of this would be Verizon uses Pega's AI technology, which is, sort of, a decisioning statistical AI technology to figure out what the right conversation to have with every client is when they interact with a client.

We do similar things for folks like Wells Fargo and Commonwealth Bank of Australia and others. We're also used across industries as a workflow platform for things like customer servicing, investigations management, claims management, onboarding, KYC, those kinds of things.

All right. And you've been in your role for a while now, right?

Yes. So I've been around Pega for a little over 25 years. My background is in our support and engineering and then deployment organization. So I spent many years doing, what I think the cool kids today are calling being, a forward-deployed engineer. Back then, it was just a consultant who knew enough to actually write software, what it needed to be written and knew enough people back in product management that if we found things you didn't like, you could get it changed. But -- I did that for many years and about -- probably about 10 or 12 years ago took on the CTO role really in a field-facing capacity.

So I spend about 50% of my time with CIOs, CTOs, chief architects at our clients and potential clients, really making sure that we understand their road maps, their architecture patterns where they're going with technology and then making sure that they understand what we're going, and we understand the map between those. And then I spend the other half of my time with my team, which is really focused on go-to-market activities. So everything from brand to sales strategy to activation and corporate comms.

Great. That's great because it's really great to get a touch point into the way the customers are doing right now, particularly around AI. So how is AI significantly impacting your customers in your key verticals, banking, insurance, health care, and so forth. What are the pain points they're trying to figure out?

Well, I think there's one pain point that everybody has, kind of, been dealing with, and frankly, we've all been dealing with for -- I would argue, since GPT popped up, which is, pressure from CEOs and boards to just demonstrate that we're using AI, right? And I think that pain point hasn't gone away. I think there's that continual sort of pressure of, are we being AI-first? Are we becoming AI-led organizations? Where I think the shift, that I'm starting to see, in client conversations is shifting that conversation towards value. So it's not just are we using AI, right? We went through -- I thought it was a really interesting bit of whiplash in the market in Q1, Q2, where it seems like literally in a couple of weeks, we went from everybody talking about token maxing and putting up leaderboards of who is using the most AI and celebrating the people who are burning through millions of tokens every week or month to a sudden realization of, wait a second, those people are spending lots and lots of money using those tokens.

That stuff is not free, and it's not going to be free. And so what we really need to do is actually how do we ensure that in the concept of like tokenomics, which is now, kind of, taken over the conversation, how do we make sure that we're governing our use of AI so that it's attached to where the actual value is. And so I'm seeing in our -- in the client conversations, I have a shift back to not just let's do a lot of AI, but where can I use this to drive meaningful value in my business.

And ultimately, that comes down to where can I use it to drive better customer experiences that help me drive increased revenue, where can I use it to drive measurable efficiency gains, not just, sort of, that generic sense of yes, we all have Copilot and we feel more productive, but actual measurable efficiency gains often measured at the process or the workflow level and things like regulatory adherence, right, the kinds of consistency that especially in a regulated industry is absolutely essential when you deploy any technology at scale.

All right. Great. And -- if you look at just the recent Forrester Wave report and you guys were cited in here, ranked very highly, as an AI platform category. Maybe talk a little bit about how you, sort of, view what Forrester is saying? And how do you differentiate yourselves out there from some of these bigger, broader companies like a Microsoft or Salesforce?

Yes, yes. So this is an interesting report that came out, right? So Forrester created this -- and this is the first time they've done this, this classification of, what they call, AI platforms. And I think Forrester is drawing a pretty clear distinction between AI platforms and the foundation model providers. And I think that's a really important distinction for -- that I see in the market as well because the push -- the thing that I'm hearing from the clients that I talk to is less and less attention being paid to, kind of, like the horse race of which model is faster this week, right?

Obviously, there are concerns, especially in the InfoSec area about the implications of things like Fable and what that means in terms of making sure that you are staying ahead of detecting any holes in your security well before a model finds it. So there are obviously implications there. But for the vast majority of enterprise use cases, the problem isn't that the model is good enough, right? For the things that enterprises need to be able to do, which is drive in more automation to handle and accelerate intake of work, handle things like research, document management, generation of outputs the versions of models that we had access to a year ago are perfectly fine.

The challenge and the unlock is how do you connect those models to the actual decisions and processes and workflows that are -- really will run a business. And so Forrester's new report about AI platforms is really looking at that layer, that, kind of, platform layer that connects what the models can do back down into the data and the workflow and the governance structures of the enterprise itself. So it's an interesting, kind of, slice, right? And you can check out the report, it's on our website. I think it's probably the first thing you hit these days if you go to pega.com.

But the interesting thing, Forrester said two things on that, that I think are really interesting. One is they said, keep in mind, this is going to be a federation, not a monolith, right? So I think there's almost a, sort of, misunderstood story in the market right now that there's going to be one winner of the AI orchestration or one winner of the AI platform layer at the enterprise. And in my experience and in talking to my clients, that's just not how enterprises work.

They have different technologies that they use for different things. And in many cases, they purposely are looking to distribute investment across technology platforms to minimize their own risk and curate their own flexibility. So Forrester is saying, like, look, you may have a platform like a Palantir that's really good at data ontologies and actually helping you map and understand how your data might feed up into a model, but you also are going to have a platform like Pega that's really, really good at helping you reimagine your workflows and then deploy and run those workflows in a way that orchestrate and use AI agents to maximize the amount of automation while maintaining a high degree of predictability and consistency.

So that was one thing. The second thing that Forrester said, and of course, we really like this, was that Forrester said that models, not insights, are actually the unlock of value for Agentic AI. So the second part of your question was about Pega's differentiation. And I think it, kind of, hinges on that. And it comes down to what I think are 3 things. I think we are about to embark on a massive process reengineering wave. I think leaders in business and the ones that I talked to, we were talking to a major U.S. bank about how they're rethinking their complaints process. We're talking to another major U.S. bank around how they're rethinking what's called the KYC, the know your customer process, when you onboard new customers. And what that's really resulting in is massive amounts of process reengineering because the organizations are realizing you can't just drop the AI onto a broken process and expect it to fix it. You actually have to rebuild the process. So we've built something pretty unique in Pega Blueprint, which basically takes and harnesses the AI from folks like Claude and Gemini and GPT.

Blueprint actually uses all those models under the covers, but it directs it at the very specific and important problem of how do I redesign my business processes so that I maximize efficiency and I use AI in the right places where it really adds value as opposed to using -- not using it in the places where I don't need it, where the decision can actually be encoded in the business rules that is pretty deterministic and taken repeatable. So Blueprint and that ability to help lead our clients on that process reengineering and redesign work is one key differentiator.

The other key differentiator is this need that I think clients have to be able to continue to run stuff with a high degree of predictability. I think there was this -- when AI first, kind of, popped up and agents first started to come up, there was this, sort of, idea of like, well, we're just going to create a -- take a bunch of documents, we're going to shove it in the agent. The agent will consume the documents and magically, the agents will just run all the work, right? Well, it turns out that's not going to happen for 2 big reasons: One, agents just have an inherent lack of predictability to them. They're probabilistic being, right? And much of the work, not all, but much of the work in enterprise does is actually deterministic.

The way a bank processes a complaint or the way a bank onboards a new customer should and must follow a repeatable set of steps. So the bank can audit it, so the bank can get economies of scale, so that the bank can actually tell the regulators, they're doing the right thing. So what we've done is we use Blueprint to actually build that recipe out once.

So I know what those steps are, and then I can run it repeatedly and consistently. That doesn't mean I can't use agents. I can use agents throughout that process. I might have an agent at the beginning that actually intakes the complaint from the customer and make sure we capture all the information right the first time, so we don't have to go back and get additional information. But that agent is actually being informed by the workflow. So it knows exactly the data that it needs.

I might have an agent that I call in the middle of the workflow to find out whether or not this particular request is fraudulent. But I don't want that agent to reimagine the whole workflow that would be risky and frankly, really expensive from a tokenomics perspective. I'm going to have that agent do something really small, which is like take this, check it for fraud, come back with a fraud score risk so then a human can make a decision or a rule can make a decision of whether or not we escalate that.

So that ability to run the work predictably and do it with a predictable cost. In fact, we just announced it at our user conference that we actually aren't going to charge any of our clients a per token fee, we're just going to charge them the case fee. So how many new clients do you onboard? How many complaints do you process? We're able to do that because of how our architecture allows us to execute predictably.

Don, just on that point, if you're -- if I'm running a -- if a bank is running a process like you're -- example you just gave, and you're in the middle of the process and you have to kick off and use an LLM somewhere. So where is that charge coming? Where is the cost of that going to show up for the end customer?

So what we've built into our model is an uplift to our case price that allows our clients to use agents across the life cycle to say that new complaint. But because the way we use the models and because we're using the agents to do very surgical specific things, when we look at, kind of, our math of what it takes to use agents, even if we're using a 40% to 50% of the steps in the process, we can actually manage that margin pretty effectively and ensure that you don't get runaway token costs. The place where things get really expensive is if you ask the agent to reconceive the entire process every time. That's pretty expensive. We want to do that once with Blueprint and then just repeat it again and again and again consistently. So that's how we're able to, sort of, offer this as a per case uplift rather than a running token meter for our clients.

Got it. Got it. I think as we said in an earlier conversation today, you and I -- you can go out and use AI to code the process. But why would I not do that?

Well, we have competed since -- as long as I've been with Pega, against the idea of some of the stuff I want to build myself, right? And I think there are 2 big reasons why we're seeing clients continue to look at Pega as a workflow engine even as they can build this. And one is down in that engine itself is a lot of code that is non-differentiating to the client. We get used, for example, Google uses Pega to run a lot of its operational workflows. And I once asked an engineer at Google. "well, why didn't you just build this workflow there yourself?" And his point was, well, that's not Google's unique capability in the market. Building a workflow engine is not we're good at. We're good at network, that search, that ads -- at like -- so I want to focus my engineers on the stuff that actually adds value and differentiates us, not the core componentry.

So one, we get that. But the other thing that I think is also really important is the stuff that we end up doing for our clients has to stay transparent and has to stay changeable. I need to be able to see where my business rules for how I handle a client complaint or how I process a claim or how I onboard a client or why I made this offer to one customer and didn't make it to another. That has to be visible. And if it's buried in code, the effort to extract it, the effort to change it is dramatically increased, the cost, the total cost of ownership goes up, the risk of the business goes up. And that risk compounds when that code is actually being generated by a bunch of agents who are not particularly good at writing human-readable code and they actually tend to write a lot more of it. So what we're starting to see is clients are coming back and telling us like, look, I used my agents to code something, and then I want to do something really simple like change the label on a field.

The problem is nobody knows where that is. So I got to either send a human being searching through the code or I have to go ask the agent to do it, and the agent sometimes will find it in the right place and sometimes won't. And so we want to make sure that those business rules that are essential to how these organizations run, sit in a layer that is visual, right?

That is where I can actually see the process. I can see the rules, business people can look at the process model and literally drag it and change it in real time or now with Infinity 26, they can sit and literally ask an AI, but the AI will visually change the process. So they'll actually be able to see and validate the change that the AI made and that transparency and that changeability that you get from our, kind of, visual approach to doing this that has built up over the last 30 years, like that is hugely important for the kinds of work we do for our clients.

Along the same lines, and again, you and I chatted a little earlier about this, but I think it's really important to understand, if you're a customer and you're looking at Pega, do I use my LLMs to access this? Is this my interface to my workflows now and I just do that? Or do I -- am I steeped deeply directly in Pega like I've always been and build my workflows that way.

So I actually think the answer is going to be a little bit of both, right? And maybe the best analogy I can use with this was up until about 10 years ago, if you wanted to go travel someplace, your only option was to go stay in a hotel, right? Or like maybe you're in Germany, you could find a Ferienwohnung, which is like a little traveler's house or something, but mostly stayed in hotels. And then Airbnb and Vrbo and some of these other things popped up. And we had this idea of like being able to get home shares, being able to actually share or take somebody's property over, right?

For some use cases, like I'm traveling with my family and we're going on a ski trip, and I want to actually be able to put the whole family in house and have a kitchen and cook meals. It's like doing an Airbnb is great, right? But I've gone on a business trip down to see some clients in Sao Paulo, Brazil for a big event tomorrow. I don't want to show up and try to track down an Airbnb after my flight lands at 09:00 PM. I want to go to a hotel, want my room to be ready.

I want to get my loyalty points. I want to have a bar and a restaurant that I can order food from. Like -- so I think it's important to understand that just because a new way of interfacing with technology, in this case, agents has showed up doesn't mean that it actually replaces everything else. I think it becomes additive. So that's a long way of saying, in the -- we've added to Pega. And we have the advantage of about 10 years ago, we made the choice to make the architecture headless. So we made the entire architecture of Pega API-driven and that was because we were noticing that clients had all these mission-critical processes in Pega, and they wanted to connect off to a bunch of different front ends, some of which were built for Pega users to use, a lot of like back office and middle office workers, some of which were customer-facing, like their front-end websites, some of them were other tools that they are already given to a certain population of users.

So there are a lot of our clients who -- like Salesforce is their user front end, but Pega is the workflow engine behind it. And we built an architecture that worked across all of that. That made it really easy for us to start implementing things like MCP, which stands for Model Context Protocol, which is basically a way to let other agents and AIs know what services and tools you have available for that agent to use.

So in the latest version of Pega, every workflow you build or every workflow you have in that Pega instance is available as an MCP skill. So any agent, anywhere can call that workflow. We've actually made the entire development environment of Pega MCP. So we have this new concept called Infinity Studio, where we took all that design time power of Pega Blueprint, and we pointed it at build time. So I can literally open up Claude inside of Infinity Studio and ask it to change my workflows for me or redraw my Pega UIs or add validation rules, so I'm increasing the speed at which people can build Pega and actually reducing the amount of specific Pega expertise you need to deploy this stuff, which is all great. But I'm keeping that visual layer so that everybody can see what these processes are doing.

And I think both at design time for builders like that, but also at run time for end users, there will be a mix. Take like the complaint process we're working on for one of our banks. Customers may actually interact with an agent, where they're just chatting either via voice or text and they don't have to fill out a form. The agent just ask them what they need to know to get the complaint started. But eventually, that complaint might be get reviewed by a human being whose job it is to sit all day and make sure that the complaints flow through, right, and validate any exceptions and double check the work of agents.

Well, it's probably best for that user to sit in a more traditional kind of forms-based workflow where they can click on a work list, open up the next thing, quickly look at the data, click Approved, move on. right? So I think you're going to need to build for both, and that's the architecture that we've inherently had in Pega and that we're exploiting going forward.

As your MCP access, are you seeing customers start to use this? Or is it still too early?

No, we've already seen customers start to play with and actually deploy Pega as a workflow with another agent as the front end that they use to, say, intake into the workflow. We introduced back into Infinity 25, the ability of Pega as a workflow to call any other agent via MCP. And we've seen clients embed agents that they've built outside of Pega into their Pega workflow, so that the workflow can orchestrate them and use them at the right point in time and really connect them to a business process. So we're seeing -- and that adoption, I think, has been accelerating because I think clients are becoming increasingly savvy about how they begin to piece these things together.

Is there -- what's the revenue model impact for you guys if somebody starts putting a whole bunch of front ends in or embeds a whole bunch of agents along the way?

Like we said, we -- many years ago, we moved away from charging per seat per user. We ultimately charge by the number of cases. So how many complaints do you process? How many customers do you onboard? How many claims do you deal with, how many exceptions do you resolve, like that's the number we care about. And so customers connecting this stuff up to more channels and different channels outside of Pega generally just leads to more volume, which is ultimately where we think the customer gets value, and then that's also where our licensing model value.

That's great. Just want to shift over a little bit to Agentic Process Fabric, which has been out there for a little bit. Maybe just help us understand what you're doing there and why that's important?

So as Forrester, kind of said and as I believe, there is not going to be sort of 1 monolithic architecture inside of our clients. Our clients -- these organizations are pretty sizable. But what that means is they're going to have agents and they're going to have workflows in a bunch of different places, right? So just take my example, when I log in as an employee, there are things that I want to do, there are workflows that live in Pega environments like we run all of our own sales automation. So if I want to update an opportunity or create a new lead, that's a process that runs in Pega on our sales automation, but there are other things that I want to do that are workflows that don't live in Pega, like I might want to -- I need to update my profile in HR or I need to approve time-off request for one of my employees, right?

And today, right now, the way I do that is I swiveled chair between a whole bunch of different applications. I walk into a sales app, right, but where I think the world is going to move is applications become less about being different front ends that you log into and more about collections of business functionality that I need to be able to reference. So it makes perfectly sense that I would have an HR app that is separate from my sales app because the team that's going to inform that HR app is going to be different than the team that's going to inform the sales app. It makes perfect sense.

But as an end user, I don't want to have to go hunting between those two. So what we've done with the Agentic Process Fabric is build a directory, a registry that allows us to capture where those workflow capabilities live. They may live in different Pega apps. They might be workflows that live even, again, in apps outside of Pega.

But I can build that registry in 1 place. And what that then allows me as a user to come in and do is instead of worrying about where a particular workflow process outcome I need lives. I can just go to the fabric and say, "Hey, I want to do this," and the fabric is going to say, "Great. I know where that workflow is. Let me go kick it off for you, here's the information I need, it's off and running." So the goal is to help simplify the end experience for our end users and accept the fact that architectures are going to remain pretty federated, but I need some way to bringing that federation together.

Very interesting. And then Blueprint itself, maybe talk a little bit about what kind of advantages Blueprint brings to your customers? And of course, you've now extended that into Infinity Studio, like what's that doing for you with, like, new potential prospects?

Yes. I mean Blueprint has, in a lot of ways, changed the conversation for us from a selling and prospecting perspective. And what I mean by that is Pega ultimately is a platform. And sometimes, our experience in the sales process is the process can sometimes feel a little conceptual. Like, we have a platform. What does your platform do? It does workflow. What's a workflow? You can have this, kind of, theoretical conversation with the client. With Blueprint, because I can literally take any client use case, and in really just a couple of seconds. And by the way, I encourage anybody who wants to try this out, go to Pega.com/blueprint, like give us your email address and then start typing in the name of a process, right?

You can literally in a couple of seconds see what the workflow looks like, see the steps, see where the automations would be, see where Blueprint recommends you have agents do things. And then you can literally hit a play button and try it out. You can actually see what the experience would look like. We've now added things like you can literally take that Blueprint and plug it via MCP into Claude or some other front end and you can literally have Claude talking to your blueprint. Like it's really, really powerful. So what it does is it allows us to jump in and instantly focus with the client on the use case that's going to drive the business value for them.

So we shift from a technology conversation to a value conversation and it allows the client to, sort of, visualize and experience what they could look like right away in the first meeting. Like we don't need to send a team off to build the demo or do anything, we're literally in the first meeting showing them what they could look like. So it dramatically accelerates that. And that's all sort of focused on getting the design right, right, getting the process right.

With Infinity Studio, which is a part of the Infinity 26 release we GA-ed last month, we've taken that AI capability that was in Blueprint to design your workflows. And now we've pulled it into how you do the build. So if you think about -- you got to get the workflows laid out, you've got to get all the stages and steps and that's like 40%, 50% of the work, but then I have to do things like wire it into my existing systems and make sure my data model is aligned and make sure my security definitions about who's allowed to do what in the workflow are all appropriately defined and make sure that the validation rules I want to have on every screen match up with what the business wants.

That's sort of the build stage. Well, now with Infinity Studio, I can do all that build with an AI system as well. I can literally ask it to go update this or change that or add a step here or fix a validation. And so without having to know nearly as much about Pega, I can actually have the AI do a lot of the Pega configuration build for me.

This ultimately, this means your customers will have -- need less resources tied to running your systems?

So I think -- look, the #1 goal for us is to get the client to the value faster, right? Because the value of Pega is we're going to give them a workflow and a process, whatever use case they're running that is better than what they are currently doing, more efficient, more responsive, better adherence to the regulatory, more auditing, more control. So there's a whole bunch of business value tied to that better, right? Like one of the banks that we're working with are on some of this agentic stuff, like they're looking at 6- to 9-figure use cases like business cases in terms of the better they get. So the faster I can get you to that better, the faster that like you start take claiming credit for that, that's ultimately better for the client.

And what Blueprint does is Blueprint allows us to compress the design phase. Now Infinity Studio allows us to compress the build phase. So I need fewer time. Hopefully, I also need fewer people and I need fewer like deep technical Pega expertise, right? I can get to that value picture faster. And then by putting agents into that workflow, I'm actually getting even more value. So I've also increased the value side of the equation as well. So I'm getting there faster and the value that I'm getting at the end is more.

Interesting. Along the same lines, I want to ask you a little bit about -- on the legacy application modernization moving it to your platform or others. What's happening in that area? Are you seeing the speed up with AI?

Yes. So I think AI has done 2 things for legacy transformation when I've talked to clients about it: One, it has increased the urgency. So like I say, the problem isn't the models. The problem are connecting the models into your existing data, your business logic, your workflows, et cetera. And if that business logic and data and workflows are buried inside of legacy systems that were written on code 30 years ago that I have very little ability to change and very little confidence in my ability to change it. I'm not going to be able to get any of the agentic value I want. So there's an urgency to do legacy modernization. But at the same time that AI and agents have created urgency, they've also actually reduced the barrier because it turns out agents are actually pretty good at going through and understanding what's inside of a legacy app and analyzing code and analyzing old documentation.

And we've now worked both on some of our side but also with partners, we're very tightly partnered with AWS with a product that they have called Transform, which is, sort of, the interpreter of this stuff. But there's actually a direct plug-in using MCP again so that Transform, if it's got something that's a workflow, it calls Blueprint to then redesign that workflow. The other big thing that we've seen from clients is the desire of I don't just want to lift and shift these legacy systems, right?

Yes, there's benefit of getting stuff out of the code. But the process that I engineered into a COBOL system 30 years ago is probably not the right process for my business. So as I go through that modernization, I also want to be reinventing the process itself to make it more efficient, to make it more customer facing, to make it better work with what AI can do and now automate more and more of the process. So the power of Blueprint is not only do I get the technology shift of I've moved from an old legacy technology to modern cloud-based technology, which is what Pega is, but I also can do that business reimagination of how the work is done. So I'm adding more efficiency and more productivity to the process in a really measurable way.

Earlier today on another call, you and I talked about your view on LLMs and the choice of different LLMs and open source versus proprietary and so forth. Maybe just how do you guys see it? How do you see it? And where do you think this goes?

Well, look, I think the market loves the horse race, right? The market loves the horse race. So who's got the fastest LLM today and I think there's lots of interesting debate going on right now around open source models and the cost of those. There's obviously a bunch of geopolitical security questions around some of the sources, some of those open source models, which I'm not going to dive into. What I think to me is far more interesting is how do we plug the power of the models that we already have into solving the real business needs that our business has today, right? Like I talked to a lot of clients and what they tell me is like, "look, I don't need most of what Fable does." Like, yes, my Infosec team needs Fable because they need to be constantly checking and making sure there are no holes in our security layer, great.

But to like automate how I do complaints, I don't need Fable, right? What I need is I need Sonnet, I need Opus, I need GPT-5. I need some of the stable models that are out there. But more importantly, I need them actually integrated into my processes to do the things the models are uniquely capable of doing, not to replicate the deterministic decisions that I can do in far more cheaper and far more dependable technology and consistent technology than the models.

So what we're really focused on is what the world is increasingly referring to as a harness, right? How do I harness? How do I pull the power of the model in. But for us, that's how do I direct it at solving the problems of the workflows in the business that's designing them with Blueprint, building them with our new Infinity Studio capability and then running them so that I'm deploying agents in the places where the agents add value, and I'm doing the deterministic things, the repeatable things, the predictable things when I can, which we find as 80%, 90% of the work anyway.

Interesting. And of course, the deterministic side of things is much cheaper for the customer.

Well, it's much cheaper. And I think there's been this shift. I hear clients using the word deterministic more and more often because I think there's this realization that when we first -- when agents first popped up, like I said, it was we're going to throw agents at everything. And what clients are realizing is, well, that's far too expensive. The stuff that's deterministic, keep deterministic because I know how to do it. I know how to run it cheaply. I don't need to pay for a lot of tokens and surgically use the AI to do the stuff in the context of that deterministic process that I couldn't otherwise do deterministically.

Like that's things like mapping and handling unstructured data, researching across vast scopes of automation, doing a deeper level of analysis and putting scoring around things. Like there are things that the AI will do uniquely that I couldn't automate before, but it's at very specific points in the process. It doesn't work when you try to have to do the whole process because then it becomes both very expensive and very unpredictable.

Interesting. One of the things you guys have talked about in the past at some of your user conferences really is sort of moving towards an age of autonomous enterprise. Yes. Just give me your perspective on that. And so where are we on that path?

I think we are starting -- I think most organizations are starting to build the architecture for it, right. And I think the -- we see -- I was reading somewhere in some reports in some place that like some organizations are saying, "Well, we have thousands of agents deployed." Yes, I probably have, in the team that reports to me, a couple of hundred agents running. Most of them are like little individual agents that people have built to like monitor their e-mail in the morning or like -- and that's all great. That's fine. I heard -- I heard a presentation from the Head of the Federal Reserve Bank of San Francisco.

And she was basically saying that like the problem with this kind of productivity stuff is it shows up everywhere except in the numbers. Like I have no way of actually like knowing from my team's perspective, like does that agent that's handling your e-mail in the morning? Like is that making us as a team more effective? I don't know, maybe, right? Where I think the autonomous enterprise is moving towards is let me look at the things that the actual business needs to do. I need to process 10 million customer service requests every year.

I need to do them following our rules. I need to do them in a way that keeps my customer really happy, and I want to reduce the cost that it takes me to do it. So now I focus on our workflow process, a thing that has an outcome associated with it, and I start applying the AI to add the autonomy where I need in parts. And I use the AI again at the beginning to be that autonomous design agent that actually helps me get the workflow right to begin with. And that's where we're really focused. And I think that's where a lot of our clients are on that journey. It's like I'm starting to move from, yes, I got a bunch of agents everywhere, great, to what are the ones that can actually really measure the value of what they're doing?

Are customers looking at this then from like a system level for their organization like abstracting up a little higher?

I think it depends on who you talk to in the organization, right? Architects got to architect, right? So every architect I talk to, they've got their boxes and their layers of what they want to build inside the system. And so I think they are, and I think that's why organizations have architects and architect disciplines, right? That's where I come from, frankly, in my background. So you've got to get that system architecture right.

But then the next step is you actually going to have to take that system architecture and you have to apply it to real use cases, right? There's no ROI in an architecture. There's only ROI when I apply an architecture to an actual process that is how my business runs, where it can drive actual efficiency gains or actual customer satisfaction increases.

So yes, you've got to get the systems and there are people that are actively doing that, and we're having a lot of those conversations. But we're also trying to have conversations around what are the real use cases that are going to drive value.

Great. Just I wanted to touch on before we finish up here. A couple of areas that we haven't talked about. One is the area of process mining, which you guys bought into a few years ago with an acquisition. It seems like that has probably become more important of an area now with AI.

I think so. I think it's a -- I think it is an interesting input, but it's not the only input, right? So I think process mining and process mining technology, what it does is it looks at like the logs of systems that are running and tries to deduce the processes of what people are actually doing. And I think that's useful. Like again, I think that's a very useful input. But I also think our clients also struggle a little bit, and I think this happens across the board, which is most of our clients actually don't want to re-implement what the users are currently doing. They want to implement something different, something better, something that's more efficient.

So the important thing for us is, yes, that process mining is an interesting input, but we view it as a feed into Blueprint. And keep in mind that Blueprint is also -- not only does it have all these powerful models, it's got our own AI database of industry best practices and ways in which we've done this. We've actually opened Blueprint up so that our partners like EY and Cognizant, they've actually injected their own industry expertise now into some proprietary versions of Blueprint that they run. And so to me, the interesting thing is not the process mining data. It's great that we know what we're currently doing. But how do I intersect that with the best practices so that they can actually push my client up and away from the as is and towards a better version of that process that's really ready for agents.

I have two more questions for you. One is, sort of, a legacy question, if you will, and the other one is, sort of, the future. On the legacy side, you've had a small business in robotic process automation, screen scraping, driving desktops, all those kinds of things. What's happening there? Is that going away? Or is AI helping that?

I think like many things, AI is actually helping us deploy those bots faster, but we've always felt that like RPA was a Band-Aid. But to me, RPA is a quick way to go and patch a pothole on a really messy street. And you can get away with that for a while. But ultimately, sometimes you just need to come down and repave the thing, right? Like -- or sometimes you need to actually say that road was not the right road to begin with, we need to put it in a super highway. So like we've always felt RPA as, sort of, a shortcut to either interface out to systems that we didn't have an API to, so we can pull in some data when we needed it or to create some quick wins and value for the business so that we actually could fund the deeper process redesign work that needs to happen. That's going to continue to be there. But my hope is as we continue to accelerate and make it faster to actually redesign the whole process with Blueprint and then build it with Infinity Studio, we don't even need those stopgap measures anymore, right? That would be an ideal world for me.

Right, right. Another future-looking question is, what's your -- I mean, you talked to a lot of customers you see inside a lot of very large institutions of all sizes, all variety of sectors. What's the view, your view on AGI? And are we getting there? Are we a long way off? What's this -- we've had so much advancement in the capabilities of the LLM in the last 24 or 36 months. What's this going to look like in 2 years?

So I'm a skeptic. I actually don't like -- we may get to some form of like, one, I don't think anybody actually can define what AGI is. Two, I actually don't know whether LLMs, which are essentially text and pixel prediction machines actually -- whether that's the architecture that ever gets us to AGI or whether it's that architecture plus a whole bunch of other AI architectures we haven't built yet. What I kind of try to draw a separation between what I think are the big questions. And it's certainly interesting to talk about like AGI and the future of human intelligence, et cetera. I actually don't think that's what my clients are dealing with.

My clients aren't asking me about AGI, they aren't asking me about -- what they're asking me is, man, how do I -- how do I use this stuff today in a way that actually is meaningful to my customers and meaningful to my employees and actually changes the trajectory and the profitability of my business. And so like I try as fun as it is to, kind of, debate the big questions, I try to focus a little bit more because my clients, I think, want us to on the pragmatic questions.

Excellent. All right. We're going to wrap it up here. Thanks very much, Don. Great chatting to you, as always, I love your insights, and thanks for participating today.

Thanks for having me. Bye, everybody.

All right. I think we're ready to kick things off. I'm DJ Hynes. I'm the senior software analyst here at Canaccord. I say it every session, but this is the 46th year that we put this event on. We couldn't do it without the support of the corporates that come and bring all the great content and the investors that ask us more questions.

So thank you to Pegasystems for being here. We have CFO, Ken Stillwell.

We're going to do this as a fireside chat. I have questions that should get us through a half hour, but I also want to make sure that the audience knows you're welcome to participate. So if there's questions, raise your hand, we'll work them in the conversation. These are obviously for you guys.

So with that said, Ken, maybe -- look, Pega has been around for a long time, but I still think sometimes investors struggle to understand what you guys are doing. So maybe just a high-level intro of the business, and then we can kind of unpack some of the finer points about what's driving growth.

Sure. I thought you were going to say I've been around for a long time.

I'm smarter than that.

I feel like that's true as well.

So if you think about like in large enterprise organizations, there's lots of work that needs to be done that goes through a relatively structured set of steps and processes. And large organizations are not really able to buy these solutions kind of off the shelf. So there's not like -- I can't go out and say, "Can you give me a loan origination application?" There's not -- the market -- first, the market for that is nichey, but also every single bank might want to have a different, like if you're a wholesale bank versus a retail versus a mortgage broker.

So there's these use cases that are very structured, very common, typically end up with consumer or constituent-based workflow touch points. And many of them are regulated. So companies have to -- large organizations, like Canaccord or like Bank of America or American Express, large banks, have responsibilities to regulators and to their own internal control processes to execute work in a certain way.

You have maybe 2 options in how you do that. You can go write custom applications, which is what many companies did for decades, 50 years, like we know of all the technical debt, and I'm sure you use some of the systems that were built on this. So everyone's seen it. Or at least if you haven't seen it, you kind of can appreciate that.

Then the other way is to get some type of a platform that you can build on or configure, like Pega, to be able to build the structure of this workflow. So those are really your 2 options. Because there really isn't a commercial off-the-shelf option. It's not like you can buy something like HubSpot to be able to do dispute management in a regulated environment for Federal Reserve transactions. Like that's not something that is out there.

So we've really competed with other platform companies like ourselves, companies like Salesforce or Microsoft, or even Adobe in some of the marketing use case, where they take some of the solutions they have and try to do something similar to what we do. But we are really a true workflow platform, so this is kind of our thing. So naturally, clients come to us because our platform is more purpose-built for workflow applications.

Or you can go back to trying to write your own code, which I think is very unpopular these -- over the years, and maybe has some curiosity around AI, where people say, "Well, geez, could I write my own applications?" But there's a really specific set of reasons why people don't want custom applications, and that's largely just around change management, trying to manage those, how do you operate with them?

So that's kind of where we sit between we don't really compete with commercial off-the-shelf. If we do, it's either we're the wrong fit or they're not going to be able to do the use case. And then we compete a little bit with kind of build-your-own. And the business has really grown as people have moved off of these homegrown COBOL mainframe environments into a more modern kind of workflow platform.

Yes. Makes sense. The scope of what you guys can do is enormous, right? There's almost endless opportunities. Blueprint has played a role in some of that opportunity discovery, maybe, I guess, I would characterize it as. Maybe just talk a little bit about what Blueprint is. I mean there's been lots of customer enthusiasm for it. So what are you doing and what's driven that excitement?

So maybe I'll touch just quickly on the problem that we had and why Blueprint solves that. The problem that we've historically had is, on the front end, trying to help clients identify which systems or what applications they might want to modernize, and then trying to figure out, visualize what might look like.

So if somebody said, listen, I have a -- let's use the dispute example. I have a dispute system that I built in 1975, that has -- that was -- worked fine for a number of decades, but now I have to make all kinds of changes because of the regulatory environment, information security, whatever. And I want to put that on the cloud. I can't use that system. So I've got to move to kind of someone different, so to speak.

So I think what that process would look like would be a lot of mocking up screens and whiteboarding and a very overhead-heavy process, which led to like longer sales cycles and, quite frankly, a lot of investments clients had to do on the front end to try to figure out what -- like which systems they might want.

What Blueprint is -- and for those of you that haven't seen that, I would encourage you to go to pega.com/blueprint. What Blueprint does is it allows you to really just, in like a very easy, user-friendly screen, say, "I sit in this vertical, I'm trying to solve this problem," and it builds the workflow for you. You could put in the personas, the users. You can talk about really down to the granular scope of what you're trying to do. You can pick different fields. You can mock up the integrations you have. Do you have a Workday integration? Do you have Oracle? And it essentially gives you a visualized view of this is what your application would look like.

Once again, it is very much in a demo environment. But it just speeds up that whole ideation process on the front end and the design process to kind of get to like almost a prototype of what you might do. Whereas before, we would literally have to close for meetings, and then in the meetings, close for a demo, and then in the demo, close for like -- it's just dragged everything out.

Yes. So the customers that have leaned in to Blueprint, what have you seen from them in terms of their ability to design faster and put product into production and ultimately pay you guys more? I'm curious...

So we've had examples with clients over the last 12 months where a client would go into Blueprint, mock up, so to speak, an actual use case, take that template of what they got, and go and actually configure the application to be able to go live inside of 90 days. That will be unheard of, historically.

The challenge, however, is that Blueprint didn't easily allow you to go from that demo environment into a build environment. We just made generally available Infinity 26, which has something we're calling Infinity Studio, which now allows -- that's the build environment. So we first started with the design view of how you could ideate and design, but many clients would still get a little frustrated with they couldn't take that Blueprint into production. They had to take the artifact and go and actually configure that application.

So we now have a way that you can import that Blueprint into Infinity Studio, and it gets essentially the development. And then you can use AI to basically finish out the application. And so it's much faster. So we've seen early wins with clients that have just used Blueprint to be able to speed that up. We anticipate this being a completely different way of building on the platform.

Yes. I mean Infinity Studio has been only, what, GA for a few weeks now. Any early feedback response?

So what we did with Infinity Studio, we released it to about 20-ish of our clients about 3 months ago. And we actually had kind of an early adopter program where we gave them a beta version, and they went in and used it a ton, gave us feedback. So we got a real -- a bunch of good feedback that we actually did incorporate in, some other feedback that we'll incorporate in the update that we're doing to it in the next couple of weeks.

So I think we -- that whole process of getting it in the hands of our clients has been -- that's really a great way for us to catch the use cases, make sure the actual use cases and the experience is covered.

But in terms of using it, I think what has been -- I would say a couple of thoughts came up. One, clients want to understand, so like if I'm doing something in an AI model, how do I actually leverage and share that into the Blueprint environment? So we have the ability for you to share, connect to any of the models. If you're doing something, we have MCP connections to all the different models. We actually have our own native models inside Blueprint. So that's a big opportunity for our clients.

Another thing is clients really have never -- in Pega, the concept of building a workflow based on a prompt, based on a discussion is really a very modern concept that most of them, they would do like kind of drag-and-drop kind of more like work on the screen. So it is that experience of being able to say, "I need to add a step. And here's what I'm trying to get done. Give me some ideas." Like that whole experience, I think, is very powerful.

Yes. You're doing pretty good with these product questions for a CFO. We're going to get to the numbers at some point. One more on the product.

So PegaWorld was in June. And you talked about your no per token cost model. What's unique about the architecture that enables you to do that? And why is it important to customers?

So if you would have asked this question in March or the beginning of April, I think most clients would have said, "What do you mean? Tokens don't cost anything. I pay $20 a month and I get" -- I think everyone now knows like, yes, the bill started coming, right? And this is a very, very -- we got to pay for the $1.8 trillion of infrastructure. So we know that. That's a reality. That's just capitalism. Someone needs to get a return on their investment.

What our job is, to help our clients, is to only use AI when it is needed to be used. And then when it is used, to use the right model. So what we do is we actually understand where AI should be used in the development or in the operation or in the actual kind of innovation of changes, and we allow the leveraging of the model. Then the second piece is picking the right model for the right activity. You don't want to use a frontier model to basically -- to create an automated call wrap-up in a customer service call. That's ridiculous, right? You might want to use OpenAI 1.5, right?

So the reality is there's 2 dimensions to that, and we are built -- the way our architecture is built is to have model selection to be able to pick exactly what model based on the activity. And we take on the risk of that. And we charge a fixed AI-enabled price for a piece of work. So that clients have complete certainty of what they're going to pay, we take care of the tokens. And the way, like I said, the way we do that is we use AI incredibly efficiently around where it should be used.

Yes. I mean the natural follow-up to that is like how do you guys forecast how much AI is going to be needed, and how do you price these projects? Or is it just we know what we're getting for this and we can throttle back AI spend to preserve the margin that we want?

So I would draw an analogy to cloud, right? We charge someone for a certain set of work on the cloud. And to be honest with you, that work could -- that could vary from A to B in terms of the amount of compute that you use, the storage, et cetera. So if we know the use cases well enough, we know the parameters of what that cost would be, and we could do that with AI as well.

And there's always lots of tools that you have around AI, around throttling and governing, like picking the right model. Availability is another issue. So we just -- we're very comfortable with the risk of that, right? We know that -- now the cost might vary by 500%. But for us, that's manageable given the economics of the relationship between us and the client.

Yes. Okay. Maybe we could transition into some of the numbers questions and kind of current business performance. So ACV is kind of the anchor metric that folks pay attention to. I know the first half of '26 wasn't quite as good as had been hoped for. Maybe talk a little bit about kind of what's causing some of the softness and maybe what you see going into the second half and what might change?

For those of you that are not aware, the first half of the year, I think, was certainly an unimpressive growth in ACV. So it's disappointing, I would say, activity in the first half of the year. What was behind that? A few things connected to that.

One, we had a very strong start to 2025. And I think that carried our confidence through knowing that we were going to beat our targets for 2025. We saw the shape of where renewals were and what the pipe looked like for '26 in late '25. And I think on us, I think we were just too accepting of that shape. I don't think we did some of the activities we could have done in the back half of the year to try to build more pipe, to get a bigger working set, to pull some more opportunity into the first half. I think we were maybe a little bit too complacent on that, if I'm self-critical.

Second thing was, at the beginning of the year at our sales kickoff, we said this is a different selling environment in '26 going forward, which is clients are not going to just self-select and self-adopt all of the solutions and we just take purchase orders. Like not that that happened all the time, but that did happen from time to time. We said there's going to be -- every single vendor is going to be in front of clients telling them that their version of AI is going to solve all their woes and that they should shut off every other system and move to them. So we know that's going to happen.

We have to change our mindset, and we said this back in our sales kickoff, from a farmer mentality into more of a hunter mentality. Not all the time, but when needed. And we had a bunch of activity measures that we rolled out at the beginning of the year that, quite frankly, we could see in March or in April, we weren't really making progress on that transition. And that, I think, didn't help us in the first half of the year.

All of that, coupled with AI being very distracting to enterprise buyers in the first half of the year, which is probably not a surprise to anybody. So I think we just didn't set up the environment to be able to give us any room for error, and we didn't do some of the things that we said we were going to do to get in front of clients to make sure -- and I think that led for us to be surprised by deals that didn't close in Q2. But in hindsight, we should have seen that coming.

