ExlService Holdings Stock

We'll get started. We're excited to have EXL and Rohit Kapoor, who is the Chairman and CEO. What we'll do -- I'm Bryan Keane, of course. I cover IT services here at Citi. And we will run through my fireside chat of questions. And then if you have a question in the audience, feel free to raise your hand. So with that, Rohit, thanks for coming.

Thanks for having me.

Maybe just to set the stage a little bit, and we were talking prior to getting up here a little bit about the evolution of EXL and the business and IT services. And maybe you can talk about kind of key strategic pivots you've made along the way.

Yes. So EXL, we've been in business for 27 years. And actually, this year, we complete 20 years of being a public company. So we started out as a business process outsourcing company. We made an early pivot in 2006 to move into data analytics. Today, we are a data and AI company. I think when we think about the journey over the last few years, the last several years, we've been investing in building up our capability around data management and really beefing up our credentials out there, which I think has played out quite nicely.

And now we've started to focus in a lot more on the model. And so we just acquired a company called iMerit, which allows us to do model evaluation, red teaming, rubric creation, chain of thought reasoning and start to kind of really play around with -- both with foundational models and frontier labs, but also start to bring that same technology and that architecture down to the enterprise clients.

Got it. And so when you think about the environment that we're in today, some customers are struggling more than others. How are EXL solutions really helping to solve kind of current problems?

Yes. So look, the biggest thing that's happening right now is how do you make AI work effectively in the enterprise. And what that requires is using some of these new capabilities that are being spawned out by AI and applying that in an enterprise environment. That sounds very simple, but it's not easy. Our approach to that is that we lean in by having a very, very strong understanding of our clients' business. So we go deep into the domain, and we have very strong contextual understanding of our clients' business, their workflows and how they use their customer data, how they think about leveraging capabilities across the enterprise. So that's the starting point.

The second is we've got this capability built up around data management, which really allows us to help our clients have a clean data estate that can be usable for AI. And then finally, using AI, when you first apply, the first time you apply the model, the outcome is actually pretty bad and the quality results are pretty poor. So you have to work on it iteratively to improve the accuracy and bring down the cost of using AI. And so by bringing together this domain knowledge and contextual understanding of our clients' business and workflows, having a mastery over data and knowing how to correctly apply AI into the workflow, that's what allows us to be successful with our enterprise clients.

Our AI services, we track the efficacy of that. And we are at a 94% success rate in terms of enabling AI in the enterprise. The industry statistic on the same dimension is closer to 30%, which means a lot of clients are trying to use AI, but only 30% of them succeed in terms of actually getting to the business outcome and being able to deliver this at cost and get the ROI associated with that investment.

And it's shown up in your guys' results. I think last quarter, you reported 16% revenue growth, and that growth rate has been accelerating. And as you know, that's a significant divergence from what most investors are seeing in some of your peer groups. So when you take a look at your model and you take a look at some of the other low-growth models or 0 growth models, how would you compare and contrast the 2 models and why the differentiation?

Yes. So the first thing I would start off with is enabling AI in the enterprise is not really a technology solution that you're trying to build in. It's actually a business problem that you're trying to solve for and be able to get to a business outcome. So frankly, the work starts from the business side first. And most of our relationships are with the CEOs and the COOs and the heads of businesses. And those are the people that are driving this change going forward. Technology is an enabler and it's a necessary part of making the transformation and the change.

But the entire initiative in terms of that transformation is being led by the business. And it's not like previous technologies, it's not being led by the technology or the CIO function. So I think that gives us a big edge because our knowledge and understanding of operating workflows and understanding our clients' business, that gives us a huge advantage. The second part is our portfolio. Today, 61% of our portfolio comes from what we would categorize as data and AI-led services. And that part of the business for us is growing very rapidly. In the second quarter, that grew at 30% year-on-year. So it's a large part of our portfolio, which has got rapid growth and a huge amount of demand associated with work that we do out there.