Going to the back half of the year, our pipe is incredibly strong, and very big growth over last year July 1. So if you had July 1 to July 1, pipe growth were up significant, and more than what we actually need to grow just to hit our back half of the year target. So the pipe aspect of it, I think, is very solid.

Second thing is most clients that we talk to that I would put in the distracted camp in April, I would say are very much less distracted now. Many of our financial service clients set up their AI gateways. They've set up their compliance and control. They've picked their models. They even pick -- in many cases, have their budgets established on how much they're going to spend on token usage. So we were nowhere near that 90 days ago. So I do think that is a positive factor.

And the third piece is all those activity measures that I mentioned around making sure that we're doing all the right outbound stuff, those have -- dramatically better in the last 6 weeks. And we're watching those, almost in tune to a forecast call, where we're like, let's do a forecast on our activities. How are they improving?

So I think it just needed management attention. And the combination of all these things, I think, quite frankly, we were a little complacent, we were probably accepting of the back-end shift. We probably minimized the distraction with AI in the middle of the quarter -- the middle of the first half, and I think we just didn't do some of these activities. So I feel very good like we are doing all the right things and we have a great working set for the back half of the year.

Okay. At the top line, the bookings environment was a little bit disappointing. The bottom line, the profitability has been quite strong, right? I mean you delivered record free cash flow. How do you feel about the path? I mean you have targets out there for '28, right, $700 million plus in free cash flow. How do you feel about the glide path to getting you there? And what are the key levers?

So the unfortunate reality of our business is a lot of our billings and collections don't happen in a straight line through the year, so they tend to cluster in Q1 and Q4. So I don't anticipate Q3 being like -- given that we had lower bookings in Q2 and it's not a big billing collection. Q3 will probably -- we probably are at risk to have a little bit of negative cash flow in Q3. Q4 will be a really strong cash flow quarter as is typical.

And I think if you look at where we are with the year, if we don't make up the ACV bookings or ACV growth shortfall in the first half of the year, we have pressure on our cash flow target for this year because, naturally, that -- those are -- some of those costs are cemented in the organization.

So this year is probably like a little bit kind of cash flow will probably be kind of more flattish kind of year-over-year. But if you say that, how do you get from like $500 million number, somewhere around that, to a $700 million number? Some of that is we just will calibrate some of our costs, right? Because if our growth rate is lower, naturally, we'll reconcile some of our cost spend. But we have kind of baked into our model operating leverage improvement over the next 18 months or so, which sets us up for that $700 million plus number in 2028.

We really, what needs to happen is we need to finish the year strong in the back half. We need to have an ACV growth rate that's double digits in 2027 and 2028. And we need to basically just get the operating leverage in the business to get our free cash flow up into the mid-30s, which is where our target is. And if you do -- if you cascade that out, it's an over $700 million number.

And it sounds like -- I don't want to minimize that. But I would also say I'm also not minimizing when we went from $22 million of free cash flow to $500 million in 4 years. Like we know how to do that. We actually know what we need to do.

Yes. I want to talk a little bit about -- and just transitioning back into product and strategy a little bit. But the role of AI agents in the enterprise and kind of where you see Pega's opportunity and what role you could play in helping customers govern and orchestrate inside a large enterprise.

So I have this vision of if you think about the power of AI is to generate thought based on data, so I'll use the "thought," but to generate thought from data like a human being could do, except at extremes and in a much faster way.

So if you think about that aspect of AI, what we really do is we govern the actual way the work needs to be done. So if we put that instrumentation or that governance around how an agent can actually execute work, but guide it through a set of you have to first get the FICO score, you have to make sure that the underwriting documents are distributed and disclosed, you want to make sure you get the appraisal, you have to do all the underwriting calculations, make sure you got the warehousing, like using a loan origination example, that's the structure of the workflow.

The human beings don't need to be involved in any of that, right? Agents could transact. So I do think there's a governance aspect. You've heard the concept of like harness AI. Harness AI is a little bit different than governance, but has the same concept, which is, left unstructured and uncontrolled, AI will do varied things. Some good, some very bad.

So the whole concept is use the power of it, but allow the structure, allow the harness to be able to make sure that it's doing it exactly the way you want it done. Now if you have use cases where you don't care about how you get it done, then you shouldn't be using workflow. Workflow is not relevant to it. But these are -- we're talking about where you really need to do things in a very specific way, many times because that's the way the law requires you to do it.

Sure. Look, many enterprises are moving kind of from AI pilots into full-scale deployments, or now we're talking about measurable business outcomes. In your customer base, I'm curious kind of what examples of compelling ROI metrics have looked like and kind of how repeatable those feel.

One really big one that we've seen with our clients is testing. I think -- I would say almost every one of our clients has done some level of AI-enabled testing on any of their -- could be their systems or application. Like that's been a very obvious one.

The reason why that's so obvious is you're telling AI what you want it to do. You can see what it does. And it can speed up all the work that, even when you're doing automated test cases, you have to actually create the test case. AI can do that for you. That's a big one.

Another big one is a lot of the data mining, data analysis, extraction, analytics, like really giving you intelligence, business intelligence. The reason why I think that's a really great use case is the intelligence is not driving a decision necessarily. The intelligence is going to a human being to make them smarter. You still have the human judgment as a control vehicle for that.

Another one they have used is a lot of the code, a lot of code writing around like changes or bridges around existing applications. Most enterprise applications do have some level of customization things that sit kind of at an extraction layer around the -- abstraction layer around the application. And I think the agents are actually very good at doing those kind of very tactical pieces of activity, and they're very transparent.

Where we are not seeing clients use it in a big way are places where the agent writes a level of code that a human cannot keep up with. And I know that sounds really cool, but the reality is it's unbelievably risky, right? Because you really have no idea what's being built. So we have seen clients really draw a line to say, the things we need to do, we have to have governance to understand what the agent is doing.

And we've seen some really interesting validation of that, is a lot of the red team, they call -- typically call red team testing, which is the testing that companies do around some of the AI or other tools. What they found is that the agents will drift into taking liberties around what it does to try to accomplish an end goal, even when it might actually even compromise the model rules that natively guide them. So we know that's going to happen.

So if we know that's going to happen, you really just -- you need to figure out how to govern that. And one of the ways to govern it is to not allow the agents to do so much that human beings can't even tell what they're doing. And I think that's where the line seems to be.

Yes. We're bumping up on time. I told the audience I'll give you a chance to ask questions. So if there's anything out there, I can work it in the conversation. Otherwise, I can ask kind of a wrap-up question.

Ken, just thinking about if we look out 3 years from now, which I know a lot can change over the course of 3 years, how do you think AI is going to affect Pega's business in the most significant way? And how do you think it's going to impact your clients' business in the most significant way?

So I think the thing that Pega tried to do with our workflow was to create structure, automation and efficiency around repeatable work. And I think that AI will create tremendous efficiency, tremendous leverage around repeatable work that -- and be kind of complementary to things like Pega on the workflow. I think it's a way of really taking human time and precision and, quite frankly, tolerance, away from work that humans kind of are not really that good at doing, right? And so I think workflow does that in a way of like repetition.

I think AI is going to -- I think we're going to have a world where it really matters how you give instructions to the model, and it really matters how you manage the regulation of the model, like on the front and the back end. And all the actual work that is done, I think, will be heavily influenced by AI.

Yes. Yes. Well, there's some huge opportunities ahead. You're going to execute against them profitably, and will hit that free cash flow target.

So Ken, thank you very much for doing this. We appreciate your support.

Good morning, everyone, and thank you for joining us on the second day of Oppenheimer's 29th Annual Technology Conference. I am Param Singh, the senior analyst covering storage and infra software. And we have with us today Ken Stillwell, Pegasystems COO and CFO. Ken, thank you for joining us today.

And before we begin, just for the audience, we have a question bar for you that you can send in questions or you can separately e-mail me at [email protected], and I can ask the question on your behalf.

So again, Ken, thank you for joining us today.

Thanks for having me.

Great. So I want to start at a very high level, right, because maybe some of the audience may not be as familiar with the Pega story. So you've been a public company for, say, 30 years. Maybe you can kind of take us on a very high level, what Pega does? How is it positioned from the next wave of benefit on the enterprise software and some of these AI dynamics that we're seeing in the market today?

Sure. Pega -- to maybe hit at a reasonably high level, what Pega does is there's a lot of use cases that large companies have that tend to be either regulated or high internal control heavy things where you need to execute work in a very deterministic, consistent, predictable way. There are not off-the-shelf, so to speak, or out-of-the-box type solutions for many of these use cases. So companies have a few options. They can write their own, which has always been an option. They could buy a platform like Pega or someone similar to Pega to be able to configure the actual workflow and the use case similar to what is an application that they may have been able to otherwise buy kind of off-the-shelf. But since these use cases are really specific to the industry, really specific to the company and sometimes the company differentiates the workflow based on their own advantages, companies love to do that configuration on a platform like Pega.

So we've always competed with writing your own code. The challenge with writing your own code is that the application isn't predictable. It isn't sustainable. It has a massive change overhead. So what we've really been -- our tagline has been "Build for Change." It's not just that you can actually build the workflow on Pega. It's that Pega is built to be able to evolve the workflow in a way that's very business friendly, that's very user or interactive. And now what we've done is we've inserted AI into the upfront design, into the actual build and maintain and evolve stage so that you can really use AI when you're developing your app, when you're evolving your app, when you're modernizing your app, and we have AI in the actual workflow.

So when you're in a workflow step and you want to automate something or you want to use an agent instead of using a human being, you're completely empowered to do that, either with our AI or by calling your own agents or through your own gateway. So we've taken this concept of like a platform to be able to build workflow-based applications and really evolve that into an agentic workflow experience where you're leveraging all the capabilities of AI, both in the design and when needed at the run.

Thanks for that, Ken. I want to dive into a few different aspects of that. So firstly, obviously, you're kind of focused on delivering predictable outcomes, predictable cost for the client. How important do you think this is when enterprise move from more AI experimentation to more production deployments? And then we can kind of talk about some of the products that you have today available to customers when they deploy these AI-centric applications? And how are they benefiting from it?

Sorry, repeat the first part of your question. I didn't follow the first part of your question.

Yes. No. So you were talking a little bit about predictable outcomes, right? So when people build their own code, you have a wide variety of results and it's not standardized and there's other issues, accessing data and getting your results. So the predictable outcome piece of it, probably the most interesting piece. And I wanted to understand how you've incorporated some of the newer products that you've introduced into the platform over the last 12 to 18 months to deliver those predictable outcomes?

Got you. So if you think about -- let's pick a use case like a loan origination for a bank, which is one. So you're going to go in and you're going to apply for a mortgage. And the mortgage -- the process for a bank might be different depending on if you're a wholesale bank or you're a retail bank or a mortgage originator. So the process is never going to be exactly the same bank to bank, but it is going to involve a series of common steps. It's going to be a call to grab a credit rating, let's say. It's going to need some type of an appraisal step maybe, some asset underwriting. But through that process, although there can be some variations, there are regulatory steps in there.

There are things that require disclosures, things that require anti-prejudicial type activities to make sure that you stay compliant with the Fair Lending Act, for example, but also State lending acts, also national rules and regulations, et cetera. So that workflow needs to be consistent, predictable and always producing the same outcome. Generative AI cannot solve that problem because it is not producing a predictable outcome. It is producing a uniquely generative outcome each time that you actually ask it to do something.

So at the end of that, if I say I would like to go to the regulators and confirm that I have complied with the Fair Lending Act and let's just say, the discriminatory clauses of the Fair Lending Act, how will I do that, if I cannot tell you predictably that my loan origination process was without fail exactly the same, whether you applied, I applied or someone else applied. And that is a very big problem for those use cases.

Conversely, if you say, "Well, that's okay. I'll use AI to write my own system to be able to basically write a workflow system." Well, one, the AI agents do not have the domain expertise that Pega has. We don't share all that domain expertise into the public forum. So agents are going to know what they know. They're going to make best estimates or best guesses to be able to write code, but they're not -- but they don't really understand workflow.

Then when the application is built, you're going to -- any change that you have to make, guess what, it doesn't go back and deprecate all of the code. What it does is it just adds more code on top of it. So you have this constantly evolving code base. And I think we know firsthand because we use agentic engineering in our own R&D process. We know that like if you are not very careful with the agents, you end up with an amount of code that is unmanageable. Even agents aren't able to actually reconcile all the inconsistency.

So we feel like either option, generative AI cannot solve the problem. Writing your own application using agents is really a doomed process because it's just impossible to manage long term. It's not to say that you can't do it. You can. It's just enterprise clients for predictable systems that need to evolve with a regulatory change that happens often is just going to be very challenging to actually accomplish that. So that's our differentiation. That's our moat, so to speak.

Yes, Ken, there's some very interesting points you brought up, and it kind of brings me to kind of days of old. I want to date myself a little bit back when you moved to first higher levels of orchestration and software, right? You were doing C and you were compiling it. Back to assembly code, you would never get a consistent result across different compilers, different operating systems. And it feels like we're seeing more of the same where if you use the same AI to generate code, and you'll get a different result in a different application each and every time, and there's no consistency even to get the same result. Let alone, as you mentioned, the second piece of the problem that there's this whole code sprawl. And then finally, who owns the code because who's going to own the error correction, who's going to fix it? If you haven't built a code, you don't know how to fix the problem to begin with. So multiple different issues, I guess, in kind of using agents to build this. It's a very interesting.

Well, we've quickly hit a point where the speed and the complexity at which an agent can build code has far surpassed a human's ability to actually look at it. So what you're doing is if you decide to build applications, what you're doing is you're putting your complete trust in the AI models that it will do something that is right for you.

You don't know like -- I'll give you a great example. We've run a bunch of tests around AI. And I'll just give you an example of something that we found the agents do, even though the guardrails that the AI models themselves say shouldn't happen. If you tell an agent to accomplish something, agents are inherently going to try to do it like a human does it, which is they will cut corners. They will figure out a way to do that.

So if you tell it, "I need you to do X", it will try to go and hack someone's password to be able to get credentials to go in and do. So it's doing that in the code it will create. It will create bugs. It will create trapdoors, backdoors in the code to give itself options around how they can -- like there are behaviors that we have seen. And if you could talk to companies like CrowdStrike, et cetera, and they'll tell you they've seen this across that really are like almost unpredictable outcomes that are coming from AI and our clients are seeing that, and they're actually saying, "Huh, I got to be really careful here" because the models themselves don't actually know what the models themselves are doing.

I think it is a very risky thing to just -- that goes back to my point. If you're going to write and build applications, you better have a human actually know what's happening because if they don't know, you're completely captive to whatever a model does. And that's risky.

Right. And then there's a whole validation problem, too, and you don't want to, like you said, add in backdoors or other kind of ways for malicious actors to kind of hit this because they're using AI, too, to access your code, test your code and kind of create backdoors into your application, your data sets. And that's very, very critical for the enterprises. So that's a very interesting point, too, Ken, that you mentioned here.

The other piece you talked about was the cost factor of it, right? And we are now seeing some of these token economics break. So I want to understand from you, what is this AI cost reckoning that you kind of talked about earlier today? And versus what people were thinking 6 months ago where tokens tended to be a lot more free, if you will. So anything you're seeing that from a customer perspective when they speak to you?

Yes. So the whole token situation is really like almost illogical, but very fascinating that we -- this wasn't obvious to all of us, which is -- so I established a $1 trillion data center or data centers, and I'm going to now use a piece of that data center. We all know that we have to pay our fair share of whatever the cost plus profit is of actually that data center provider running it. So that's nothing new. That's just capitalism, right?

Like so I think that's -- but then the question becomes, well, if that's true, shouldn't I use just the right amount and not overuse it? For example, the utility industry is very similar. I have electric in my home. I actually turn my air conditioning off when no one's in the house or I turn it up higher, right? Why do I do that? Because that's really just a smart way to be efficient. Why would I have my air conditioning on 65 degrees in every single room nonstop when no one's in the home, right? So we don't -- we won't want to use AI in a silly way.

Additionally, there's things that logically don't make sense, like if -- why would I actually run a certain set of energy when I actually don't get any utilization from it? I might do a cheaper thing. So wouldn't I use a model that's cheaper, right? Why would I actually go to a frontier model? Like can I actually use a model that's less expensive and actually gets the same result?

So I do think there's 2 different things going on. One is when do I use AI and the other one is which model do I use. Pega's commitment to clients is that you don't pay for tokens because we basically are fixing the cost of using AI within Pega. And we are telling you on the back end, we will manage the token cost because we will only use AI when it should be used because workflow should be used in many cases, AI should used in many cases. We'll figure that out and we'll use the right models, right? And so that we'll actually -- and that's where we take on the ownership of that. Clients are very intrigued by that, right?

And in some cases, they almost can't believe that we're willing to do it. But the reason why they can't believe it is that everybody else is just pushing the token problem to clients. They're just saying, "We'll give you an agent $200 a month, and then you have to pay for all the tokens." No one is really trying to address the real elephant in the room, which is the cost of all this compute is incredibly high.

Right. No, absolutely. I mean that seems to be a lot more real, and I've heard enterprise complain about it more and more. So I guess going back to your platform, right? I remember when you first put out Blueprint, right? For me, that was phenomenal, right? Just kind of working through kind of testing it out and seeing what you delivered. Now as customers have been engaging with you on Blueprint from conceptual sales to more an experiential one, what are the biggest surprises you've seen on the positive and the negative side with Blueprint?

Well, I think the positive for Blueprint is really just the experience. The clients just are amazed at just the ease of being able to like walk through that process and really understand kind of how -- what's my problem? How do I build that workflow? How do I advance further into seeing what this will look like that? I think that's like been a great needle mover for us in terms of engagement with clients.

Honestly, the negative is actually probably -- the negative is probably the fact that we didn't have Infinity Studio until recently. So what clients would say is they get excited about Blueprint and then they'd be like, "What am I supposed to do now?" Like I want to get into building the application, but we didn't really have that agentic experience until the release of 26. So getting our clients on to 26 and they experience 26 is hugely helpful to kind of connect that.

So I would say like in a weird way, it's like the excitement of Blueprint almost becomes the negative because we couldn't take that journey further until recently. So I think it's a great sign of the relevance of Blueprint, but also a message around how fast we need to move to be able to support the entire life cycle.

No. That's a great point you brought up, Ken. And maybe we can touch upon a little bit more on Infinity Studio. As things move to machine speed, right, how that design, time to build, deployment, to, say, product evolution shifted with your introduction of Infinity Studio? And what's some of the early feedback you've heard so far?

We had a set of clients early stage that use Blueprint kind of in almost the pre-GA, like before we actually made it available. We had about -- I think, about 25 clients that actually we got feedback from. It's a new process that we've instituted to almost have like -- kind of like a beta release type scenario where clients contribute. And so we got a lot of great feedback, experience feedback. Honestly, you find some bugs in that process, right, because clients test things that maybe are use cases that you might not be able to test in your development process. So that was like very helpful. And then now we're like just starting to get clients engaged in 26. So I think the feedback will come in the coming months.

Great. Now Ken, all of these are phenomenal things, right? And the product road map and the portfolio looks very strong today. But when I kind of tie to some of the numbers that showed up in your 2Q earnings, and you've been very candid about it, but I wanted to kind of better understand some of the challenges that you saw in the first half, looking back, what were some of the drivers of ACV growth that were softer? What was some of the AI confusion? What were some of the internal execution issues or maybe go-to-market issues that you have talked about that kind of surprised you and maybe you're addressing at this point?

Yes. I think upon reflection, even adding more color to what we said at earnings, I think it was a confluence of a bunch of things that happened in the first quarter, right? One, we were having -- we had a really good start to '25. We're feeling good about getting through the '25 year. I think maybe we got a little bit lazy in terms of focusing on pipe in the first half of the year and making sure we had a lot of backup pipe. We also knew that with AI, we had to up our level of engagement. Maybe I don't think we were fast enough to do that like to get in front of our clients with our story, with our message around Blueprint, quite frankly, even talking about Infinity Studio that was coming. And then couple that with a market that was very enamored with AI and very confused and trying to figure all this out. So I think those -- the confluence of those factors, I think, put us in a situation where we had a first half that was that was disappointing.

I think that when you look to the second half, I think the market is much smarter on AI now. Not to say that there won't still be confusion, but they are definitely. I think every -- all of us are smarter on AI, right? The fact that people understand generative AI and deterministic workflows, and that's commonly talked about like across lots of -- that's a big advancement versus not really understanding that concept, 4 months ago, 5 months ago.

I think our pipeline is much stronger because we did have a lot of focus on the back half tied to renewals, but tied to just -- that just happened to be the timing of when there were deals flowing in. And then also, we now are really leaned in on this activity, these activity measures and really saying like, "Are we engaging? Are we hunting? Are we getting new contacts to be able to make sure that we're reinforcing like a hunter mentality versus a farmer mentality, which historically, we've been an account manager, farmer-type sales org. So I think like all of those are like we're almost doing the opposite of what might have been a challenge in the first half.

Yes. No, that's good to hear, Ken. So maybe a little bit more on the hunter versus farmer mentality, right? I know it's the middle of the year, but have you made any thought -- given any thought to kind of shifting the way you incentivize some of the sales teams or some of the newer hires to shift to this hunter mentality in terms of compensation or other incentives? And also maybe incentives around selling some of the newer products, especially around Infinity Studio?

Yes. So there's a carrot and a stick approach, right? I mean I think there's more of a stick approach on doing the activities like this. We really don't want to incent people to do what is mandatory to do, right? So this like if you want to work at Pega, you have to do these activities. I think that's the stick approach. I think the carrot approach is we are trying to really incent new logo, new workflow activity. Sometimes that's by having sales teams that might have a slightly better accelerators and slightly lower quota when they're selling new logos. So that's kind of -- because new logos are obviously harder, longer. If you're going to build pipe, you got to close. You probably have a lower win rate on new, like they're not -- like the pipe isn't at the same level of quality. I think most -- I think in any company, you'd see that.

And then there's also like some sweeteners, like I said, on the accelerator side. So I do think there's incentives that are healthy in the system to drive people to that. But then there's also like we just need to enforce the activity that we expect as a minimum table stake. So we need to be outbound in front of our clients. And the reason why that is so different for us is because at Pega, over the decades, our clients would find their own workflows on Pega at times, like because they have their own centers of excellence, and our sales teams were more enablers for them to find those workflows. And with a new logo, you can't do that. You need -- they don't know who Pega is, like they don't have a center of excellence. So we -- and even when you're getting into new buyers in your existing. We're going into -- if we're going into Bank of America and we want to go from the retail bank to wealth management, those organizations don't talk to each other. That's like a brand-new logo. So you need a hunter mentality there.

No, no, that's a great point, Ken. That's obviously some of the sales execution issues that you can address. But there are also some issues that are kind of beyond your control, right? And we've seen that across the software landscape in the first half as more hardware costs have taken over the entire budget taking away from software. So what have you seen right now in terms of elongating sales cycles and client caution on software spending? And maybe what kind of gives you confidence that the behavior might start to shift back into spending more on software?

Well, I think -- so I think there's 2 factors there. One factor is the actual dollars, like the dollar factor, which is I need to -- I'm going to spend more for using AI. I'm going to build out even some of the infrastructure, the security, et cetera. So those dollar distractions, I think, are more temporary than they are long term because there's a ramping up of like I got to build the infrastructure piece, right? Then there's an ongoing spend of like the cost of using tokens. I think that will just become a normal part of how that gets a part of the IT budget. And quite frankly, agents are largely going to reduce the number of heads these organizations have.

So it's not incremental spend as much as it is a shift from having less people in their organization and using the AI agents to essentially be a different spending category. So I don't think that long term, that's going to take away from transformation spending. I think it's going to more, when people retire, they'll hire less people, right? Like that's kind of how I think this is going to play out. And many of the large banks have said very similar things.

Some companies have actually went out and done big layoffs in advance of what they think is going to be AI. I'm not sure that makes a lot of sense, right? Because you don't really -- I mean that's not a great way to compel your organization to adopt like, I'm going to fire everybody and then you try to figure out how to use AI to get the work done. I don't know that, that's healthy. But I do think there's an obvious efficiency that we all know with AI.

There's a different aspect to that, which is the mental distraction, which is when I say like, "Hey, I need you to -- you've got to go and cut the grass in your house, but I just found a water leak in your basement." You stop everything and you go down and you fix the water leak and you finish cutting the grass when you actually have addressed. So there's a mind share shift. That's what happened with AI as well, where people just said, "Oh, I've got to spend a little time here and make sure that we understand our security protocols and how we're going to build our gateway and how we control? And what does this -- what the agents have access? Which agents are we going to use? Which models are we going to you?" So I do think there was a little bit of like almost a mental like a focus shift.

So there are 2 different things. One, I think there was a big cost investment to get the infrastructure set. I don't think that will repeat. I think the focus shift, that actually is diminishing as well. And then the ongoing spend of like tokens, so to speak, the companies -- which companies will spend on tokens, I think that will more offset head count spend in these organizations over time.

No, no. Ken, that's a great point. But the term that's been thrown around is client confusion around AI, right? So have you started to see that subside? Or what are some of the indicators we should be looking for as they address some of the issues you just mentioned or as you call it, client confusion?

Yes. I think the question for me is do companies understand -- first of all, are they prepared to use AI? Like do they have like the security parameters, the education, the knowledge because that's a big piece. So let's assume they have that. Then I think the next phase is what are the use cases for AI? And I think clients have -- they seem to have landed on this model that I've heard often now from clients, which is it's a 20/80 rule. And why I say 20/80 is 20% of the applications that we have should probably be disrupted by AI, right? We don't actually need to have those. 80%, AI is going to augment those applications. They can't replace them.

I'll use an example, ERP. AI is not going to replace ERP. That's one I'll use that. However, could AI report -- or excuse me, replace Power BI? Yes. Yes, I think it could. Are people going to be ready to go there yet? Maybe not. I mean I've actually heard some clients talk about replacing the data visualization, like Power BI tools saying, I know the agent can do it. I just don't trust the agent well enough yet, right? So there's a lot of work trying to -- but I think we will -- I think, obviously, things like that agents are built for, right? Which is to feed -- just to use data to feed insights, right, which is why pivot tables exist, right, which is what we all have used over time.

So -- but then there's other applications and other use cases that really have a very predictable approach, a very structured approach, need to be governed at a level that it's just sloppy to do it with AI. And I don't think AI really -- I don't think they -- it's built to do that. The whole concept of AI is that it's coming up with ideas. It's generating things, right? It's not governing like AI is not a governance tool, right? It's a generative tool.

Yes. No, no, that's completely understandable. Now before I jump on to the financial side, right, because you do have a dual role as COO and CFO, I just want to remind the audience, you can type in your question on your dashboard, and we can ask on your behalf. Or you can e-mail me at [email protected], and I can ask Ken a question on your behalf. So with that, I want to jump into some of the financials.

Look, you have talked about second half ACV coming back. I think you mentioned that's 2/3 of your ACV. How do you think about that? Is whatever you've lost in the first half kind of be captured back in the second half or the whole pipeline gets pushed into '27? And how much confidence do you have that you can at least meet the 2/3 ACV you had set out earlier in the back half right now?

Well, so the things that give me confidence in the back half is our pipe, they're being very strong. Naturally, that's an important one because our pipe is -- it's not 100% certain, but it's a credible measure, right? So I think our pipe being strong is certainly an anchor of that.

Second thing is our engagement levels, we've -- our activity levels have already noticeably improved just in the 6 weeks or so that we've been really pushing this. And that is helpful not to build new pipe in the back half of the year, but to really be on those relationships. So the pipe is there or new opportunities that could pop up that we're actually hitting those hard, and we're in front of our clients.

The third thing is the -- this is more of an anecdotal one, but is informed by actual experiences. I've been -- I probably see a client a week, right, in terms of like either a meeting with a client or a client being in our Waltham office or talking to a client, probably more than a client a week, probably a couple of clients a week. And the activity level and the conversations we're having are very real, are very normal in terms of the word I use the word "normal", like they are the same types of things that we've helped clients with for decades, and AI comes up throughout that conversation.

And I think it's -- I think there is a level of normalcy that I see in terms of those. That does give me some comfort that things are not frantic, right? That people like -- versus if I went back to March and April and May, looking back there, I would say there was a more kind of confused frantic nature of where -- of what people were thinking. They just didn't know what the future was going to be. They didn't know what was going to be expected of them. Their CEOs were all saying, "Give me AI use cases, my Board wants to see them." I don't -- that doesn't happen at the level that it happened before. It's very much more like kind of getting back to some level of normalcy.

Yes. That's interesting. Now Ken, as you think about new ACV, right? How much of an opportunity do you think you have with existing customers? And that could be building more applications because AI is becoming more ubiquitous or more data that's getting through every application, right, because you now need to consume more and more data to get more accurate and better results versus the other piece, which is expanding your customer base. And you kind of have been talking about the last couple of years that Pega is going to be reaching out to a broader base as you have a much more automated platform. So how are you thinking about new ACV coming from existing customers, new customers and within existing customers, segregating between application building and more data consumption?

Yes. There's -- so it's -- the size of the opportunity outside of our customer base is pretty massive. The time line to capture that opportunity is more of a journey, right, as opposed to -- because you've got to build -- you got to get partners, bring in -- get partners bringing leads, get closing deals and consistency, outbound, brand, all these things. So I think that we will grind through that because that's kind of the only wheel. And then we'll get some flywheels that will start to emerge on certain activities. So I think it's a massive opportunity, but not something you can go capture in like October, right? It's going to -- that's going to be something that's a longer journey.

Existing clients can actually turn much faster, right? Because clients, they have -- they're bigger, they tend to have a lot of spend, and they may already be through some of the contracting hurdles and comfort with using Pega. We might already meet some security, internal security parameters, et cetera. So I do think the opportunity set is bigger outside of our client base, obviously, because we only have 700 or so, 750 clients. But the speed and the impact of size of closings are probably slightly skewed with existing clients just because of the intimacy relationship and visibility we have. So both are interesting. One is important for the future and one is probably important for right now. And I would say that like -- so we're trying to balance that activity.

Got it. In terms of getting new logos, right, you have been kind of working more with the channel. And I did notice that you were kind of listed out as a leader on Forrester Wave's AI platforms, right? So how important is validation from a third-party platform like this and when kind of new customers are evaluating you as a potential AI vendor here?

I think that it's very important from my history of seeing how clients use the Gartner and Forrester type reports. Those firms put a lot of thought and do a lot of research to try to make their content credible to all the people that subscribe to them. And what they really like landed on is that Pega is in a very small group of companies.

By the way, none of our real direct competitors are anywhere close to us, right, that suggests that like we are a leader around leveraging AI in workflow, leveraging AI in enterprise applications. So I think it's very meaningful for clients to see that because it basically gives them a validation that moving forward and expanding with Pega versus some of the other companies that might be followers or quite frankly, just in their infancy of even getting into the maturity curve, I think that's a very important differentiator.

And I would add one thing. It is not lost on our clients that the companies -- some of the companies that we compete with that have massive budgets to try to influence the Forrester or Gartner, weren't been able to do that, weren't able to do that. So the fact that a company our size with our limited resources in terms of that can shine so much better than others, does make clients pause and say, "Well, maybe the gap is even bigger than what Forrester is showing when you factor that in."

Yes. No, hopefully, that turns into better new customer acquisition, right? And it's good to see recognition. Kind of switching gears a little bit to your cash flow, right? So despite also the near-term challenges and some of the macro pressures, you've always been a very strong free cash flow generator. I would say, probably one of the strongest free cash flow generators in enterprise software. One, I wanted to understand how important is this metric for you? And two, how do you kind of balance cash generation and margin expansion versus investing in growth here?

Yes. So it probably starts with my background because naturally, we're all influenced by what our experiences are. And for good or for bad, Pega is influenced based on what my experiences are as well. So coming from a private equity background, I have been -- really learned over time that companies do a -- not just public companies, but some -- but public companies are a victim of this, I think don't do a great job of really managing trade-offs and managing decisions, hard decisions. And I think generating free cash flow at a 30-plus percent level and even higher being a Rule of 40-plus type company is really just a disciplined commitment more than it is anything else. It's a commitment to run a good business, to make trade-offs, to make the hard decisions to really understand like why they -- why you invest in something or don't invest in something.

And I think that over time, there's been a skew towards growth at any cost, right? Because there was a time in the market where making money didn't reward your stock price. It would just grow, even if it's bad growth, even if it's unsustainable, even if it's unprofitable, grow anyway. There is still some of that always seeps into the market. In the market we're in today, there's much more of a realization of like, if you're not turning that into cash flow, it's not valuable, right? So it's certainly not as valuable. So I just have that, that's kind of in my DNA.

And I think one of the things I'm most proud of at Pega is my ability to influence the organization, starting with the Board, to our CEO, the whole way through the organization to understand the importance of being a profitable company. It's not just so that you can generate cash. It's because it's really a measuring stick of whether you're running the business well, whether you're making good trade-offs. And I think that, that just has to be part of our DNA, which means that if growth rates vary because of market conditions, you have to calibrate, you have to adjust, you have to make the corrections. If you see opportunities, you look at those opportunities as I want to invest money in that, but only if I'm going to get the yield for that investment that I should get. So if you build that into the DNA of the company, I think you will see behaviors change. And that's what I think we -- that's what we aspire to be as a company where all through the organization, we make the right decisions.

Got it. I know we're getting close to time, but I want to sneak in a couple, if I could. One is on the use of cash, right? As a free cash flow generator, there's always a question from the investors. And I want to understand, if you were to prioritize organic investment, buybacks, acquisitions, how would you rank order them in terms of importance? And specifically within acquisitions, do you feel there's a gap in your portfolio today that could be easily addressed by acquiring niche companies? Or do you think organic investment is a better way to go in filling any kind of portfolio gaps?

So I look at a buyback or return of capital, and I look at an investment like an acquisition in the same lens. I can buy back shares. And if we're trading at 10x our free cash flow, I can get a 10% yield immediately on that investment with zero risk, right, or very low risk. And over time, I should be able to really reap the benefits of that investment. That's the way I think about. So I would look at an acquisition in the same way. How much am I paying and what am I going to get for it? Incredibly like risk adjusted, how much I'm going to get because nothing is certain. So I think that's -- that then what that does is that puts, I think, a high bar on acquisitions because you really want to make sure that you're doing the ones that actually add value.

Now in terms of gaps, if you have a gap in your portfolio, which we don't believe we have a major one, we always have things we need to close. But if you have a gap in your portfolio then your whole risk-adjusted return calculation changes because some of the reason why you do an acquisition is to preserve your existing position with your platform. So certainly, we don't -- I don't believe we've done an acquisition in the recent past that I would put in that category. But that's kind of how I think about it. So like what's the actual net return? And then are we closing a gap? I think most of what we see is we can build organically, but we are always looking as well.

Understood. And one last one was -- and I think I ask this for everyone. What's the one thing do you think that's underappreciated about Pega? And the one thing that keeps you up at night?

One thing I think that's underappreciated is the importance of a deterministic workflow where clients need to follow a certain predictable, consistent way of doing things. I think that is -- I think there is a misunderstanding of that versus like a Power BI type use case. So I think that's misunderstood.

The thing that I think keeps me up at night is, to be honest with you, I think I do struggle with some of the irrational behavior that happens with companies based on investor preferences. So investors have historically, they see a trend. They always overcorrect in the early years of a trend. They kind of assume things are going to happen faster, but they minimize the long-term change. And that short term, they shift value between companies. And then those companies sometimes irrationally respond to that.

So I'm going to give you one quick example. Companies that just make up AI revenue. They go, "Oh, 30% of our business is now AI." And I'd say, "Well, you're only growing 70%." So what happened to the -- if 30% is now AI, what happened to that business that went away? The reality is nothing changed. All they did was they just added a small feature and they checked the box and they say, "Now we're AI." That's largely done because they believe investors want to hear that. And I think -- so I do think that is a problem in the system that there's this perverse incentive to like position things just as that as opposed to just being very transparent and honest about what your business is and what it isn't. So I would say that we try not to do that. We fight that at every turn because we want to try to have our business be as real and transparent as we can, but it is a problem.

Understood. Ken, thank you so much for all the color and insight. And I, for one, I'm very excited about Pega in an AI world. So thanks again for your time today.

Thank you. Thanks, everyone.

Hello, everyone. Thank you for joining us, and welcome to the Pegasystems Second Quarter 2026 Earnings Call and Webcast. [Operator Instructions]

I will now hand the conference over to Peter Welburn, Vice President of Corporate Development and Investor Relations. Please go ahead.

Good morning, everyone, and welcome to Pegasystems Q2 '26 Earnings Call. Before we begin, I'd like to read our safe harbor statement. Certain statements contained in this presentation may be construed as forward-looking statements as defined in the Private Securities Litigation Reform Act of 1995. Words such as expects, anticipates, intends, plans, believes, will, could, should, estimates, may, forecasts and similar expressions are intended to identify these forward-looking statements.

These statements speak only as of the date the statement was made and are based on current expectations and assumptions. Because these statements relate to future events, they are subject to certain risks and uncertainties that could cause actual results to differ materially from our current expectations for fiscal year 2026 and beyond. Factors that could cause such differences are described in the company's press release announcing our Q2 2026 results and in our filings with the Securities and Exchange Commission including our annual report on Form 10-K for the year ended December 31, 2025, as well as other recent SEC filings.

Investors are cautioned not to place undue reliance on these forward-looking statements as there can be no assurances that the results contemplated will be realized. Except as required by law, we undertake no obligation to update or revise any forward-looking statements to reflect subsequent events or circumstances.

In addition, non-GAAP financial measures discussed on this call should be considered in conjunction with and not as substitute for our consolidated financial statements prepared in accordance with GAAP. Constant currency measures are calculated by applying the June 30, 2025, foreign exchange rates to all periods presented. Reconciliations of GAAP to non-GAAP measures can be found in our earnings press release.

With that, I'll turn the call over to Alan Trefler, Founder and CEO of Pegasystems.

Thank you, Peter, and thank you, everyone, for joining today's call. Ken will walk you through the first half financial results shortly. But before he does, I'd like to spend a few minutes discussing some major market shifts that we've seen and a lot of the tension and confusion in the market and why we believe it creates significant opportunity for Pega.

The market evolution shows a structural shift in software. We're still in the early stages of the fundamental transformation driven by AI. Across the industry organizations are rethinking how software is designed, built, operated and evolved. And for the time, AI providers acted a little bit like drug dealers, offering their products for free or charging $20 a month for what felt like unlimited usage. To many users, the experience was magical. But at the same time, the frontier model providers have been investing literally billions or trillions of dollars building the data centers required to power AI. And now they're going to need to seek a return on that investment, making a significant share in the economics of the market.

These companies are under pressure to generate meaningful revenue. And what was once available for free or for all you can eat licensing is priced now by token use with the attendant anxiety and ambiguity. And it's not done, more of this is coming. The challenge for enterprises is token consumption is opaque until the bill arise. Many of the tokens these models consume are reasoning tokens. They don't show up in input or output, but are used by the model itself as it loops through increasingly cloud logic. These reasoning costs become surprisingly and prohibitively expensive.

Now this cost uncertainty is leading many, many organizations to sort of freeze and trying to figure out what's going on and take a more deliberate approach to technology investments as they assess the economic environment. Decision cycles have lengthened as clients seek greater clarity around technology strategies, AI priorities and the reality of highly variable token costs. At PegaWorld, we unveiled the solution offering our clients access to AI agents with no token costs. Our clients' demand for predictable outcomes and predictable costs plays directly and uniquely to Pega strength.

Our clients see the opportunity to use AI to deploy software faster and more effectively. And to see the power of AI agents to automate work that could not have been automated before. But the value from this technology will not be measured in the lines of code generated or the number of agents deployed. The value comes from better business outcomes with greater speed, control and efficiency. For decades, we have delivered software that optimizes and executes the workflows and decisions that run our clients' businesses.

AI is making that software much, much easier to design and build and continues to evolve. As our tag line puts it, "build for change". AI dramatically expands what is possible. But the requirements of enterprise systems remain the same. They need to be predictable, governable, secure and cost-effective at scale. So our goal is simple: help our clients avoid AI chaos and the mess that comes from it by delivering predictable outcomes at predictable costs.

Our approach is different from the 2 misguided approaches that others in the market are offering. The first of this guides approach is that enterprises should just use AI agents to code up anything they want generating millions of lines of code. Now look, Pega has competed against coding yourself for decades. And the reality is it went through an open source cycle where everything was going to be open source and people would code themselves of those technologies and tools. It went through an inexpensive fingers in India cycle.

But the problems with code and our moat against it remains the same. In fact, with AI, the problems with this alternative get worse. Code is hard to change. It's hard to understand. And AI generates more code with less visibility making those limitations even more pronounced. Clients report to me that simple things like changing the label on a field become really, really hard when that field is buried in millions of lines of code that no human has ever reviewed and wasn't structured for people to really understand.

The code is only readable by coders and it doesn't fit into a model that business and IT can jointly see. Pega provides that model. Our system is built on a structured and visual foundation of workflows business logic, personas and integrations. These remain transparent, understandable governable and adaptable over time. We represent the logic in business metaphors by stages, steps and decision rules that clients can easily understand and change.

With the release of Pega Infinity last week, we took a significant step forward with the introduction of Infinity Studio. Together, Pega Blueprint and Infinity Studio now create a more complete AI-powered life cycle that helps clients design, build, operate and then continuously evolve enterprise systems, all in a model that both business and IT can work in. That capability reinforces what has always differentiated Pega, our ability to help enterprises build for change. And in an AI-driven world, that advantage becomes even more valuable.