And then finally, our Operations Management business and our digital operations that we run. When you take a look at total operations, which means it includes the piece where we are embedding AI into the operations, that's growing at 10%. So frankly, we are seeing a lot of demand on the operations side as well as on the data and AI side. And so everything seems to be kind of working well for us. We are also fortunate that we don't have specific client situations or specific geographies or specific types of work that we do that we don't have a grow-over problem. So essentially, for us, this is a great time for us and a great position to be in where we can be adding a lot of value to our clients and continue to build and grow our business.

Do you see budgets shifting more or wallet share shifting more to EXL versus going away from maybe some of the larger Indian IT companies or some of the larger multinationals?

Yes. So what's become really important in the AI era is business outcomes are really important. Having companies that are going to stand behind their commitments and their promises is really important. And just the size and the brand itself is not going to carry the day. So today, we are able to compete very effectively against some of the much larger players, against some of the IT players who have deep relationships for decades with the CIO and with the CDO. But we are able to kind of be able to make a headway out there because of our demonstrated ability to deliver results.

So just thinking about the operations business, and you talked about, I think it grew roughly 10%. And that includes the traditional operations work and operations where you're embedding AI into the workflow. Can you just spend a minute on that transition? What does it look like in practice when AI gets introduced into an existing operations engagement? And how long has this been going on? Are you 50% penetrated, which goes to 100%? Just give us the road map there.

Yes. So look, this is a multiyear journey. It's not something that can be done quite easily. Just to kind of give you a sense of that, that 39% of our revenue, which comes from digital operations. That's broken up into more than 2,000 unique processes. So these are fragmented processes. Each one of them is a unique use case. Each one requires a unique solution. And so it takes a fair amount of time to be able to embed AI into those operations and into those workflows. We necessarily need to have access to the clients' data, the clients' technology. We need to make changes to the operating workflows.

So it's a very time-consuming and a very complex process. We've been at this journey now for a little over 2 years that we've been trying to embed AI into the operations. And we would categorize the level of maturity into 4 different levels, which really starts at L1 going all the way to L4. And L4 is what we would call as autonomous AI and -- autonomous agentic AI being embedded into an operating workflow.

I would tell you that right now, we are still operating at between L1 and L2, which means we're just doing some of the basic things that can be done with AI and be it in the form of data extraction, be it in the form of querying, be it in the form of providing basic recommendations and helping and augmenting our colleagues who are working on this process.

So it's literally at the early stages of that happening. But what it's doing for us is it's opening up the landscape for us very, very meaningfully because, as you know, today, the penetration of work that is currently outsourced under digital operations, the penetration is about 20% to 25%, which means 75% of the work is still being done by our clients, and that can be done in a much more efficient way.

So any time we are able to demonstrate the effectiveness of AI and deliver an ROI to the client that is positive, they gain more confidence in us and they give us more work to be able to apply AI into. So that expands the work that we can do with them. And then, of course, once we do it with one client, there are other prospects who want to do the same thing with us. So that expands our ability to grow the business across multiple clients. So for us, the net recurring revenue associated with our total operations business, it's at about $1.1 billion. And that means we are able to continuously keep adding on to the quantum of work that they're doing, and this is a growing pie for us.

And how has the pricing of the operations model changed since you've introduced AI? And then as we get into L3, L4, how does pricing look different from where it is today?

Yes. So pricing in the commercial terms are shifting over much more towards outcome-based pricing. It's also shifting over where we need to take the risk of making some of these AI investments upfront.

So clients will come to us and say, why don't you make the investment on the AI? And then whatever the gain is, we can share in the gain that you're able to deliver to us. So there is an amount of risk transfer that's taking place. The commercial model is changing. But the way in which we manage this transition is we try to do it in a manner which is going to be helpful to the clients, but it is also not going to introduce a huge amount of risk for us.

And what I mean by that is there is a base level payment that the client makes to us. And then the productivity and the efficiency gains that we are able to deploy, we share in those gains. And then as we move towards outcome-based pricing models, we typically will have a couple of examples where we've done that kind of work before. And so we know what the metrics involved are. And therefore, we can price those types of work streams accordingly.

And so the data and AI, which is the majority of the revenue growing at 30%, can you just describe some of the drivers driving that growth rate? And how does that evolve? Can it stay at those growth rates? Does it accelerate, decelerate the kind of law of large numbers? How do we think about that segment?