While with Blueprint, we applied powerful AI reasoning at design time before an application goes into production enabling our clients to reimagine how they get worked on, create better processes and see them working in an application, they can design in minutes. Blueprint takes in information about our clients' business and create a system built around the scalable structures of workflows, personas, integrations and decisions and structure is critical.

In contrast, if clients generate code directly with tools like Claude or Kodex, where is the architecture, how is the business launch captured and understood. And code-first approaches become opaque, complex difficult to govern and extremely expensive to change, especially at scale.

When we developed and released Blueprint, it really opened up people's eyes to how they could reimagine their businesses. It brings in our decades of experience and our Blueprint agents are able to really come up with things better than either the inputs they're giving. It's enormously exciting. And I'm constantly having clients tell me, this is one of the most novel uses of AI they have seen. There's really nothing else like it out there. Blueprint enable clients to reimagine their business processes to be better to see how those processes translate into working systems and engage with a working system early in the sales cycle. Blueprint is transforming how we engage clients and prospects at the front end of the sales cycle, moving Pega from a conceptual sales process to nisperiential and product led one.

In retrospect, when we started with Blueprint, we really focused on new clients and new applications. We have not yet brought the full power of Blueprint to help existing clients reimagine and rethink their existing big applications for 2 reasons. First, we were learning how to use it. And secondly, in helping clients reimagine existing systems is, in many ways, a more tricky and complex problem. But now with Pega 26, Infinity 26, which we released last week, we've made the power of Blueprint AI available for clients to reimagine and improve their new and existing Pega applications.

This release brings Blueprint AI from the design time use and to build the deployment and to evolve using this Infinity Studio capability. Our completely reimagined builder environment enables our Pega Cloud and Client Cloud clients to leverage Blueprint AI to deploy new applications and to improve existing ones.

Blueprint AI helps businesses and helps IT leaders reimagine how organizations get work done. Infinity Studio extends the power of Blueprint AI into the application development and deployment, creating a continuous path from idea to execution. This is especially compelling for our existing clients who can use Blueprint to modernize and evolve existing Pega applications, helping them reimagine what's possible. But it also is terrific for new clients who can answer the question, how are they going to continue to evolve after their initial build.

Furthermore, we've made the whole Pega development environment available via MCP. So people who are very technically oriented could use Claude Code or open AI codecs or any coating agent to be able to initiate their Pega applications. These improvements dramatically lower the barriers to entry reducing training time and accelerating productivity and will enable more workflow creation across the enterprise. Infinity Studio will make it easier for our clients to build and extend their workflows. We launched it last week, and we'll be rolling it out aggressively through the rest of the year. But Infinity Studio is a really big deal for our clients and prospects.

I said there were 2 misguided approaches to AI, and let's talk about the second for a moment. The second approach says, hey, just use AI agents to reason through every process at run time. Now people know this approach brought risks. I mean agents built on LLM don't execute with the consistency that enterprises need for most of their workflows. It also turns out, however, that it can be hugely expensive. We've got a calculator of pega.com that shows you the difference between this and our approach and it's pretty staggering.

Our approach is fundamentally different, use AI extensively at design time to Blueprint AI and design the workflows and get them right and really make them excellent, but once you get them right, run them repeatedly at scale, thousands or millions of times, only using the AI selectively in the runtime steps where it makes sense. Highlight the metaphor of chef. Great restaurant doesn't reinvent each dish each and every each and every paper. They take the time to design a recipe or set of recipes that work really well at scale. And that's what we do with Blueprint. And then when it comes time to execute the dinner service, the whole kitchen follows the recipe predictably and consistently tuning it only when needed. That's our workflow running in production.

Clients know how to execute effectively at scale, providing the perfect balance between AI inspiration at design time and AI used consistently at and run time. And this also means we're not using up massive amounts of costly reasoning tokens and run time. And it's how we are able to offer our Agentic AI as an uplift to our case base price with no variable per token costs. We use AI selectively for specific and well-defined tasks, where it adds value in run time, like a summarization or reading documents.

But core workflows remain deterministic, predictable in the outcomes they deliver and efficient in the cost it takes to run them. This is a key and structural differentiator for us. It's hugely important to allow our clients both ensure they deliver the outcomes they want predictably and to ensure their AI costs or tied to value. We've taken this power even further. With Infinity 26, every single workflow in Pega, both new and existing is automatically available through MCP. That means any agent built on any agent platform can find and invoke a Pega workflow. And the workflow instructs the agent to operate predictably and consistently. And because the workflow does the reasoning for the agent, Pega helps our clients make even agents build outside of Pega more cost effective.

So in conclusion, our clients tell us they want to see increased efficiency and better results from AI. They also, however, want better outcomes of installation of runaway token costs and measurable results in increased efficiency and better processes for much of the work they do, they don't want to re-reason a business process every time it runs. And instead, our approach really resonates. We imagine with AI, execute predictably continuously evolve and improve it. And this is what Pega delivers, giving clients the ability to design an effective recipe for executing worker design cut.

And then what uses requested business outcome, the menu at the restaurant contains the proven recipes that organization has been how to do extremely well at scale and that are consistent with regulators' needs and the efficiency the organization wants. Clients, I believe are continuously -- are increasingly recognizing the value of our approach, and we have a strong and differentiated story today. The clients who saw the PegaWorld who I met with extensively, we're extremely excited. And you can see Infinity 26 to sell in Kerim's keynote, which is available on pega.com.

It's also becoming increasingly clear that while the market is still early in its AI journey, many of the key trends, I think, are moving in our direction. Increasingly, clients are coming to us to ask how they can manage business costs while achieving business value. As organizations move beyond AI experimentation, and try to bring ROI to production deployments, they will avoid approaches that generate massive amounts of brittle code or highly unpredictable economics. We think that these companies will succeed with AI by becoming those that combine innovation with structure, control and economic discipline.

Some of the things we hear just seem like madness. People talking about trying to control thousands and thousands of independently operating agents. We just don't see how that works. And candidly, I've talked to a lot of customers who don't see how that works either. Our approach instead really builds on our traditional workflows, inspired by AII for way more aggressive design and the use of AI to do the pieces that it needs to do have run time.

And we remain committed to how we help our clients get through the confusion that has been candidly obligated. But so much noise in the market about SaaSpocalypses and software being debt, et cetera. I think some software is definitely under threat, unquestionably. But the types of systems we are to build them in code would be extremely complicated, hard to update, hard to accept, and that build for change is important for our clients.

So these principles relate to how we are going to work with our customers to work with them. But we don't just want to help our customers bring efficiency to their businesses and help our customers run well-managed businesses. We need to also operate our business that way. we remain committed to getting through the current period of wild confusion and generating strong cash -- free cash flow along the way regardless of market conditions or how long it takes for the sort of understanding here to stabilize. We will be using and leveraging our AI technology and capabilities to improve our efficiency and create additional operating leverage. And we will temper our spend accordingly vis-a-vis our free cash flow needs.

With that, let me turn it over to Ken to provide more color on our first half. Jim?

Thanks, Alan. The first half of '26 had challenges for 3 primary reasons. First, as we explained in February, our renewal portfolio significantly weighted toward the back half of the year, resulting in a more typical seasonal pipeline pattern. Because of a meaningful portion of our net new ACV comes from cross-selling and upselling into our existing client base, fewer renewal opportunities naturally result in fewer expansion opportunities given our typical contract length renewal timing is inherently a long-term dynamic in our business.

Second, as Alan explained, unprecedented change in software market created significant buyer uncertainty organizations wrestle with fundamental questions about how AI would reshape software development and whether they should build more capabilities themselves. The market entered a token maxing mindset where organizations encouraged even celebrated token consumption and then whiplash to the opposite extreme, where companies sought to tightly monitoring control token usage. The resulting uncertainty made customers more cautious and contributed to a more confused and longer buying cycle.

Third, we didn't execute well enough on our go-to-market change to drive deeper and broader engagement with our clients and prospects. As part of that effort, we are increasing prospecting activity, expanding executive level engagement, identifying new workflow and legacy transformation opportunities with not only existing clients but also strengthening our focus on new logo acquisition. We are also using Blueprint to help shorten sales cycles powered by the combination of Blueprint and the newly released Infinity Studio in Pega 26. These are the right changes to improve pipeline quality, conversion and sales productivity over time.

Our progress in the first half was slower than we anticipated, but we remain very confident this is the right approach. With that context in mind, let me turn to our financial results. Annual contract value growth or ACV growth, is one of the most important metrics in our view the best indicator of underlying execution. That's because ACV growth provides a clear view of the business momentum than revenue growth in a subscription model.

Pega Cloud ACV increased by [ $165 billion ] as reported year-over-year, growing 22% as reported and in constant currency. This growth reflects the continued expansion of our cloud business and reinforce the success of the subscription transition we began in 2017. As a result, Pega Cloud remains the fastest-growing and most important component of our subscription model. The growth moderated to 27% at the end of last quarter in constant currency. We're watching that trend closely and remain focused on improving our broader ACV growth trajectory.

Our overall ACV growth rate was offset by decreases in maintenance ACV and subscription license ACV. As a result, total ACV grew 7% as reported and 8% in constant currency year-over-year. We expect Pega Cloud ACV to continue increasing as a percentage of total ACV over time and still believe it can ultimately reach approximately 75% of the total. And that mix shift toward Pega Cloud will continue to put pressure on maintenance and subscription license ACV growth in future periods. Over the longer term, however, a greater concentration of Pega Cloud ACV will create a more predictable, higher quality revenue stream, improved cash flow visibility and strengthen our ability to compound shareholder value.

While our total ACV growth was below expectations, we continue to operate a period of significant market disruption. Several software companies have recently noted delays in purchasing patterns where the business is not going away, clients are frozen in the confusion. It remains difficult for us to assess the magnitude or duration of potential IT spending reallocations and an impact on software growth. But more broadly, clients are still focused on legacy transformation and using and refining their AI strategies but also governing usage, managing token costs. So this -- although this is a great long-term trend for us and our value proposition, it still may continue some delay in investment decisions.

Moving to cash flow. Even with slower ACV growth, the durability of our model is evident in our cash generation. We generated $288 million of free cash flow in the first half 2026. The a record that reflects the strength of our subscription model and our disciplined approach to managing the business increasing free cash flow over time is one of the most important measures of value creation and business health. It also provides strategic flexibility for capital allocation, which then brings me to my next topic, as we discussed during our investor session in June, we intend to deploy a substantial amount of our free cash flow toward opportunistic share repurchases.

In the first half of 2026, we repurchased 9 million shares for over $360 million in the open market under the prior authorizations. That cash expenditure represented well over 100% of the free cash flow generated during the same period, and total common shares were reduced by 6 million shares in first half of 2026. Share repurchases remain very attractive use of capital and represent a meaningful opportunity to create long-term shareholder value, especially in a disruptive market that we see around SaaS. We remain confident the long-term prospects of the company and our strong cash generation provides us considerable flexibility.

While we're pleased with our capital allocation results, one of the most common questions investors have been asking is what are we seeing in the demand environment? As we exited the second quarter, we began to see a more balanced discussion emerging around AI economics and deployment costs. Clients and prospects are increasingly focused on measurable business outcomes, governance and total cost of ownership rather than just experimentation alone. This shifts favors Pega's differentiated approach and creates an opportunity for us to more effectively communicate our unique value proposition and our approach to AI cost containment.

Let me be clear. Pega does not charge or token, rather than monetizing for token, our AI monetization strategy is based on the business value that clients create on our platform. Clients should be rewarded for driving outcome, not penalized for AI usage. Our monetization approach features 2 key elements. First, Blueprint makes it easier and faster for clients to create and deploy applications on the Pega platform. Given our case-based pricing model, the more workflows clients run on the platform, the more value that they create and then the more ACV that we generate.

Second, we apply case price uplift for advanced AI-powered run time capabilities, including innovations such as Agentic Process Fabric. This approach aligns our economic interest with our clients value creation and success as clients drive more value for Pega and expand adoption across the enterprise, both parties benefit.

Before I conclude, I'd like to provide a few forward-looking thoughts on our business. As a reminder, we provide annual guidance at the beginning of the year. We do not issue quarterly guidance or typically update our outlook during the year. given our back-end loaded renewal portfolio and our slower-than-expected start to the first half of 2026, we definitely have our work cut out for us in the second half. That said, we expect the market disruption of buyer confusion to remain factors in the near term. I'm optimistic though that our ACV growth over the long term will be stronger than our Q2 results would indicate.

I also thought it would be helpful to share that when we model our full year net new ACV ad for 2026, we assumed 1/3 of that add would be in the first half of the year and 2/3 of that add would be in the second half of the year. We will work hard to recover as much of that first half shortfall as possible, but it will be very difficult. The second half requires stronger execution that we delivered in the first half, particularly in expansion activity, new logo contribution and conversion of our healthy qualified pipeline.

From a mix perspective, now that Pega Cloud ACV is 57% of total ACV and continues to be the fastest-growing element of the business, we expect continued pressure on maintenance and subscription license growth rates as clients migrate to Pega Cloud, as I mentioned a moment ago. In addition, as more and more buyers move from the experimental phase of AI into the ROI stage, that shift plays to our strengths. As AI costs come under greater scrutiny, our outcome-based pricing model provides a clear and more efficient path for clients to generate and measure return on their AI investors.

As we iterate on our annual -- as we reiterated at our annual investor session last month, we expect to generate $700 million plus of free cash flow than 2028. Slower ACV growth in the first half of '26 does not change that objective, but it will require us to reevaluate certain investment priorities. Our 2028 free cash flow objective is supported by multiple levers, including cloud scale, continued mix shift, sales productivity, gross margin improvement and disciplined investment prioritization. We will make appropriate adjustments to ensure that we remain on track to achieve or exceed this target.

Strong free cash flow enables long-term shareholder value creation and our commitment to the Rule 40 performance reflects our belief that the world's most valuable companies combine durable subscription growth with disciplined cash generation. In conclusion, we remain optimistic our latest technology enables clients to achieve predictable outcomes at predictable costs at a time organizations are struggling to justify the economics of Token masking and broad-based AI experimentation that failed to deliver ROI.

Our investments in Blueprint and Infinity Studio [indiscernible] Process Fabric and the broader Pega platform are designed to help clients deploy AI at scale within a governed framework that accelerates productivity and business transformation. We made some critical architectural choices that even if you take -- even if it takes a few quarters to recognize are going to be game changing. The idea of design time and runtime being respected in their own ways is massively different in the approach of our competitors.

Looking at it yourself to appreciate the differentiation and how hard it would be for someone to emulate. We continue to see strong engagement from both new logos and existing clients, growing Blueprint adoption and increasing interest in solutions that help organizations move from AI experimentation to AI-powered business outcomes. And the market is increasingly rewarding companies that can combine AI, workflow automation and enterprise transformation within a governed production-ready platform.

Pega is uniquely positioned at the intersection of all of those trends. While execution remains our top priority for the balance of 2026, our long-term conviction has only strengthened. We remain confident in our market opportunity, confident in our ability to deliver substantially growing free cash flow and confident in our disciplined approach to balancing growth and profitability and will sustain strong Rule 40 performance and create significant long-term value for our shareholders.

With that, operator, can you please open the line for questions.

[Operator Instructions] Your first question comes from the line of Steve Enders with Citi.

I guess I wanted to dig in a little bit more on just what it is you are seeing on the demand side. It sounded like exiting 2Q, but maybe some of these deals were beginning to unlock and things were getting over the finish line. But can you just maybe give a little bit more clarity on have you seen deals get over the finish line now? Are the deals that pushed from 2Q? Like are they starting to close? And just, I guess, how does that make you feel about I guess, the broader kind of opportunity for these delays to undo into the back half of the year?

So we are seeing movement. We went through a period I would say, early to mid-second quarter, in which the level of confusion -- look, I've been doing this a long time. So I've seen other enormous moments of confusion. But this would rival anything that I'd see. People just weren't sure what they should be doing. And I would say even today, there is enormous confusion that is percolating in lots of these organizations. And trying to figure out what to do, they're trying to figure out how this all fits into our future. And the reality is though these companies have serious things they need to get done.

So we've had excellent engagement from customers. The things we announced in Pega, which obviously was just in June, have generated a lot of enthusiasm. The idea that we have an architecture that is understandable. You can actually understand, hey, big difference between agents that do a lot of their thinking and design time and use it, that's something people can internalize and understand. And the fact that we translate that into a new token cost model, has gotten a lot of candidly excitement and provides real reassurance of these clients that we have a thing to do.

So I'm seeing see things starting to move. But I'll be honest, in the summer, the third quarters are lousy time for return around, it's just in general. And we are working it, and I have a lot of confidence that what we're doing is the right thing. And candidly, what we did a couple of years ago were choosing this architecture. I think it's been enormously indicated and customers do see the difference. But in terms of unlocking, yes, they will be unlocking in the second half. how quickly it will happen, that's candidly part of this great uncertainty, I think a lot of companies are dealing with.

Okay. Okay. That's context around that. And then on, I guess, just the investments that you feel like it may be a little bit more kind of discretionary or at least to make the $700 million in free cash flow work in a few years. Just -- I guess is this, I guess, making you change how you feel about the own investments that need to be made in your business? Or I guess if rubber meets the road, just what does that look like? Or how does that maybe take shape or change strategically, how you think about the business and the investments that need to be made to grow here?

Some of -- we really enjoyed after years of capable not operating that way, we've really enjoyed the benefits of being a Rule of 40 cash flow generated company, and we are committed to doing that. We have enormous opportunities to change the way we operate and become more efficient. And we're seeing that happen, and we're doing that. We will temper our investments to make sure that we don't go back to the spend culture that I think we probably had more of if you go back a decade. So I'll turn it over to Ken, who's going to be the enforcer of this.

Well, I think, Steve, I think what really the simplest way to think about this is that we work very hard to build the muscle of the discipline in the organization to run as a Rule of 40 company. And based on where our growth trajectory is we need to always be looking at opportunities to rightsize the amount of investment spend that we have based on where we are and our future growth trajectory. So that's just -- to me, that's just running a good business.

The specific opportunity we have ties to a lot of the opportunity around AI in our own operation, which is areas where we can optimize where we can not only optimize by leveraging technology but also optimized by looking at places where we might be able to organize more effectively and be able to get better outcomes by thinking about themes of work that we do that could be better coordinated and also just quite frankly, looking at ways that we could drive efficiency and how we deliver value in all the different parts of the business.

I would not put us -- I would not say that any part of our financials are best-in-class and cannot be improved. Going from gross margin, the whole way through to spend in each of our functional areas. That's really what you should hear from us is that we're going to continue to run a good business, investing in the right areas and make sure that we're delivering value for shareholders that we believe rule and certainly free cash flows lever in that value creation.

Your next question comes from Raimo Lenschow with Barclays.

Can you kind of mention the 3 areas that kind of impacted the numbers. And I was just trying to -- the cohort we knew about the go-to-market changes as well, and I heard your comments there. How do I have to think about like how much of what we're seeing here now is kind of stuff that the go-to-market changes and the cohort, which is kind of in your control, so you can control the controllable versus the talking maxing that we see in the market. So how much is in your control really to change what's going on here? And I have 1 follow-up.

Yes, that's a great question. And there is a connection between those 2 themes that I think will make sense to you, which is with the whole AI kind of disruption, what that really -- that happens kind of right in the middle of us really pushing harder to grow into new logos, grow into new work flows into new use cases. So there's a tremendous opportunity with Blueprint to be able to unlock in a much faster and more efficient way opportunity with both our existing clients and new logos. So that is a motion that is different than the way that we've operated at Pega over the last quite frankly, 40 years.

So that was a change for us. I think maybe we underestimated some of the ways that we needed to really manage that change internally. So that is a big part of what happened in the first half of the year is we just didn't move as fast as we would have liked to in terms of making that change. And we believe that is a tremendous value unlock even in a confused market because there is lots of opportunity where people are looking at solving this problem with a deterministic workflow approach and certainly with the best platform to do that in Pega. So I think those things are -- there is a relation there, but a lot of this is in our control.

Yes. Okay. Perfect. That's good to hear. And then the other things on the guidance. I mean, a common pattern would be like, look, you had a tougher first half to kind of derisk guidance somewhat. Just kind of -- can you kind of -- and you talk to that a little bit, Ken, but like I'm still slightly confused why not use this opportunity.

Well, so we're -- we've stayed away from the pattern, whether you agree with it or not or whether others agree with it or not, we saw the pattern of being in this constant kind of chasing, trying to reguide guide and reguide quarters. Our business is a business cycle that doesn't happen in a 90-day period typically. So we've kind of tried to stay away from that.

I think what we're trying to give some color to is that we had a lot of work to do in the back half of the year in the existing model, right? 2/3 of our business was going to be in the back half of the year. That's a good working set for us to go after. It's just going to be very challenging to make up the gap that we already experienced in the first half. So hopefully, that gives some color around how we're thinking about the potential range of outcomes.

But to be honest with you, I think reguiding to a number in an uncertain environment, I don't think is super helpful for anybody, including us. We're going to try to -- we want the number to be as big as possible, obviously. But I think that helpful guide of like we originally said 1/3, 2/3, certainly, we have significantly underachieved on the 1/3 in the first half. And if you just assume we can't make that up, but we will still work hard to achieve what we originally thought in the back half. That's hopefully some color to help you get a view of that, Raimo.

Your next question comes from the line of Devin Au with KeyBanc Capital Markets.

Would love to just get a little more context on some of the deal elongation commentary. I know you gave kind of that commentary of expecting to add 1/3 and 2/3 of the new you for the year. in the first half and second half. But would love to just get maybe more context, maybe you could kind of quantify the magnitude of deals that might have slipped from 2Q into the second half or beyond that? And are these deals that were kind of pushed out, are they concentrated in any specific vertical or geos. Just any context there would be helpful.

So let me start just by saying one thing. So the thing that we're not seeing which I think we view as a positive is what we're not seeing is clients not wanting to engage. Second thing we're not seeing is we're not seeing clients our opportunities go away because the client says, oh, we're not doing that. We're only going to use AI. We're not doing that. We're going to -- we chose not to do any transformation, for example. So we don't think that the market is actually deciding the transformation is not important.

But a lot of those pipeline deals that just did close, right? They elongated. So we -- when that will unlock and when that will correct, Devin, I think it's a very hard thing to predict. But I think the thing that I would be most worried about that I'm personally not seeing it's just -- if deals just go away, right, pipeline deals just go away. We're not seeing that. We just are seeing a little bit of a fine buyer dynamics in the market. Alan, anything to add there?

No, no, I think that's accurate. People are just confused. It's really almost a max confusion moment. It's starting to make more sense. Candidly, it was wonderful that earlier this year, suddenly take started costing something. People realize that they have an economic decision to make here. Because before that, it was all magic and no expense. Candidly, I think we have a compelling story even in a free token competitor environment because we offer the predictability.

You need a level of determinism. I don't see anyone else out there who's able to use AI aggressively to be able to redesign and reimagine the business and then use AI selectively to make you deliver outcomes at scale at a reasonable cost. And we're going to work really hard to push [ 26 ] out to a lot of new customers and have them get hands on with that, probably faster than we've done historically. And I think that will actually help us online customers as we enter the third and fourth quarter.

Got it. I appreciate the additional context there. And then maybe just a quick follow-up on the free cash flow side. I mean just given the potential of a kind of more muted ACV outlook for the year. Can you just kind of speak to the confidence in the lowering the $575 million outlook for the free cash flow side? Was that kind of guidance to hinge on the ACV growth to accelerate for the year? Just any color there?

We certainly -- we would -- I mean, we certainly would expect ACV growth to accelerate from where it is now through the back half of the year. But certainly, any ACV shortfall puts pressure on our ability to hit that $575 million for this year. So I definitely wouldn't suggest that the ACV landing spot and the free cash flow for the current year are completely unrelated or disconnected. There's certainly a relation there.

There are decisions that we make around spending that are in-year decisions that we certainly will be much more thoughtful about given the first half performance. But we still feel like the cash flow durability is strong, but I would be that would be remiss to suggest that the ACV shortfall would have some cash flow impact associated with it.

Your next question comes from the line of Patrick Walravens with Citizens.

Alan, so as I think back to your June 8 remarks, I think you had a pretty good sense this was coming, right? Because you made a comment back then it's very similar to what you're saying now. You said there's still enormous amount of confusion. It's going to take months and quarters for the confusion to abate. So what are you seeing at the beginning of June because you still have 3 weeks to go in the quarter. Like was there a really big deal that poster what was -- what makes you suspect this is going to be a problem so early?

Well, I'm not sure so early. The reality is, when you were talking to customers after the whole SaaS populous narrate, people were wondering what do we do right? Should we be writing this ourselves. One of the interesting challenges for us is that, particularly since we sell to a lot of very large organizations and to, for example, banks. Those have been primary targets of the people trying to get customers to become agent, right? I mean everybody is talking about that. They hear it over time. And you can just tell they're massively confused with what they hear originally from companies like Microsoft and Salesforce. And then recently, just the overwhelming drumbeat of Anthropic and open AI and others coming with their coating solutions.

What I'm encompassed by is as recently as last week with the heads of technology of very large financial institution. Going into saying, we really don't want to be in a position to maintain all this code, the amount of code that this stuff generates and the lack of sort of sensible structure around it, it's -- somebody just needs to look at a package system and say, "Hey, I can understand this. So when I go and I want to change something, it makes a difference. That's what we need to build on. But it's not unreasonable that everybody is trying to reevaluate things. When you think of just all the noise that hit. And I think, Patrick, I think a lot of that hit really in Q2.

All right. Great. That's really helpful. And can I ask is -- I mean, you mentioned like OpenAI and Anthropic is Salesforce and Microsoft. Are the companies -- is it like is Sierra starting to pop up? Is Decagon starting to pop up? Are those types of vendors contributing to the confusion for your customers?

Well, Sierra has been out there for a while, and the customers have been experimenting with them. I've yet to see the sort of ground swell that I think was promised in some setting for some of those. I think it does present some confusion, opportunities to the customers. because I think organizations are unsure. So I do a Sierra, should I do a Workflow should I do direct open AI or Claude sort of interface, everybody is in this party shouting at the customers. And in that environment, it's just incredibly noisy. And what I'm going to say is that we have a distinctive story I've never been happier that we made some of the decisions we made 3 years ago.

Your next question comes from the line of Mark Schappel with Loop Capital.

Ken, given that this year, is more weighted toward the back half of the year? And given that some of the deals in the pipeline didn't close in 2Q, what is giving you confidence that the current delay in purchase decisions is just temporary rather than kind of a more durable shift in spending priorities.

I think that's a very fair question that I would -- I don't know that I would say I have complete clarity to refute the suggestion that this may go on longer. What does give me confidence is our pipeline is growing nicely. And our late-stage pipeline is up -- is very strong over last year. And we're not seeing clients not want to engage.

And the last point I would make is that the pipeline and the discussions that we have are companies that are use they're thinking about AI priorities as well. They're still kind of engaging with us in very healthy conversations around how we can help their transformation needs. So it looks like the activity that we're seeing is, I would say, real than it will, that it sees real [ pipe ]. But the question you're asking about, like, how do I know for sure that the confusion won't continue for a prolonged period of time, I would say, I don't think anybody in the market can guarantee that.

But I would say we feel confident that the activity is rising. The pipeline is strong. The clients are real. We know these clients in many cases. And our engagement around Blueprint has been a real big difference maker for us in -- in building that. It's -- right now, it's just down to us continuing to execute I mean I think somewhat of this is you just as a software company for all this confusion, you just need to grind through it. You just need to work through and just stay focused on your objectives I think we're in that kind of market right now.

And we have the benefit of having real structural differentiation I think it can be hard for people who don't want to spend the time ticking in and looking. But if you actually go look at the alternative approaches that we have and company who's writing massive amounts of code or generating dozens or thousands of agents, there's a big difference when you guys workflows at the heart of it. And to be blunt, we are the best workflow company out there by far.

That's fair. And then, Ken, would you say that renewals are holding up better than net new business? Or are you seeing pressure on both?

I would say the net new business and the expansion with existing clients is where some of the some of the kind of -- the freezing happened, I think, in the first half of the year.

Your next question comes from Patrick McIlwee with William Blair.

So we heard that Blueprint helps cut your average sales cycle roughly in half, which seemingly supported a material acceleration in your revenue over the last year or 2. Can you talk about the significance of Infinity Studio 26, if you think that has a potential to have a similar effect on your implementation time lines and do you feel -- as we think about that, do you feel that, that dynamic alongside some of the frozen but not lost deals you've talked about provide any kind of spring loading of demand do you feel like you have heading into late '26 or into '27?

Yes, I think that's a good question. So bringing the Blueprint ANA capabilities, into Infinity Studio is a really, really big deal. What we have done originally was focused very much on how do you completely reimagine the design process. And it can be that was a sufficiently hard problem that deserved our focus. But what it meant is that after somebody wants to begin using the system, the whole concept of building for change required you to return to a more adequated environment. And customers told us, no, we don't want to do that.

We want to be able to continue in this sort of accelerated mode of thinking and exploration. I think having a pretty study will be available with Blueprint AI technology is going to be a really, really big deal. And candidly, I think it will do more than have the build and delivery experience. I think it's going to completely change it much in the same way the blue did. So we're very excited about that. But candidly, it's good in the market now for a week. So we're going to get some real experience. I'm sure we'll talk about it at the next call.

Okay. And wanted to ask if you could quickly provide some thoughts on the proliferation of open source and open weight models, SaaS evolving space. But what implication is you believe.

I think it's actually sort of great and inevitable. The reality is that these model makers are going to find that they've become largely commoditized. And it's an interesting thing to see how quickly that is happening.

We have reached the end of today's Q&A. I will now turn the call back to Alan Trefler for closing remarks.

Thank you. Obviously, it's a challenging moment, but last week, actually, we were -- we opened the NASDAQ to celebrate our 30th anniversary. And this being my 120th earnings call frightening to say. We've seen a lot. We've seen a lot of technology change. We've seen a lot of market changes and a lot of market ships. I just want to assure people that I think we have a really good understanding of how to react strongly, but smartly. And we are going to do that, and I appreciate your support. Thank you very much.

This concludes today's call. Thank you for attending. You may now disconnect.

Everyone, if you could take your seats, we're going to get started. My name is Peter Welburn, and I am the Vice President of Corporate Development and Investor Relations for Pegasystems. I'm so excited to welcome you today to our 2026 investor session.

To get started, I put our safe harbor up here on the screen. Certain statements in this presentation may be considered forward-looking statements as defined in the Private Securities Litigation Reform Act. These statements represent our views only as of the statement, the data statement was made and are based on current expectations and assumptions. Investors are cautioned not to place undue reliance on such forward-looking statements, and there is no assurances that the results included in such statements will be achieved. For additional information about our safe harbor, please check out our disclosure materials on pega.com or the most recent information in our public available information, such as our 10-K.

To ask a question today, if you raise your hand, we have two mic runners in the room. They'll come over. If you could identify yourself, first name, last name and the firm that you're working with. We are audio streaming today, and there is going to be a recording. So we like that for the transcript. If you're listening on the live stream and you want to send in a question, you can send it to me at [email protected]. In addition to that, you can send an e-mail to [email protected] and we'll try to respond to those as well.

In terms of the agenda for today, we're going to have Alan Trefler, our Founder and CEO, come up first. Alan has customer obligations for today. So he's not going to be able to stay for the entire session. So what we'll do with Alan is, Alan is going to come up and talk about our strategy for reimagining business with agentic AI, and then he'll take some questions. Alan will probably go about 20 minutes. And then he may be able to stay for a little bit longer after that, but he's going to have to go meet with customers after.

Next on the agenda will be Don Schuerman, our CTO and our Head of Marketing; and Carie Whalen, our VP of Solution Consulting, they're going to give a product and a go-to-market update. Don is going to talk about some of the most recent and exciting new product capabilities that we have, and Carie is going to do a demonstration of those latest capabilities. So that will be great to see. Then we'll have Ken Stillwell, our CFO, and Chief Operating Officer, talk a little bit about tokenomics, and then he'll give the financial update, talking about driving durable cash flow, which is a huge focus for us, and we've had great success with that. And then Ken will wrap up with a Q&A session.

My expectation for today is we'll run about an hour and 45 minutes, plus or minus 15 minutes. And every year, when we do this investor session right after Christmas, I go to Ken and I say, "hey, Ken, we've got the investor session coming up." He's like, "Peter, the investor sessions in 6 months." I'm like, yes, I know we got to get started. We got to get planning. So I actually talk to our investors, talk to our sell side. We try to craft an agenda that really fits your needs and answers the key questions that you have.

So I'm very excited to have Alan come up here and talk about our strategy. So Alan, if you want to come on up. Thank you.

Well, then I know what they are because I have a couple of favorite slides. So let me thank everyone for coming. It's always a nice one. I think investors have a chance to really understand what's going on in the company. And that's especially true at this time of immense confusion in this market. I mean just craziness.

But giving you a chance to actually see how we position ourselves, see the product and where it's going, talk to actual clients and understand what their motivations are. I think that should provide, hopefully, great insight for you. And I'm going to show you a couple of slides and then just throw it up into any Q&A that you might have for the remainder of the time that I've got here as well.

The critical thing about Pega, I think was reflected in my keynote this morning, which hopefully, all of you or many of you at least had a chance to see, which is in this midst of all this confusion, Pega actually has a very cogent strategy to bring predictability of outcome and predictability of cost to a technology which though enormously exciting has neither and this was not something we just stumbled into. This is something that was very much planned contemplated, tested, validated and exercised and plays to the history we have that goes back decades.

When we saw what was going to happen with generative AI, and it was hard to predict how fast this would keep happening but at the end of 2022, it's pretty clear that something remarkable had occurred that was going to give us both the opportunity and the requirement to rethink large chunks of our business, we had to answer a bunch of questions.

One question is, well, where and how would this fit? And the answer is, of course, that it fits in more than one way. But the primary way that we thought it would fit would be to help you organize this agentic approach to workflows. That the workflows which we had built our company on and we're such a core asset, our understanding of how workflows work, our understanding that they're constructive of stages and steps and service levels, a greatly nuanced experience, having done this through many generations, we said we can use AI to enhance that understanding by being able to address the things people find hard about using our technology, but to use the technology in a way that will give them extra advantages compared to the alternative.

So this idea of taking the workflow is the central element of what we wanted to create. Using AI at design time using Blueprint as its name to both challenge and stimulate and capture our AI and our knowledge of how workflows work, now being able to also incorporate the IP of partners, being able to do this at a design time would allow us to really exercise the workflows have candidly many more of them than you might have historically, so they could be even more precise, but to do it more easily with less training and education with greater reliability with greater quality assurance.

And we thought, boy, that would just be really excellent because we've been trying for years to make our products easier to use, more accessible to greater populations, available to more of the market and even heavy going. We've been making progress, but it was a lot of work. We said we can use this AI through this Blueprint concept to just radically change that and move from a very conceptual way of talking about workflows to a very practical one, which is, hey, describe your business, and we're going to show you what it would look like if you wanted to run it in a predictable fashion.

And we said, in this environment, you will have agentics a couple of different ways. One, you will have conversational agents. You will have people who want to talk to applications because applications, the old way of applications being thought of as rigid boxes and screens, we thought that, that was going to go away. And I would say that, that decision set turned out to be pretty good. And we said we're also going to want to from the steps in a workflow, call agentic technology, call like a risk control agent that one of our customers may have written for the bank or call an agent that would do document processing and parse fields off of documents or just call it out on directly.

To summarize something and create a summary, but we said if we do this right, all of those calls should be highly deterministic, they shouldn't hallucinate because that's not where the language models hallucinate, they do really well with like micro speech translation and conversational elements or being able to move fields around, they did fabulously at that there. Where they hallucinate is when they start to reason, when they start deciding how they're going to go about doing things. And we said, well, let's use design time to challenge them to reason, to get the best thinking they have, but actually show it to a person and if I'm showing it to that person be able to actually have a reliable, predictable workflow that's going to run at run time.

And then at run time, have that work in a way that can call agents but also just hold together in this sort of predictable system. And of course, being able to open not just to our conversational agent but that any agent on the front end should be able to call us as a resource knowing that if they use us as a resource, they'll have the reliability of workflows in what they do. And then to carry this along to say that we have customers with not just 1 or 10, even many dozens of Pega applications, we're going to want them to be able to hook these into a fabric so that the idea of a fabric control agent, which is candidly really an awful lot like an application control agent.

It basically takes a set of stimuli, looks to see if there's a workflow that knows how to deal with those sorts of stimuli if it does, snaps to it and executes that this would let us achieve the vision of excellent predictability, it would also let us break down the silo walls between apps because being able to snap to different ones means you can get to the workflow wherever you want to get to, and then get to another one that might be somebody else.

And this is the original vision. And we also thought the good thing about this is that this is going to be very frugal with its use of all these tokens that the LLMs are giving us for free. Because I will confess, we were perhaps a little suspicious. We got more suspicious when they suddenly announced that they were going to build data centers for -- remember, they announced they just send us hundreds of millions of dollars. And then it was billions of dollars and with hundreds of billions of dollars.

And now no one talks without using the keyword here as well, and maybe we're going to send them to. We said they're building technology to drive consumption of tokens. And we shouldn't put ourselves at the mercy of having them drive that unless there's some real added value. And by the way, not only is there no added value having them drive the consumption of tokens by doing reasoning at run time, there's a huge negative in which you can't describe to people how things are going to work until after they happen. So we went hard down the notion of burn the LLMs, tokens like crazy at design time, really exercise that. For everything you design, you're going to run it, hopefully, thousands, tens, thousands, hundreds of thousands of times. When you're running it, only use the LLM for those very narrow tasks where you need to do a translation or something needs to happen.

And by the way, in a lot of those cases, you can lose a lot cheaper LLM because if all you're trying to do is pass parse some language, you don't need a $1.7 trillion parameter model. So we would have all sorts of optionality. I can't tell you how thrilled I am now that in the last 6, 7 weeks, this term tokenomics has come out because in the early free drugs days, which were, say, February, you never heard that. You never heard that. I hear it all the time.

And sometimes, it's nice to have an architecture that was built to take the customers someplace that should be very special for them. And candidly, that's what led us today to say that in Infinity 26, we're not going to charge for tokens. It's not because we're using so many tokens but are going to underwrite it, which, by the way, is what the models did is that this style of use treats tokens as if they are something that should be treated with respect, which, by the way, I think they should because the other consequence of tokens, is burning forests.

And the incredible amount of electricity takes to run a geographical processor unit to come to a conclusion, which -- and when I'm over in Europe and I was talking 2 weeks ago to some folks in describing what this architecture was. And I said, and by the way, this burns way less trees. This is terrifically better for the environment. And they said, yes, we still like that. Now here, I don't know, it depends on the times, et cetera. But given that it costs money, everybody likes that as well.

So I think we're in a terrific position with our architecture and with this positioning here. Despite the fact, I cannot overstate the complete amount of confusion that has been created by the LLM companies basically who we use and love, right? But declaring war on software and trying to say that the entire TAM of the software industry is something they're just going to wipe out. I don't think they're wiping it out any time -- actually ever, certainly not anytime soon. And there are parts that are vulnerable. There are elements of software that inevitably when big inventions happen, have to change.

But the ability to manage work in a predictable way by using intelligence and then leveraging the intelligence isn't something that I see going away. In fact, I see that something that's going to be required much, much more. That's what our best and -- for those of you who are investors, I hope it makes sense to you. I will be happy to answer any questions.

Looks like Steve Enders from Citi has a question.

Steve Enders from Citi. I want to ask just on the tokenomics discussion. As this has kind of come in to fold over the past few months, like -- where are customers at in terms of their understanding of the budget consequences? And is there any change in terms of their posture for how they're thinking about leveraging the model vendors versus looking at Pega or other software to help them through these challenges.

I think the customers are trying to figure it out. The customers are hearing amazing and wonderous things. They're trying to figure out what's right. The level of skepticism and suspicion has massively risen in the last month. And this conversation that we're having, I will tell you, is resonating with the senior executives that I was talking with. I was talking with the CIO of a Fortune 50 company who has a travel ban and wasn't sending anyone to PegaWorld.

And on Thursday, talk here for half an hour and said, look, I need to show you a slide and basically showed, well, it wasn't quite done yet, but that slide as I looked on Thursday. Anyway, I just met her two people who she said. She said, we've been talking about this a lot. We're not sure what's going on. And you guys obviously know something that we want to learn more about. So I think we have an opportunity to do some education here, but the cacophony out there is huge, and it's a very confusing moment. Having said that, when I see our customers get up, talk about Blueprint when I see what we're doing with Blueprint because Blueprint, remember only took us part of the journey.

When we talk about now bringing the AI Blueprint into the whole entirety of the journey. And being able to do that as soon as this summer, I think that the customers are going to be a lot more excited and receptive to them.

Blair Abernethy with Rosenblatt. Just wanted to ask you a little bit about how you see the opportunity within your installed base now, particularly around the ability of Blueprint and the other technologies you're adding to accelerate the transformation off of legacy.

So the -- I think the installed base has been hampered by the fact that when we built Blueprint, we built it really as a way to get started and to conceive a new application. I mean it was conscious decisions. You couldn't do everything, particularly when trying to do something as aggressive as that application of technology. So we made a very conscious decision that we would use Blueprint as a vehicle to get people to understand and reimagine what could be done.