Yes. So the data and AI part of our business has a few different service lines as part of that. It's got our payment integrity business. It's got some of the work that we do with our IP and our platforms associated with insurance and with health care. It's got our analytics and AI services part of the portfolio.

And then finally, it's got AI solutions as part of that portfolio, and it's got data management. So those are the 5 different elements of service that we provide under that bucket. Each one of these has got a very healthy growth rate. The TAM on each one of these is actually very large and growing. And particularly, if you take data management, that's become enormous already, and you can see that going forward that, that's going to be even bigger as such.

So for us, while the growth rate of 30% is a good, healthy growth rate, the opportunity set for us is pretty large and deep out there. And so we think that there's legroom for us to continue to kind of build out there. The high growth rates are really around AI services and AI solutions. And there, the -- once you have success replicating that AI service or the AI solution, I think that's a lot easier. So we have to make a few bets out there, which we've done in the past and a few of them have been successful, and they actually drive up the growth rate.

And so you've kind of described here AI being a tailwind for the business and opportunity to expand TAM. Can you just give us an example of workflow where you introduced AI and how it grew the scope in that project?

So yes, Bryan, keep in mind, we do AI services in 2 different motions. One is on a stand-alone basis, and that's growing and has got a big TAM. And the second is when we embed AI into the operating workflow.

So let me give you an example of stand-alone AI services. We do a lot of work helping our clients leverage some of the models -- the foundation models that are there and applying that into consumer experience and into CX. That's become a good and growing practice for us. Another area for us has been all around data extraction. And so we have a tool which we've created called XTRAKTO, and that allows us to be able to use AI to extract data from one platform and embed it into another platform and do that autonomously, which previously would have been done by humans.

Then we built up an agentic AI suite of offerings called EXLdata.ai. And that allows us to be able to leverage AI for getting our clients' data estate in order and to be able to modernize their data architecture. So all of these are examples of capabilities that we've built, which allow us to deliver stand-alone AI services, and each one of these is becoming large and meaningful and growing.

The flip side of this is -- so if we apply AI to customer experience, when we handle some of that volume of work, we are able to embed that into the workflow that we are managing for our clients and take on more responsibility for them. So that would be another example where we would have done this or we're building up now capabilities in insurance and underwriting or in claims.

And there, we're making that decision-making cycle. We are collapsing the time frame, and we're making that decision a lot more accurate. And so it frees up the time for the underwriter or for the claims adjuster, and it adds a huge amount of value to our clients.

So last quarter, I think you guys raised the full year guide of 13% to 14% for revenue growth. That was up from 10% to 12%. And headcount growth, I think, was 12%, so slightly below that raising guide. So a lot of discussion on headcount in the industry and delivery. How do you guys think about that relationship between headcount to revenue growth? And what is that going to mean for -- going forward to the model? And what does that mean to margins?

Yes. So look, our viewpoint is that as you leverage AI, your headcount growth should be lower than your revenue growth. And that's happening for 2 reasons. Number one, we are participating in more complex and higher-value services that we are providing to our clients.

And number two, we're delivering a lot more productivity benefits to our clients and therefore, reducing the number of headcount that is required to deliver the same level of service. Both of these should be helpful in terms of driving margin because if our revenue per headcount is much higher, that should be a much higher margin producing service line. And so that's what's happening with us is as we get to size and scale in terms of some of these services, we're able to optimize the margin associated with that.

Got it. At your Investor Day, I think you guys noted about 25% of revenues currently touch EXL developed IP. And there's also a lot of debate on how much service companies will keep developing their own IP. Can you just explain what that means in practice? What kind of IP is EXL developing? And why does it matter competitively?

Yes. Look, any time you have a revenue stream which uses EXL IP, it produces differentiation. It produces higher value and therefore, our ability to charge a higher price, and it makes it much more stickier as such. So for us, 25% of our portfolio today touching EXL IP, that's a good thing, and our intent would be to continue to increase that as much as possible.