And as part of that, we really didn't have a great answer for my customers' existing systems who Blueprint would not affect very much. Now if they were building a new workflow that many of our customers have added a new workflow, they can add a new workflow at Blueprint, and it works great, and we've got clients who have done that. But in terms of adding on to an existing application, which is an awful lot of what customers want to do. We didn't have what I would say is a great answer of how we bring AI into that part of the development process.

Now we do. By taking the Blueprint capabilities and putting them in this Infinity studio, I think it really is going to enable existing customers to look at evolving their existing Pega systems tremendously more. And I think it will make them a lot more receptive to the whole legacy transformation message because you're not making them do something new. We never intended to leave our old customers at sea. We just had to go about this in a certain way to have a chance to be successful with something that there was this consequential to change. Does that make sense?

I have a question for the audience. Do people understand reasoning tokens? -- reasoning token? Some people are nodding someone. So something really wonderful happened in the last 6 weeks. Claude and OpenAI both added these new models. They -- first, they claimed they were dropping their token prices. So they dropped some token prices on some old stuff. But they added new models that don't have dropped token prices. And these models, you'll see them if you use them, and I'm sure all of you are using these at some point or another, say things like thinking or canoodling or they're going through a thought process. What they're doing there is generally called reasoning. If you want to talk to your LLM about this, they'll tell you exactly what I'm about to tell you, which is you used to think you know how many takes you were using. You used to think that you'd know that if you asked a question or put something in or uploaded a document, you might use 100 input tokens.

And then the thing would think and it would give you maybe 400 output tokens. Now it turns out the input tokens are 1/3 the price of the output tokens. But all right, I got 400 tokens. I can calculate the price, et cetera. I kind of know what's involved. By the way, the whole fact this industry called them tokens, I think it is just an example of the BS that's going on here because there's a mathematical relationship between tokens and words, and they could have called them words, but that would have been more transparent. So tokens, that's obviously a lot more mythical here as well. Anyway, you got these input tokens, you get these output tokens. Do you think that's the tokens you're using? No, no, no, no, no.

Your LLM, when it's doing that thinking is generating tokens for itself. If it actually decides as many of these do, that it wants to split the problem up and create what's called the subagent to go off and research a little bit of something, it uses tokens to go talk to the subagent. And then it pulls those tokens back together to try to figure out what the conclusion is. It's not atypical for the total number of tokens that you get charged on to be 5 to 10x the number of tokens in your input and output, 5 to 10x. And you don't see them to your bill.

Now they may have changed this in the last 2 weeks, but 2 weeks ago, I asked Claude, so can you tell me how many tokens we just used? I don't have that record. You'll be able to see that on your billing. This is, I think, indicative of why when these new technologies come out, I think good organizations, wise organizations always ask how can this operate against us. And I'm so glad we did when we were making the design decisions about how and when and where to use LLMs because they're not done yet. They have to raise a lot more money for them to get the IPO valuations they want.

Austin Cole, Citizens. Maybe just to continue the discussion there a little bit. Like on the one hand, we're seeing token usage just go up, which is indicative Anthropics revenue exploding. On the other hand, you talk to some of the customers here in large enterprises that have massive barriers in terms of their cyber teams and IT teams.

But if AI is anywhere in the conversation, there's a million approvals that need to take place before anything gets implemented. So what -- what are you seeing that gives you faith that maybe this AI kind of token usage only in the design phase really rather than run time? Like is that a value proposition that's going to resonate for in terms of getting more adoption of your solutions through those barriers in cyber? Or is it the case that people are kind of just jumping in with token usage just because AI is a mandate and it needs to happen. So how do you see those two playing out?

So I think that be able to point out to people the difference between design time and run time and say that you get to cure rate, you get to put a human in the middle of the workflows you're doing, and then you get to execute them. Like if you like, my chef recipe an allergy this morning, you get to approve the recipes. And then the sous-chefs will go off and we'll cook them all. I think that has, and I know it has provided a lot of comfort to some organizations who are extremely concerned about reasoning at run time. So I'm certain that, that is helpful.

Having said that, there's still an enormous amount of confusion here, and it's going to take months, maybe quarters for the confusion to a bank just because look at how obscure some of the languages that people end up using and talking about here. I think that the fact is somebody can use a Pegasystems to do serious automation without actually using the AI at all because we have the workflow engine there at the heart of it. And I take a lot of comfort in that. It's not like there's like magic that's been clued in. This has been very carefully architected in to the way that workflows work. And I find that customers seem to understand that.

Thank you so much, Alan, for answering a few questions. We're going to move on now to Don Schuerman, our Chief Technology Officer and Head of Marketing.

All right. Just a color answer to that question, just with real-time kind of fact. I was meeting yesterday with a European energy customer. And -- there's a lot, especially in Europe, increasing concern about even using some of the U.S.-based models, right? So there's a whole bunch of European models. They're actually -- they've made the decision to stay off cloud and deploy entirely on their own GPUs for a whole bunch of security reasons.

But I love Blueprint at design time. And the reason they love Blueprint at design time is their security people actually feel much safer because they're not putting their PII data. They're just using it as a design time tool to mark out their processes.

So actually, as our clients have increasing sort of security concerns and in some cases, especially in Europe and APJ, regionality concerns about the use of these different models, especially in the cloud. Design time gives them a nice way in to be using it where there's a lot less risk mainly because there's no PII data flowing through it.

All right. So I'm Don Schuerman. I'm Pega's CTO. I also head up marketing, which people always ask me about. I always say that CTO means Chief Translation Officer, especially today, a lot of what I end up doing and my team ends up doing is translating technology into value and also making sure that we translate our clients' need back into what Kerim and the product teams are building.

But the other thing I think increasingly is marketers and CMOs are increasingly becoming system architects. We're increasingly actually building marketing stacks that connect agents and workflows and some of the data decisioning that we do to actually activate and execute our marketing. So it's an interesting time to kind of bring some of these left brand right brand things together. I'm going to be talking a little bit more about that tomorrow.

But I want to focus on today just sort of where we think Pega's differentiation is, where we think we can offer some unique solutions to our clients. One of the things that you'll hear throughout the conference is a real emphasis on, first and foremost, this idea of reimagining work. If I listen to what McKinsey is saying, if I listen to what folks from Bain are saying, Gartner, there is an increasing realization that the value from AI is not going to come from just dropping a model into your business. right, for an enterprise.

The value has actually come from the real hard work of reskilling teams, of rethinking and redesigning processes and ways of working of, in some cases, reorganizing parts of the organization. Like there is a true reimagination that needs to happen inside the enterprise, a true we talk a lot about digital transformation. There is an AI transformation that needs to take place. And we are really excited and have great feedback and response from our clients around the ability of Blueprint to play a role in that specifically getting them thinking differently about how they are either workflows and how they start plug agents and AI into it.

So we're continuing to lean into and really, I think, use Blueprint as a key tool, both early-stage conversation engagement with our clients, but also deeper and deeper into the design and delivery process to really help them through that transformational journey. The next thing we are going to be talking a lot about is this need to run predictably, right, Predictably both in terms of outcomes. The models increasingly, we had Gartner speaking here yesterday. Gartner was citing a stat that some of the models are getting to 85% accuracy rates. I've heard other things from the vendors approaching 90%, 95%.

But when I go talk to someone who runs operations at a bank, 95% accuracy is a 5% failure rate, right? They can't live with that. And the interesting thing is if you start building out these agentic systems where I have multiple agents talking to each other, what I'm really getting is 0.95x 0.95x 0.95. So my overall accuracy starts dropping and dropping and dropping. That does not work for the mission-critical processes that drive regulated businesses like banking like insurance like the U.S. government, like you've heard from in the breakouts on the stages today.

We have an ability, because of our strong background in the workflow space to run this stuff with predictable outcomes. To have it actually run in agents, running self-service agents and conversational agents that talk to customers and employees, plug in agents to automate tasks that maybe couldn't have been automated before, like research document validation since the sizing information. But orchestrate that in a way that we are using the workflow to get guaranteed responses to keep the things in the business that need to be deterministic. The added benefit of that is it's not just predictable outcomes, it's predictable cost.

And that's what Alan has been talking about, that's what we announced this morning. We are tying our pricing for AI built in Pega to the amount of work that you do, the number of cases, the number of customer service requests or claims you process or investigations you resolve, not the number of tokens you use on the back end. Token is a measurement of usage, not of results. And we actually want to tie our pricing to the work being done, and we believe we have the architecture that allows us to do that in a way that both delivers incredible value to the clients, while also protecting our margins.

So some of the things that we're doing to make all that possible. What Kerim announced today is really taking that design time power of Blueprint and pulling it into Infinity Studio. And what we've done with Infinity Studio, and I'm frankly amazed at the speed at which the product team was able to do this. But we basically took the build environment for Pega and completely redesigned it around AI. We took all the things that we loved about Blueprint because we have tens of thousands, hundreds of thousands of clients using Blueprint, seeing that experience.

We took all the things that we're working on that experience. We pulled it directly into Infinity Studio. So there's now an incredible consistency across those experiences. We took the AI engine of Blueprint that has our best practices and increasingly our partners' best practices for how to design these workflows and how to apply them to different industry use cases. That is now available directly inside of Infinity Studio, which opens it up not just for new workflows, but for our clients who are looking to evolve and extend all their existing apps on Pega.

And then we added to Infinity Studio, all the power of the AI coding agency went off. So we take Claude, we take GitHub Copilot, we take OpenAI CX -- we take Ciro from AWS. We have our own offering there. We plug that right into Infinity Studio. And the important thing is we're not just plugging it in, we're actually injecting through some really smart sort of MCP skills, all of the best practices of how to build in Pega directly into Infinity Studio.

And as I'm going to show you in a second, I think it dramatically lowers the bar of entry for people being able to come in and build meaningful mission-critical applications in their Pega environment. This is the only platform that hails that full life cycle that lets you reimagine the work that you want to do that lets you take AI and use that to accelerate your build not only accelerate it, but make it better like ensure best practices are followed to ensure good architecture and design is follow it as you build it out, and then run this stuff with a high degree of predictability in the outcome, a high degree of predictability in the cost that you're going to pay and give you the foundation and the right architecture so that you can change it and evolve it.

Like one of the things we're seeing with some of these coding tools, they're really slick at building out a prototype of an app. Right? You can ask Claude Co. to go generate a bunch of stuff. And then a developer asks that Claude to go do something really simple like change the label on a field. And the label on that field is buried somewhere in 1 million lines of generated code. And the more lines of code Claude has to go through, the less successful it is and consistently finding it. And you, as the engineer, even though you know this is a really simple fix, you didn't write the code, so you don't know where that label lives.

So what should have a really simple change becomes an excreasingly complicated and expensive change. Because of how we architected Pega, we avoid that because we know that our clients, the value comes not just in the first release, the value comes in the third and the tenth and the 100th. So we want to make sure that continuous change stays there.

A couple of things I'll point out that we added into both Blueprint and Infinity Studio that I think are worth noting. First is this integration designer. And I'm more than happy. I'm sure [ Kuvan ] his team would love to kind of take you through some of the gory details of how this maps into things like gas and MCPs and we could just talk about all the various 3-letter acronyms this thing supports. But here's why it's important. The #1 challenge of making the stuff work in the enterprise is getting it integrated into the stuff that already lives in the enterprise.

These mission-critical workflows are not self-contained inside of one system. They're not self-contained inside of a single platform ecosystem like Salesforce. They need to grab data from Salesforce, and they need to update data that lives in an old mainframe system. They need to pull customer information from Adobe. They need to pull all those pieces together, right? That integration is both a large part of helping our clients understand early in the sales process, the level of effort that's going to be needed in order to bring the solution to live.

And it's also a big part of the work that then actually needs to get done by us, our partners, our clients during the implementation. So integration designer both pulls that conversation earlier in the process. So we know even at the blueprint phase exactly what the integration work that is going to be required is and then it uses the power of AI to accelerate and automate a lot of that integration work so we can deliver faster and more accurately. So this is, I think it was a really powerful addition.

The other thing that we've done is this Infinity studio experience, right, which, again, leverages Blueprint, both from the design experience, but also from an AI engine capability directly inside of the studio. It does things like automatically generate your implementation plan for your application and then actually uses the AI agents that we can plug in to automate all the steps in that plan or many of the steps in that plan for you so that we can accelerate value. And this is just going to keep getting smart and keep getting better.

So I think this, again, in terms of accelerating that build process I also think it's really important in AI. We love to talk about speed, and I think speed is a great and I think we want to continually to compress the time between when we start talking to our clients to when they actually get value, I'm all about that compression. But I think the thing that people also don't take into enough account is, if you do this right, it also can help us build better stuff.

Blueprint doesn't just help you build faster processes. It helps you build better processes that are more efficient that deliver results to your customer faster. What we've done at Infinity Studio doesn't help you just build Pega faster and actually encodes the best practices that we know make for a well-architected application and helps ensure the application you deliver will be easier to scale, easier to maintain, easier to grow on. So it's not just faster, it's better.

And the way I look at what we've been doing with all of the tools is trying to simultaneously lower the entry cost while increasing the value we can deliver, right? Pega has always been able to deliver pretty massive value to our clients. We've been talking about 40% improvements in efficiency, 30% improvements in cross-sell, upsell rates. So it's long before AI existed, right? So there's already a pretty massive return and benefit that we give to our clients.

With the first reason of Blueprint, we lowered that entry cost., right, because we made it easier to conceive and understand what you wanted the workflow to do. So our initial conversations with clients got easier. We could more quickly get to the point of what we wanted the application to do. We added more capabilities to Blueprint. That further lowered the cost. It made it easier to get something to closer to something that we could deliver.

We also started adding Gen AI connectors. So your processes could automate more. So not only were we reducing the cost, we were actually increasing some of the value that we can deliver, right? With Infinity 26, we've done that even further, sort of step change in terms of both Infinity Studio starts using AI to automate the build.

So again, the entry cost, the skills needed, the time needed to deliver something goes down. But if you start building those workflows with agents inside them, you're automating more work, you're automating things that previously were manual. We're talking to some of our clients about really massive, like 8-figure business cases floating around some of the agentic workflows that we're doing. So that value goes up, the barrier of entry goes down.

And that's with the continued push that we want to make, not just for Infinity 26, but 27, that's our goal is to continue to sort of bridge that gap because that's the value ultimately for our clients. That's why the clients choose is the gap in between there. So I often get asked not just by investors, but by customers, by my fellow employees at Pega, in a world that, as Alan points out, is incredibly noisy, right?

I mean a couple of months ago to me, the state of the market was greatly summed up when Anthropic dropped a blog post and IBM's stock price went down 11%. You guys remember that. Anthropic dropped a blog post, not a demo, not a product announcement. They put up a blog post where they said, yes, cloud could help with the mainframe. Like that's what they said. And IBM stock dropped 11%, right? So we are just -- there is so much noise and swings and hype in this market. So it's really important for us to be very precise on where I think our differentiation is. And I draw our differentiation in three areas.

First, our ability to help our clients reimagine how they get their work done. That is the profound step that enterprises need to take to move from sort of just we've deployed Copilot to we are actually transforming how the business runs. You actually have to reimagine the work. We have the best reimagination tool on the planet in Blueprint.

Next, we need to be able to run that work and we focus on the mission-critical work because that's where the value is, and that's frankly where the hard problems are, and we're interested in hard problems. You have to run that mission-critical work with predictable outcomes and predictable cost because we're probably going to get sick of people on this stage saying predictable. We're going to keep saying it, predictable outcomes and predictable costs, right? We are able to do that because of our grounding workflow architecture that is proven to operate in a regulated industry.

We are the audit system of record for how our clients do things like handle payment exceptions, how they handle fraud, how they handle due diligence in KYC and CLM. We know how to build a governance system that can withstand the pressure of a security in an audit team. So we bring that now to bear on actually starting to deploy agents across that life cycle. And of course, we do it with a predictable cost because we cover the agent so that you're not burning time wasting, reasoning to figure out a process that you already know how to do.

And then finally, we've got an architecture that allows us to continue to change. [ Crib ] showed in his demo I think it's very interesting to easy to get excited about things like command line interfaces and I type into my chat and it does stuff. And sometimes you just want to add a field to a screen by dragging and dropping it. So we want to give you that flexibility so that our clients can change and evolve these systems and not build out this compounding set of technical debt that starts to happen when you've got AI agents generating hundreds of thousands and millions and millions of lines or [ code ].

So we've got an architecture built from the bottom up to really support that change and support the scale that's needed to do this for real at the enterprise. So that's the differentiation we are leaning into. That's the reason the clients are here talking to us couple of days, and we're going to continue to build out on that advantage as we take our products and our strategy forward.

So with that, I want to get Carie because Carie talks to our clients about this stuff each and every day. I think she is going to just be a great person to show you what some of this stuff looks like.

All right. Thank you, Don, and hi, everybody. Nice to see you here. My name is Carie Whalen, and I lead our solutions consulting team here in the Americas. And what my team does is sort of the technical side of our sale and we stick with the customer through that sale and implementation and then obviously expand Peg with them. So we spend a lot of time listening to our customers' challenges, what they want to work on and then presenting Pega solutions to them.

Carie, I think what we should do is we should just start calling your team for deployed engineers since that's the term that everybody -- that seem to be the term of art for what everybody wants to do. And when I hear you describe what you do, which is you're a bunch of technical experts who spend time with our clients to really understand their business, help them envision what their business could do and then stick with them through implementing it and getting success. Boy, that sounds a lot like a forward deployed engineer in a lot of ways.

We do. We do spend a lot of time with the customers, both in presales and post sale. So what I'm going to do -- I know you guys have seen Blueprint a lot. I know you've seen the demo that Kerim did this morning. What I want to do today is kind of talk more about what it looks like when I'm in the field with customers and how we work with Pega that way.

So Blueprint has changed my life and my team's life because it used to just be that over there. Can you guys see about whiteboard? That was what I used to have to use to talk to my customers about their business challenges and I'd whiteboard it out, I don't even have that great of handwriting, right? So it was circles and squares and trying to get those things in front of them, take a picture of it, document it, come back a week later and try and demonstrate something to them and see if it resonated.

Now imagine how much better my life is, my customer's life is and my team's life is like a using Blueprint where I can just come in and say, hey, I'm talking to a banking customer in retail banking and I'm going to give you guys an example today that I actually worked with a customer on around their complaints challenges. So a lot of banks get a lot of complaints. And in this case, we're talking about 2 million complaints handled per year at this bank. And right now -- or actually before Pega, they were using 19 different systems, and it would take weeks to process a complaint sometimes in order to get that done.

And keep in mind, the complaints for a bank it's actually a pretty fraud moment, one, because of the customer relationship, but two, because banks have an increasingly complicated set of regulatory obligations on behalf of a customer complaint. If complaints are related to fraud, if complaints are related to discrimination, if complaints are related to, you sold me a credit card that I shouldn't actually have been allowed to have. And now I've got like all of those things are moments of truth, not just for the customer relationship, but actually for the bank's very licensure in existence. So you have to get this stuff right.

So back to my interaction with the customer, I sit down with them and I say, okay, what is the thing you want to get done. Tell me a little bit about what you do today. Tell me what you want it to in the future. And we write out a functional description of the application, which I paste it in here. You guys don't want to see me type that whole thing.

And I tell you know what, I need something that can handle complaints, the information, make sure we're communicating with the customer and we need to do it in a certain amount of time. So all we give -- that's what we give the LLM, that's what we're giving degenerative AI and Blueprint that's now going to take all of Pega's best practices that Don talked about and take that into that generative AI that's going to help me design it. Now we can also do more now. So one of the capabilities that I love within Blueprint that I use a lot with customers is the supporting assets capability. So most of our customers have process documentation.

They have Cobalt code. You heard about that this morning from [ Unum ]. We might send that into AWS transform. We then upload that information up into Blueprint. So in this case, I'm uploading a complaint specification. I have a walk-through video of their current mainframe application that they're doing that I'm giving Blueprint. And I've done some AWS transform on that.

All of that is going to feed into that generative AI design that Blueprint is going to do for me, again, without my trustee whiteboard and bring back something that my customer and I can then react to very quickly. So this is something we do in first meetings with customers to really get them excited about seeing their problems in Pega very quickly.

Now the first thing you come to in Blueprint is a case. And a case, just think of it as our way to organize all the information that our customer needs to get work done. So it's like a digital brief case of their data, their integrations, their personas, all the stuff you've heard about, we put all of that into a case and then we build out what we call the workflow details. And Blueprint is going to do that for me.

Now again, I want you to look at the whiteboard and see how blank it is when I walked into the room. With Blueprint, I just don't have to react to a blank page with the customer. And 90% of the time, when I'm pulling this workload up with the customer, they're saying, oh my gosh, that is my workflow. How did you know this is amazing. So I get them to like get something to react to a very quickly where we're not just standing there and asking them 100 different questions.

And I think it's also -- one of the things we talk to clients a lot about and having spent a lot of time talking to clients about their processes over the last deck or so. The big challenge the clients have is also often a challenge of imagination, right? The subject matter experts on their teams have been doing this stuff the same way for years. And so their inclination often is to just automate the thing they're already doing, right?

We call that Paving the CAL Path in -- that's a very technical term. But this is a moment where Paving the CAL Path doesn't work. You actually need to rethink where these processes go. You need to rethink what you used to do, I was meeting with one of the largest banks in the U.S., they're using this to reinvent their KYC/CLM and the head operations globally for all of their CLM work was basically saying, there's a whole bunch of stuff in my process that we do just because we've always done it, not because we actually need to do it.

And Blueprint is her way of like cutting, it's the weed whacker for all of that. It's like cut through all the mess, get me a clean version of what the process could look like. So I can build something that is now ready for me to start dropping agents in. And the other thing you'll notice is Blueprint is actually even suggesting where agents, all the purple spots on this are places where Blueprint is saying, you could drop an agent in here. You should be dropping an agent in to do this. It's actually pushing you towards the agentic theater.

Yes. And again, it gives us something to talk about. And it's great because the customer will be like, no, no, no, that's not how we do it. I do this. And they go, okay, I can go ahead and make that change. And I can actually -- a pop-up there. I can actually use an AI assistant to help me. So I don't have to click and go into and code anything. I'm a business user and I can get my business users for my customers, actually hands-on sometimes. I'll just turn the laptop and around and I'll say, why don't you tell the AI assistant what you want it to do?

And in this case, I want to create an alternate stage that gathers more data and evaluates that the complaint can be auto resolved if there's a low-value fee dispute. So I'm able to quickly tell the AI agent, how I want to edit my Blueprint and it's going to go off and work on that. It's going to check with me before it does anything because I don't want it editing something for me necessarily. And it's telling me that it's going to add that additional step. So I'm going to say, go ahead and proceed with adding that.

Now for the customer, they're able to see now an alternate stage that they want it on the screen. It's already formatted, again they can react to it. It's given them some new ideas about how they might want to handle it, where they can put those more deterministic rules in the workflow where they can put those agentic flows and workflow and do all of this during design time. Don, I was thinking about this when Alan was talking about design time versus run time. So we spent a few weeks on this like really refining this with a customer and getting the design done and interacting with Blueprint. They're running at 2 million times per year every time they get a complaint, this runs. So that's a pretty big different...

I'd much rather invest all that token is getting the design right and then the 2 million times I'm running it, I certainly don't want AI re-reasoning through that workflow every time.

Yes. So the big takeaway for everybody in this room, I think, on this, is the AI capability in here that gives me that assistance as I go through the Blueprint. And then also the guidance that I get from where I should put maybe some agentic processes to speed up my workflow. Now the next thing, and this is the new thing that Kerim talked about that I am super excited about as a former implementation consultant, the thing that takes the most time when you're implementing any product is figuring out how you build the integrations to the systems that you're going to need to interact with, right?

It takes a long time to get these requirements. It takes a long time to figure out what that looks like. We are bringing all of that forward with this new capability in Blueprint with this integration screen. It may not look that exciting, but it is very exciting to me and my team because it means that we can get to that build that much faster. I can get an offer on the table during the sales process very quickly. because I now know how many integrations I have to create.

So it's been a huge game changer for us in the field and for our customers to be able to see this. So just a quick refresher, if you didn't see it this morning, on the left-hand side, I'm able to actually take inbound events. So of course, I'm going to get files and things like that when I get a complaint, I'm able to define what those inbound inputs are going to look like into my workflow. I'm going to be able to define what my application data is and what I need there. And then the integrations themselves, I can quickly add the different systems that I integrate with. Our customers have all sorts of different things in their ecosystem. They might have sales force.

They might have Workday, but we're always working to integrate those and having these prebuilt integration capabilities as well as the ability to add anything we want from an integration perspective, just speeds up that process. So this is huge for us and for our customers in getting these things done very quickly and having those integrations client.

If folks were in the keynote, right, one of the things that [ Omron ] from AWS talked about was what we've got to be calling 333, right? So 3 hours to understand a system, 3 weeks to build a prototype, 3 months to get something going. This is the kind of capability that allows us to drive that because we're actually accelerating the hard and important work that it takes to [ be ] client to both prototype that they can really understand and get their hands on and then ultimately take that into life. So that's why these investments are so important for us because they actually drive that capability and that certainty outcome that our clients are winning together.

And once we've defined our integrations, another important stuff is figuring out who's going to interact with this workflow, right? So that's our personas. And Blueprint gives us the ability, again, in the presales process, we're already starting to build with our customer what it's going to look like, who's going to be able to interact with this, what data will they have access to?

How can I set these up in the very beginning. So we've got our customer service representative. We've got our compliance officer as well as our customer defined here. And again, this helps us capture that and design that and start the build very, very quickly. I can also turn features on and off within Blueprint if I want to.

So in this case, maybe I want to turn on e-mail and then we get to a summary. So I've been up here maybe 10 minutes, maybe a little bit more. We just built the design of an application. So when I show this to the customer, I say, "Hey, guy, we just built five personas and channels. We just built a bunch of workflows. Isn't that cool? They're like, yes, we just got that through that so quickly.

And I say, okay, I'm going to blow your mind because now I'm going to -- oh my god, no, my chat is one. Now I'm going to show you what my application is going to look like for you. So I can go directly into preview my app with the customer and show them that we're able to see the look and feel of what their users are going to experience with Pega. This is sort of that jaw drop moment for the customers where they go from, hey, that's a cute design. That's really nice, Oh, wait, it's an application. Can I touch it? Can I feel it?"

So we've moved from sort of showing customers what we can do with Pega. Oh my goodness. I don't know how to get that to stop. Okay. So this is how we get the customers to get their hands on Pega very, very quickly. We can see what it looks like to go into the mobile application. We can see what it looks like to actually talk to your workflow. And this is my favorite thing that we do with customers on. I know you like this, too, where they can actually come in and hear and have a conversation with the agent and interact with their workflow. So I can actually ask this workflow. How do I complain, right? So we just built a complaint process, tell me what it is I can do to...

My kids ask that question all the time.

I'd like to lodge a complaint. I can also call this blueprint. I know it sounds crazy, but I'll actually pull out my cell phone and let a customer call their Blueprint and their workflow that we just created. And they're able to interact with it and say, hey, I have a complaint, I met this bank and the teller is making me very angry. And they can see how Blueprint will react to it and how Pega will react to it. So it's a pretty effective sales tool and getting them, again, to experience it, not just display it.

Now the other thing that both Don and Kerim mentioned that is super exciting for us is this MCP agent integration. So think of this as the way that we just created a workflow together again in a few minutes, I am now able to make that available for MCP for anybody to call me to do a Pega workflow. So if somebody is using cloud, they can call me through MCP. They can run the workflow that we just created. This makes it so quick and easy, literally, you just copy and paste lines of code and you're ready to go.

I mean, this is -- for those of you who are -- folks use Claude or any of the opening AI tools, how you can like customize Claude, you can add connections, you can add skills. Basically, the workflows that carried [ out ] in 10 minutes and really 5, if I had stopped interrupting her, we are basically now skills that you can use inside of Claude inside of Open AI inside of like instantly, right? Like -- and this is -- this means now for our clients. They've got different front ends, they're using different agent front end. Pega's workflows will pour any of them. And the instant new plug our workflow in that starts acting predictably. That agent doesn't do a lot of reasoning. So the cost you're spending on it drops because it actually is just following the process. So it's a way to start embedding mission-critical work into any agent that our client has.

We're at the end of our Blueprint.

Excellent

And Blueprint has given me some recommendations. So it's even guiding me in the design process on best practices, like hey, you may have missed a few things. We're good, though, I feel like we've done a good job of building this out. So I'm going to take my blueprint and pull it into that Blueprint AI capability that Don was talking about an Infinity Studio.

Now it may look the same as Blueprint, but what this is -- and that's on purpose, is it looks more like Blueprint than ever has before. In the before days, before Infinity Studio, Pega actually had two different design studios that our customers could use, App Studio and Design Studio. And it meant that you had to go in and code some things, you have to be fairly technical, kind of hook things up.

With Infinity Studio, we are making it very easy to design your and build and produce your applications very, very quickly. So it should look and feel a lot like Blueprint to you because that's what we're trying to do. So if you'll remember, we brought in that workflow that we built around the complaints, right? So you'll see the intake, the evaluation, all that stuff is in here. And now that I'm in Infinity, I want to kind of refine this a little bit more, and I want to think about how I'm going to put this into production.

So again, I -- in moving into a world where Infinity doesn't need the most technical users to implement this. It's going to make it a lot easier, open us up to a lot more types of users it. And so in this case, I'm going to use my agent to help me. I'm going to add an agentic investigation step, generate some test scripts, and wire in something we call the document summary agent. So we have this new capability called Doc AI that I'll show you once we get in there that's going to help me add a user on the back end of Pega, analyze that stuff.

So I'm going head go and my agent is going to run, and it's going to do my work for me. And it's going to come back and let me know what it did. Now in order to go to production, I have to create some test scripts, I didn't want to have to do that. the AI agent just did that for me. It added my new investigation agent, and it's all right there.

Now I wanted you guys, the visual of what Don was talking about before as far as hundreds of thousands of lines of code that Claude is creating. And if I need to make a change, it's a heck of a lot easier to come in here and look at a workflow and say, "Hey, I actually need to change my business rules in this initial valuation. I can click on the initial valuation. I can come in here and I can edit my business rules, right? So it's a much easier way to maintain the change that's going to have to the regulated industries, maybe I changed my rules in the bank as far as who I want to allow to have a specific credit card, and that business logic is very easy and visual to change.

And not just change transparency right? Because if I'm a bank, if I'm running mission-critical work on this stuff, I'm constantly being asked, well, what are the business rules? What are the controls? Where are you checking right? I can just literally show it to you. I don't have to go find it somewhere in a bunch of code that was written by an agent where I don't actually know where the rule lives anymore, right?

That, again, for this type of mission-critical work that our clients do is a game changer in terms of how they can use this stuff with an increasing degree of certainty and confidence.

So we built our workflow. We're going to put it into production. And one of the things I want you to see a part of this is we've got a combination of those deterministic rules, right? So I have very specific rules as a bank as far as how I'm going to handle a complaint. I'm not going to let an agentic reason come up with the way that I'm going to handle a complaint, right? So that's built in. But we also have agentic capabilities already in here. So I want to know if this is potential fraud, right, when the plant comes in. So I can actually call an agent that's going to calculate the fraud risk score for me.

I can also have an agent that goes off and does an investigation for me and get that done. So after we have built this out, we are able to then launch this into production and get it ready to go for the customers who are going to interact with it. And again, that could be in any channel, right? So when you think about our center out architecture, you have to think that we're building this once right in Infinity and then we're able to then push it out during implementation into any of those different channels.

And that increasingly now that channel is agents, right? That MCP connection that Carie showed you now means that for our clients who are saying, "Hey, we want to deploy self-service agents. In fact, we're working with a bank on building an agentic complaints workflow like this. And the way that customers are going to enter those complaints in is not by a form, they're going to go to an agent and say I want to register a complaint. And so now we've plugged these workflows in. So when the client says that, the agent isn't randomly asking them stuff. It's going through the workflow and saying, what data do I need to collect? What's the steps I need to do what do I need to tell is going to happen next. All predictably driven through that agent.

So we're going to take a look at that live on my U+ website. So this is the bank that Pega's created to show you what this might look like. So U+ already has their own chat center set up, and this customer comes in and I am upset and I want to log a complaint with U+ bank because there's been a charge on my credit card. So I can very easily start a chat with this chat agent.

Now on the back end, what Don just said, is it's going to use that workflow that we just built to come back to me. So it's empathetic. It says, hey, Larry, I'm sorry, that you got charge that $29 late fee. Is that the one you're referring to. So it automatically kind of understood what I might be complaining about. It also allows me to respond and take in information from me and go back and forth, again, using that workflow. And in this case, I'm asking that to go ahead and process that. So that's kind of how we integrate with the different channels.

Now that could have been again the phone that could have been a different interface. But in this case, that's what it looks like to be a customer interacting with the workflow. Now I'm going to take you to the back end of Pega if I'm a bank. So now we are users of Pega in the bank, the people that process those complaints within the bank, and we are going to actively work through this complaint.

So what I see when I log in as this complaints adjudicator is all of the different complaints that might be out there for me. I can see the work volume that I have, and I can even see note on some cases that are out there. I see that there's one coming in from Larry Clark about his dispute. So we're going to go ahead and get that work done. Now again, on the back end, this is the workflow to keep me through the process of getting that work done.

One of the things I can see as a user is the supporting documents. And again, I plugged that document AI capability into this application. So it will automatically summarize the documents that I'm feeding to it, giving me a very quick view of what that looks like. So what I'm showing you guys on this back end is the ROI that our customers are seeing when we automate their workflows and make this that much easier.

Remember before, they were using 19 different systems to go in and find that file. They were trying to find out where Larry was. We're bringing that all together in one place for them. And that's really the impact that we have for these banks from an ROI perspective. So I'm going to go ahead and work on Larry's case, but it happens that I'm kind of new to the bank. And this happens a lot with our customers have customer service representatives with very high turnover or back office workers with very high turnover. It's hard to keep everybody trained.

But luckily, we're using Pega and I have this Gen AI coach that's going to use that generative AI, that LLM to help me summarize the case and then I don't know what to do next is the user. So I can actually ask the Gen AI coach, what's the next step I should take? So we can actually guide the back office users, again, making it easier for them to do their jobs, making it faster for them to do their jobs and complete these complaints that much more quickly. So it's telling me that Larry is a premier customer. We should probably waive the fees and say we're sorry, maybe give them a little extra. So I'm going to go over here.

And again, a time-saving capability that we built in here is I don't have to type up my notes. I can actually just fill out the form with the AI and double check it as a human and make sure that, that looks right. So we have worked on Larry's dispute. We're now going to be able to get back to them very, very quickly. So I'm going to submit that and the claim is completed, right? So we've addressed the complaint.

And then to finish the story with Larry, Larry gets a text as part of the workflow that says, hey, Larry, we've completed your -- we've taken a look at your complaints, and we were happy to refund you that money. So you guys have seen how we are changing the design that we're working on with customers upfront, taking a lot of time out of my sales process, making it much easier for customers to get their hands on upfront. We built that integration step into the design to make sure that we can build more quickly and get those offers out the door more quickly for sales.

And then you've seen what it's like to be a customer that interacts with Pega as well as a user that interacts with Pega to really get that work done quickly.

Awesome. Thank you very much. Carie. All right. There we go. A couple of quick things we get the slides up.

I just want to -- there are two questions that come up inevitably in investor conversations and even sometimes when I'm talking to clients. And I thought I'd just share with you how we are answering them when they pop up. So the first is, we get asked, what can I just code all this? Like do I just do I need Pega? Could I just have Claude write this for me?

In many ways, if you look at Pega's history, we've been competing against build-it-yourself basically our whole existence. That's pretty much every year, I look at our competitive data. And every year, the #1 competitor that we have is a build-it-yourself option, regardless of what other vendors are out there in the market. So -- but to me, even with the Claude code capability, this comes down to two arguments for me. And they both are TCO.

So I call it TCO Squared. The first is the total cost of ownership argument I'm going to show you a little bit about what I mean by that in a sec -- with I think eventually everybody has to show an iceberg slide, so I'm going to show one. But also, I look at TCO as meeting transparency change in operations, right? The transparency for what our clients do is so important. They are running mission-critical work. They have mission-critical rules. And having those rules buried in a bunch of code that their auditors can't see doesn't work for a lot of these regulated mission-critical processes. With Pega, even if you're using Claude code to write it, it's not writing code, it's writing visual assets. It's writing business rules, it's writing processes. You can still go in and see a business person can review and validate the business doing what they want it to do. That transparency also means the stuff is easy to change.

So as this evolves, if I need to add fields, add steps, change rules I'm not hunting back through millions of lines of code that I didn't write. I'm actually just going right into the visual element, and I'm making the change. Or now I'm asking my coding agent to go make the change, but the coding agent knows exactly where to go because the meta data and the context is all captured in the rules.

And then finally is the operation piece, right? The TCO of this I think we often get distracted by just how cool it is that Claude can write code. It's great. We use Claude to write code. I've been in engineering for a long time. the tipping of text into files is easily less than 20% and probably less than 10% of what it takes to buid an app. There's a whole into this stuff at the top of the iceberg that we don't even see. It was actually what do you want to build? What is the right process, what are the rules we need to obligate to? What's the outcome we want to deliver to our customers.

And by the way, most of these stories about people using Claude code have them going off over them by themselves over a weekend of building stuff. Yes. But how do you get 20 stakeholders in your business team to agree that you've built the right stuff, that the process is right. That the rules are right, that the teams are going to actually support and run with the new application. Well, you need a design tool that actually lets people see and test and validate and prototype and debate and arrive on the business process. And that's what we're doing with Blueprint, right?

We can do the build stuff. We can now use Claude code to accelerate the coating stuff. The other stuff, the under the water stuff of actually operating a mission-critical workflow. You need -- you have to have work list. You have to have a service level agreement. You have to have all this stuff. The packages does. And by the way, none of those capabilities are differentiating for a bank or an insurance company or a telecommunications company. So if their teams are spending their time building that, they're not spending their times on things that actually differentiate their business.

So we want our clients to focus on designing and building processes that meaningfully change the efficiencies of what they do and how effectively they respond to and engage and support their customers. And by providing a platform that does that, we allow them to focus on the stuff that matters, not maintaining the stuff that doesn't. So the other question that we get is what about this purely agentic reasoning? Do we even need workflows?

Maybe in the genetic world, there is no workflow. There's just agents, which sounds really cool, except the fact is that businesses by their nature are determined as [indiscernible] like there's a lot of stuff in the business that is deterministic, not probabilistic. Not everything, right? There's work that probably could be purely agentic some of the work that we do in marketing that actually involves like coming up with creative designs and like that's, yes, I can use agents and a lot of that.

Actually, I do eventually need to workflow because I want to make sure the right people approve the asset and the legals looked at it and a whole bunch of other stuff has to happen. But a lot of stuff that needs to run in the business is deterministic. It needs to be predictable, right? I want to follow a set of rules. So if I use my reasoning design time to get those rules and processes right and reimagine for AI, then I can execute them predictably, both with predictable outcomes, right? So guarantee that I'm going to follow the rules, the bank needs to follow, but also predictable cost. I don't want to be burning the reasoning tokens on rethinking the workflow.

And we put a little calculator on pega.com that does this because it's important to understand this calculation is quadratic. It's not linear. Because what happens is as an agent -- if you have an agent reasoning its way through a long-running workflow, at every step of the workflow, the context window that agent needs to manage gets longer and longer, right? So step of the workflow, adds context to the workflow. Now the contracts won to us longer. Next step adds it. And every step, the agent needs to reread the entire contact wind of the workflow to figure out what next to do next. So if I've got a 3-step workflow. The agent power required to think through that 30th step is 30x what it started when I was beginning, right? So that means that your agent reasoning costs go up quadratically as the workflow gets longer.

With Pega, we don't do that. We manage the context for you because we know the context, we know the steps that need to happen. And we focus the agents when we use them on doing the very specific thing in that step. So the context window stays small. The context window staying small those two things. It minimizes drift, which is what causes hallucinations inaccuracy. And two, it dramatically reduces the cost in terms of the number of tokens that you need.

And with that, to talk a little bit about that, I'll hand it over to Ken to talk about tokenomics.

Thanks, Don. So one real quick thing that Carie was talking about earlier. The process before Blueprint was there would be a whiteboard session, she is the example. After the whiteboard session, then you would -- we would close for what would be to go away and try to build a demo. That might take months that by the time we go back and show a demo, you show it to the stakeholders and they say, I don't remember putting that on the whiteboard. And so there was this iteration process of how -- remember, we -- all of that time, we do not -- we're not able to put a financial offer in front of the client.

So that's the big change that we've seen is getting an offer in front of the client so they can consider how we can help them as fast as possible because they're looking at all kinds of other alternatives. And I think naturally, without AI, this would have been hard to get to -- but we -- this is really not a driven by, AI I think, this is actually the way clients want to experience transformation. So this is something that we -- quite frankly, we would have done -- we were moving in that direction without AI. It's just that AI really was a big compelling event for that.

I just want to clarify a couple of things on tokenomics and then we'll get into some financial discussions on the model. Don touched on this. As prompts, prop chains and processes expand, there is a geometric calculation of how much the agents actually work of how many tokens are used, right? Because when you do one process, you have a single prop. As you add multiple processes the spider web of the prompts and the repeated prompts are pretty excessive. Don mentioned we have on our website an AI cost estimator. But when you think about the cost of using props versus using workflow.