Examples of where we would have EXL IP are -- so we run the Payment Integrity business. And in the Payment Integrity business, we've developed about 8,000 algorithms. And we are able to actually -- any time a new client comes and signs up with us, we are able to take those 8,000 algorithms and apply that to the claim data set for a new client.

And therefore, we are able to discover and identify fraud waste and abuse a lot quicker, a lot faster and deliver value to that client expeditiously. We have built up our TPA platform. So we own a policy administration platform. We own a population health care management platform and many of the operations of our clients run on these platforms. And we've embedded a lot of AI now on these platforms. So these are all on the cloud. They are all technologies that our clients leverage, and they're dependent upon using these platforms for providing services to their end customers.

So the reverse of this question is always, is the software industry going to become more competitive with services since they own a lot of the IP themselves? Can they start competing with EXL and others? How do you think about that?

Yes. Look, I think the software companies will certainly want to compete with us and with the foundational model companies. At the end of the day, I think you're going to see competition come at it from all different angles. You're going to have the foundational model companies who are going to come in and say, use their model and they'll be able to get you the productivity and the value benefit immediately, and you don't need anything else. You'll have the software companies, most of whom who have the data sets of their clients sitting on that software, and they will be able to apply agentic AI and be able to deliver that value.

And then you have companies like us who are services companies who built up special use cases, and we are trying to kind of create efficiency and benefits for our clients on a direct basis. I think the key is going to be who is able to actually deliver and guarantee those business outcomes to the client and who can be a partner for our clients for the long term.

I think with our track record over the last 27 years, we feel we are in a great place to be helping our clients. We've built up very strong partnerships and relationships, and we continue to extend ourselves. And so the pathway for us to be able to grow continues to be very strong.

Many of -- in the industry, the vendors complain about or highlight the discretionary environment is weak. And so that, that's slowing down deals, there's elongated deal cycles.

Can you explain for you guys, it's probably not quite dependent on discretionary and the cycles maybe are not as -- they're still long, but maybe you guys are just -- your close rates are better. Can you just compare and contrast the demand environment for what you're seeing versus maybe some of the peers are talking about?

Yes. So clearly, enterprises are spending a lot more money on AI and token cost has gone up. They're spending more on infrastructure, and they're spending more on AI. And as a consequence of that, they're cutting back anything that was discretionary or which wasn't as important in terms of their budgets.

From our perspective, the work that we do is actually all on the growth side. So it's actually benefiting from the additional investments that they're making in AI. And everything that we run for them in terms of their operations, embedding AI into that is a core priority for them, and they can't really shut down operations because that's something which they need to do 24/7 and continue to provide that to their clients on an ongoing basis. That's not discretionary work.

The places where there was a lot of work being done around managed services, application development, SDLC life cycles or call center type of work, I think all of those areas are getting compressed and clients are looking at ways in which they can cut back expenses in those areas. And that's where I think most of the compression is taking place.

How long is the sales cycle for you guys? And have you seen an elongated sales process as people are trying to understand the real benefits from AI?

Actually, on the AI side, the sales cycle is pretty quick, and the work gets done pretty fast. But the size of the engagement is small. But once you demonstrate credibly the ability to deliver value to the customer, that expands very rapidly. So it's all a function of being able to demonstrate credible success and then being able to scale up as a consequence of that.

And what about the pipeline for the operations business? Is there still enough, I mean the TAM is there and you got probably a lot of people doing it internally and they probably say, well, you know what, EXL can probably do this better than us. Is that what drives a lot of the pipeline?

Yes. Yes. So I think there's a fair amount of pipeline on the digital operations side. And clients are basically saying, if you can leverage AI, you can leverage automation, you can leverage intelligence, I'd much rather give that to a player and get the benefit associated with it right now.

And therefore, the earlier emotional battle that they used to face about whether they should outsource this or not, that no longer exists. And that's a lot easier for them to be able to outsource the work because the value proposition is so much more compelling.

You mentioned iMerit, I think, in the beginning. Can you talk a little bit about what that acquisition brings you and why you thought that was necessary to bring that on the portfolio?

Yes. Look, iMerit is our first acquisition in this new space around model evaluation. Our hypothesis is that going forward in the future, enterprise clients will use foundational models, but they'll also use open weight models.

And so you will end up having a hybrid of models that you can use, which are off the shelf and you will also use open weight models that you can train and develop and have more sovereign and proprietary model sets. In order to be able to train sovereign and proprietary models for the enterprise, you need a capability of doing model evaluation, model training, model curating, being able to do red teaming, being able to establish rubrics for doing that evaluation. And so our thesis is that we'd like to be in that position to be able to help our clients on that.

Keep in mind that EXL at EXL ourselves, we've developed 11 models that we have trained on our own using some of these open weight models that we've procured. So -- and we've seen the efficacy of that, and we've seen the cost-benefit analysis of that.

And that for us is very, very compelling. So we think enterprise clients rather than being beholden to any one single model or a couple of models will want to have much greater control of models that they can have as proprietary, that they can use their own data sets and their own contextual data and not be dependent on any third party for that.

I think recently, you guys expanded your credit facility to up to $1 billion, and you're generating strong free cash flow. I think you got modest debt -- net debt on the balance sheet.

So how do you think about more M&A? Where would you maybe tackle some other things that you don't have in the portfolio today? And then two, what about being more aggressive with the stock buybacks, considering you guys are kind of the leader in organic growth these days?

Yes. Look, we are in a fortunate position that we don't have much leverage on the balance sheet. And we've got financial institutions and banks willing to partner with us on that. So we've just renewed the $1 billion credit facility, and we have enough room to be able to do more acquisitions and do more buybacks.

On the buybacks, we've been very, very explicit and transparent in terms of the size and scale and the time line with which we would invest. So we have a time line of 2 years where we want to invest $500 million and do stock buybacks, and we are on the pathway of being able to do that.

Typically, it ends up being about $200 million of stock buyback in any given year. But if for whatever reason, the stock is more depressed, then we can be a lot more aggressive about it. M&A is certainly something which we would love to be able to do a lot more of and be able to bring in additional capabilities. I think this is a very rich environment for looking at M&A.

The biggest challenge is the financial metrics and the valuations. So many of the start-up companies, which have got capabilities, they are very nascent capabilities. They only have it with a couple of anchor clients and the valuation expectations are way above public valuation levels. So for us to be able to identify and be able to transact on any one of these acquisitions, it's always a challenge. But whenever we find something which is strategically compelling, where there is a financial arrangement that will work for both sides, and there's a cultural fit, we're going to go ahead and do that acquisition.

Okay. Lastly, I wanted to ask, I know Vivek announced that he's leaving after his tenure at EXL. Can you talk a little bit about maybe filling that role and kind of the bench you guys have?

Yes. So Vivek has been with us for 2 decades. He came to us from the Inductis acquisition. He's a terrific leader and did really well at EXL. But also, we are fortunate that we have a very deep bench at EXL.

And we've been building up this bench over the years, and we will continue to invest in this. Earlier in this year, you saw us bring in Bhupender Singh as the President of the company, and we added to our talent base in the international geographic footprint.

We have leaders under Vivek who can easily handle the health care and the insurance portfolio that Vivek handles today. And so we're not really worried about it from a client relationship and a business standpoint. And we'll continue to evolve our operating and management structure so that we continue to have succession and we have a bench strength out there.

Okay. With that, Rohit, we'll leave it there. Thanks for being here.

Thank you.

Hello, and welcome to the ExlService Holdings, Inc. Second Quarter 2026 Earnings Conference Call. [Operator Instructions] Also, as a reminder, this conference is being recorded today. If you have any objections, please disconnect at this time. I will now turn the call over to Andrew Thut, Head of Investor Relations and Capital Markets.

Thanks, Mariana. Hello, and thank you for joining EXL's Second Quarter 2026 Financial Results Conference Call. On the call with me today are Rohit Kapoor, Chairman and Chief Executive Officer; and Maurizio Nicolelli, Chief Financial Officer. We hope you've had an opportunity to review the second quarter earnings press release we issued yesterday afternoon. We have also posted a slide deck and investor fact sheet on our Investor Relations website.