And keep in mind, it's not that we are not -- sorry, using tokens for reasoning, all through the process versus the way Pega does it is we use AI where it is relevant and where it is necessary. And so some examples of if you -- in this model, we ran a very specific set of steps and stages in a workflow and we model out what that would be if you actually re-reasoned and went through the process. And it's anywhere -- I think we're a little bit conservative actually on the $151,000. It's a multiple of cost and as the process or as the workflow becomes more complex, that gap becomes bigger because the more -- that more you're pushing scale transactions through those are done through the workflow without any tokens.

So the more transaction -- so I think this is just a kind of a framework of how our calculator work. And I want to I want to jump in. I know that we're -- I think we're even running a little bit past our time, but we'll try to be respectful of getting everybody out of here by 2. By the way, Peter, what time is it right now?

Just after.

1:21, okay. So we've got a few minutes. So once again, repeat safe harbor statement. It's Peter talked about it earlier. The reason why it's here again is because we filed this last section as an 8-K, and I noticed some of you have already actually pulled up the 8-K and are starting to look at the slide, so you're probably ahead of me on that.

But I want to go back a little bit to the conversations we've had. I think one of the most misunderstood pieces of Pega's story. And I always get asked, like what do investors not appreciate? What do investors not understand. Understanding the solution is always a challenge to make sure that because investors are at different points. Some of you are very technically astute.

Many of you just look at the financial modeling. It just depends. Everybody is a little different. But I think one of the things that I think is very misunderstood is the ability for Pega in the business model we have to generate significant amount of free cash flow. And for that free cash flow to be highly leveraged as the company scales. And so I think that is more understood now than it was 3 or 4 years ago, but I still think it's very misunderstood.

I think -- and I wanted to just kind of highlight a little bit of the -- of just going back of what we said over time and how we've actually delivered on those results in the last few years. So we went through a pretty significant transformation in our business. as many of you know, where we moved to a subscription business. And subsequent to that, we moved even harder into cloud into Pega Cloud.

That business in 2018 was about $570 million of ACV. We actually modeled out pretty closely to where our trajectory has been through the first quarter in terms of we gave three scenarios, if you remember, we gave -- we gave a base case, a bear case and a bull case, it was basically a range. And if you look at where our trajectory is now, it's pretty much at, I would call it, the base case or the mid taste that we actually modeled out. And so -- but really, what we've seen is as we've moved along this subscription journey, our rule of 40 measure, which we've used and we've talked about for probably going on 10 years.

You've heard me talk about this. We moved from what was a 10-plus percent rule of 40 calculation to now we're trending over 40%. And in a business, it's an enterprise buyer with a very high retention rate with highly leveraged components of our costs, like our gross margin is leverageable. Our R&D costs are leverageable. Our sales and marketing costs are not completely variable to booking meaning you do get some leverage there. These are all components that help us continue to drive that free cash flow margin up. Pega Cloud has been a big part of this. And why is Pega Cloud so critical? Pega Cloud One, it is a leading indicator on where adoption with our clients is going.

Naturally, our ACV growth is less than 29% because Pega Cloud is growing much faster than the other components of ACV. But as we continue to expand Pega Cloud, that becomes a bigger and bigger force for potential ACV acceleration. And you'll see on this chart, in the last -- from Q1 of '24 to Q1 of '26, we've had a pretty noticeable increase in Pega CV. The important clarification here, some of that is because clients have begun migrating more consistently to Pega Cloud. But the majority of our Pega Cloud growth is net new spend on Pega Cloud. So this is not just a solely migration plan.

The reason why you can validate that as you look at our term ACV for example, and you don't see our term ACV precipitously declining, and it's actually even growing slightly. So you can tell that there can't be that many -- that much migration happening from term to Pega Cloud or else you would see a pretty steep drop. You will see maintenance ACV going down, but it's still going down kind of mid- to high single digits. And that's our smallest ACV component. Those would be the reason why there's a difference between term and maintenance is because maintenance has some legacy perpetual buys that happened years ago.

So that would actually -- maintenance would actually come down faster. So what's the big lever point we have with Pega Blueprint, right? We have -- we [ struggled ] to Pega to expand into net new organizations. That could be a new buyer within a client of ours or it could be a brand-new logo -- a new logo. Why did we struggle with that? Admittedly, the reason why was the conversation on the whiteboard discussion. If we're going to target a net new organization before Blueprint, what we would do is we would hire a salesperson. That salesperson would have to be trained up to be very technically astute because remember, there is no blueprint.

So they have to actually understand the vertical, understand the Pega platform, know the horizontal use cases potentially. And then be somewhat vertically industry aligned. So that salesperson has to spend in the past, we wouldn't even send them out into the field for sometimes 6 months to 9 months because they were trained had to get certified on Pega, which means they're not building pipeline until maybe the end of the first year into the second year of their existence. So when we were going to hire new sales teams and attack new logos, we had to know that the first year, we were probably not going to get much reduction out of that salesperson.

And hope that we got it in the second year, that's a very costly proposition to expand into new logos. Now with Blueprint, you saw what Carie did, we could actually close and we have numerous examples of a brand-new logo just in the last few quarters pitching Blueprint in a first meeting. Two or three slides on what Pega is right to a blueprint that has led to us closing and getting clients to live in many cases, inside of 90 days.

Now I'm not talking about like the entire enterprise customer service for Bank of America applications. But these are enterprise companies. These are not -- these are not SMB businesses. So they're -- so this is a big change that we're seeing around Blueprint and the ability to use it to get into discussions to new discussions to people that don't know Pega without a sales team that has to be really technical around -- without using Carie's team or our professional services team to build custom demos to shrink that time to when we could old pipe. So this is a really important opportunity for us at Pega to be able to get into organizations that don't know us, that don't know Pega.

Now I'm not suggesting that, that's easy. We don't have a household brand. We are spending time and effort to try to improve the brand, but we're not a brand that everyone knows who we are. It's not an obvious thing what workflow is and what that means and how that plays into transformation. But there's an opportunity to really attack a lot more organizations than we've ever been able to do.

And Pega Cloud is our exclusive pricing mechanism with any new client. There are situations where a new client may choose to buy Pega Cloud for some reason, like a government agency that might not be able to run something into the cloud. It does happen. But our primary pricing mechanism that we use and our exclusive offers that clients start with -- or excuse me, sales people start with our clients is Pega Cloud. So it really means that any of our new growth with the new logos is going to come off of a blueprint that's on Pega Cloud. It's going to lead into Infinity Studio which is on Pega Cloud, which is going to leverage either our AI or call to their own AI models on Pega Cloud.

So Pega Cloud is a big accelerator tied to Blueprint. Some interesting early insight we've had around new logos. So our total pipeline year-over-year is up just under 30%. That is driven -- some of that is driven by new logo pipeline being up 65%. Now I will be very transparent that new logo pipeline is admittedly of a somewhat lower quality than pipeline that may be an upsell or an expansion off of an existing application if a client say is expanding, adding a case type growing.

But this is what we hoped to have [ seen ], which is the ability to drive significant new logos that we're engaging. And when we -- when we're looking at pipeline, we're talking about where there's been a conversation with a client where we have we have structured an offer to that client or we're in the process of structuring and offer where the size or the solution is known and the size of the opportunity. This is not a pipeline where it's like, I hope I can sell $10 million to XYZ company.

This is the early signs of us really seeing new logo engagement, driving pipeline that hopefully will then lead to a accelerating our growth. What has to happen here is we've got to basically really prosecute this process and make sure we're learning from the engagement that we have with these new logos, and we're understanding the competitive dynamic because it might be a very different competitive set than our traditional kind of expansion with existing clients.

On this slide is not included on this slide, is any momentum that we are starting to see from autonomous partner selling. You showed -- Alan showed on stage the partner Blueprints, -- if you remember, talked about the branded Blueprint like Cognizant, for example, that program has just been kicked off, and we're seeing -- we're kind of -- in some cases, we're seeing really good momentum. In other cases, we're still kind of working through the process of getting in front of the sellers of the system integrators.

And the system integrators are very much changing their business models. Time and material is going away, right? They need to sell outcomes. They really need to be -- they need to take more risk to help clients transform. So we we're putting Blueprint in front of those sellers and really trying to get that co-sell motion co-selling meaning we help when needed, but they are the ones engaging with the client. That's largely new logos as well. That is not in any of this data.

So we're -- once again, that is early -- when we modeled the year, we did not expect to get any contribution from autonomous partner selling in our guide. So that's something that we are watching closely, but it is a really big opportunity because that's how we can get to the 10,000 to 15,000 clients through partners. And really, quite frankly, leveraging their brand. So a lot of times, people talk about moats.

They kind of -- what's -- why does Pega exist? Why can they continue to exist? Why won't you be destroyed by AI. And I think that's a question that, quite frankly, has nothing to do with AI. You should ask that question of yourself in any business. Like why are we different? How do we win? We have objectively, we are rated always with one of the best, if not the best product in all of the segments that we operate in. We are built around repeatability, consistency, governance, trust, workflow executes consistently, predictably. And that is very critical for enterprise organizations.

Don used the example, I think, on one of the slides you talked about a 98% or [indiscernible] a success rate, which meant a 5% error rate. Alan and I met with one of our larger banking clients, the CTO and the CIO. And he talked about how his team was bringing these AI projects to them. And he was like, it's really interesting, getting. He said one of the struggles I have, though, is they're bringing the use cases saying AI, can get this 90% right. AI can get this 92% right?

I don't doubt those numbers. His comment to us was, what am I supposed to do with the other 8%, right? How do I actually go to a regulator and say, my system will execute this way and I'll screw up 5% to 8% of the time. Now there's a difference when you say my system is built to execute 100%, but we will have errors. We will have humans, we'll have fraud, we'll have mistakes. That's different than actually saying the system is built to not execute at 100%. So I think for regulated, for deeply critical process or control workflows, which is the majority of what we do at Pega, that's critical. Build for Change is one of those really like underplayed piece of, if you want to change something, Don and Carie talked about it a little bit, you can go in and change it right in the UI.

Imagine having to go in and deprecate code or try to deactivate code and write new code. And how would you know what's actually even if you look at the AWS transform tool, it can actually -- it takes -- it ingests Cobalt code. One of the things that can't do well yet is it doesn't know how much that code is being called. So it actually can tell you what the code is supposed to do. But there's other tools that you have to do to try to see like how often is the code actually being used. Very complicated to try to change anything that's old, which is -- which, by the way, was one of the fundamental differentiators of Pega and the low-code or no-code solutions that we've sold for decades. We understand industry use cases.

The biggest value proposition is of Blueprint is when you go into Blueprint, you're not doing a Google search. You're actually calling on Pega's proprietary knowledge of how to do that work. So that's a very important differentiation. And we view Pega as being the orchestration of these agents that are kicking off or asking a question are being requested by a user to actually go into some level of work the guardrails, the hardness, as you might call it, to ensure that AI is driving or initiating the right -- and then when the activity happens, the activity actually happens in a predictable way.

One of the important takeaways I hope that everyone will take away from today is we've made commitments on how we're going to improve structure the business and we feel like we've delivered on them. And we're going to continue to focus on the importance of us delivering on what we said we were going to do. We talked back in the 2022 to 2025 the actual numbers. Back 3 years ago, we set targets for '27 and '28.

We said we wanted to be a gross margin business of 80%. We're kind of -- we're approaching that now. We said sales and marketing, we wanted to get to 30%. At the time, we were in the 40s. we're down to 30. I think there's more room to go there. I think AI is a powerful enabler for sales efficiency as well. R&D, we said we were going to be at 17%. We were above 20%. We're down to 16%. I think there's a lot of AI efficiency that we've started to see. AI in development can come one of two ways. Either you can actually get more done in your road map and/or you can actually reduce the amount of people needed to actually get the work done.

I mean right now, there's a kind of a balance that companies are taking to try to figure out where they should go on that. So I think we'll get likely a little bit of both of that. And our operating margin, which was single digits, is approaching 30%. So we feel like we've made bold commitments on what we said we were going to deliver, and we feel like we've delivered those. I'll show this as a cash flow slide.

In 2022, I said -- or actually, it was 2021. 2021, I said, this business can generate $300 million of free cash flow in a few years. Some of you in the room, probably there's a lot of new faces here, but a few of you in the room and other analysts or investors said came to me and said, why would you say $300 million? There's no possible way you'll generate $300 million.

Like that's -- it's almost -- it's -- you're going to lose credibility by saying that. When we got to $200 million, I said this business is going to generate $500 million in a few years. I had some of those same investors, by the way, some of which are actually long-standing, still investors of Pega came back to Peter and I again and said, you have to -- you can't -- like that's a ridiculous number. You're not going to get to $500 million. We did $491 million last year. We put $700 million out there. We had people come back to me last year and say, why do you keep like it's $700 million. I mean I get $300 million, congratulations, $500 million. How are you going to get to $700 million? That was before we guided. That's before we finished with $491 million.

At the time we did that, we were tracking to $440 million of free cash flow for 2025. And we put $700 million out there. The reason why we are confident in our execution is because this business model is a model that has a level of predictability and visibility to it. Not every business model does, right? If you're Dunkin' Donuts and you have to bank on weather and how many people are going to buy coffee every day, that's not as predictable a model given macroeconomic changes.

Our clients are not subject to deciding they're not going to do disputes anymore or they're going to shut down their customer service applications. We are always at risk to competition. We are always at risk to switching. We all risk to pricing pressure. Those are -- but the foundation of what we have is a very sticky platform. And the ability for us to have that visibility and manage our cost structure, which admittedly over time was not really one of our core competencies, I think it is now. And I think that we've got to just keep executing.

So I think one of the really important takeaways is we've made commitments on how we are going to generate free cash flow. We've delivered on those commitments. We continue to be focused on this -- as we take this slide. I mean, this is one of the most important slides to me personally around how we can show to ourselves that we can run a good business, right, which naturally, we're doing it for all of our investors, but there's a pride level of here of like running a good business. And this is very important.

And if you go through the halls of Pega and you ask what role of 40 is, I guarantee you, every single employee will know. I guarantee you they'll understand specifically calculation, and they'll know how important this slide is. Now this is a hard needle to thread. An organization because you want to grow, you want to put investment into growth. It's critical, but you have to have a business model that allows you to leverage the investments. And that's why we look at Rule of 40, and that's why we're so committed to managing the business with this level of discipline.

The next piece of this is, okay, so you're starting to generate cash now. Let's go back 3 years with $600 million of debt, we had maybe $300 million of cash where net debt was between save $300 million of net debt. We weren't generating a ton of cash. The cash flow started to increase. We paid down the debt. We started buying back. Last year, we paid down $470 million of debt and we bought back a few hundred million dollars this year. We bought -- we started to buy -- I think we bought back $170 million in Q1, I think, somewhere around there, $167 million.

You're starting to see share count come down. And the reason why you're seeing is because the other piece of this is we don't have a significant amount of stock-based compensation as a percent of revenue. If you compare us to our peers, we are in many ways, half of what some of our peers are in terms of the amount of stock we'd have to buy back just to offset dilution. So our focus is to pay a dividend, and we've heard from a number of investors that having a consistent and methodical increase in the dividend shows a level of confidence in the free cash flow.

To optimize debt, we don't want debt. What if we have debt, if we, for some reason, we decided to do a transaction or accelerate buybacks or whatever we might do, we'll pay down debt. We don't believe that debt is necessary as part of our capital structure. And then we will use a substantial amount of our free cash flow to opportunistically buy back shares. Naturally, when -- the way we structure it is when the stock -- we do the typical 10b5-1 plans that you've seen other companies do. We buy more when the stock is lower, we buy less when the stock is higher and we have a lot of flexibility there.

But there's a meaningful opportunity for us to take down the amount of shares, which increases free cash flow per share, which is I know important to all of you. So just a quick snapshot of that. Over the last 3 years, we've returned $600 million to shareholders. And in Q1, it was about 80% in the first quarter. Naturally, that will change because our cash flow is not consistent quarter-to-quarter. Q1 and Q4 are our two biggest cash generation quarters. But just kind of highlighting the use of proceeds, so to speak, from this significant cash generation business that we have.

And that's really -- this is really just highlighting that point of -- some of you have asked a very good question, and I'll maybe just give you maybe 1 minute clarification on something. You said you bought back approximately $500 million of stock last year but the share count stayed relatively steady. That was -- what happened is we have -- our compensation plan typically gives up to 50% of our compensation is in options. We had a series of options that got exercised in 2024, 2025, which as you can imagine, took our -- took our share count up. That's not something that would happen every single year.

And you saw in Q1 or Q1 or you had more of a normal quarter where you actually took down. That's our -- by the way, Q1 is our biggest vesting of RSUs because we kind of have an annual vest that happens in March. The other quarters in the year are much lower. So you're seeing this trend now as we buy back shares, the share count coming down. And I think you'll continue to see that. So there's a really kind of meaningful opportunity here to use this free cash flow to take share count down.

There's another question that -- and to be completely honest with you, I wasn't as in tune with how egregious some of the stock-based compensation is of some of the other companies in the technology space until I continually heard the point in the last year because I know all of you know that with multiples coming down, there's a lot more focus on the after diluted free cash flow. Like -- and I think many of you are focusing a lot more on that. So as I started to hear that, I've always been independent of that conversation, I've always focused on the way to think about our measurements is the percent dilution of our market capitalization and the stock-based compensation as a percent of revenue.

So that's kind of -- and what -- when you compare these numbers to our peers, I think you'll find that we're probably in the top quartile of companies that around Pega. And I think that is not -- that does not mean that we are underpaying our teams for stock-based compensation. What I think that is really a signal is that I think we've used stock-based comp as a proper and equitable part of compensation and not over-enriched large populations of the workforce not paying attention to this number.

So I think there is -- I think this is -- I actually applaud all of you to actually look at the after diluted free cash flow because I think it's been ignored for way too long, and I think it is a number that's used. So we feel like we're in really good shape in terms of how we think about our equity pull and how much we use for stock-based compensation and how that helps us be able to buy back more shares after we offset dilution.

So the summary of this is we have a recurring business model that is heavily influenced by Pega Cloud. Pega Cloud tracking -- continuing to track towards that 75% number that I had talked about a few years ago. The operating margin continue operating margin, free cash flow margin. Those actually are starting to become closer aligned now because the volatility of the model is not as bad on a 12-month basis now that we've kind of got through the major pain of the cloud transition. The operating margin will continue to expand. We'll continue to get operating leverage, which drives into our free cash flow margin.

Our free cash flow margin right now is about 30% ballpark. There's no reason why this business shouldn't be a 35% to 40% free cash flow business. I think we have all the -- we have all the dynamics in the business to be able to support that and still be a double-digit grower. So I think that's a -- it's a very valuable business that we'll have if that comes true. And then using that capital to be thoughtful about where we put it to use.

Naturally, if we saw an acquisition that made a tremendous amount of sense, then we would decide to do that and slow down buybacks or take on some debt or whatever was required. It's -- it's hard in the environment we're in, where multiples are and certainly Pega's multiple to argue that you could pay -- you could get a better return than actually using that cash to buy back the shares of Pega. So that's been -- that's certainly been our priority over the past year.

Key takeaways. Pega Blueprint gives us an opportunity to get into markets that we couldn't get into before. I mean, this is not a Q1, Q2, Q3, Q4, Q1 of '27. This is a -- the business can be structurally changed to be able to attack a market that we could not attack. And I think it's really important to not think about this as like Blueprint will just like somehow help us in the next 90 days or the next 180 days. This is how we want to change the nature of how we target the organizations that we can attack.

The subscription transition, I would say it's largely done. Naturally, as you're moving to Pega Cloud, you're going to always have a little -- you're going to have a little bit of variability there. But this has really helped us anchor significant profitability and significant free cash flow. And then using that free cash flow to be thoughtful about capital allocation can help choose up the free cash flow per share by actually taking down the total number of shares and creating a natural buyer in the marketplace, which is us.

And so this is who we are and who we -- and how we're executing for all of you. And I just wanted to reinforce how committed we are to continue to drive value for our shareholders. And we believe you cannot drive value for shareholders without running a good business and a good business means that you're making difficult trade-offs and that you're driving margin and free cash flow in the business. So I'm going to stop there. How many?

1:50?

Okay. So I'll open it up to any questions that you guys might have. Why don't we just start with Devin, and then we'll go to Steve.

Devin Au from KeyBanc. I just want to go back to the slide where you show all the go-to-market metrics influenced by Blueprint. I mean it's great to see total pipeline up 29%, and I think new logos up even more.

[ 2.65% ] and then and about double in the number of new logos in the pipe.

Yes. Really great to see. Maybe my question here is, when I look at those really strong metrics and when I kind of look at your near-term guide, ACV in the mid-teens, can you kind of like help us understand where are the main kind of gating factors of kind of conversion lag or what you're doing to accelerate that lag in the pipeline?

So admittedly, much of this pipeline changes early stage, early stage, and we don't have a lot of we don't have great statistics or data on the conversion rate because we don't have -- if we had 5 years of data, we'd have a lot more confidence, Devin, in terms of how we think conversion is. So I would say that this is -- this is a great metric. It's also riskier pipe.

And so that's -- it could turn out that this is -- that the win rates are pretty reasonable on this. It could turn out that our win rates are not as exciting as we want them to be. We're not really there yet to know. The point of this is the number of logos that we're engaging with, and we are growing pipe significantly, and that will impact the pipe overall pipe because new logos will become an increasingly bigger percentage of our overall pipe. Steve?

Steve Enders from Citi. Maybe going back to the free cash flow discussion. It seems like there are a few areas where you were calling out there's incremental margin opportunities for sales and marketing and R&D and getting efficiencies there. I guess, why not maybe update some of the guidance on the free cash flow side if you're seeing that incremental opportunity? And maybe how does that kind of inform how you're thinking about incremental investments as you kind of get through the efficiencies versus letting that flow through to the bottom line?

So we wouldn't be messing around with '26 at this stage, Steve. What you're really talking about is the 700-plus number, which is why I put a plus there, right, which is naturally how fast our ACV grows will be dependent on that and how much AI actually drives efficiency in our business will drive that.

And naturally us running a good business and making the difficult trade-offs. So I think we -- because we're not -- we don't want to get in the habit of constantly like changing the number just slightly. We're just highlighting that we are still on track for that commitment that we made last year. And we've -- where you can kind of see our I would say, our credibility building to where we are now.

Pat McIlwee with William Blair. First off, is there a plan to monetize the Pega Blueprint song you were playing earlier because it's shockingly catchy.

Sorry?

Joke. The Pega Blueprint and song, you were playing earlier. Shockingly catchy. So we heard customers on stage today talking about how scary it can be to endeavor into the modernization of legacy apps. You've long talked about the kind of pot of gold that is that legacy modernization opportunity, and it seems like the capabilities of Blueprint and Infinity are pretty consequential and actually unlocking that opportunity.

So my question is how much of this recent acceleration in your cloud ACV growth is attributable to that? Like how early are we in addressing that opportunity? And how much as you think about the trajectory of the business, how much of that is still to come?

I think very little of what you've seen so far would be material legacy transformation that's been driven to Pega. I think clients are -- my read on talking to clients is they are very focused on it more than they ever have been, but still not moving as fast as it's not like they've a light switch and they're just transforming. I do believe over the next 5 years, it's going to accelerate.

I think clients are -- and largely, I mean you heard on stage one of our clients talk about Cobalt developers. And the fact it's out of the curriculum of 85% of [ college ] classes and that there's nobody graduating with cobalt experience and nobody wants to do it. And quite frankly, cobalt engineers are retiring and quite frankly, dying because of the code because it's so old, right?

So I think that, that is a very big catalyst. I also think [ Mythos ] and some of the other tools that are out there that have really been able to expose vulnerabilities -- all these systems have unsupported open source that's onboarded, old versions. The companies -- I mean, Lotus notes, I mean, there's 50,000 companies that have Lotus notes, that isn't even a company anymore.

I mean like -- so these are tools that you can't really let accessible to the internet. You can't then tie it into data lakes and AI and all the things you'd want to do to enable the -- so I think that is a very big compelling. That's where AI is the most compelling because it makes you get your systems modernized, get your data together so that you can actually mine your own data. So I think that's a very big -- that's what's different. So I've never honestly, we've talked about legacy transformation, and we've talked with AWS, and Matt would say -- well, well, originally Andy, when Andy was running AWS, he would say we're 10% of the way there. I think AWS, what [indiscernible] were 15% of the way through the transformation. But I do think in the next 5 years, it's going to accelerate noticeably.

1:56. Kind of if you want to wrap up the two, you might have time for one or two more question.

Thanks for the time, check, Peter.

It's Blair Abernethy with Rosenblatt. Just wondering if you can walk us through sort of a picture of your installed base today, what sort of versions are they on? How many are very, very current or within the last, say, 2 years because it looks like 2026 -- '26 looks like it's a pretty substantial change. And so I think hugely beneficial if you guys can get more of your [ base ] faster?

Yes, Pega Cloud. So the Pega Cloud is probably 80-plus percent on the last two releases. So it's pretty significant. I would say the other 20 is like in the process to getting on to one of those. So if you go off of Pega Cloud, it's -- they're not as current, but still like they would say the shape look similar. So I would say we probably have 60% to 80% of our clients are going to be able to get to '26 like if they wanted to right away.

And I would say the other ones, you're probably looking at realistically probably a year or 2. So -- but those -- there's an important point on the ones that have moved slower -- and I -- this will probably not surprise you, but it's worth mentioning. -- clients that move slower for upgrades are typically much stickier clients. That's a good and a bad thing. It's a good thing because they're sticky. It's a bad thing because you can't get them real-time access to governments, for example, people that tend to move a little slower. So that's -- so it's not that clients like don't want to move. It's that they -- sometimes they have a lot of process to get through to be able to go to be able to -- and there's some rules some companies will not go on a current version.

There'll only be one version back. That's not -- and that's typically more like high security, high levels of third-party security.

I think Lucky you had a question, too.

Lucky Schreiner with D.A. Davidson. A great presentation. A philosophical question, you emphasized the high cost of the frontier models. And how Pega lets customers drive value at a reasonable price, which makes a lot of sense. But can you maybe level set for investors how you view the relationship with the model providers moving forward? Particularly as you talk about using them to drive internal efficiencies and the model improvements help customers get more value out of their Pega applications. And so I think the goal is to clarify frontier models aren't going to eat all of software, but maybe is it a case of classic [ coopetition ] moving forward? What's the relationship?

So that's a great question. And I will draw a parallel to. You shouldn't use AI for things like deterministic work like we've talked about that, we strongly believe that. You also should not use frontier models when they are not needed. Frontier models are incredibly expensive, 100 million 3x as expensive as maybe some of the more older models that might be able to be just as helpful to be able to solve certain use cases.

So I think you got to be really the best-in-class companies are going to think about what am I trying to do? Should I use AI, should I not? How -- where do I use AI? And then which model do I use? Do I always want to be on the current model? That's a silly answer. Anybody that says that is going to change quickly because the cost -- the ROI is not going to be worth it. So I think people are going to think about the models. We actually look at each model, although they are commoditizing, they still do have differences. So we look for -- we have a lot of optionality.

In Blueprint, we use four different models like depending on different activities and different things that you're doing. So I think or my view, and I don't -- like this is somewhat philosophical, but I do think grounded and feedback we've heard from clients that I think people are getting smarter around tokens, smarter around the models that they use, where to use AI and there's a whole ecosystem of companies that have been built to manage AI spend, right?

So I think we are in a mode where clients are going to leverage AI, but they're also be really thoughtful about what they're getting for I'll use two final points on this. One is, for those of you that -- like a perfect example of the hallucination that can happen, which is well intended, I was at the William Blair conference last week, and I apologize if some of you have heard this conversation. My fireside chat was 12:30, at 9:50, Peter Welburn, opens up his screen, flips it over to me, and there was a tip trends press release that said Pega stock drops based on negative commentary made by Ken Stillwell in his fireside chat at William Blair.

I hadn't presented yet. So what AI did was it saw the stock price, it knew I was presenting and it drew a logical conclusion that, that must have been why the stock price dropped because so completely AI. We notified tip trends. They took it on right away, and we understand that is a very common thing that's happening. That is not really a hallucination. That's just AI doing a probabilistic association, and they just got it wrong. Imagine if they did that for loan originations for credit card increases for payment processes, for ACH exceptions for debit card replacement, like just think about all the use cases we would be disaster of a society if that's actually a lot.

So thankfully, we imagine though, if that trends one went out 1 minute after my presentation, I wouldn't have really had any way to dispute that. I mean it actually said, based on investor sentiment they didn't like what Mr. Stillwell had to say. But thankfully, it happened before, so I could actually refute it. But this is a problem. If models are -- AI is just going to go out there and just make things up because they think probabilistically, it sounds right. You cannot.

You cannot run scale transactions and companies through that. And I think that's the most important anchor of deterministic versus probabilistic. Then when you get into probilistic, it's like use the right model, just be thoughtful about it. And I think clients are trying to figure that out.

And just one point of clarification, I was a new story that was generated, you said press release.

Oh, I'm sorry. Thank you for the clarification.

And certainly, we can keep going answering questions, but I also wanted to point out for the people that are in the room innovation hub is open today. I've highlighted on the screen what I consider to be the top 5 things for you to visit. If you guys want to take a quick snapshot of that with your phones if you're going to go down to the innovation hub. I would go to those areas and focus on those.

So we can certainly answer a few more questions?

I actually have to leave because I have to go meet with the analysts with the non-sell-side analyst. So our industry analysts to talk to them. So thank you, everyone, for coming. Enjoy for those of you that are staying, enjoy the innovation hub. We actually have a concert this evening. Please partake if you're around. And naturally, we're here if you guys have any questions and to reach out. We're still in our open window for another 10 to 15 days or so. So thanks, everyone.

Good morning, and thank you all for joining the Pegasystems session at our Growth Stock Conference. I'm Pat McIlwee, and I'm a research analyst in the software group at William Blair as a part of which I cover Pega. I'm required to inform you that a complete list of disclosures and potential conflicts of interest are available at our website at williamblair.com.

Today, we're thrilled to have the Pega team back at our conference, including COO and COO, Ken Stillwell; as well as Peter Welburn, who leads the IR team here in the audience. So welcome to Chicago. Ken, you mentioned to me that Pega hasn't done a fireside or a roadshow in Chicago in a few years' time. So it's great to have you here. And I think especially timely given that we're just ahead of your investor session in Pega's annual user conference next week. So for investors here who are not familiar with Pega, can you just start us off by giving them an overview of the company's solutions and why the Pega platform is particularly interesting today?

Sure. And thanks for having us. It's actually been a while since I've been in Chicago in general. So it's good to get back. That's true.

Good is a good month. So if you think about in large organizations, they have -- and when I think of large, I think of like banks, insurance companies, health care companies, governments, where you have either a B2C business model or you're supporting constituent management like in the public sector, there's a number of use cases to support those consumers, those customers, those constituents, things like health care claims, managing credit card approvals, loan originations, onboarding, change of agile.

There's just a series of actions that need to be really structured and managed consistently, sometimes because it's regulated, like managing a credit card dispute and how Visa or Mastercard requires the banks to manage those disputes or other things that might be more driven because it's the internal control processes of the organization to do in a certain way. So when you have those deterministic workflows, work that needs to be done exactly the same way through a series of steps and stages, and be able to know on the front end and know on the back end that you actually executed that work. That's typically called enterprise workflow. Pega is the leader in enterprise workflow. And so sometimes the solutions are more horizontal. They look like onboarding that might be very similar across different verticals. Sometimes they're very specific to an industry or a vertical like know your customer in banking. And so we've been helping clients for more than 40 years doing essentially an alternative to either writing their own application or trying to buy a commercial off-the-shelf solution and try to make it good enough to meet the -- so we've kind of functioned as this low-code platform where you don't have to write code, but you can get the level of specificity and configuration that you need for your use cases.

Okay. Okay. That's great. And with more -- to kind of build on that, right? So more than 3/4 of your revenue comes from highly regulated industries like you touched on. Can you talk about why those customers rely on Pega? Is it that trust that you've built over 40-plus years, the security, the services component? What's the secret sauce?

So we tend to sit in this kind of convergence of a number of different factors. One, the scale and the volume of transactions. Not many systems are able to manage what could be billions of interactions in the course of a year in that scale and multiple kind of instantaneously having multiple threads of transactions. So there's one is like a scale differentiation. The other one is that what I talked about, about being highly configurable.

Now highly configurable does not translate into customized, although some clients do like to build customization around Pega. It's really just around the ability to configure a set of work and to be able to iterate and change the nature of that work over time. That's a -- there's very few vendors that actually have enterprise workflows that allow you to do that. Another dimension of that is the level of security that we have. And security, meaning not just native to the platform, but we have over 100 different industry certifications, everything from PCI to HIPAA to FedRAMP to IRAP to ISO 27001. So in any country, in any vertical, we help our clients support their third-party certification requirements that sometimes they are because they're regulated. Other times, it's just the nature of the business that they're in. And another dimension is the ability to really drive the very robust structure of the work and be able to separate the work. We have something called a case, which is not only do we have the actual workflow, but we have the ability to contextualize each incident or each activity in a way that is unique, but also related to other incidents that look like that. Many of our competitors manage that just in a database, right? They do indexing and they have rows and columns, and it really prevents the ability to understand deeper associations and relationships around the metadata that is associated with each individual transaction. So people typically buy us for the combination of all of that. And that really -- everything that I just said really fits tightly with enterprise needs. Large companies have scale transactions, regulatory matters, consistency, repeatability and the ability to manage as the business changes.

Okay. Yes, that's great. And so in your overview, you said the word deterministic. So I'd like to ask you to kind of elaborate on that because I think it's still not completely understood in the investor community what exactly Pega does, right? It's providing secure, reliable, deterministic workflows that work for an enterprise every time. And then what foundational models like Claude do, providing more probabilistic calculations, where there's overlap, where there's a distinction and kind of where there's a harmony between the two?

Sure. So I'll start by saying there are 2 types of work or 2 types of -- I'll use the word workflow, but 2 types of processes. One type is probabilistic, generative, where you would expect there to be an error rate. The error rate might -- you might want it to be 1% or 0.5% or 10%, but you would expect it to not execute exactly the same every time because it's going to use the data that it has. And even when given all the exact same variables, there is a slight risk that the probabilistic model will pick -- if left with 2 equal choices, may pick one time and one another. And that's just the way the models are built. There's nothing incorrect or errodous about that they're built to be inaccurate. They're built to try to get it close enough. And then there's deterministic, which is really not focused primarily on the outcome. It's focused on the process. How is it that you will go through the work? Good example of a probabilistic action or a deterministic action. Probabilistic would be if you come to a website of a large credit card company and they want to speculate exactly what rendering of a picture or a call to action or an offer they give you, that would be probabilistic. They're not going to get it 100% right. They might see that you're coming in from the Midwest and that you're over 50 years old and you have a family, so they might show a picture of a family sitting under a tree in a corn field because they feel like that might be the most relevant. They might have that completely wrong because you're from France and just happened to move to the Midwest and actually that picture doesn't resonate at all with you. But that's not a problem. That just means they got it slightly wrong. And then there's a probabilistic workflow. I'm going to approve a loan. And I have to follow all the state guidelines, discriminatory lending guidelines, credit guidelines, wholesale lenders, whether it's FHA or VA, very, very structured, and you cannot get it wrong. Might you -- might an underwriter make a decision at a stage in that workflow that could be wrong? Yes. That's where the judgment fits. That's where the probabilistic fits with the deterministic. But the structure of how you process that loan must be the same every time.

And really Sorry, correct...

Deterministic. That is deterministic. Sorry, I want to be misleading.

Or that...

Probabistic.

Yes, sorry. Probabilistic is where you can have an error rate and you're making a best guess. Deterministic is where you decide the work that is done on the front end. But in a deterministic workflow, you will have probabilistic AI that's used as well. The example would be that underwriting decision. In the underwriting step in that workflow, there may be some judgment. You might look at loan to value, you might look at things in there. Someone might have to make a human call on that. You could make an AI call on that. And you realize that maybe you give a loan to someone that you slightly -- maybe you shouldn't have because they -- maybe there was a credit risk you couldn't anticipate, but that doesn't undermine the process of which you went through to approve that loan.

There wasn't an intentional discrimination against the borrower. So in consumer industries, there is a -- as you might imagine, there is a high bias to protect the consumer. And then if you go across the world in different countries, there's an increased bias around protecting GDPR in Europe, protecting like sovereign information, not sharing information out there. So there's so many rules and regulations across consumer industries that it's very important that you understand where does a workflow need to be deterministic and where can a workflow actually be probabilistic. And when it's probabilistic, I think that's where AI can play a role.

Okay. Yes, very helpful. I think it's an important distinction to call out. So just given this is a generalist conference by nature, can you touch on the pricing model? I think that's been a big concern across software, the software sector at large recently, and Pega's pricing model is a little unique. So can you just kind of clarify?

So I'm going to -- maybe I'm going to go back about 10 or 15 years because we changed our pricing model with -- had no relation to AI or any of the things that are going on now. So what Pega does is it takes what otherwise were human activities that would be managed manually across maybe a structured set of workflow steps, and we automated that into a system. When we automated that into a system, what we would do with our clients is we would build efficiency so that if they had 10 people that might have been needed to manage a certain body of work, they might only need 5. In a licensing model that's a user model, that's kind of counterintuitive that we would go out, help a client take their headcount down by 50%, and we would then get 50% of the revenue associated with that. So we did have a licensing model that was a user-based model 23 years ago, and that -- some of our contracts still do have licensing components that are user-based. But we made a big shift to move to what we call a case.

A case is a unit of measure in Pega. Think of a case as a piece of work. So we license based on the pieces of work. A piece of work could be a dispute on a credit card, a loan origination, the number of clients that are onboarded, different measures, the number of card replacements that you might have for lost credit cards. So that unit of measure of the case is how we license. And we feel like the more that the system automates work, the more cases that it does, the more that Pega should receive compensation because we're automating and driving efficiency. So we have -- so you might call that a usage model.

So essentially, a case is a piece of the use of the technology. In an AI world, that become -- that's become now much more obvious. But we have 75-plus percent of our contracts that are exclusively case-based. And the ones that aren't -- that have users they're typically on purpose clause driven, like they're like -- you could use it for this purpose and this number of users and cases. We typically have both. So we've -- we were ahead of that challenge, but not because we saw AI coming. It was more around just our value proposition, made more sense to charge based on usage. The analogy I use is if you were using AWS for your cloud, you would -- AWS would never charge you based on the number of employees you have. They would charge you based on like the number of CPUs, storage, processing, et cetera. And that's very analogous to Pega.

Okay. Very clear. And so I think whether or not it's completely misled, there's a fear of disruption associated with some of this automation technology right now. But when I've spoken to customers of you and your peers alike, it seems like they're leaning more into these trusted platforms more than they're trying to move away from them, right? Can you talk about just how those customer conversations look when you're talking to enterprise-grade customers that are looking to roll out AI automation at scale?

So a few -- maybe a few thoughts on that. So one is when this -- when AI really started to get more visibility maybe in the like kind of November, December, maybe even January time period, there was a lot of confusion even with our customers. To be honest with you, I think there's still quite a bit of confusion with investors. But there was confusion with our customers around this concept of deterministic versus probabilistic work.

So originally, it was -- there's this AI thing and what can AI do and we should try to experiment and see. I think that carried into the investors thinking, well, why couldn't AI just get rid of all of these SaaS companies, all of the -- all this technology. And I think that was kind of the first wave, which I think has largely been settled down now. It certainly with customers, where I think they're not confused. I mean they know that 80% of the applications that they have are deterministic and they're not going to use an agent to go execute that work. But there's probably 20% or so that you probably don't even need a software application and an agent or some type of a prompt could actually execute what you need to. So I think they're honing in on that. The next step of that was, well, okay, so I'm not going to displace it. I still need a software product but could I just write my own? Could I actually use now, then the model started to say, well, we can help you write code. And then there were tools like cursor like that would help you be like kind of almost like a development harness to be able to help you drive using the models to write code, which we do at Pega, which we've been -- we're not fully rolled out on that, but we're in that journey as well. Then that kind of takes you back to, well, why do you -- why would you want to write your own application?

In some cases, you write your own application because there isn't something you can buy, to be honest with you. I mean it's just your use case is unique enough or you write your own application because what you could buy isn't quite the perfect fit or it's just too costly to buy versus you just actually writing it yourself. So I think there's definitely going to be applications where the companies decide, I can actually build my own, and it's going to be faster, cheaper, easier to support. Then you get into the bucket of why would they try. And many of our clients, we -- I've had this conversation over and over again where there's a couple of dimensions. One, error rate is one. So when you have a situation where you cannot have error rate, there's no such thing as like I'll accept a 1% error, where the system that you're building is highly complex. It's going to manage a lot of scale and may be subject to regulatory or control processes. You run into that risk of, is it better for me to build my own ERP system? or should I buy an ERP system that actually I know is hardened to be able to support all those controls. I think when I say that example, most investors would even say, yes, that's kind of ridiculous if someone would go try to build their own ERP system. But there are a lot of enterprise systems that have the same level of sophistication and discipline that you would see in some of the ERP modules. So I think that's kind of -- that's one of these decisions that companies will make.

One of the biggest challenges that our clients are seeing with the AI models is -- and I'll get it to the last one, which is cost. But the middle one is the level of imperfection that you get. For example, I'll give you an example. This morning, I was finishing up one of my investor meetings and Peter, our Investor Relations Vice President, pulls up a screen. And the article was from Tip ranks, which is basically like -- essentially, it's Benzing Tip rank. And the title said, Pegasystems stock retreats based on comments made by COO and CFO. So we read the article, and it said, Ken Stillwell made comments at the William Blair fireside chat that caused the stock to go down. That was 3 hours before I'm sitting here. that was an actual article that went out.