As a reminder, some of the matters we'll discuss this morning are forward-looking. Please keep in mind that these forward-looking statements are subject to known and unknown risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements. Such risks and uncertainties include, but are not limited to, those factors set forth in yesterday's press release and in EXL's filings with the Securities and Exchange Commission from time to time. EXL assumes no obligation to update the information presented on the conference call today.

During our call, we may reference certain non-GAAP financial measures, which we believe provide useful information for investors. Reconciliation of these measures to GAAP can be found in our press release, slide deck and investor fact sheet. With that, I'll turn the call over to Rohit. Rohit?

Thank you, Andrew, and good morning, everyone. EXL delivered strong second quarter results, posting revenues of $595 million, up 16% year-over-year and adjusted earnings per share of $0.59, an increase of 22% year-over-year. We entered the year with positive business momentum, which has continued to strengthen throughout the first half with broad-based growth across every segment of our business. We sit here today with very good visibility into the balance of the year and are looking forward to a strong finish to 2026.

We continue to differentiate ourselves with industry-leading growth. As enterprises move from proof-of-concept to AI implementation, our expertise, solutions and services sit squarely at the center of the demand vectors where investment dollars are focused, delivering measurable business outcomes and economic efficiencies. Our deep understanding of client workflows and domain context, combined with our competencies in data and AI engineering is creating a tailwind as we help clients solve the acute challenge of making AI work effectively in the enterprise.

We are pleased with our results that reflect the strength of our data and AI-led strategy and our focused execution. Over the last 2 earnings calls, we have made additional efforts to bring transparency to our financial reporting. In addition to providing a revenue breakout across both data and AI-led and digital operations, we now provide revenue from total operations as well.

Data and AI-led revenue has accelerated over the past 4 quarters, growing 18% in Q3 2025, 21% in Q4 2025, 28% in Q1 2026 and now 30% year-over-year in Q2 2026. Data and AI-led services and solutions represent 61% of revenue with broad-based growth across data management, AI services and solutions, payment integrity and data and AI-led operations.

Reported digital operations revenue was down approximately 1.5% year-over-year, and I want to be explicit about why, because this is important. This decline is by design, and it reflects the evolution of our business mix. As we embed AI into operations engagements, that work becomes more IP-led and higher value. And the related revenue moves into our data and AI-led category. For this reason, we believe the best way to evaluate the health of our operations business is to look at total operations, which includes both digital and AI-led.

Our total operations revenue in Q2 were up 10% year-over-year, continuing a trend of healthy, consistent growth. As we go to market with an AI-forward value proposition in operations, it strengthens our data and AI-led performance and vice versa. Combining operational expertise with proprietary data and AI capabilities, we help clients unlock greater productivity, faster decision-making and measurable business impact.

As AI adoption expands, the value of our operations relationships deepens, enabling us to identify new use cases, accelerate deployment and drive sustained transformation. This creates a mutually reinforcing cycle that delivers greater value to clients while supporting durable recurring growth for EXL.

We saw strong performance across each of our 4 operating segments in the quarter. Insurance grew 15% year-over-year, representing 1/3 of our revenues. Q2 was a defining quarter for EXL's insurance practice, translating multiyear AI investments into demonstrable client outcomes. Insurers continue to accelerate AI adoption across underwriting, claims and customer experience, and we are seeing strong deal activity across market segments.

Healthcare and Life Sciences grew 22% year-over-year, representing more than 1/4 of our revenues. Payment Integrity continues to be a significant growth driver, and we are seeing strength in analytics, AI services and solutions and operations. Payers and providers are under meaningful cost and regulatory pressure and are turning to EXL to apply AI at scale to improve productivity and outcomes.

Banking, Capital Markets and Diversified Industries grew 11% year-over-year, representing a little under 1/4 of our revenues. Deal activity was strong in the quarter, and we remain confident in continued progress through the year.