Now we called them and they took the article down, but this happens all the time, right? This is called AI slot, right? It's just -- it's out there. Nobody knows if it's right, nobody knows it's real. Enterprise company, I mean, that's just funny that, that happens today, but it happens all the time. We have to constantly be watching because the information that gets out. Now you're an enterprise company, you're Bank of America, you're William Blair. How important is it that you don't actually let AI decide that when you're trying to make a bill pay on your bank account that it decides you paid that vendor too much, so pay a different one.

Like how do you catch that? How do you control that? So these are the types of decisions that companies are making like do I really want to go try to build my own? And by the way, for those of you that are not aware, you should ask around on this. AI models now build code that is 50x faster than the human's ability to review the code, which means we have no idea what it's writing. When we write it at Pega, we have no idea. So what you have to do is you have to throttle how much you can. You have to look at the code, run other models to test it, run test models. And hopefully, you actually can know what the -- that never happened before.

In the whole world of coding, you never had a situation where one person could write code faster than someone could actually review it. So these are big, really big challenges that our clients are trying to figure out.

Okay. And so to shift gears to kind of the upshot of AI and how you're leveraging it within the platform. So Blueprint, it has been kind of revolutionary for you guys. It's been incredible to see how that tool has helped your go-to-market motion, taking your sales cycles down materially. Can you just talk the audience through what that has meant for you and what that is?

So prior to AI for Pega, if we wanted to work with the client, we typically had to go through a very manual and quite frankly, very human-intensive discovery session on the front end. That typically involved whiteboard sessions, operational walk-throughs, lots of collaboration, trying to get people physically together and then, quite frankly, realizing that, that took cycles to be able to really figure out like what do we want the reenvisioning of an application to be. We're trying to move something or trying to build something new, there was almost like a village that would have to build the like view of the -- and unfortunately, that could take quite a bit of time.

So what that meant was slower ramp for salespeople, harder to get pipeline deals in, early-stage pipe moved slower. So these were all challenges that, quite frankly, we just accepted as part of our business for decades. What blueprint -- what Pega Blueprint is, is what we did was we took the AI models, and we built on top of it all the knowledge of Pega, specific knowledge of Pega, all the workflow history, how does the workflow work? What are the use cases? What are the personas, what are the typical integration points? So in an actual like agentic interface, you could chat with this application and build your workflow. And now the workflow that you build in that is not necessarily going to be one that you click a button and go right into production because these are enterprise companies, but it got you so far -- it gets you so far down the path compared to what we had to do in kind of before AI.

So it's been a massive revolution for us in terms of how fast you can get from concept to a design where you're actually looking at the application. At the end of blueprint, you can click preview and it shows you a working application. What we're announcing I guess we've already kind of leaked this out. But we're talking about a PegaWorld next week, next week is our user conference. We're going to talk about the next phase of that, which is the Blueprint experience goes into finishing the build of the application, something we're calling Infinity Studio, which is essentially keeping that whole Agentic experience until the point where you can actually go live and into production.

We know right now that, that has taken 50% of the actual time and engineering effort to just get to the point where you could decide what you're going to build. What we want to really do is make this as agentic and as automated as we can to get to application to go live. So that's kind of our -- that's what Blueprint has done, and that's how we're extending Blueprint into the build phase.

Okay. Yes, that's great. And then can you just talk about -- so a lot of large enterprises are still running mission-critical on these legacy applications. Can you talk about what the implications of this technology are in terms of your ability to go and address that opportunity in the enterprise?

So I don't know what the percentage is, but I've heard different percentages, anywhere as low as 10% and as high as 25%, which is the percentage of applications that have actually been modernized in large enterprises. So I've heard Amazon talks about between 8% and 10% of applications have been modernized. I've seen more aggressive ones in the 20% to 25%.

Whatever you believe the number is, it certainly is nowhere near 50%, and there is a lot of work to do in terms of getting these typically like homegrown systems running on ancient infrastructure into a more modern world, whether that be on cloud, public cloud like Pega Cloud or whether that be managed on a virtual private cloud. What Blueprint does for us, which is just massive, is it allows us to go after new logos and new workflows in a much more aggressive way because the upfront selling process, the upfront solution process is so much faster. If you think about in the previous world before Blueprint, if Pega wanted -- if we wanted to target a new organization, the first thing we had to do was hire a salesperson that would target that. The next thing we did was train the salesperson for 3 to 8 months to get them certified on Pega, then they would start calling the company.

But remember, the whiteboarding session example, that might be a 6- to 9-month pipeline building. So we had salespeople that we would hire and they might not build pipe until their second year working there. And that's if they follow the path. Now we can hire a salesperson, they don't need to be certified on Pega. All they need to know is how to get to pega.com/Blueprint. That's the extent of what they need to know. Blueprint is right there. They can engage with the client in a first meeting -- the other thing is with new logos, if you think about a company that knows Pega, like Bank of America, I'll use that example because they're a large, many decade client of ours. We don't go into Bank of America and say, let me tell you what Pega does. They already know. If we go into a brand-new client, they don't know what Pega does. So we're going to go into that brand-new client. Blueprint is an easy way to show it and say, let's walk through one of your problems, onboarding a client, managing a dispute. You pick whatever that vertical might be. It just makes the whole conversation. You're immediately going into a demo that's very specific around the customer use case. And that -- the level of confidence that gives our sales teams, how fast we can ramp our sales teams, how quickly we can attack new logos, these are all brand-new things for us.

And you can correct me if I'm wrong, but you guys have actually quantified your sales cycle might have been 12 months before, on average, it's been cut in half. Like last quarter, you highlighted some deals that went live in 90 days.

We had one that went live in 42 days which is -- may seem like 42 days for not knowing enterprise software, may say, well, that's still 1.5 months. But like, I mean, to take an enterprise application and actually go from whatever they had before into a working application inside of a quarter is nearly unheard of in enterprise. So we've had a handful of those in the past 2 quarters.

Yes, pretty impactful. Yes. Okay. So we can get more into the financials in the breakout. But just one -- so there was some noise in the first quarter on the ACV growth. There was some noise around the license revenue, the renewal timing, a little disruption within your federal pipeline. How should investors be thinking about current ACV growth versus the growth that you expect over the next few quarters?

So when we guided the way we -- so there's a little -- in our business, many -- much of our growth comes on the back of a renewal cycle. So if a client has a renewal event, typically, that's when our ACV is our equivalent of ARR. When our ACV increases, the customer typically makes that commitment based on the usage or systems that went live in the previous year. So we're tied to renewal cycles.

In 2025, our renewal cycle was not back-end loaded. In fact, actually, there were more compelling events in the first quarter, last year, meaning 2025. And then in 2026, when we guided, we had exactly the opposite. We have more compelling events in the back end of the year and not as many in the first half of the year. So it creates this dynamic of just difficult compares in the first half of the year and easier compares in the second half of the year.

So it makes our growth rate kind of bounce around a little bit because we measure a trailing 12 months. And so that's really what we had talked about. Now separate from that in Q1, we had a few kind of isolated incidents that caused our bookings to be slightly lower than even what we would have modeled. Like we would have modeled about $25 million of net new ACV in the first quarter, and it was about $20 million. So we were at about a $5 million gap. Some of those were some of the government shutdown and changing to the processes that they've had, had some deals slip a little bit. Just these are renewals with expansion. So these are not deals that we have to win. They're just paperwork situations.

So we had a couple of those. And so we've had a few situations in Q1 that were for various reasons that caused Q1 to be slightly lower than what we had modeled. But we've -- we had said from the very beginning, and we still feel that way that first half of the year, tough compare, back half of the year, easier compare. So it will cause some growth gyration through the year.

Okay. Got it. So there's some more mechanical factors at play than anything necessarily concerning and you guys still feel pretty good about that mid-teens ACV growth target?

I think probably the only thing that's still -- it does concern me, but I don't know how to quantify because it's not an empirical concern is supply chain disruption from the Middle East. I still don't know how to -- I don't know how to measure that risk. I don't -- it could be nothing. It could manifest itself into something, but I think that's the one that I'm just still kind of not sure how to -- we don't -- we're not in the energy space, but I think I'm just more worried about the macro impacts that could happen. Europe has started. Europe has been under a lot of strain with the Ukraine, but I think with natural resource with having some shipping delays and certainly running low on inventories for oil and gas.

Those are some areas I'm watching. That said, the consumer has held up pretty well.

Okay. Great. Yes, I think we're just about of time. So we'll wrap it up there. Thank you, Ken, very much for being here.

I appreciate everyone coming in, and the breakout will be in Jenny Be upstairs on the second floor.

Great. Hello, everyone. My name is Alexei Gogolev and welcome to JPMorgan Boston TMC Conference. Today, we're delighted to be hosting Pegasystems management team. First of all, Alan Trefler, welcome, Founder and CEO of the company; as well as Ken Stillwell, company CFO. Alan, happy to have you here. And first of all, if we could maybe begin with a short overview of the business, what investors who are new to Pega story, maybe talk about what Pega does and provide maybe a few use cases to better understand the business.

Well, I'll let Ken kick that off. He talks to the investors all the time.

Okay. All right. So Pega has been around for quite a while, helping traditionally or historically enterprises with scale transactions. That isn't exclusively companies that work in the B2C industry, but there's a lot of connection there between B2C business models and a significant amount of volume of work that needs to be automated. And that volume of work also tends to be very structured.

Deterministic workflows is the term that's more commonly used these days. We've always thought about the power of Pega being to be able to build something once and run it millions of times the same and predictably and then allow the change to be manageable in terms of the evolution and how we innovate and how we change the -- either the work process or the workflow because things do evolve.

We've been on a journey over the last 10 years or so where we had historically a user-based perpetual license model in the earlier part of our business model. And that has dramatically changed over the last 5 to 10 years where now we have more SaaS or Pega Cloud business. We're all recurring, and we have a volume-based or a usage-based metric is our primary licensing metric.

Sometimes we have users and cases. Case is a unit of measure that that we think of at Pega. So the business has went through a pretty significant transformation over the last few years. We went from a business that was $500 million and largely perpetual to a business that's approaching $2 million and generating a significant amount billion -- $2 billion and generating a significant amount of free cash flow as we went through that transition.

And more recently, over the last couple of years, we've launched something called Pega Blueprint, which if you haven't seen it, you should go to pega.com and take a look at it, you can test it for yourself. It's really our way of leveraging AI and how we can help our clients reimagine or envision the future of what they want to transform in terms of their technology platforms.

And maybe just to touch on AI a little because it's such a wild and crazy part of the lexicon these days at all moments. We've been heavily involved with AI since 2010. In 2010, we went out and acquired a company that were specialists in statistical AI. That's machine learning. It's the part of AI, which I think people are overlooking now some, but is still incredibly valuable.

And the reason we did that is we had, since our inception, been experts in rules engines and process automation. How do you have processes that make sense according to the various either legislative or business policy or other types of rules. And you could really see that being able to do machine learning off of that and pull that sort of knowledge and learning into the workflows would make a lot of sense.

Of course, since 2022, we've done massive changes candidly to our business model as well as our products, which are reflected in this Blueprint AI technology, which we're really excited about.

Well, that's a great segue. Maybe we could discuss, Alan. So customers, they either buy, build or they configure solutions. Where does your low-code Pega solution fit into that landscape? And you talked about Gen AI. How does that change customers' decisions?

Well, I think Gen AI has massive implications, and I hope not to contribute to the crazy hype that's going on around agentics and AI these days. But we're pretty neck deep in it and it's having a pretty substantial shift in our business.

If you think about one of our customers, they typically have hundreds, thousands of workflows that run and describe their business. They think of that as being the way they want to have standard operating procedures, and if they have to pass an ISO regulation or they have to pass something, you actually have to even document those and show those to people sometimes. And that's candidly a good thing.

What we've been able to do with the Blueprint AI technology is use the full power of these frontier models to be able to apply AI in anger as it were at the time that a customer is reenvisioning or reimagining how they want to do part of their business. This might be moving an older system to a newer environment. This might be taking 6 or 7 systems that come together as a result of a merger or acquisition and blending them together.

And what our Blueprint AI does is it lets you do that and lets you rethink it in ways that leverage our literally 4 decades of best practices. We knew a lot about workflows. We've been able to incorporate those in a language model technology that is part of our Blueprint AI that really can bring that to our customers when they want to think about a new way to either engage with their customers or engage in their back office or do those sorts of operations.

So what it really is, is instead of the traditional model where people would either buy an off-the-shelf software product, figure out how to wire it up to your various back ends, figure out how to hook it into how their users or their customers might use it. This lets you instead literally create something that's yours, that is specific to your back-end systems as a customer and specific to how you want to engage it. That's fully agentic in architecture, which means that there are things that can be automated, they just naturally are automated.

But by using all this tremendous AI power at design time, -- and then at run time, when people are actually using it, being very selective about the use of AI. We can do a couple of really interesting and unique things. One, the systems this creates are remarkably, remarkably better and really allow customers to do -- you can take an old system and convert it and you get something that doesn't look like the old system.

It looks like something that is the way you would want it to work. But more interestingly, by applying AI at run time selectively, we don't actually charge our customers for tokens. They're able -- all this stuff that's finally caught up in the last 4 weeks where people are suddenly worried about token expense. Of course, they should be worried about token expense. $1.5 trillion is being spent on data centers and somebody's got to pay for it.

We are so, I would say, smart in the way that this uses the AI and uses tokens that we really get the best of all the worlds. And I think this is going to be pretty exciting as the confusion abates in this market, there's a lot of confusion. But as it abates and it will, I think the companies that have done the right things structurally are going to be the ones who are going to be able to take the lead for themselves and for their customers.

Excellent, Alan. And could we maybe talk more about Pega's competitors? And how is it changing? Obviously, all these hyperscalers and SaaS platforms, they're expanding their agent strategies. How has this changed recently?

Well, we're very involved with both AWS and with the Google GCP lines because we run -- we don't maintain our own data centers anywhere. When somebody uses Pega Cloud, which the majority of our business now is and the significant majority of any new business runs on Pega Cloud, it's actually something the hyperscalers are really quite happy about.

And we have -- for example, AWS is going to be with us at PegaWorld, and I think talking about how they're working with us and doing things. They have agents that actually know how to translate legacy systems, and we integrate with those so that we're in a position where, for example, AWS Transform, which is an agentic capability they have to read like old COBOL code.

We will take that as one of our inputs when we are reimagining how one of their systems could work. Of course, we take lots of other things as inputs too. We'll take user manuals, we'll take what's on the customer's website. We'll take all sorts of pretty much anything you throw at it. Blueprint will digest as part of making a new solution. So I think we're pretty well aligned with the hyperscalers there.

The noise that's in the market where people worry that all software is dead, I think rumors of the death of software are greatly exaggerated, particularly if you pay attention to the different types of software that are out there. There's just a lot of, certainly, there are companies that are dead, but there's a lot of different types of software.

I think we're awesomely positioned to give the customers the ability to manage the workflows they need to manage to run their business, but to do it in a way that is both innovative and deterministic. And I think that's a big advantage over a lot of the other things I see out there.

I think I'll just add one thing. I think leaving, say, agentic engineering or code writing out and you think about the competitive landscape, I think there's a pretty deep misunderstanding around companies that don't really do workflow, but essentially custom build process connections to be able to try to replicate that activity that don't really have the structure. It's very hard to manage change. It's very hard to get repeatability. It's really just another version of custom code.

And then there are workflow providers that are purpose-built for really straightforward, simple use cases like a ticket management system. And I think that there -- I think many times, all of these companies get thrown into the same bucket in terms of workflow. And what we pride ourselves is that our clients get value in building enterprise scale.

When I say enterprise, I mean repeatability at scale, millions, hundreds of millions of repeatable things that happen that need to be done in a very deterministic way. And I think lots of companies say that they will help that workflow, but they really are just another version of custom code or more simple use cases.

And deterministic does not mean not varying. I mean it's really important, and this is where the AI helps a lot in making it so that you can have decisions and subtlety and other capabilities that will do the right thing for every customer. But they'll do it in a way that, first, you don't reimagine it literally every time a customer shows up. And they'll do it in a way that ensures that you're treating 2 customers the same way, which in lots of businesses is considered a good thing as opposed to reinventing something for each one of them.

That's a very important point, Alan. So with generative AI disruption, point solutions, and, kind of obviously low-end workflow companies, how does Pega's architecture and product suite position you against very big competitors like Salesforce?

Well, it's interesting. I think Salesforce, Microsoft, ServiceNow, they've all done something, I think, is wonderful. They've all created something called a prompt studio. And they want people to go in and "create agents," which are pretty easy to create, whether to do one that really does exactly what you want to do is so easy, not so much, but they create agents by putting English language prompts in.

And you listen to -- ServiceNow is a great company. You listen to Bill McDermott talk about he's going to have a AI control tower to let the thousands or tens of thousands of prompt-driven agents that you create to magically be able to interoperate, find each other, call each other and do something wonderful. I personally think that approach, which is the general approach for some of those other companies I mentioned and candidly, is the preferred approach for people like Claude and OpenAI because it generates staggering numbers of tokens to have this happen. That preferred approach, I think, is madness.

So in Pega, if you want to have an agentic process, you create a workflow. And that workflow, doable by a person only, we'll go to that person. If a step is doable by a person, we'll go to that person. But our super agent is able to read any workflow in any Pega system and execute it. So what do you get? You get the power of the AI creativity, but at design time.

At run time, you're using the AI very narrowly for language translation and for selecting the correct workflow, but you're executing a workflow where you could actually tell somebody what it was going to do before it did it. And we happen to think -- it's not candidly whether we're 10% ahead or 20% ahead or whatever. We are structurally doing this the right way. And I think that, that will over time come out.

It's interesting. There are examples or there are analogies to this workflow discussion that Alan is having that we would never pause to think that doing it using agents or AI. For example, imagine if you just scrapped your ERP system and what you said was, I'm just going to ask an agent what the GL transaction should be every single time. I mean it's -- we would look at that and think like there's no possible way.

You wouldn't even -- how would you even audit that? What would you get as a result? How would you have -- so there are situations that are very parallel to this discussion that Alan is having around I need to make sure that when I do a dispute on a banking transaction that, that follows a very deterministic process, largely because that process may be regulated.

And there may be some variation, as Alan said, because something unique comes up or there's an exception or there would be variation with a human interacting with that workflow. That's where AI is really powerful to augment the workflow. But I do think the concept is very well understood in other kind of analogous use cases. The workflow is done because you need to repeat it at scale.

And the thing that I think people miss because sometimes they think of workflow systems as being just kind of ticket tracking systems. When you build a workflow right and particularly when you use like Blueprint AI to design your workflows, it can put a lot of discrimination, a lot of selectivity into the branches of those workflows.

So it can -- no different types of customers, no different types of risk profiles, no different types of steps, but it can show them to you. It's not that it's figuring it out every single time you go through. And I find that, and I think customers will find that increasingly comforting as they hear people, Julie this morning was talking about an AI control plane.

I think we're going to go through a phase where this stuff has to go out of control before people realize that it should be brought back into -- well, it just should never have been done that way. I mean there are other ways to do it. And so we represent the alternative way to do it.

Makes a lot of sense, Alan. And so as enterprises move from experimentation to ROI-driven implementations, can you talk about some of the themes you just highlighted, the AI governance and explainability for your customers, this approach that you have towards this strategy?

I think it's a difficult time for customers because the hype cycle has been in full gear for a while. There are lots of things being said, the whole declaration of war with the entire software industry where the AI models have basically said the entire software target addressable market really belongs to them. I think customers are trying to figure out some of this stuff. And we're moving into operationalizing more, but the companies are still trying to figure out the key elements of their architecture, I think.

And I don't know how much longer that will go on for, but it's going to go on for a bit. The reality is the world is moving very, very fast. These things are coming at them very, very fast, and they are contradictory. And so this is an opportunity, I think, for people to make decisions that will be very consequential, either good or bad. And I think some of them are being a little cautious because of that.

Having said that, a month ago, we were token maxing, right? You guys know token maxing? Burn as many tokens as you can. Jensen Huang gets up and says, "If you're not spending $0.25 million a year per engineer on tokens, you're just wasting your life." And I think people have reconsidered that. And I think that reconsideration is going to come fast.

And it's perfect because candidly, we figured out in 2023 when we started that this free lunch was not going to exist at some point in time. And I'm really pleased that I think between now and the end of the year, we'll be out of free lunch territory. People will realize that somebody was expecting to pay for all these data centers.

I think it's -- to add on to what Alan was saying, in the last -- inside of a month, I would say, in the last 2 to 4 weeks, almost every client conversation that I'm in has some part of the conversation that says, hold on, how many tokens are you going to charge me for, right?

So I think the clients are becoming much more sensitive because of their own experience. Naturally, there are the very highly publicized like I ran out of my token budget by February. You will see more and more of those use cases. But clients themselves are starting to understand there's variable pricing models. I think that everyone kind of knew -- I think we all knew in our stomach that like it was going to come. It just came really fast, right?

I mean like the fact that the realization of like this is a very costly way to do repeat scalable transactions. It's not efficient at all. It's not the right way. It's the reason why we went away from writing custom code 40 years ago because it's incredibly hard to manage. Writing -- by the way, writing the code is like 20% of the problem, right?

The 80% of the problem is operating it, scaling it, changing it. And that problem becomes much, much harder with the proliferation. That's why we existed. That's why Pega, we existed to try to give our clients an alternative to that. So I do think the token thing is not something that's going to go away.

I just think the conversations are going to continue to escalate around how do we use AI in the right way. That's not a point to say don't use AI. It's a point to say, use it like anything else when it is appropriate, not use it just for everything indiscriminately.

Thank you, Ken. And fascinating conversation. I'm sure people in the room have a few questions. And if you have a question, please raise your hand. But I wanted to just to clarify. So how do you see enterprise technology changing around these ideas that you just mentioned, the center-out thinking, and where should Pega fit into that future?

So I think the future will involve in any enterprise of any complexity, any $1 billion-plus company. There will be no single solution. So part of what it will take to make an enterprise successful is to be able to have a collection of technologies able to interoperate in sort of a sensible way.

And being able to use AI to create a fabric as it were that enables you to run processes across these technologies is exactly what we do and what we've had to do. So I think that it's incumbent on us to first get customers to realize that there will be applications in the future. There will be -- because it's very convenient to build your like customer onboarding system as a system or your lending system as a system or your customer service system as a system.

There will be applications, but the way you will access them will no longer be by coming in, in the morning, logging on to a computer, logging on to 1, 2, 3, 20 applications and figuring it out. The applications need to be able to reveal themselves so that you might access any application in your environment through a chat. And by the way, that chat is also going to come from multiple companies. It's not -- I know everyone is aspiring to be like the portal that you use.

I don't think customers are going to go from that. But the different applications and chats will have to interoperate in our world at the workflow level, at what is the piece of work that they're trying to do or what is the piece of data that they're willing to reveal and bring back.

So this fabric idea and this idea of the multiplicity of applications is absolutely centered to use the term center out. The idea is build your applications, not around the GUI at all, because GUIs are changing, if not always optional and not around the specific back ends.

You really want to define the processes that make your business a business, and that center needs to be accessible by people, by customers directly where it makes sense, by agents to do those different pieces of work. And that is exactly what the Pega center out architecture is and what we've been promoting and working on for a lot of years. And I think that experience that we have doing that gives us a big advantage.

I think I'll add one. I would say this maybe would fall into the category of a prediction more than a know. But I do think there's a level -- there's a parallel to a utility that is a parallel to the model providers. And I think they're -- just like you manage -- it's an energy source, right? The AI models are an energy source.

There -- so I think there is an analogous example of we need to figure out how to manage those in the right way. We don't want to leave lights on when no one's in a room. We don't want to turn on -- we don't want to unnecessarily use an energy source. We want to optimize it and use it exactly when needed and almost build in that efficiency into how we use it. That's where we think we are very aligned with leverage AI exactly when it should be leveraged.

Don't leverage AI. It's a very inefficient use of energy or of power when not used in the right way. So I draw the parallel to like a utility or a source of energy because I think it is very -- and my prediction is in 10 years, we will view AI models as an energy source. They're literally physical centers building processing capacity.

Amazing. Anyone has a question? I think I see you, hand over there.

You guys have laid out a very strong case for how your position in heritage sets you up well. I'm wondering if you can kind of address the success of Palantir's approach, especially in light of the comments they've been making recently about the death of software.

Yes, no self-interest there. What's ironic is I think what Palantir does is as subject to attack by the LLMs as anything. I guarantee you Claude can build a brilliant ontology, which is how they talk about what they do. But look, Palantir is a custom development shop, and they build -- they have a lot of -- they have capable people and they build extremely intricate and sophisticated custom software.

I mean that's how I see them. And they say that they have "products," but at the end of the day, I think they got to send a whole slew of those forward deployed engineers in to actually get anything to work. And there is a role for custom software. Whether everybody is going to customize their software on top of a Palantir ontology, I don't think in my lifetime.

Thanks for being here, and I hope to see you guys at PegaWorld in June.

Thanks for the plug.

I've got 2 questions actually. One was -- so you guys were discussing the competitive landscape earlier, especially against larger competitors. I'm more curious to hear about how you guys are fending off smaller, say, AI, younger competitors who seem to be gaining traction in the space. That's question number one.

Question number two is, so you mentioned that customers -- your customers are having a difficult time trying to figure out AI. By this time next year, how far along do you think customers will be along their AI workflow and orchestration paths?

I'll answer the second question first. I think they'll be pretty far along. A year is a very long time at the pace that things have been going. And new truths are being revealed every month. Tokens is the truth for this month, but there will be a new truth coming out. And the customers are smart. They'll figure it out and they'll get to a good place.

In terms of the smaller competitors, the amount of capital and the amount of noise flooding in this space is mind blowing. I had a traumatic experience a month ago. I went to -- I was speaking at an AI conference, and I drove down 101 in San Francisco. And there's billboard after billboard advertising AI.

And I had this enormous dot-com flashback that hit. So look, there's a lot of craziness and insanity going on. But I do think at the end of the day, making some of the critical architectural decisions will differentiate the companies that are successful from the companies that aren't. And that's true for vendors like us.

And I actually think that's very much true for companies like our customers. And so eventually, we'll see some of this froth subside a little bit. But we may have to get through the next round of IPOs before that sort of comes down. That's why next year is probably the right time.

I think you're hearing a lot. The token maxing token cost is just one element of it. I think clients, we use -- listen, we are big users of AI across every part of our -- all of our functions in the organization. ROI is elusive, right? Like when you use an agent to get an extra hour out of your time, what do you do with that time? Like what -- and it's not obvious in every case where there is a real ROI. And there are some where it is obvious, right?

If you can do self-service deflection and get away from people on a call to talk to somebody, it doesn't negate the fact that you need the system to actually manage that work. But there are use cases that are, I think, are more visible in terms of the ROI. But a lot of the experimentation that's going on right now, I think, will channel into the very targeted use cases where there's ROI.

And I just don't think companies will be able to experiment forever because there's a lot of spending being done where companies ask companies that use it. It's very hard to demonstrate the actual value that they're getting for the organization. So I think that will fix. I think one thing to add on new entrants, there's a tremendous moat, as you might call it, that we have knowing workflow at the level that we understand it.

The use cases, the vertical use cases, the scale, the variability, the knowledge we have is not publicly available, right? This is things that we know by working with our clients for decades.

I think that is a pretty significant barrier to entry when you're trying to do things where you want to be best-in-class and you want to connect the relevancy to the business problem. So I think that -- that's not the only thing we need to do, but that is a helpful moat in terms of new entrants.

Great. If we don't have any more questions in the room, I wanted to ask about the blueprint. We've talked a lot about it already. But considering that the SIs and hyperscalers, they're using a lot of the partner branded blueprint and embedding their own IP on it, how is this motion scaling? And what role will partners play in the go-to-market strategy over time?

So this is all pretty new. So just to explain what it is, if you go to pega.com blueprint and you create a blueprint, which is a pretty interesting thing to do. We're glad to help you with the demo, but you can even do yourself. The system really will walk you through the reimagination of a business process.

And whether you decide it's a business process of onboarding a new customer or if you want to go into the llama rental business or something else that you get to see what -- it's pretty amazing what Blueprint can do when it does something that we have no idea about, but it just understands concepts like what it takes to run a business and workflows and other things.

It will actually do some pretty amazing things. We decided that we wanted to recruit our partners to be a part of this go-to-market for their own good, for their own benefit, not for Pega's benefit. And so we added a capability, which really has only gone into the system as of the beginning of this year that enables a partner to -- when they -- when a staff member of that partner, like, for example, when somebody from Cognizant logs on to Blueprint, they sign on with their Cognizant credentials.

And the top of the blueprint screen talks about Cognizant. And Cognizant has a vector database that we've given them that we can't see into where they can put their best practices into that vector database. And when Blueprint runs in that context, it is going to create a Cognizant influenced, Cognizant empowered blueprint that they can go talk to the customer about, hey, because Cognizant's IP is in here, this will do a better job of reimagining your legacy system or bringing these 2 different things together, et cetera, than if you had done it just with the Pega elements.

By the way, the other player in this party is the customer themselves. They get to upload in the context of Blueprint, their IP, their business objectives, their business plan, things from their website, all of that is part of this distillation into a blueprint that can be specific to that customer's back-end systems to the actual interfaces of the back end or can be specific to the way they talk or some of the language that they use in them.

This partner-powered blueprint, we don't yet know how it's going to turn out because we're really hitting the pedal hard coming into PegaWorld next month. But I think it's a pretty exciting opportunity. And the fact that we had so many partners, big companies want to sign up to create and put their own IP in, we think is just a good early signal for what can happen.

We're going outside the Pega practices in the system integrators to the actual end sellers that are selling legacy transformation broadly across the industry. So these are not people that necessarily know Pega, which is a market that we've never touched before in terms of the visibility of it. That's why...

And we need to work it. We need to market to them, and we need to get them excited about it because they're not, as Ken said, the couple of thousand people, they're the tens of thousands of people or hundreds of thousands of people in some cases that are outside of ever having known Pega.

Alan, Ken, thank you very much for this very insightful conversation. Appreciate it.

Thanks, Alexei.

Thank you.

Thank you for standing by. My name is Karli, and I will be your conference operator today. At this time, I would like to welcome everyone to Pegasystems 1Q 2026 Earnings Call and Webcast. [Operator Instructions].

I would now like to turn the call over to Peter Welburn, Vice President of Corporate Development and Investor Relations. Please go ahead.

Thank you, Karli. Good morning, everyone, and welcome to Pegasystems Q1 2026 Earnings Call. Before we begin, I'd like to read our safe harbor statement. Certain statements contained in this presentation may be construed as forward-looking statements as defined in the Private Securities Litigation Reform Act of 1995 and Words such as expects, anticipates, intends, plans, believes, will, could, should, estimates, may, forecasts and similar expressions are intended to identify these forward-looking statements.

These statements speak only as of the date the statement was made and are based on current expectations and assumptions. Because these statements relate to future events, they are subject to certain risks and uncertainties that could cause actual results to differ materially from our current expectations for fiscal year 2026 and beyond.

Factors that could cause such differences are described in the company's press release announcing our Q1 2026 results and our filings with the Securities and Exchange Commission, including our annual report on Form 10-K for the year ended December 31, 2025, as well as other recent SEC filings. Investors are cautioned not to place undue reliance on these forward-looking statements as there can be no assurances that the results contemplated will be realized. Except as required by law, we undertake no obligation to update or revise any forward-looking statements to reflect subsequent events or circumstances.

In addition, non-GAAP financial measures discussed on this call should be considered in conjunction with and not as substitute for, our consolidated financial statements prepared in accordance with GAAP. Constant currency measures are calculated by applying the March 31, 2025, foreign exchange rates to all periods presented. Reconciliations of GAAP to non-GAAP measures can be found in our earnings press release.

And with that, I'll turn the call over to Alan Trefler, Founder and CEO of Pegasystems.

Thank you very much, Peter. I've just gotten back from a few weeks on the road across EMEA and the U.S. and including an AI conference last week. And it's interesting because I think we're pretty practiced at separating it from was real, but there is a lot of confusion out there.

Nonetheless, I am here in consistent themes from leaders of clients and prospects and partners. In a world of constant disruption, clients want and need innovation without sacrificing reliability. They want solutions to reimagine how their businesses work while still running them predictably as delivering measurable results. This means platforms architected for scale, interoperative and continuous change, where AI is governed, explainable and artists in workflows rather than bolting on. That's what Pegasystems are, hard for enterprise AI. Blueprint to help reimagine how work should run and have people rethink their businesses.

And then the Pega platform to operationalize it with confidence and involve a regulatory demand to go. There's a lot of noise about the future of the software industry is out, and it's creating some real confusion and some real moments of doubters and buyers. Some investors I have met it sure what the future looks like and our even questioning the long-term viability of enterprise software vendors.

Well, we think AI will be good for some and bad for others. And for Pega, it will be guided. The reality is that enterprises don't succeed based on the alternative of coding ask using AI. They succeed based on whether they can design the right outcomes execute the predictively and evolve safely over time. The assumption that AI generated code can replace architecture, it is backwards.

In mission-critical enterprises, AI increases the value of platforms that are architected for predictability, governance, interoperability and continuous change. And that's us. When outcomes matter with customers, regulators and systems that must evolve for decades, AI generated code still needs structure. [ Shipy ] for the types of things we do, very small things. You can just got together. But AI doesn't replace the need to deliver a business system. Alternatively, if people are using AI to just dynamically reason each process over and over.

What we're seeing that's now running up costs and giving nondeterministic outcomes. At the moment, you weaken your enterprise platform, you make your whole business weak, putting AI in the middle in an ungoverned way. Well, that's I think just a recipe for disaster. So whether you use AI to generate house that you want to be able to orchestrate and pull together, whether you use AI to be able to run or handle certain parts of your business where you want the creativity of agentic AI interactions or whether you want whether you want AI to be able to pull together and orchestrate multiple business functions with a harness like Pega driving that. In all of those cases, Pega adds tremendous value.

Let's talk about how mission-critical enterprise software really is still, enterprise applications has always been around a continuous life cycle regardless of technology. It's not a single build moment you need to design and lie on what the software must do and how it must perform. And that design is really going to involve collaboration for many parties and having a collaborative environment by Blueprint that brings the power of the Internet, the power of agaves practices and the power of a customer and/or partner thinking all together in a way that they can understand, experience and improve is absolutely central to get it to a great outcome. You've got to build it, and there are lots of ways to build it, but the great news about something you've done in Blueprint is basically built.

You need to be able to execute or operate it to run it, scale, secure, make sure that this performance that's being watched and managed. And with TagarCloud, which you'll see is really, really continuing to grow beautifully. We give our customers a place to execute that is without parallel. And then you need to be able to evolve it and respond to change as the cycle starts together.

This cycle is high stakes and it's absolutely critical to get businesses, not just what they want to get done in 2 weeks or 4 words to 6 weeks, but to get to operate over the years of the business. The Pega model which is at the heart of a Pegasystem is the key to most of these key factors. It's the thing that lets you design it, if you collaborate, it makes the build trivial it actually executes it and orchestrates the AI.

And best of all, it lets you go back to it and have a structure that you can look at, you can understand and you can direct change from. And that ultimately to us, is how this life cycle operates in this new AI distraction page. While LM dramatically accelerate the build, they place these other key factors, nor they going to be able to. That's why clients do [indiscernible].

Some people are going, "Well, I don't we just got a software. And certainly, AI can generate core quickly, but prompt to code alone Well, sure. It doesn't tell the enterprise what should change. And the gap we have is in coding speed is understanding what's there and making sure you don't accidentally change something with unintended consequences.

When you're operating at the speed of the product, it's actually easier to do that, not harder, particularly if you haven't put out a nice solid architecture that makes what's going on visible.

Now we do want it to some people who say that they believe that AI on the execution. Why do I need a workflow engine at all? Why do I need harness at all, why don't you just particularly turn everything over to general purpose AI agents and to manage it and have say control power watches, what's going on and reports out, it keeps things in line.

But I'll tell you, these rare systems that are difficult to test expensive to run and nearly impossible to have offset with. RMs are incredibly sensible -- sensitive to even the tiniest bits of additional data. And our new version of RLM, and let's look at how quickly they're coming out, can often behave differently from the one you used just the day before.

I think it's safe to say that for many times of work, in provision and proposition is not a reasonable business. strategy. People want predictability and reliability. But the other thing was really broke last week is that this approach to AI reasoning is becoming cost primitive. I knew growing discussion about the cost of Gen AI, our teams are bouncing between token meshing, in which they try to tell the team to use as many tokens as possible to rationing [ cores ] to usage caps to supplying bills.

The concerns are real, but they reflect the misapplication of AI using the wrong AI at the wrong time. When you ask Gen AI to reason that run time over and over, again, for processes you are already validated. Every interaction becomes a new experiment it consumes telcos. You end up paying repeatedly for the same thinking, which is expensive and predictable and hard to scale.

Instead, do it blue predates, do the super heavy reason to get design time, what Gen AI can brilliantly explore options, now at work for close when you collaborated pressure test decisions. Then use the right AI for the execution, focusing on consistency and speed. Costs become predictable and value scales with confidence. Gen AI is expensive, but this application is and the smart organizations will stop paying the LLM to relearn their business every 5 minutes.

Success in the enterprise doesn't come from AI recently everyone flat. It comes from executing redesigning work reimagine work within clear governed structures. Our architecture uniquely allows enterprises to design intelligence into how work gets done, not bolted on after this.

Now since we last spoke, we introduced new 5 account into [indiscernible] Blueprint. And this combines a speed of AI augmented design, with security and predictability that Group print get. You can try it out on adcoms blueprint. Remember that Blueprint facilitates the reimagination of critical work, not just the development of applications. And that risk imagination goes beyond process alone. It includes refining roles, decision rights, skills and experiences, AI can be applied intentionally to these rather than accelerating what already exists.

Users interact with Blueprint designs in natural language now, describing changes by typing and speaking and the result are enterprise-ready governed workflows. We received continued validation of Pega's leadership across the industry, from clients, partners and analysts who see and work with Blueprint AI. Recently Forster named Pega as a leader in customer service solutions, recognizing tegcustomer service, peggaboomprint, and Pega Process Mining for automation and agenetic capabilities. So we're also losing awards for our software.

We've already this year, received 4 awards for innovation related to how we're leveraging AI including a Product of the Year award. Now we love receiving reports for our work. But personally, it's even better seeing our clients win awards for the work that they do with their software.

Just last month, the National Health Service, which provides 24-hour digital and telephone-based out service to Scotland 5.5 million citizens, received the public sector award for work leveraging Gregor software. There's these sorts of recognitions reinforce our strength and the need to be able to orchestrate complex service journeys and apply AI predictably. Now this is not the medical at all.

If you take a look at how this is playing out, we recently had one of our customers Proximus, Belgium's largest telecommunications operator use Pega to modernize our mission-critical B2B installation application moving from a fragile legacy tool orchestrated cloud-ready solution. They built their first prototype Blueprint in 15 minutes and went live in weeks. And numerous other great names, NGEN, Vodafone, National Australia Bank have really been able to drive change include redesign and include extensive automation, all AI-powered.

I love the customers are excited about this and that they're going to be becoming a copegoworld in quantity to talk in detail about what they're doing. And these same stories that you just heard and others will be shared to Pega oil in June in Las Vegas because the way that I think we all learned is by seeing what other clients are doing. And it is such an honor and it's wonderful the customers are willing to come and do that. It's from huge 7 to 9. And I would say it's a must event. A chance to interact with thousands of transformation leaders from around the world and see incredible new developments at over 200 different AI-powered demos.

We have these exciting peanuts lineup was nearly 100 more customers from 6 organizations presenting detailed breakout sessions. Of MetLife, will show how a highly regulated insurer move from AI experimentation to AI at scale. We will discuss large-scale legacy modernization, leveraging Pega Blueprint and AW is transformed for decades of legacy core system.

And I would say that is also exciting is the breadth of industry Wells Fargo talk about how they highlight AI decisioning across billions of customer interactions. So we're going to have great customer stories, but I'm also going to tell you that this year, we're going to have a tremendous product together that we're going to do to leasing. Because this is going to be a very substantial year for the product. We've already seen a Blueprint has done and Blueprint AI has fundamentally changed the upfront design and reimagining of how people should work with systems.

What we're doing this year and what you'll see us being able to show at Pega is how Blueprint AI is moving into the entire development and support suite. So that, that interface, that AI-driven guidance and that power will operate from the monthly to visualization and the exception that you get from Blueprint, all the way through to how you complete the system and how you support a production system.

I think this is the most consequential change to the underlying technology that I have seen, and it's there to support the Agentic process fabric technology we have that then allows all of your Pegasystems and even non-Pega systems to be able to operate as a connected orchestrated network for the next generation of technology.

I think only Pega has the efficient runtime intelligence, the deep design time skills, the experience with these key workflow arises, and it's going to be able to put in your hands the way for you to make our harness ovens. We look forward to continuing the conversation and we in the investor conversation on Monday, June 8, at noon in Las Vegas, we're also hosting an investor session.

So thank you all. We're working hard. And for the numbers, let me turn it over to Ken.