International Growth Markets grew 15% year-over-year, an acceleration attributable to ramp-ups and new client wins. This quarter, we welcomed Bhupender Singh as President and Head of International Growth Markets. Bhupender brings a track record of building and scaling multibillion-dollar businesses in complex international markets, and he has hit the ground running, architecting our EMEA and APAC go-to-market, deepening client relationships and building pipeline. International represents one of our largest long-term growth opportunities, and Bhupender's appointment reflects our commitment to capturing it.

Let me make our differentiation in the market more concrete with a few examples from the quarter because the thread running through all of them is the same. You cannot deliver strong business outcomes without deep understanding of the clients' domain and their data.

First, in health care. We went live at a large national health plan with their first-ever customer-facing agentic AI module, delivering a high deflection rate and significant ROI for the client. When the client independently benchmarked our solution against that of a leading hyperscaler, EXL outperformed on every measure. What became apparent is that while technology and AI capabilities are necessary, combining deep contextual knowledge with data and AI is what creates exceptional value, and that is where we differentiate ourselves.

Second, in insurance, we entered a competitive multi-vendor hackathon at a global carrier to build an AI-based data ingestion solution. Our approach, leveraging a strong understanding of the client's domain resulted in us presenting the best solution. That win positions us as their agentic partner as they reimagine their data estate. It is repeatable, referenceable work we can now deploy rapidly across our client base.

Third, a capability that has increasingly become more important and integral to scaling AI services is token optimization. As enterprises operationalize AI at scale, token consumption has become a dominant constraint on cost, speed and reliability. Working inside client workflows, we are able to reduce client token consumption by as much as 80%, helping them conserve spend and make their AI systems dramatically more efficient without compromising quality or latency.

None of this is possible as a simple technology plug and play. It requires deep knowledge of the workflow, experience of the regulatory context and understanding of ontologies of the data estate within the industries we serve. Also during the quarter, we hosted our Investor and Analyst Day in New York. The core message was straightforward. The AI opportunity for enterprises is immense and capturing it requires partners that can make AI scalable, effective and accountable inside complex regulated environments.

We laid out our view that sustained AI outcomes depends on 3 things working together: the right data, deep domain context and proven AI capabilities, coupled with trusted execution at scale. This is the framework that guides how we build, how we invest and increasingly, it is what we hear directly from clients as they move from pilots into production deployments.

We also outlined our investment priorities to extend our competitive advantage, continued investment in proprietary IP, solutions that move us up the value chain and targeted M&A. 25% of our client revenues today touch our proprietary IP. Strong free cash flow and an underlevered balance sheet gives us the flexibility to continue our share repurchase program and pursue acquisitions of products and solutions that allow us to better serve our clients' needs, which brings me to the most significant announcement of the quarter.

Last month, we announced the acquisition of iMerit, which we expect to close on July 31. iMerit is a recognized leader in AI model training, evaluation and reinforcement learning, and we view this deal as a transformational pivot for EXL. It brings established relationships with leading foundation model companies, a new and strategically important client segment for us. It also deepens our vertically specialized AI capabilities and expands our total addressable market into high-growth AI tech sectors.

The landscape is also shifting in a way that makes this timely. Gartner predicts that by 2028, open source Gen AI models will underpin more than 50% of enterprise use cases, up from less than 10% today. We believe this shift will be especially pronounced in the regulated industries we serve, where domain knowledge, context and compliance are absolutely critical.

Deploying AI reliably in the business-critical workflows requires industry-specific data, rigorous evaluation and constant reinforcement learning. By combining iMerit's capabilities with EXL's domain expertise and AI platforms, we will be well positioned to help enterprises build, fine-tune and operationalize AI that performs reliably in production, a natural extension of the data and AI-led strategy we have been executing for years.

The strength of our business performance and the addition of iMerit give us the confidence to raise our guidance for the full year. We now expect 2026 revenue to be in the range of $2.39 billion to $2.415 billion, representing 14% to 16% growth on a reported basis, up from our prior guidance of $2.3 billion to $2.33 billion. iMerit accounts for approximately $28 million to $32 million of that revenue for the remaining 5 months of the year.