Thanks, Alan. As discussed last quarter, the rhythm of our business was expected to return to a more typical seasonal pattern in the Q1 of 2026. We entered the year knowing the first quarter would also be a challenging comparison given the $60 million of net ACV ad in the first quarter of 2025. And which was very much an outlier and roughly 20% higher than any other quarter last year.

It's no doubt, and it's an interesting start to 2026. With all of the AI experimentation that Alan mentioned, the federal government shutdown 2 wars, both in Europe and in the Middle East, clearly puts pressure on the entire environment. So it's not surprising as well that Q1 did have a lower growth rate.

We continue to believe in the durability of demand for our platform, especially for our cloud offering. Pega Cloud -- in the first quarter of Pega Cloud revenue in the first quarter of 2026, increased year-over-year from $151 million to $205 million, and also grew 30% if you look at that Pega Cloud revenue growth on a trailing 12-month basis. Pega Cloud ACV grew 29% year-over-year as reported and 27% in constant currency to just over $900 million over -- an over $200 million jump -- it's very exciting to see Pega Cloud ACV now rapidly approach the $1 billion mark.

As we've said, ACV growth and mix is reflective of the evolution of our business. Pega Cloud ACV now represents about 56% of total ACV. Our focus on growing Pega Cloud puts pressure on both term and maintenance ACV as well as revenue. Naturally, as Pega Cloud ACV continues to grow as a percentage of overall ACV, it will impact near-term and in-quarter revenue for term and maintenance.

Moving to free cash flow. Free cash flow reached $207 million in Q1 of 2026, marking a strong start to the year. As a reminder, our free cash flow is primarily driven by our operating efficiency in our ACV growth, which serves as a proxy for subscription billing growth.

We remain confident in our strategy to drive free cash flow and ACV growth for several reasons. First, expansion within our existing client base remains a core go-to-market motion, with our sales team continuing to successfully cross-sell and upsell into our installed base.

Second, we're accelerating new logo pipeline build with Pega Blueprint as a key enabler. Blueprint makes it easy for sellers to showcase the power of the Pega Platform, while enabling buyers to reimagine their legacy mission-critical workflows. As a result, Blueprint is already driving meaningful pipeline creation across both new logo and existing clients. We expect this new pipeline will begin converting into ACV in the second half of the year as deals progress through the sales cycle with a faster motion, thanks to Blueprint. This is also an unusually high level of new logo pipeline growth, which is just awesome to see.

Third, we're already seeing early proof of Blueprint's ability to accelerate time to value. Last month, I met with a large health care organization. This existing client of ours use Blueprint to design and build 2 new applications, on going live in 92 days and a second in 70 days. A strong example of what our platform can do powered by Blueprint.

Fourth, we're seeing renewed interest in legacy transformation as more enterprises look to leverage AI and the cloud to modernize their operations. Blueprint is unlocking these legacy transformation opportunities by simplifying how clients reimagine and redesign their workflows to drive growth, reduce costs and improve customer experience.

Together, Blueprint and Pega Infinity create a powerful combination, Blueprint to design and reimagine the work and Infinity to run it, reinforcing Pega's position as the platform of choice for large-scale mission-critical workflow transformation. Unlocking legacy transformation is just one way Blueprint is transforming our business. Early signals show Blueprint is accelerating pipeline growth, and helping us capture new clients. For example, in Q4, we signed a new financial services logo leveraging Blueprint's new legacy transformation capabilities with plans to migrate more than 30 applications from a legacy application platform to Pega Cloud.

Blueprint is also driving meaningful go-to-market efficiency, where deals once required a full bench of supporting roles. Today, our client executives can now cover far more ground with our clients when leveraging Blueprint. Finally, we're seeing R&D benefits as well -- our new genic engineering approach will enable us to execute our product road map more efficiently, allowing us to increase our pace of innovation.

Since Blueprint runs on Pega Cloud, we can deliver new features and capabilities rapidly to clients and prospects. We're excited to share more about this new approach with you at our upcoming investor session in June.

Moving to capital allocation. We continue to maintain a balanced approach prioritizing investments in long-term ACV growth while returning capital to shareholders as appropriate. In Q1, we returned more than 80% of our free cash flow to shareholders, repurchasing 3.5 million shares for $167 million under our repurchase program and paying $5 million in quarterly dividends.

As of March 31, 2026, our shares outstanding decreased from the end of 2025 by 1.6 million shares. Looking ahead, we will continue to opportunistically return capital while maintaining strategic flexibility. Our buyback reflects our unwavering confidence in the durability of our cash flow. As you know, these buybacks are accretive to earnings and on and also combat stock-based compensation dilution. They are made possible by the strong and durable cash flow.

Next, a few thoughts on modeling. We provide full year guidance at the start of the year, and we typically do not issue quarterly guidance or update our outlook during the year. As I mentioned earlier, our renewal portfolio is back-end loaded this year, which means we expect to have higher level of business activity in the second half of the year.

The shape of our pipeline also influenced the timing of term license revenue, which is largely recognized upfront in the quarter a client contract is renewed. As a result, we expect term license revenue to be more heavily weighted towards the second half of 2026. At the same time, our focus on driving Pega Cloud ACV growth also puts pressure on term and maintenance ACV.

The success of our Pega Cloud sales efforts is already reflecting the shift and we expect it to continue as Pega Cloud ACV scales to 75% or more of our total ACV over time. Put simply, a portion of our Pega Cloud ACV growth is displacing term and maintenance ACV as intended, and we expect this dynamic will persist as we march toward our cloud mix goal.

In addition, we're beginning to see a meaningful change in how enterprise clients are thinking about AI. The economics of AI are changing. Frontier models providers are tightening monetization. And the era of low-cost subsidized all you can use experimentation seems to be coming to an end.

As a result, AI usage is increasingly treated as what it is, a true operating expense. Every API call must be justified with clear business value. Given this change, buyers are moving out of the experimental phase of AI into the ROI stage. This transition to profitable AI plays directly to our strengths. Pega has always been focused on delivering measurable business value. AI is not just about efficiency. It's about generating tangible returns, and that's exactly what Pega is built to do.

Importantly, our pricing model is aligned with the shift toward outcomes. Pega prices based on cases, which is a measure of the amount of work that the Pega platform executes. Tying our economics directly to the business value delivered rather than on users or seats. This stands in -- in contrast to many model providers where pricing is driven by usage of metrics like tokens or API calls.

As AI costs come under greater scrutiny, we believe our outcome-based pricing model provides a clear and more efficient path for clients to generate and measure return on their AI investments. As Alan mentioned earlier, we're holding our annual investor session at PegaWorld on Monday, June 8, at the MGM Grand in Las Vegas.

During the investor session, we look forward to providing you with additional color on several of the topics that I discussed today. We also plan to provide more insight into how we envision clients driving legacy transformation with Pega and how we're progressing against the long-term targets we laid out last year. We also plan to give you insight into several key Blueprint metrics, including the impact of pipeline build and deal progression and what is most interesting of some of the metrics around new logo momentum.

In closing, we look forward to seeing you on the road at conferences and non-deal road shows over the next few months and our investor session at PegaWorld in June, which we encourage all of you to join us. Please also note that we plan to participate in the NASDAQ opening bell ceremony on Monday, July 13, at Nasdaq MarketSite in New York to celebrate the 30th anniversary of Pega's initial public offering.

With that, operator, please open the line for questions.

[Operator Instructions]. Your first question comes from Alexei Gogolev with JPMorgan.

Ken, would you mind providing a bit more color on acceleration of ACV growth through the year I remember you talking about client compelling events and renewal cycles driving potential uplift in the back half of '26.

Yes. So there's 2 different -- there's 2 different factors to that. One is our renewal cycle is tipped toward the back end of 2026, which is more -- that's more the usual distribution than unusual. But in 2025, that was reversed. There was not as many compelling event in the back half of the year. So that's 1 factor.

When there are renewal cycles, that is typically an event where clients, if they're going to expand their relationship and to do it around that renewal cycle. So that's 1 factor. The second factor is we've put a renewed interest in new logo focus with Blueprint. And as we build pipeline -- and that will naturally grow and the conversion of that pipeline will grow the opportunity tends to sit towards the back end of the year as well. So there's 2 different factors that really tip our business momentum towards the back end of the year, which is very different than last year where we had a very, very unusually tipped towards the front end of 2025.

And Alan, in the past, you spoke about AI adoption disconnect. Can you talk a bit more about what you're seeing in terms of the narrative in the industry and update us on the trends from Q1 in terms of Agentic adoption?

Yes, I think that there are some things that looked at properly, you have to really laugh or cry that's the case maybe. I talk to people, look, I spend my whole time out in the field talking to people about what they're doing so much going on when people talk about LLM, People talk about the word Agentic, People -- we talk a lot about LLM technology as we make very heavy use exact or databases, which is a way to use in technology, but to do it in a very cost-effective fashion. And what I encounter is there were some people who was actually [indiscernible] about this, where they think that they will be at their most successful when they use the MLM for as much as possible.

And I'll be honest, that's just a -- the ones do magical things. But what you want to do is use them for the right things, not for the parts of this problem that are statistical and not for the parts of the problem that should be planned out in advance, they be planned out with that a Blueprint does, but should not be just planned and we plan and pay this incredible tax for rethinking what you already know.

Some people are just so enamored with the LLMs. They're in love with it. And I think some of the enterprise is outdated, have told their teams, hey, we just want you to use this stuff. But I can appreciate that they're trying to get people to understand it, but that's not going to be remotely what ends up sticking, not just cost, but also lack of reliability. And it's like most unrgreen thing you could do in terms of the electricity and all of that. So I think understanding proper use of LMS is absolutely key. And to be honest, I think we deal it. And what I see with the others, structurally different path in just about all cases.

Your next question comes from Raimo Lenschow with Barclays.

I just wanted to stay on that new logo focus that we have. Like if I look at you guys over the years, you have been a really the high-end provider, very good for complex scenarios, et cetera. But on the new logo side, that was always like a bit of a question, we had a B2B installed base. Talk a little bit more about that new focus on new logos. I can see how Blueprint is really going to help you here, but I just want to understand that a little bit better on that one.

And then the Ken, one for you also on the maintenance side, I hear you that the push towards Pega Cloud will impact maintenance, the numbers we saw this quarter, are they indicative of what we see for the year? Or is that -- were there other factors in Q1 that we should be aware.

I'll touch on the maintenance one. I think you will see as we continue to move towards Pega Cloud, you will see maintenance go down over time, and you will see term license be flat to down as well. Even though some clients will still continue to run on client cloud. I think you will start to see that shift, not certainly not 100% at any point in the foreseeable future, but it will move in that direction.

I'll touch on the first question that you asked because so the way we think about it, and this is just a framework -- if you look at a company like Gartner, they have something like 15,000 clients. I think Forrester has slightly less than that, but still many thousands, probably approaching 10,000 clients. The types of companies that would go and seek advice from a Gartner or Forrester clearly have enterprise spend of some level of size.

We believe all of those organizations are an opportunity for Pega. There are others that don't actually subscribe to Gartner as well. And we think the universe is very large. We're not talking about building down to tiny organization to get that opportunity. There's just a lot of companies that we've not historically sold to -- it is a newer motion for us, but not a brand-new motion. We have always added a few new logos. It's just that Blueprint changes the dynamic of how fast we can engage with the new logo and the speed at which we can validate if there's interest.

And that was the reason why we never really pushed hard on that in the past because we didn't -- we -- in some cases, it might be a 2-year sales cycle to see if there was momentum and that was the reason why we were very thoughtful about new logos.

With Blueprint, that really changes the game.

Yes. In Blueprint is a great starter. But the thing that -- for that, but there are a couple of other things we've done that also changed that game. First, it much you design things that you would have had to be a lead system architect have had years and decades in Pega experience some gas that really do. And now it just happens.

We've got a lot of that expertise built in. And every month, we build in more. So we've radically changed the trading required curve. We've radically changed the expertise required curve and candidly, all of these also adjust the cost curves as well at the same time that the improved met delivery. We've added this syncratic education, which is a way that Blueprint we can now may be easier to teach people about their gaps as opposed to having them to go through big formal courses.

So there's been a huge simplification. And when we sit and we talk about how we want to go from the companies that we would really sell to and have as customers in the global. And we're going to continue to really try to do great work for them there. The 2, say, 10,000 as a much more an easily acceptable population of multibillion firms that we can go after. We now have a tool that's well equipped for that.

And between the fact now, that it runs on Pega Cloud and the Pega Cloud is tied in to our predictive diagnostic cloud, which does a lot of self-maintenance, a tremendous amount of making sure performance runs, handle scaling be on [indiscernible] , we're just in a position where we can really go after this. So it's not just the choice to go after it's also years of product evolution and business evolution that has made this possible.

Your next question comes from Steve Enders with Citi.

Okay. Great. And good to see everybody last week at our conference. I guess I wanted to start on kind of the AI discussion and the focus on becoming the harness for enterprises. I guess I just want to understand a little bit better about maybe what this means tangibly for your customers in terms of use cases that you see as you kind of try to become the harness layer kind of how you're thinking about that adoption curve within some of these bigger customers that you have?

Yes. What I see is that customers in approximately as we just mentioned, I think of some of the others look to the use of RAI powered by Blueprint, but still able at critical points in the workflow to call even a non-Pega. The idea is that we can actually use our agents, customers agents, but it all is in the context of a business objective that they were able to think out and design. That I believe gives the customers a level of reliability and auditability that they can't come close to with any of the other alternatives there.

So I see customers who take the moment to understand that they don't want to reason everything all the time. You guys probably all use Cloud, GenAI and OpenAI, was using cloud this morning, putting some questions in it starts explaining to me that it's reasoning in terms like tracking, [indiscernible].

Well, when it's doing that, it's heating itself telling us I mean we're paying for these journeys of intellectual research that it's doing. I am thrilled to do that when it's not the exact same stuff that was done that one. And people who think that they're going to handle credit card disputes by turning them over to a to figure out the detail of nuance individual customers are missing the chance to bring stability and efficiency into those operations. And when we explain the harness concept, I could think customers really get that.

I'll just add 1 additional thought there. If you think about the value that Alan mentioned of the concept of Pega functioning is a harness, got the efficiency, you've got the risk management that Alan mentioned. You also have the resiliency aspect because we're using multiple models and being able to actually use the right -- the models are -- they have variability in their performance and their speed and their and the context, so I think we're able to really create this best of all of the models in terms of leveraging it when providing that value at design time and selectively at run time.

Okay. That's helpful context there. Maybe just on the ACV dynamics here. I think it would be helpful to kind of know kind of what the net new EV was if we look at kind of 4 25% constant currency like what that was for the quarter? And then I guess on the levels that came in this quarter, I guess does this kind of come in as you were expecting? Like was that the level you were assuming when you guided for the year? Or does this maybe change how you think about what the ACV growth for the year will look like?

Yes. So we -- I'll talk about Q1, and I'll also actually mention kind of the first half that we had talked about at the beginning of last quarter. First half last year, we had a significant amount of net everything I'm saying is in constant currency, Steve. We had a significant amount of constant currency growth last year in the first half of the year. That was unusual and will not -- I mentioned will not repeat this year.

We were going to be more back-end loaded. So the constant currency growth in Q1 was somewhere around $20 million. I would say it was in a few million dollars of where I thought it would be. It was probably a couple of million dollars lower than what I thought it would be, but pretty close. It was -- I would see more of a rounding error than something that was significant in terms of the growth.

And I would say Q2, once again, we don't -- Q2 is not a big renewal quarter either. It's really Q3 and Q4. So that's -- I think the year is not that difference in the way that I envisioned it playing out in terms of just the numbers. And we knew the cash flow was going to be stronger in Q1 because Q1 is typically a strong cash flow quarter.

So I would say kind of across the board, it wasn't dramatically different than our plan.

Your next question comes from Rishi Jaluria with RBC.

Maybe 2 for me. Ken, let me start with you. In your prepared remarks, you talked a little bit about some of the macro or at least the macro backdrop that we've been seeing and obviously, with everything with government and to our political tension and obviously, the prevailing AI side.

There's a lot going on there. Maybe can you be a little bit more or expand a little bit more in terms of -- what have you seen so far this year as a result of kind of all of these, and let's put AI side for a second because obviously, you spend a lot of time on that. But very specifically around some of the geopolitical stuff government et cetera. How has that impacted your business so far? And as you think about things going forward, I know you're not updating guidance and that's in line with kind of your historical practices. But just how should we be thinking about that potential impact on your business for the rest of the year? And then I've got a follow-up.

Okay. So once again, I'll follow your leave AI off to the side. I think on government and the government shutdown and some of the changes the government has made, there were a few deals and a renewal to that actually did slip out of Q1. We don't believe that those are like lost deals. It's just more that the process by which we go through like procurement changes in the government definitely has caused a little bit of confusion in Q1, specifically more in March. So that's -- I don't step that will extend for a very prolonged period of time.

But probably Q2 might still be a little bit of confusion as like GSA starts take deals more directly, et cetera. So that's -- there's no doubt that there is some backlog of work that needs to process through the government that happened in Q1. On the war, I think that -- look, the war is to worse, by the way, not one right. Are both of those ores are very impactful for Europe. -- right? And 30% or so of our business comes from Europe. So I think that it would be accurate to say that there are people there's a potential for a derisking that would happen just because the impact of higher oil prices, temporary inflation, good flow being disrupted, et cetera, and not just the Asia Pacific area, but also parts of Europe that are dependent on those same regions.

So I think government, yes, some delays. I think that will probably clean up through the rest of the year. the war and how long that stays outstanding well has a risk of hurting the spending environment across IT and everything else. I mean, just because of the because of the disruption of the supply chain. So -- and we started to hear some conversations with that. I would say that I could point to deals in Q1 necessarily, but I think it is definitely something we're watching.

I think there's been more of a push to go for some of these "sovereign clouds," which AWS, for example, is working on one. But just having that as an extra complication, just has the ability to drag things up.

Yes. Now thankfully, we have cloud choice. So we have the ability to work with different hyperscalers and regions. But there's definitely there's definitely some tension between U.S. providers and other parts of the world. And we have to do our best to manage through that as this war continues these worse, I should say, continue on.

Okay. Okay. That's very helpful. And then maybe, Ken, I wanted to expand, and this is for both of you. Ken, you talked about this idea of maybe the kind of era of subsidized unlimited tokens might be doing -- and I think everyone's experiences with Claude and the likes kind of shown that as there have been a little bit more at least throttling some of the usage a lot, and I think that makes sense.

But just to maybe expand on that -- can you talk a little bit about -- as that kind of trend plays out, number one, what does that do to your own cost structure with blueprint and where you are using the LLM for the design and ideation side, not necessarily in run time, as you've been speaking about?

And then number two, does that maybe change the nature of some of the conversations where maybe in the past, customers have said, "Hey, we're going to try AI for everything, whether it makes sense to do it or not or use outlooks for everything, whether it makes sense to it or not? " And maybe that can change the nature of conversations and how has that been showing up yet?

Yes. I was really seeing those last week, actually, after [ Atronic ] announced its price changes. It's going to be fabulous for us because Blueprint, yes, Blueprint is consumptive as anything else. But when you design something once and run a 200,000 times, the design cost is not really relevant, so that's really nailing it. I do think it's great that the tokens have started to approach closer to reality, they're still very, very heavily subsidized.

And I think [indiscernible] will persist because people are trying to push the numbers up to when you guys take them public.

Your next question comes from Devin Au with KeyBanc Capital Markets.

Maybe just for Ken on the first one. I know you've mentioned some geopolitical disruptions in EMEA that's ongoing. But when I look at your revenue performance in the U.S. and APAC in the quarter, it seems like both regions were down quite notably. I know revenue isn't the best metric to assess the business quarter-over-quarter, but would love to just get some more color on kind of what drove the downtick specifically for those regions in the quarter.

That's solely just the timing of term license revenue, Devin. And how that compares year-over-year and quarter-over-quarter from Q4 to Q1 -- in terms of the business activity, I don't believe we've seen any impact kind of bookings or new business in either of those regions. My comment was more, it would be reasonable to think they would be under pressure. But we have -- the revenue is just related to term license revenue. It's not structural. It's just the timing of accounting.

And we're -- and we hate with revenue behaves the way it does. Nothing would make us happier than just be able to report everything on a recurring rate.

Understood. Yes. I appreciate the context. And then just a quick follow-up. I know you've kind of talked about a little bit on your remarks on the new bid zoning capability like a release at Blueprint. Would hope you just speak to -- for you to speak to how kind of usage engagement have kind of trended since that release came out for Blueprint. I mean has that -- have you seen any sort of early signs or signals on greater expansion activity from users using that 5 coating tool?

We're getting great comments on it. It's right on the face of Blueprint. There's a little panel on the left is Blueprint, an AI assistant and any -- on any of the pages. If you say, hey, as an insurance policy to this travel request, it will design the data structures in the field, but I say right into the blueprint. So you don't have to get it right upfront. Peter would be thrilled to demo all that to you. But anybody just go on to do it. It's absolutely central to what we're doing and great feedback.

Your next question comes from Patrick Walravens with Citizens.

Great. Alan, 2 for you. So you talked in your script about the long-term viability of enterprise software vendors and you said, "Well, we think AI will be good for some and bad for others, is going to be bad, that's like this question.

Well, we've seen have been really bad for. There are some products that general AI has just made a feature. For example, we use -- there was a company we used to license the document processing software. And if a customer wants, for example, peel fields off of a physical document, they're really good.

Now you can just do that by having the customer call the LLM. And so there are, what I would describe as point features that massively changed or going away. I think that there are also -- a lot of the low-end workflow companies, guys like Asana, on Monday, have really, I think, suffered in the market. They were called work management companies which was a moniker sometimes applied to us.

But I will tell you, we never really competed with them because the types of things we do are so fundamentally different. I think the types of things they do, which often tend to be kind of a small little system for a 10-person work group are going to be the types of things that somebody might be able to just count. Ramping that up to do work across even a 500-person company a 5,000-person company, which is our bread and butter. I think AI just has a tremendous amount of value to that and doesn't really open [indiscernible], as I said.

And then the second question is this is a little out of that field, but I'm sure you have a point of view on it, and I am feeling it fits into your remarks somewhere. So SpaceX buying cursor or maybe buying cursor with -- if your $60 billion, was a $10 billion breakup fee. What does that tell us about what is going on in the AI world?

I think I would have to rely on guys like you to tell me. Look, I think there are so many -- last week, I was driving up 101 and there was a billboard at the billboard of AI company after AI company that I had not heard of many of them. We've got this enormous, enormous collection of code rights, some of whom have become instant unicorns. And what that tells me that AI is in parts of it are in the bubble phase, and that will all shake out whether space ex mix cursor one of the few survivors, There'll be a couple of survivors. Whether Cloud kills them all. I don't know. I'm not fighting in that rate. I have no interest to get righted to it.

I'll just get one little point that we heard last week at the AI conference we were at Pat, which is Cursor is sort of a harness right. And so I do think -- I do think it may be a program -- but for programmers. But I do think -- but I do think it kind of like suggest that like the AI models really need to be covered, right, in different ways or different.

When I use the in harness, which is a word somebody else use kind of like it, it's is really thinking about being a harnessed run time. Our Blueprint harness a design target guys used to decide to make sure you think about this in the right way. us to think like in the right structure in order, et cetera. But I think you need a design time harness and a run time harness. And I would agree [indiscernible] is a good [indiscernible].

That's for the AI models, right? So I think that's the 1 thing I could read into that.

Your next question is from Mark Schappel with Loop Capital Markets.

Thanks for sneaking me in here. Ken, a question for you. Could you just talk about what portion of your pipeline is now, say, AI-driven versus more traditional platform.

So I think -- I'm going to reframe your question because I think what you're suggesting is how much of our pipeline is led by Blueprint. And I would say almost all of our new pipeline growth is connected to a use of Blueprint in some way, which I would put in the AI camp. In terms of our AI accelerators that we have, like we talked about, like knowledge, buddy, coach, et cetera, some of the specific run time AI accelerators that we have. as we typically think about those as a premium markup, so to speak, on the value of activity that happens through the platform, you want to think about all of our new pipeline that's been added, certainly, any new logos, any new workflows, those are led by Blueprint led by Pega AI.

Okay. And then, Alan, I was wondering if you could just comment on how the -- how demand for the legacy scale modernization programs you're seeing is evolving, especially in the government and regulated industries.

So we're engaging. It's slow, but we actually have a number of these legacy transformation projects going on now. And I'm pretty excited about it. It's such a big market. So we're building up our expertise. We're getting some good examples. And when you come to Pega you'll be able to see some pretty amazing things in support of that.

This concludes the Q&A portion of our call. I'll now turn the call back over to Alan for any closing remarks.

Thank you very much, everybody. We're working hard. We appreciate our investors. I really, really hope to see all of you at PegaWorld, you should fire up your AI agents and have them book your reservations from June 7 to 9 in Las Vegas and Ken, as we mentioned, on the 8th, we're going to have a very, very good and very important investor session, and we have a lot of new things to show. So it should be awesome. See you there. Thanks.

Ladies and gentlemen, that concludes today's call. Thank you all for joining. You may now disconnect.

All right. Welcome, guys. Thank you. We're here with the Pega team, have Ken with me. So Ken, we're going to kind of keep this conversation throughout, and we're going to try and hit a lot of topics. We have time to do it. So no issues on that.

Maybe just to start off, Pega has a long history, I think 40 years or so at this point. You've been with the company about a decade. So just kind of give us some flavor in terms of kind of the company's journey, the evolution and kind of what's gotten it to where it is.

Sure. It's interesting, like an investor said to me, like kind of half-jokingly, but it kind of like you think about he said, when you're at a company 10 years, you're kind of like a founder, right? Because like when executives are at a -- and I thought that's an interesting, like it kind of sounds funny, but because strategies do not last for decades, right? Like so you have to -- well-run companies have to adjust and change.

And so the last 10 years has been an interesting journey for us because when I started, we were a perpetual business. And we decided, well, we saw what was happening in the market, and we knew what -- how clients wanted to buy, and we understood the economics of moving to a subscription business. Then right -- shortly after that decision, it really was obvious to us that the anxiety around cloud was reducing across the world in governments and regulated industries. And so we kind of very quickly then flipped into this cloud discussion of like one -- maybe one step further in that recurring model.

And then we went through a cloud transition, and we kind of knew that we had to anchor ourselves on this Rule of 40. So the business model transition was a big part of my first probably 5 years of anchoring that. And then COVID hit. And then -- and now the whole environment around the way people interact with each other, interact with dramatically changed and didn't change just in the short term as even though we're back in the offices, it has fundamentally changed the expectation of like the available engagements where people are not in the same place. So that leveled the bar in terms of engagement software.

And now we're at maybe this next kind of inflection point, which is the power that AI presents for us and how that can change the pace at which companies digitally transform, the ability for us to really make these systems more best practice hardened in terms of having less customization and freelancing around how enterprise applications are built so that we don't actually have the situation we have 50 years from now where we have to modernize all of the systems that we build because they're all custom. So I think that, that's -- like it's just been an interesting. In 10 years, it feels like we've seen a few evolutions in the business, but also driven by what was happening in the marketplace. So it's never boring.

I agree. And my takeaway from here is I'm going to now go present with my leaders from Morgan Stanley that actually viewed as a founder given that I've been 10-plus years in the seat. So we'll see how it goes.

I mean what have you waited for?

Okay. So just we unpack kind of Pega a bit here more specifically in terms of kind of the key products and key use cases that are being solved. Maybe just kind of give us a flavor of what those are and how that's unfolding.

Yes. So this is probably one of the areas where I would say many investors are not -- either not aware of maybe need to be reminded. Much of what Pega does is, all of what we do is deterministic workflow. Let's just start there. Why is deterministic workflow needed? Typically because there is a regulatory compliance internal control standard that needs to be met. Many of our systems with our clients, if you think about our client base, our clients are selling to consumers or they're governments supporting citizens.

So you have a citizen or a consumer on the back end, which means lots of the use cases have either established laws or certainly standards about how that work needs to be done. So things like fair lending standards, credit card processing, the way we handle customer data. I mean you could just go HIPAA, PCI, all these things are standards that were built to protect consumers. That then requires the work to be done in a very, very deterministic way, predictable, reliable, zero tolerance for errors. That's what Pega does.

Pega is the workflow that powers these processes and activities and transactions that have to go through a very deterministic set of steps to execute. It's not just get it done, has to be done in a very defined way. And that's the business. That's who we've been for 40 years.

Okay. And is it the reason you specifically called out deterministic versus nondeterministic just to really anchor in the points of, this is something that is known, repeatable, et cetera. And to your point, there's 0 margin for error. So I think we're probably leading into maybe why there's some protections or some moats here as it relates to your offerings and kind of what you're delivering. Is that?

Yes. So the connection to deterministic is that it needs to be decided on the front end. So the workflow has to be decided on the front end, has to adhere to that process. And if it doesn't, there's either regulatory or business brand or quite frankly, the legal risk for that. So that's the connection. So then when you think about, well, why couldn't I just say build a custom application that doesn't have workflow or use a coding tool to speed that? It's the main reason why people don't do that is not the cost of engineering. It's the inability to actually have a functioning workflow application that helps you stay regulatory compliant.

I got it. Okay. So let's now hit Blueprint. Blueprint is a big part of the company. It's evolved quite a bit from when it first was announced and kind of what you're doing with it. You all have said that it's core to how you operate. Maybe just kind of double-click on Blueprint for folks and maybe share some of the specific customer needs that drove the expansion of Blueprint's capabilities. And I think right now, more than anything, everyone is asking questions around AI at this conference, and obviously, there's a lot going in the world. But like why is this critical for enterprises right now and the needs that they have?

So one of the biggest challenges that Pega has had over decades is the cost of getting started and the cost of getting the transformation of the application. So if you've got a use case that is running on some legacy mainframe, COBOL application, whatever it is, and it works, but it's not modern.

The first question that you would go through is, well, how much will it cost me to design, to redesign and to rebuild this on a platform like Pega. And the time frame and the cost is a pretty big investment. And so clients will be very picky on what they did. And they -- quite frankly, that cost of system integrators and hands on keyboards and even the front-end ideation, it just really was a -- it was a headwind to transformation.

When we saw AI, we immediately connected what we saw with the models to how we could address that issue. So we built Blueprint specifically to attack that upfront design problem of clients not being able to visualize and design what they wanted the workflow to look like because it was just too costly and took too long. So that was our target that we went after.

Blueprint started kind of more as a simple ideation tool, like you had a couple of screens and you can maybe produce a PDF, right? If you fast forward to where Blueprint is now, we're going to -- we'll be announcing tomorrow morning before the market that Blueprint has full vibe coding capabilities in it, right? It's not -- you're not just actually seeing the drop-downs, you're actually going to see the actual like kind of vibe coding experience where you don't -- you could -- if you wanted to use the kind of the UI and the drop-downs, you could or you could go right in and actually talk to the blueprint, tell it what you're looking to do, ask it for suggestions, ask it for -- have it ask you questions to help actually.

So that's going to be -- that's the experience we envision how every Pega application will be built going forward. So we still have some work to do in terms of extending the capabilities of Blueprint to cover every configuration use case. So we're not there yet, but that's our next phase is really to make it so robust that when you interact with Pega, you will no longer drop down into an App Studio or Dev Studio experience where you're configuring inside Pega, everything will be done in a vibe coding front end of Blueprint.

And you might say, well, why couldn't I use another AI tool if I wanted to do that? No AI tool is going to understand workflow like Pega does. There's no -- you can search the Internet, pull every piece of data, it's proprietary to us. Only we understand how to do those workflows, those deterministic workflows. So the differentiation is our moat is this is our thing.

So you've removed the friction, if you will, from people designing new systems. And obviously, the initial kind of headwinds were the complexity, the time and then obviously, the money you spend and more times than not, you get it wrong because you didn't do the upfront plan.

Right. And skill sets resources or...

Blueprint, you're taking this 40 years of history of you guys building system designs and schematics and understanding certain workflows and permutations. And therefore, you then have this now vibe coding element that can tap into all of that and give you this dynamically real time and get these systems out.

That's exactly right.

Okay. Perfect. Okay. So just maybe a bit more specificity. Can you maybe just share an example of a customer using Blueprint today to maybe drive AI transformation?

So do you mean to use Blueprint and AI to transform like a legacy application?

Yes. Beyond kind of like the divide cutting, is there anything specific that's happening?

So I'll use -- I'll keep the name because I don't know if we have the right to talk about what clients are doing and such. But we have a client that I talked to recently that is essentially reforming all of their customer experience so which means customer support, customer engagement, upsell, cross-sell, everything. There basically, there are a series of workflows that some of which are on -- are custom built, some of which are on a competitor of ours. And they're using Blueprint to redesign each one of those workflows and then using Blueprint to look at the collection of all those workflows across a life cycle and they'll use that to then deploy and transform that.

So then when they're live, so now go to run time, you've designed the application, you're running the application, they'll use AI, our agents and other agents from other systems with our process fabric, which is our Agentic Process Fabric is -- think about it as like the connection of the orchestration where the agents can come and be directed to the workflow that they need to...

Kind of like [ ACV ] server issue.

Yes, like [ AAJ ] exactly, yes. So it's -- so that is like -- that is an example of that will -- is happening at multiple clients. And that would -- what would have happened pre-AI is they would have had to go workflow by workflow, whiteboard and you and I in a room and we design it and probably get it wrong and keep just -- will be 2 years, 3 years, 4-year projects.

And so we've been speaking a lot about Blueprint specifically focused on design. And you now are doing that phenomenally well. You have new capabilities that are released, et cetera. Maybe you could help me understand where else you feel you can now evolve potentially within the broader development process outside of design, whether it's kind of develop, test, deploy, et cetera. Just maybe where else can those capabilities accelerate customer deployments and implementations and deliver capability quicker?

So Blueprint is now extending into the build phase or the developed phase. It will also include all the automated testing as associated with that, that we'll be testing continuously. We have agents testing as we're actually designing and building. When you get to production and you're live, we'll actually use Blueprint in the future in the actual ongoing software development life cycle of improving the app of updating, of taking new capabilities of making changes. So really, Blueprint becomes that starting point for everything from design to build, to test, to operate.

That's awesome. And so it's fully living the infinite life cycle.

It's not there now. It's not there yet, but that's our strategy.

Okay. Got it. On your recent earnings call, you mentioned becoming more proactive and confident in pursuing new logos supported by kind of Blueprint experiential sales approach. Maybe just kind of shed some light on that and kind of the go-to-market motion, how it's changed and kind of productivity gains that you've seen?

Sure. So when we would hire a salesperson a few years ago, it was a -- we would not -- we would really not let a salesperson talk to a client until they went through a deep training of Pega. Typically get some level of certification. So let's just call that 6 months of lack of productivity. Then they start to get into the field. By the time it takes them to build some pipeline, they're not going to close a deal in the first year. They may not close a deal in the second year. You can imagine the cost of bringing on salespeople targeting a new logo that doesn't know Pega with this very manual labor-intensive front end.

So we really felt uncomfortable scaling our account executives and new logos too fast because of that upfront cost. Now we use Blueprint. We don't -- there is no training required. Person comes right in, they could start immediately with Blueprint. It's self-explanatory in terms of how it's used. It's -- they can get in front of the client. They can be talking to a client in the first meeting and pull out Blueprint. So that, to me, is like transformational in terms of how we sold, which then gives us the confidence to start targeting new logos and to bring in more salespeople to try to further accelerate our growth.

Okay. That's awesome. Maybe just on this go-to-market motion as well. You've recently enabled partners to leverage Blueprint directly, like including embedding their own IP in the platform. Maybe how does this change your strategy with SIs? And kind of what does this mean for your ability to scale more quickly over time?

So similar to the ramping challenge with new AEs years ago, we did not sell through partners. Now contractually, a partner may have been the prime, but they weren't selling. We would sell and we would partner and they would implement. So we didn't really feel comfortable encouraging partners to sell because of that upfront training that was required.

And if you think about like a system integrator like [ PwC ] and Accenture, their salespeople are not domain experts in any particular product. So you can't -- you're not -- you can't expect them to be experts. So now we actually have partner-branded Blueprints. So in the example of Accenture or Capgemini or Cognizant or others, they'll have their own proprietary blueprint. That blueprint is used in their selling motion with their sellers. Anything they do with their clients is only visible to them. We don't have access to it. No one else does.

Then we want them to use that in terms of selling transformation to the clients. We have a 3-way partnership with AWS or Google where they're the hyperscaler. And so system integrator getting the transformation, AWS or Google helping augment and support some of the service credits. We get the platform deal, set it through the marketplace, it runs on that. So that's kind of the way the partnership is working. It's just -- it's brand new for 2026. So we will work out the kicks, but it's very new.

It sounds like win-win-win for all...

We believe it is.

Okay. Maybe shifting to market opportunity and a bit of differentiation here. Enterprise CIOs have been shifting from like AI experimentation to actually execution and deployment. Maybe just kind of perspective from you on your conversation with customers, kind of how would you characterize the IT spend environment today versus a year ago? And kind of where do you see the most durable demand?

So specifically for us, legacy transformation up until this past year was typically not in the top 5 initiatives. Maybe it was # 5, but it was not in the top 5 initiatives for our clients. Now if you look at that priority, it's in the top 3, right? It's typically cloud, AI and transformation. So -- and we think those all 3 fit together, right? They're very complementary to each other.

So we feel the pressure from our clients to move faster, to move faster to leverage AI, to use AI almost as the catalyst to try to move faster with that transformation. And there's some great technology, AWS' transform tool, some of the COBOL interpretation stuff that Anthropic release. These are all great accelerants to actually bring transformation to a company like Pega.

Okay. And we touched on this a bit earlier, but just thinking about orchestration for a second as kind of one of the key bottlenecks to scaling some of these systems. But I think this kind of plays directly into some of the strengths that you all have. I think you mentioned a mesh or concept earlier. But maybe just kind of give us some perspective in terms of how this shift kind of plays to your strengths as kind of the AI adoption broadens and people become much more receptive?

So Pega is a workflow company, but much of what we do with those workflows is the integration of the workflows into their business processes and really helping orchestrate connection to all these systems. I mentioned before the Agentic Process Fabric, which is a way to create that orchestration with agents as well as those data integrations and process integrations.

So I think the most -- one of the most exciting things that I'm seeing is that transformation, clients are thinking about transformation, I think, for real, right? I realize that change is hard and large organizations like yourself, I mean there's a lot of people and there's a lot of systems, and it takes a while. But I do think the leaders, the CIO leaders realize the value that is unlocked by actually transforming and modernizing these. And so I think that AI is a big catalyst for that. So that's like one of the most exciting things to see is this isn't tactical like trying to fix a screen. This is -- I think people are taking serious for the first time.

And obviously, this evolution has come about quickly. And I think, obviously, the -- getting the product and packaging right is essential for being able to push some of these software offerings. Maybe just help us better understand how you all are pricing your software. Is it seats? Is it outcomes? Is it something else? And maybe how does that look for this year as some of these products roll out?

So we made a decision 10, 15 years ago that has turned out to be the right one now. And the decision was that we wanted to move away from user models. And the reason why was we were going to clients and clients were saying, help us optimize our call center, for example. We've got too many people. We'd like to get more. So the result of optimization is they take down the number of CSRs, customer service representatives. And for that, Pega would get less revenue.

That didn't really make sense, right? So we kind of -- we said, look, user models don't make sense. Activity models make sense. Cases. So we charge based on a unit of work, which we call case in our system. So as the client pushes more automation through the system, we share in that efficiency through a higher -- and then as they use more cases, there's naturally a volume discount curve to incent clients at scale to continue to use us more.

Okay. I got it. And maybe the ultimate validation of what you guys have is your retention metrics, right? I think your gross retention is in the high 90s and net is kind of 12-ish ZIP code. Maybe just better help us understand maybe the moat that you've developed, kind of how that helps you differentiate and maybe why that's durable? And then kind of when you're speaking about that, as people are evaluating platforms, maybe who is it that they're typically comparing you against? And kind of why is it that your moat is actually allowing you to win in this market that's becoming, I would say, increasingly competitive with the tools that people can deploy to help accelerate their specific technology offerings.

So we have a few differentiations that would be the reason why someone might scale more with Pega. We have this concept that we call the situational layer cake. And what that means is it's the concept of dimensionality and scalability. Many times, if you're using Pega for, say, 20 workflows, 30% of the types of steps and stages might be similar across those workflows.

So once you build that one time, that's all repurposed across any of the applications. So you can really save a ton of time in terms of reuse. When you make a change one place, you can propagate that change through all of the different Pega workflows. So that's very differentiated. No one else actually takes that approach.

Another differentiation we have is we have a kind of a very seamless way of integrating real time with other systems so that the data that you see, and it's the way that we cache and store the data and create those integrations that we can seamlessly grab that data, put it in the context of a case, containerize it. And it is -- essentially, it creates a real-time or near real-time kind of engagement where other systems tend to have lags, they tend to sometimes be batch transactions even today. So those are really powerful differentiations when you're doing things at scale.

When you think about the competitive landscape, you say, well, why would somebody pick Pega over XYZ company? Nobody does workflow like we do workflow. I mean if you look at the new Gartner BOAT, business orchestration automation technology quadrant that just came out, Pega is top and to the right. We've been top and to the right every time Gartner or Forrester puts out intelligent automation, BOAT, whatever the quadrant looks like.

So it's generally recognized that, that's what we do. We do workflow. And so if you're trying to do enterprise scale deterministic, regulated 0 error tolerance, real time, a lot of reuse, ability to evolve that system, that's Pega. Nobody else compares.

Okay. Perfect. Maybe shifting to financials. Strong quarter reported. Maybe just a bit before we dive into the numbers, I think you guys have been articulating ACV growth and net new ACV dollars is kind of the best KPIs for Pega right now. Maybe just kind of explain that lodging rationale in terms of if people are outside and looking at the performance of the business, why those are the best indicators?

Sure. So we started ACV when we were going through the transition because we knew that our revenue was going to convert to subscription for perpetual. So there was going to be a lot of noisiness in the revenue as lots of other companies have went through. So we knew that was going to happen. So the growth in the business was most represented with the growth in what we build clients, which is the annual contract value. So we established annual contract value as the best metric. It's analogous to ARR.

So we -- I think that is the best measure for a subscription business is the increased spend with your clients. Then we kind of -- because of some of the noise in the revenue line, non-GAAP operating margin or EBITDA or something wasn't really a great representation for what was happening in the business. So most companies take EBITDA, subtract CapEx or come up with -- to try to get to free cash flow. And we just decided, let's just go right to free cash flow, right? There's no reason -- so we just said ACV, free cash flow, it's how much do you increase the billings every year and how much do you get back to be able to distribute to shareholders or use for other strategic investments.

Then what we also look at is we also pay a lot of attention to our stock-based compensation, right, which is kind of the other lever. And we look at that percentage of stock-based compensation compared to our revenue, our market cap, our number of shares so that we really look at ACV, free cash flow and then also free cash flow less our stock-based compensation. And that's how we kind of measure whether we're generating value for our shareholders.

Okay. Perfect. In the quarter, I think it was about 14% constant currency growth. Pega Cloud ACV was 28%, exceeding expectations. You put out initial guidance, which is an acceleration of growth in the top line. And I think the margin expectations well exceeded kind of where people were at. Maybe just kind of help us understand what is the true driver of your performance on the top line? Is it kind of what maybe is Blueprint doing or not? And then kind of what gives you confidence in the '26 numbers and your ability to go out and compound that free cash flow growth that, I mean numbers that are quite actually impressive.

So I'll touch on the growth first. Blueprint has completely unlocked value in how we can scale new logos and go after new opportunities and help drive transformation faster with our existing clients and new ones. So we feel very confident making some strategic investments in sales and marketing to help to accelerate that.

And we have a modest acceleration from 14% to 15%. That will come from new logos. So can we see -- our net retention rate, it might move around a little, but it's clustered in a pretty tight range over the last 5 to 10 years. So we feel pretty confident that that's a durable number. And then we're focusing on new logos kind of filling that gap to get acceleration in growth.

When you go -- when you think about the operating margin, the operating leverage, the free cash flow piece, we have built -- and we're quite proud of the culture that we built around profitability, right? We were -- we never had free cash flow margins before the transition above 20% and probably more like 10% to 15%. And when we went on this journey, we, as a company, we put all of our bonus plans, all of our targets, everything on Rule of 40. And we said we're going to be a Rule of 40 company. And at the time we did this, we were like a Rule of 20 company.

And we said we're going to become a Rule of 30, then we're going to become a Rule of 40, then we're going to get our cash flow. We're going to accelerate our growth. And we've done each one of those things. So I think the change that we've made in terms of people appreciating the value of being a profitable business is probably actually more impressive at Pega than actually the movement to subscription. So then you kind of flip over to -- so how are you going to continue to get operating leverage, right? So we're free...

I'm not coming off, what, 45% free cash flow growth last year?

Yes. We came -- we had -- 2 years ago, we had, I don't know, 1,000% free cash flow growth. And then we had like 60% and then we've got another 40-plus percent. And our free cash flow growth is about 17% right now, but we're still getting some operating leverage.

The key for me is what is -- what can a well-run business drive in free cash flow as a percentage of kind of that subscription base. And until we get to the 35% to 40% range, I think we have room to get better. And we're kind of a little north of 30% right now, and that's an impressive number. So -- but I think we can do better.

And like I said, the commitment to the value of that across Pega, it's not about being greedy. It's about running a good business and returning value to our shareholders and creating incredible stability for our customers. Because the more profitable you are, the more stability you have to deliver for your customers.

Sure. That's really quite impressive. And I think more importantly, your ability to kind of tailor the organization culturally that this is something that's now embedded in everyone's operating philosophy and the desire to want to go and kind of achieve these goals. Maybe just kind of one follow-up on the ACV targets for the year. I think last year, there were some renewals that I think were earlier in the year. Maybe just kind of help us understand the arc or trajectory that we should expect as it relates to kind of the hitting the ACV milestone?

Yes, I'm glad you mentioned that. So last year was a little bit of a different year where we did not have a back-end loaded renewal cycle. And so we ended up -- and we didn't in 2024. So it kind of rolled over into the first quarter of '25. So Q1 of 2025 was a very impressive quarter, but also not really a fair compared to 2026.

2026, the way our renewal cycle and the way our pipe build is to compelling events with our clients tends to be more second half loaded. Which means that our year will look more like a traditional enterprise software company where your Q4 tends to be your biggest quarter, whereas last year, Q1 was our biggest quarter. So you'll see some deceleration in our ACV growth in the first quarter just because of the tough compare, and then you'll build your way back up to the end of the year. That's the way we see it model.

I understand. So before I shift to capital allocation, any questions in the room?

Okay. All right. So the Board recently approved, I think, an additional $1 billion share repurchase. Obviously, strong growth in the quarter, great guide on cash flow, generating a ton. I think you're trading low double digits on a cash flow basis, 11, 12 multiple. So maybe what's your strategy right now in terms of buyback? Is it let's lean into this because we know we have and we don't like the price? Or is it, hey, we want to lean in, but we don't want to over-index just in case there are things we want to invest in or companies we can acquire?

Yes. I think it's more of the latter. Like look, it's hard to justify an acquisition IRR when you're trading at sub-15x free cash flow and you're growing 15%. So like that's a hard argument to make. So we feel like we should use as much cash as practical to buy back shares. We feel like that's a great investment.

That said, I've had some investors ask the question, like why don't you go out and get $1 billion or $2 billion of debt and buy back shares. I'm not saying that's a crazy idea, but it does put pressure on options. You don't give yourself options like options that something might come to the market. We might want to make an investment. So we don't want to put ourselves in a situation that we don't allow ourselves. We've worked so hard to get to the free cash flow generation that we have. We want to make sure that we use that as a powerful option of how we can drive our strategy.

So we're going to be very aggressive with buybacks, which is why the Board approved an extra $1 billion, and we've run -- largely run through the first $800 million or so of what was approved. And we've got really the potential to buy back that $1 billion between now and the middle of next year. So we're going to be very thoughtful about accelerating those buybacks, but also careful to give ourselves options.

Okay. Maybe as we just start to wrap up here, we have a few minutes left. Post earnings, I'm sure your callbacks, maybe just a view on a general sentiment. And then perhaps what are the most misunderstood or underappreciated aspects of the story that kind of while we are up here on this platform, we can just use as a kind of mouthpiece to really get that story out there pretty clearly.

I think that investors that know Pega are pretty impressed with the results that we've put up and the progress we've made over the last few years. And the feedback we've got from investors, I think they're very appreciative of us doing what we said we were going to do, which I think should be -- I think that should be a given with public companies, but unfortunately, it's not. So like we're -- so we're proud to deliver for our shareholders.

I think that they're very interested in Blueprint, very excited about the Agentic capabilities that we've released and how those fit into faster digital transformation. I think they're very excited about new logos and our ability to go after new logos. They're curious about this using system integrators and the autonomous partner selling to try to have a new sales channel. So I think those are all things that they're excited about.

To hit the second part of that question, which is what's misunderstood? I think probably the -- I only realize this now because of the discussions around AI and kind of the SaaSpocalypse or whatever they're calling it these days. I think there is a deep misunderstanding with why Pega exists. Pega exists to build deterministic workflows for regulated industries, regulated workflows that have consumers or citizens on the other side. These are things that don't just go away.

And so the reason why they use someone like Pega is because they need 0 error full predictability, scalability. And I think that we never really talked about that before because it was kind of almost assumed. But I think now it's much more relevant because it's really the reason why we have a moat, the reason why we're sticky, the reason why clients expand with us is because we are a known and dependable workflow platform for them to execute a lot of that work.

So it's [ 9,999,999 ] in terms of simple numbers.

It's -- I use the example of why did people use mainframe and still use mainframe because there's a high level of reliability, right? Pega is analogous to that in terms of reliability.

Okay. Last one, just on the Investor Day, you guys are PegaWorld this year and then perhaps dates and maybe rough agenda for people to just think about what's to come.

PegaWorld is -- we have PegaWorld in June. You can come to pega.com and get the dates. We have -- our Investor Day is on Monday of PegaWorld. And we will typically -- what we'll do is we'll have a discussion of some of our new capabilities at a little bit of a deeper level that we talk about on the main stage. We're going to actually have this year, we're targeting to have a customer actually come into the Investor Day and talk about their use of Blueprint across their organization, how it's changed their engineering organization and the efficiency.

And then I'll typically wrap up with a financial update model of our -- we call it our long-term model, but typically over the next 3 years. I'll do that. Then we encourage all of our investors to go to our innovation hub and to go talk to clients and to kind of really use it as a -- learn more about Pega.

Okay. All right. Thank you, Ken, and thank you, guys, for being here. Appreciate it.

Awesome. Thanks.

All right. I think we are live. We'll let folks trickle in. But yes, welcome, everybody, for joining us here at KeyBanc's Technology Summit, Emerging Technology Summit. My name is Devin Au. I am part of the KeyBanc software research team, and we are really pleased to have Pegasystems CFO and COO, Ken Stillwell here, joining us again. So yes, welcome back again, I guess.

Thanks, Devin. Appreciate the invite back.

Yes. I appreciate you being here. So I know Pega has gone quite a lot of interest in the past couple of years. I think people are -- most investors are familiarized with the business. So I think maybe a good place to start since we have you here, Ken. We'd love for you to recap kind of the fourth quarter results and maybe highlight a few things that you thought it was noteworthy.

Sure. So the fourth quarter, I think, was a really strong finish to what was -- probably one of the best years that we've had at Pega. We -- our net add to annual contract value was noticeably up over 2024. It was like up something like 40% in constant currency. And it was probably twice what we did just a few years ago in terms of net ACV add. So I think from a new business, from a growth and new business standpoint, a very strong year.

The majority of that was Pega Cloud, our SaaS offering, which is what we would hope to have seen happen a few years ago. We started this movement to the cloud, probably in earnest about 6 or 7, 8 years ago, something like that. And then -- but really in the last 2 or 3, we've seen this very big uptick in Pega Cloud. So we've got Pega Cloud momentum leading to accelerated growth.

Pega Cloud is now growing kind of in the 30% kind of range, which is up from a couple of years ago where it was high teens, 20%. So we've seen not only an inflection up in overall growth and inflection up pretty significantly in Pega Cloud. And at the same time, we've been able to really drive a lot of operating leverage into the business. So we've -- our free cash flow finished close to a little short of $500 million for 2025, and that's up from $22 million in 2022. So it's -- I think we've really done a great job of driving both stability and acceleration in growth, movement to the cloud and significant operating leverage to get our free cash flow margins up into the 30s.

So great finish, and we guided up for 2026, a slight acceleration. Maybe I think it's about 17% free cash flow growth year-over-year. So the business is executing really well right now.

Yes. Thank you for that. Yes, really strong results that you guys finished '25 with, and I'm sure we'll get into the guidance a little bit more. But maybe just staying on the quarter just because it's still kind of topical, especially with kind of the government shutdown for like half the quarter in the fourth quarter. Maybe just a quick kind of update on -- do you guys see any impact? I'm sure it's like a pretty small portion of the business, but just anything to speak there?

Well, the -- on the -- we have in 2026, it will be about 10% of our revenue is professional services. It was a little bit more than that in 2025. So certainly, some of our professional services was put at risk if we weren't able to work. So for the -- to the extent that we had services people doing work for the government, that was certainly something that we saw a small amount of headwind. On the license side, we didn't really see any impact in the quarter.

We -- naturally, some deals that might have closed earlier in the quarter may have taken a little longer to close. But the government was pretty good about when they got back to it like trying to catch up on things that they knew. And we're considered a mission-critical provider to the government. So they -- we always know they'll prioritize the activity that we're doing with our government. So I think that although it was painful for some of our clients. Certainly, it was painful for some of our professional services team. From a license standpoint, we kind of got to where we thought we were going to be for the quarter.

Okay. Okay. And then everyone is talking about AI. So I guess we'll do that too as well. I know that Pega has kind of evolved and I think the pricing model today is mostly consumption. So I guess the whole debate of seat base as kind of an argument there really doesn't apply to Pega. But then maybe if we look at the flip side is the vibe coding and all these like AI tools is it really easy to replicate what you guys have done for decades? Maybe kind of speak to the defensibility of Pega -- is it hard for these new encumbers to come in and disrupt your space?

Sure. So I'll hit on the first point just briefly just to close that out. So Pega has -- from -- at least in the last 20 years, maybe not from the earliest days of Pega, we viewed ourselves as a tool that could help automate, which would reduce the need for human beings. It would reduce the need for people touching systems and touching processes.

So from that standpoint, it didn't make any sense for us to have user licenses because we would go to a client and we would say, let's work with you on getting your headcount scaling or better yet, maybe taking people out of your processing center. And for that, we'll get less business from you. It just didn't make -- it wasn't -- so it was really easy for us to go to clients and say, we're going to charge you based on the volume of transaction that the system does. In our terms, that's called a case. That's like a unit of measure. And then there's essentially volume discount curves. The more a client drives more cases, the more they get the economics on incremental cases.

So that's kind of our licensing model. That's not to say that we don't have some user licenses or even some mix licenses where they may do both. But the majority of our business is usage or activity based. When you flip over to the AI question, which I think is one of the ones that was probably the most -- I think the market -- I think investors have learned more in the last few weeks even than a month ago around this question.

I think the challenge that investors had was they could not -- they're struggling with trying to discern which companies might be impacted negatively, positively or neutral to AI in general. And when you hear people say like, well, I can -- we can vibe code, AI could speed certain activities up, it adds to that anxiety around who's going to be impacted. So I think what happened originally was it feels like and still to some extent, investors have just taken a swipe at everything in software and said, I think it's all just worth something less.

And I think that over time, that will shake out to be winners and losers and probably people that aren't really impacted. We view ourselves as AI being really connected to our value proposition. We've had AI before -- I mean we had AI, and this is no joke. I mean, in the '80s and '90s, we were using AI and machine learning and people kind of almost used to laugh at us back then and say like, what do you like -- almost it was like science fiction.

But we actually had rules-based engines that created probabilistic outcomes. So we did that forever. We actually bought a company in 2010 called Cordiant that was fundamentally an AI decisioning engine. So that was, what, 16 years ago, right? So like -- so clearly, we've always been an AI-first company. What's different with generative AI is it actually does a lot of the design thinking upfront that RAI didn't do. RAI was much more probabilistic -- but now you've got the probabilistic aspect like the English language, right, where you can actually use things like process manuals and documentation and you can ask it questions and it can help form how should the application look from a design standpoint.

So for us, that's -- we call that probabilistic. We do what Pega's fundamental value proposition is deterministic workflows, where we -- where the workflow decides, it tells you structurally at the beginning, how work should be done, how a process, how a loan origination, how a dispute, how a card replacement. These are largely things that are regulated by laws, by associations, by internal controls. So they're not things that like generative AI is not going to in the moment at run time, going to be decide that they're just -- it's just going to do something.

So we view the beauty is in design, and this is what our clients, probably the biggest disconnect that I've seen with investors and clients is that clients are talking about AI design time, AI helping build and form what it is that you're going to use and then agents executing the work that human beings would otherwise do on workflow platforms. So I think it's a really interesting dynamic to see this big disconnect with investors kind of worried about vibe coding replacing every software application and customers segmenting them into 2. One, which is there's no reason to do anything but use GenAI and here is a deterministic workflow that I have to follow the fair lending or whatever the standard is.

So until there's really like Congress decides to just rescind every single law and every single consumer vertical that's out there, there's going to have to be structured to how the agents work. So we love this design time agent, deterministic workflow that's built and the agents actually are augmenting the workflow and executing where a human being would otherwise do that. Real quick example, know your customer.

For those of you that know banking, essentially, new customer comes in, you've got to do a background check, make sure they're not on a sanction list, make sure they're a real person. There's -- most banks and probably yours included, have lots of manual processes. They're doing -- they're literally like pushing paper, photocopy, OCR and things. That's where an agent can be super powerful, right? Where the workflow kicks off an agent and says, go do the following discovery, document these things, tell me whether this person has an association with a Russian oligarch or some other sense.

That to me is like the perfect world of like structure of the workflow and the agent doing the work of the human being. So that's kind of how we see this marriage of workflow and AI.

And I think you mentioned you guys do deal with a lot of regulated industries. Have you guys given like the mix of like -- I know you guys are big in financial services, I think health...

So the verticals -- our 5 core verticals are all consumer constituent verticals, financial services, insurance, health care, telecommunications and the next one would be public sector, government. If you think about all 5 of those, the common thing is there's a person on the end. There's a -- and the rules are protecting that person from discrimination, abuse, data theft.

So much of what we do is those types of workflows. I won't -- I'm not going to say 100%, but I would say 80%, 90% plus of everything that we do is something that is kind of a mandated process. And it could be from their own internal controls, but it's something that has to be done in a certain way.

Okay. That's good to know. And then maybe transitioning to -- I know you guys have been using AI and ML back in the day, but you guys did release Blueprint, which has -- you guys have seen tremendous success. Maybe just speak to that a little bit for people who are like not familiar with what Blueprint is and kind of give an overview of how has Blueprint kind of positively impacted kind of your results go-to-market motion.

So maybe the way to explain it is just to talk about before AI, what would happen. If somebody wanted to transform off of a legacy application, what they would typically do is they would get a bunch of people in a room and they would go through a whiteboarding session, and they would map out like what are our processes in that system? Why do we do it? How should it work? And through a series of what may be weeks and months of this ideation, they create Visio diagrams, they would document like here's the as is, here's the 2b.

Once that was done, that would be handed to a Pega engineer, an engineer that would engineer on the Pega platform and they would then begin configuring the workflow that tied to that. So it took a long time. It was labor-intensive. That meant that we -- clients can only do so much transformation at once. Fast forward to the world of AI, what we did was we built a tool called Pega Blueprint. What Pega Blueprint is, it is a design agent that sits on -- we use primarily -- but primarily, we use Bedrock and Claude, but like we can use really any model underneath it.

Clients can pick their model if they want to use a different model. The model -- what it does, it goes through an experiential discussion of what your workflow is and it helps and it uses all the content that only Pega has around how workflows execute, all the knowledge that we have of 45 years of being the workflow leader to build up optionality on how those workflows would execute. And what's really cool about it is you could actually take a process manual, a video on your phone, you could get an AWS transform dump of the COBOL code or you could go to Claude and get Claude to read the COBOL code and you could take that as an input, put that into Blueprint.

It will immediately represent what that system looks like in a workflow structure. And then you can go through -- so it's just rapidly speeding up the point at which you can make a decision on transforming a system. And that's been the biggest hurdle for us growing faster and for us doing more is the amount of time and money and specialty resources it took to transform each system.

Got it. Okay. And any numbers you can throw around like concrete numbers on like how much does it influence the pipeline, give us win rate, conversion?

So we're going to -- we started -- we've been doing -- we've been at this for a little less than a year in terms of when Blueprint went rolled out. So we have our Investor Day in June at PegaWorld, and we're going to -- that's when we're going to talk a little bit about some of these metrics. We've seen improvement in the amount of pipe that's being built, the speed of pipeline transition, the win rates. We've seen improvement in all those, but we're going to -- we kind of wanted to get like a year under our belt where we wasn't -- we were just talking about a quarter or 2.

I guess we'll look forward to the Analyst Day in June. Maybe switching gears to -- since we have you here, Ken. Love to talk about the guidance. As you said in the beginning of the conversation, you guys are guiding to a slight acceleration in ACV growth. Would just love to get a little bit more color on what's driving the confidence that you guys are seeing in the acceleration in ACV growth. Any sort of context you can bring around where growth is coming from expansion versus like new logos or maybe like migration activity?

So we rolled out Blueprint last year, and we've seen an improvement in NRR in 2025. We're modeling out that, that improvement will sustain into 2026. So that improvement in NRR will continue. And we've started to hire some additional salespeople at the end of '25 to go and target some new logos.

We -- historically, the story that I just told about why it was complicated to do a transformation with Pega was exactly the reason why we were very timid on hiring a lot of salespeople and going after new logos because the sales cycle is long. Now with Blueprint, we can actually -- we have a much better opportunity to assess early and to be able to target orgs because Blueprint speeds up the front end of that selling process. So we think we're going to get the majority of our acceleration in growth from '25 to '26 from that expansion of targeting new logos.

Got you. Okay. And then I think you mentioned improvement in the NRR number. Have you guys given like where that level has been in the past? I mean 150 bps improvement is definitely great to see, but any sort of context behind that?

So we were -- in the last few years, like kind of the 2022, '23, maybe the '24, our NRR was somewhere in the kind of [ 110 to 111 ] range. And for this year, it was kind of more kind of north of [ 112 ]. So we've seen a slight acceleration in NRR. And net new logos has always been a relatively modest part of our bookings in a year because even if we had success with new logos, the reality is those first deals are not huge deals.

On average, the size of the deal is probably half of an expansion deal in terms of the dollar size. So the main thing for us is to get those new logos. Even though it might only be 100 basis points increase in our growth rate, that's maybe double the order of magnitude what we would typically do for new logos, which gives us lots of field for us to continue to grow and nurture those relationships.

And there are typically -- those new logos are going to typically be in the same verticals, in the same regions, just companies that we haven't sold to. So they might -- instead of the fifth largest bank, it might be the seventh largest bank, which is not a client of ours, or we are looking at some expansion into some additional -- maybe vertical is probably the wrong, but I would say there's some horizontal use cases like customer service. If you think of like utilities, a lot of what utilities do or customer service sounds a lot like telecommunication banking. It's the same kind of customer journey. So we'll look at some of those as well.

Okay. And then just staying on the topic, I think you guys mentioned leveraging this autonomous partner selling motion. Can you just elaborate a little bit more on that?

So what we mean by autonomous partner selling is we've never sold through partners. Pega is not -- we've contracted through partners, but it's largely been we sold and it was a contractor teaming arrangement and maybe they were the prime, but we did the selling. We've selected -- I always mix it up, but I'm going to say 10, either 9 or 11, but there's like 10 SI system integrators that are -- they use Pega Blueprint in their selling process, meaning when they're selling transformation.

And we think there's a great opportunity to use that in a way where they would sell in partnership with AWS because it will go through the marketplace, the system integrator and Pega selling the platform and that there's -- the 3 of us kind of together, AWS bringing some leads to us, the system integrator, maybe getting the client to buy into the transformation, us selling the platform. And in some cases, AWS augmenting with some service credits to help support some of the margin help on the SI. That's what we mean by autonomous partner selling. It's brand new in 2026.

We have not attributed any of that in any significant way to our growth. We view that as an opportunity for upside. And then we think that's prudent to not include it because we've never done it before. Like it's -- so to just model something in that we haven't actually executed, we felt uncomfortable doing that. Same thing we did last year with Blueprint. We modeled very little impact from Blueprint. We saw an impact from Blueprint. We be our guide.

Yes. That's always good to see, right, kind of be -- get the upside potential over time. And then just quickly on migration. I know that's -- I think you guys have said before, it's pretty consistent in terms of the activity in the past couple of years.

Yes.

Is that the expectation for '26 as well?

Yes, '26, we're expecting about the same level of migrations to '25. '25 was slightly more than '24, but it was kind of a rounding error. And then '26, we think we'll keep at that same pace. We probably have another 2 or 3 years of that pace before we start to get the Pega Cloud up where it's kind of in the 75% range. And we're -- right now, we're at about 55% of our overall ACV is Pega Cloud. So we think a few more years of kind of clipping along with most, if not all, of our new bookings plus the migrations will help to drive that percentage up higher.

Okay. And then just quickly on free cash flow. I think you mentioned you guys are guiding to [ 17% ] growth, which is great, really solid. I'd love to hear kind of puts and takes around that high teens, mid-high teens kind of free cash flow growth that you guys are putting forth kind of where leverage is coming from and kind of what's driving the confidence?

So our gross margins are pretty flat now. We're at about 80% gross margin. So we're not going to get a lot of operating leverage from gross margin, maybe a little bit, but that's nothing like we did in the past. Sales and marketing, we get a little bit of operating leverage. However, we are investing in sales and marketing. R&D and G&A will get a little bit.

So we're getting a little bit of operating leverage from R&D, G&A and sales and marketing and gross margins, just assume they stay kind of relatively consistent. But we do get a little bit of a gross margin uptick because the professional services mix will be lower. So that does -- and professional services have a very low, if not close to breakeven gross margin for -- in our business. So we feel like we're getting -- and then the offset to that is our tax rate is higher, right, because we've had some NOLs that we've run off.

So our tax rate is starting to kind of normalize more. So even in spite of taxes going up, we've still actually taken our operating leverage -- our free cash flow percentage up even with -- so I think that we can -- I think running a business that's growing in the like teens, we should be able to continue to get operating leverage each year, probably until we get our free cash flows up, margins up above 35%. 35% to 40% is probably when we will start to feel more pressure on that operating leverage.

Okay. I mean 17% growth, definitely solid, and then you guys just outperformed the free cash flow number in '25. And I think you guys have kind of like a target for '28, $700 million. How does that kind of inform that target you guys have outside?

So we were -- we kind of had this model of like we wanted to do $440 million, and we wanted to do like $530 million, and we wanted to do like $625 million and we wanted to do $700 million. That was kind of the way we thought about that. So the $440 million was now like $490 million. The $5 million and some change is now $575 million. So we're ahead of that pace.

You feel pretty confident in hitting that.

I think, yes, we're -- if we continue to execute the way we're executing, we should be well above. We'll be above $700 million.

Okay. No, that's great to hear. And then with all the free cash flow that you guys are generating, I would love to just get a thought or update on kind of your capital allocation kind of framework. And I know you guys just announced a pretty sizable buyback recently. Could you just speak to that a little bit more?

Yes. We had added $500 million to our buyback last year, and we've now all but exhausted that and now we are pretty close. And then we added another $1 billion to our buyback. So it gives us -- we have -- we're generating a lot of cash, gives us optionality. I'm not thinking I want to be overly aggressive like taking leverage to buyback shares.

I think that limits our options. So I think we want to make sure that we think there might be some opportunities to look at as we go through '26. And although we may never do an acquisition at '26, we may choose to buyback shares at an increased pace. I just want to make sure we have some optionality with our decision.

Okay. I mean would you guys lean in more to like tuck-in M&A? Because I know you guys just did one like earlier this year.

I think the challenge we have right now with any transaction is the IRR against buying back shares, right? -- trading at a 12 or 13 free cash flow multiple, it does make it very hard to justify that yield that you can get on just buying back your shares.

So -- but once again, that doesn't mean that we need to buy back everything at once. I still think you want to give yourself some flexibility. But that's just kind of my -- that's my thinking today.

Okay. Well, I think with that, we're out of time, but...

Ken, just is there anything that's changed in your strongest verticals end markets over the last year or 2 years [indiscernible] that you see any different kind of...

Digital -- so -- and this is something that you could look at any analyst and they show you this. Digital transformation used to barely be in the top 5 of corporate strategies for the largest organizations and now pretty much anywhere you read, it's in the top 3. So right, security has actually dropped down and it's cloud, AI and digital transformation or legacy transformation.

So we feel like we've become more relevant. We're not really a cloud provider, meaning cloud is just something that we -- it's an offering. So we're not a hyperscaler is what I mean. But like we completely fit with AI and we completely fit with legacy transformation. So...

Great. Well, awesome. I think with that, we can close it out. But Ken, thank you so much. A...

Thanks, Devin. Appreciate it.

All right. Look, so we're just delighted to have Pegasystems joining us. Ken Stillwell, to my left, is the CFO and COO. And what we'll do is we're going to talk a little bit about Ken's background first. And then I have -- [indiscernible] really just -- they didn't get it rewarded appropriately. But man, you guys had a great quarter, right?

Yes.

We'll talk a little bit of -- frustrating. We'll talk a little bit about the -- what's going on in the business, and then we'll open up to Q&A.

So where you live?

We are located outside of Boston.

Yes. But you live in Boston?

I live in a 3 wood from the office.

And you have kids? What do you got?

Three boys.

Three boys. What's their ages?

22, 20, 16, so two in college.

And where are the two in college?

My older son goes to Babson.

I think we have -- right there, Nick on my team went to Babson.

Nice. He is actually on the cover of Inside Lacrosse today, if you're curious, he happened to get lucky and get on there. And then my middle son is at TCU.

Last name is still well on the cover of Inside Lacrosse today.

Yes. Well it's -- you'll see us, [ 29 ]. It will be hard to miss that. But my middle son is at TCU in Fort Worth and my youngest is a sophomore in high school. So fun times and so on.

My high school daughter got into TCU. So that's on the possibilities list...

Yes. Well he loves it. It's a really great experience, a lot of smiling students.

All right, cool. So -- and how long have you been at Pega?

It will be 10 years in a few months. June something rather.

And what were you doing before that?

I was the CFO at Dynatrace. I was part of the Thoma Bravo team that took Compuware private years ago and then split it up between Dynatrace and Compuware and I was the CFO at Dynatrace.

I didn't realize that's where Dynatrace came from.

Yes, well Compuware bought Dynatrace, made a Compuware APM. And then when they carved it out, they split the business up because they had a cash cow slow grower, and they had a cash neutral faster grower. So I had a dual role there. I was the CFO, Dynatrace, and I was on the Board for the [ debt syndicate ] for both because the entities were still connected.

And were you at Thoma Bravo before? No?

No, I was with Vista before that.

You were at Vista before that? What years were you at Vista?

2011 and '12-ish.

What did they have under management back then? Not a lot, right?

Less than [ 10 billion ], I think, when I first started there. It was crazy.

Yes. Okay. How's business?

It's interesting. The actual -- the business aspect of business is actually quite solid and stable but the noise around all the business is quite irrational. But our clients are forging ahead with trying to become modern, and that means a lot of things. That could mean moving to the cloud. That could mean getting off of old systems, that could mean trying to figure out how AI fits into their strategy, different client experience. I mean, there's a lot of pieces to that. But I think in today's world, I think that if a company is not trying to fix the technical depth that they have in their organization, they're definitely in the overwhelming minority, right? I mean there's -- everybody is trying to address different parts of that.

We had -- actually, in this room, we had the CEO of Citizens and the CIO there and there. And we were talking about -- Citizens has reimagined the bank initiative, it's a big initiative, right? And it crosses a ton of use cases. And a year ago, we were still sort of an experimentation mode, and we are not in experimentation mode at all anymore. But one of the things the CIO talked about, he said, good -- lucky for us, we had these 6 pillars that we have spent the last 5 or 6 years addressing. A lot of it is around data and is getting to the cloud and getting your data estate in good order and the four other things. And that puts you in a reasonably good position to try to implement AI in all these different use cases, but if you haven't done it, it's really hard, right?

Yes, we met -- I met with Mike today.

Our Michael?

Yes. Your Michael, your CIO. [indiscernible] Citizens CIO. And he was -- he and his team were at Pega in November, and we actually went through the reimagine the bank and how our role is in that, and there's a lot of really challenging when you have these -- one of the biggest challenges that organizations like Citizens has is you end up building solutions for the channel and not building solutions for the customer.

And so one of the examples that is common in the banking industry is the level of risk and fraud management of somebody that goes to a digital channel versus walks into a branch is not the same because the systems that they're using are different. So just trying to synchronize that so that if someone actually -- if someone goes through a digital channel or calls into a call center, there's a certain set of controls that happen because they're using the underlying technology. But if you go into a branch, that might be 20-year-old technology that they're using in a branch. And this is a very common [indiscernible] how do you basically create a seamless experience, manage risk across all of those. Because in the branch or historically, culturally, you're more dependent on the branch person making a decision versus in the digital channel, you kind of assume that you can't validate through a driver's license or seeing the person. I mean it could be a bot. But in the branch, as an example, the culture was...

You walk in, you show your driver's license, you figure you should be good, right?

And the reality is that's just not true, right, I mean it's very easy...

You could see the collection of fake IDs in my daughter's bedroom, so yes. And they look great.

And the bad guys figure this out and they understand where the crack is [ in the secure ] and they go after that. So I think that's a really big challenge for financial institutions. And that same challenge exists even when there's not a brick-and-mortar and digital difference because people come in through resellers, through partners, through the actual brand. So it's a really interesting challenge that when you talk about it, it's like pretty obvious, but it's also one of those ones that is built because many of these apps were built point by point. And there really wasn't a thought about how to keep that experience seamless across the customers.

Yes. Let's go back to -- your comment was really interesting. You said that the business is solid, stable, but the noise, and I didn't actually get everything that you said, but let's talk about the noise. What do you mean by the noise? Not just the noise that's impacting the valuation of stocks, right? You're talking about...

I think that some of it -- they're related.

And they probably are related.

They're related. But I would say the noise around decisions that -- let me take a step back. There is noise with our customers around decisions that they are trying to make around how to modernize faster. Should we move to the cloud, if so, where? How should we manage that? What risk tools should we use? What's the right way to think about our customer? How do we embed AI in the appropriate areas and the technology stack? These are all -- those are all things that are -- I would say the pace of change is higher now, which causes some of that.

Then there's the noise in the investment community which is directly correlated to some of the valuation. And what's really interesting is when you talk to a client, when you talk to CIOs of large organizations. And then you hear some of the things, which interestingly enough, are -- now there's -- everybody on X or any social media channel is an expert in every industry because they could just put a blog post out there. It's a really interesting dynamic to see the disconnect between the reality of what executives are doing and what people are talking about.

And then as an investor, if you're an investor -- and by the way, many investors now, Pat, I don't know what your view is of this, but my perspective is 10, 20 years ago, when I would talk to investors, the investors understood the industry that they covered in a way that they don't now. There's a lot more generalist kind of investors, people that may not understand how a company buys software, how do they deploy it, what's sticky versus not. We're much more, as an industry, more quantitative and modeling discipline.

But there is -- there's a lot -- when you get into these situations of like, well can you explain to me which software is going to be disrupted and what isn't, you kind of have to fundamentally understand the vertical, understand the business case. And that is not as common now is what I feel like it was a decade or so ago, and that then causes more noise and confusion because you see a press release, you read something, you hear something that comes out from another company and you immediately think, "Oh my gosh, what's going to happen?" and because there isn't a deep domain expertise operating model of those companies, it freaks people out, right? And they think that, "wait, if I can write code, then maybe I should just rewrite everything." But when you actually then think about like at a bank like Citizens, I would wager a guess and the reason why I use this percentage...

Or another bank in general.

A bank in general, we'll generalize. I would imagine that any large financial institution, like yourselves, that 80% or so of the technology that you use couldn't be dramatically changed because of regulatory oversight, right? And then unless you think that all the laws are going to somehow change around fair lending and credit card...

Do you really think it's that high? You think it's 80%?

The reason why I get that percentage is, I actually heard it from one of your competitors CIOs who actually said 80% of my technology, I could never touch, and his words were, "in my lifetime or my children's lifetime." So I don't know if that number is right, but I can tell you that talking to a lot of that person's peers, it's a very high percentage. But I think most investors think it's inverted. I think they think that it's 80% is fair game to change and there's very small numbers of technology.

So this is where the question about AI comes in, which is -- because AI is a very powerful tool. And we've got to figure out how to harness it, how to get as much value out of it as we can. And even with that 80% -- let's just assume that number is right, the 80% of applications that aren't going to move, there's tremendous value leveraging AI around that, right? You're augmenting that workflow, that process where you can get human beings further out of that. Because right now, a lot of those workflows rely heavily on human beings whether that be the customer doing lots of work that they shouldn't have to or a person at the bank making judgment calls on -- or a know your customer use case where they've got to go out and do manual searches, those are all things that can be automated, right? And so that's really where the power is.

Yes. Just an anecdote. So for all of these fireside chats, I have my little keynote outlined, right? And then I have the most recent note. And I'm just going to have CoPilot do one of these for me. Such a disaster, Oh, my god. So the one that I didn't look at closely enough, like is -- like this executive spent 20 years at Amazon Web Services. And no, we didn't, right? And yes, the amount of time it takes to check the key note outlined that CoPilot is going to write, I can just bang it out.

Now flip side to that is I didn't want to come to this conference, it was like the fourth time I said this, but I didn't want to -- have you used Cowork yet? Have you used Claude Cowork?

Yes.

I hadn't used it, right? And I was like this is terrible, right? And part of the reason I hadn't used it is because, of course, we are a regulated organization. We have completely locked down laptops and there's no way that you're going to get to run Claude Cowork on this for good reason, right? So I had to get to a Mac and what it did with my calendar was insane, right? Like one of the very first -- how do you use it? What do you use it for?

I've been actually trying to build simple apps and understand like with not being a coder, like what level of expertise and just trying to understand like where somebody...

With Claude code?

With Claude code. Yes.

Okay, what did you build?

I'm trying to build things like -- managed like -- in like a travel management app or something like just trying to say, like, go try to find the delta airline schedules and merge. Just try to build something simple like that. And to be honest with you, it's actually pretty cool to see what it can do, and it can be done without someone that necessarily understands how to code.

Now this is where the trick comes in, when you're actually going to write more complex applications. And I don't even mean -- I'm not talking about ERP systems. I just need something simpler than go grab all the travel schedules for the public websites and put it into one view, something where you now have a real big dependency on the product management aspect of a human being because the AI models cannot be your product manager. You have to have a human being on the front and you have to have a human being on the back. So these systems write codes so fast that a human being cannot process even reviewing it at -- so now you're left to a model to check the work of another model that a human can't tell what actually was actually written until you see it in the wild and then you hope it works.

And this is where it gets dangerous. So you really -- you have to think about ways to -- because you can't -- you're not going to ask a model. Please go figure out what business we should be in, put a business plan together and then go right to all the -- you are going to have someone that wants to drive the strategy. And I think there's a lot of work in the middle that can be done. But you got to figure out how to leverage it in a way that makes sense.

I know. I had one CIO tell me that he said, look, historically, I had a bunch of young developers and they had one gray-haired guy who reviewed their code, now I need everyone to be a product manager because I basically -- now I need everyone on my team to be a product manager. They all have to think like a product manager.

Because someone is writing the code, it's a machine that's writing the code. But you need the human being to actually watch and give instructions to what needs to be built. And I think that's really where the -- and there's value there, and there's going to be tremendous value there. It's just kind of like figuring out how this will play out and where that opportunity set is.

Okay. So let's take this back to Pega. So how is the noise impacting you on both sides?

So I would say with the clients, we really just need to keep the clients focused on where we can help them the most, which is transforming their legacy applications, help modernizing their apps, making sure that we are also supporting them in the selection of which apps they pick primarily, we're focused on the workflow centric apps because that's relevant for us. And then using Blueprint, and so tools on the front end, you could use Claude, you could also use AWS transform to basically digest the COBOL code as an example. That information can be ingested into Blueprint. Blueprint, the reason why Blueprint is different than any other kind of AI tool is that Blueprint understands the context of workflow. That's our business. All of our intellectual property is in Blueprint where you could not get that if you just went to a standard model.

Now we can use models on the front end to feed information and then we do, and we use the LLMs as the guts of actually how the decision model actually manifests itself on the platform. So that's kind of our view is use this -- use tools to digest what these applications are, put them into Blueprint, which helps to model out the future app, then you have human beings, product managers of sorts helping to decide like what does that new experience need to look like.

Okay. Start over that was super interesting. So step 1 is?

AWS Transform, Claude code, pull the COBOL. Basically interpret the COBOL code, I'm just using COBOL as an example. Interpret the code, what is the system doing, what are the steps, what are the transactions, what are the process, what are the data, the user -- like what's happening with this custom built app.

That information, second step, gets loaded into Blueprint just basically with a simple attachment of a document, a file, a video, whatever the easiest vehicle is to get that in there. Blueprint takes that understands the context of cases and stages and steps and processes and says, based on what I understand here, this is actually what the system does.

Now a lot of times what you'll see is it will take this custom-built system. It will create a workflow that has like 100 steps in it, right? Because that's what someone customized and what it's been able to do is compartmentalize the pieces into what might be not one workflow, it might be 25 workflows that are -- that have dependencies or not relationships. So that's kind of -- that's the reality of what we're seeing.

What that then does is a human product manager is actually looking at those options and understands the business, understands the use cases and helps to kind of, in Blueprint, reorganize those workflows that then what gets productized is a series of workflows that agents can actually be called on the front end, and they will be purpose-built to understand which workflow to call. And then the agent will drive that automated work across the workflow based on the rules and the process and the compliance and the workflow. So what you're doing is you're getting speed to build best practice kind of application modernization. You're leveraging all the agents to get human beings out of there and the agents are guided to go through that process. So that's like -- that's maybe not necessarily reality for every transformation, but that's the way we would hope that it would play out in terms of the best scenario for our clients.

And this step one, where you're using AWS Transform or Claude to understand what the code is doing is huge. It's a huge difference, right? This is why IBM went down 13% in the day, right, same thing? Not that you necessarily agree with...