We are also raising our adjusted diluted EPS guidance to a range of $2.25 to $2.29, representing approximately 16% to 18% year-over-year growth, up from our prior guidance of $2.18 to $2.23. As always, I want to thank our clients, partners and employees for their continued trust and commitment and our shareholders for their continued support.

With that, I'll turn the call over to Maurizio to provide additional details on our financial results and outlook.

Thank you, Rohit, and thanks, everyone, for joining us this morning. I will provide insights into our financial performance for the second quarter and our revised outlook for 2026. We delivered a strong second quarter with revenue of $594.8 million, up 15.6% year-over-year on a reported basis and 15.9% on a constant currency basis. Sequentially, revenue grew 4.4% on a constant currency basis. Adjusted EPS for the quarter was $0.59, representing a year-over-year growth of 22.3%. All revenue growth percentages mentioned hereafter are on a constant currency basis, unless otherwise stated.

Now turning to the second quarter revenue performance by segment. Insurance revenue was $197.8 million, up 14.9% year-over-year. This growth was driven by both the expansion and higher volumes in existing client relationships. Sequentially, insurance revenue grew 2%. The insurance vertical, including revenue from international growth markets, grew 14.9% year-over-year with revenue of $233.7 million.

Healthcare and Life Sciences reported revenue of $158 million, representing growth of 22% year-over-year and 4% sequentially. The year-over-year growth was driven by higher volumes in our payment services business, expansion in existing client relationships and new client wins. The Healthcare and Life Sciences vertical, including revenue from international growth markets, grew 22% year-over-year with revenue of $158.3 million.

Banking, Capital Markets and Diversified Industries reported revenue of $133.9 million, representing growth of 10.7% year-over-year and 5.1% sequentially. This growth was driven by the new client wins and expansion of existing client relationships. The Banking, Capital Markets and Diversified Industries vertical, including revenue from international growth markets, grew 12.8% year-over-year with revenue of $202.8 million.

International Growth Markets reported revenue of $105.1 million, up 16.3% year-over-year and 8.9% sequentially. This growth was driven by ramp-ups and higher volumes with existing clients and new client wins in banking, capital markets and diversified industries and insurance.

SG&A expenses as a percentage of revenue increased 170 basis points year-over-year to 20.9%, primarily driven by higher investments in front-end sales and support. Our adjusted operating margin for the quarter was 19.7%, up 10 basis points year-over-year, driven primarily by improved gross margins. Our effective tax rate for the quarter was 21.3%, down 110 basis points year-over-year, driven by higher profits in lower tax jurisdictions. Our adjusted EPS for the quarter was $0.59, up 22.3% year-over-year on a reported basis.

Turning to the first -- to our first half performance. Our revenue for the period was $1.17 billion, up 14.7% year-over-year on a reported and constant currency basis. This growth was broad-based across all segments, driven by double-digit growth in healthcare and life sciences, insurance and international growth markets. Our adjusted operating margin for the first half was 20.1%, up 20 basis points year-over-year. Our first half adjusted EPS was $1.17, up 21.3% year-over-year on a reported basis.

Our balance sheet remains strong. Our cash, including short- and long-term investments as of June 30 was $284 million and revolver debt was $381 million for a net debt position of $97 million. We generated cash flow from operations of $90 million for the first 6 months of the year. During the first half of 2026, we spent $27 million on capital expenditures and repurchased 5.8 million shares at an average price of $30.90 per share, totaling $179 million. This includes 4.15 million shares repurchased under the accelerated share repurchase program at an average price of $30.10.

Now moving on to our outlook for 2026. While we continue to monitor the evolving macroeconomic and geopolitical environment, our strong second quarter performance, sustained growth momentum and healthy pipeline in addition to the acquisition of iMerit provides us with the confidence to raise our outlook for the remainder of the year. We now expect 2026 revenue to be in the range of $2.39 billion to $2.415 billion, including $28 million to $32 million of anticipated revenue from the iMerit acquisition, which is expected to close on July 31, 2026.

This represents a year-over-year growth of 14% to 16% on a reported basis and 13% to 14% on an organic constant currency basis. At the midpoint, the revised range is $88 million higher than our previous guidance. Based on the strong market opportunity and the need for us to continue to stay ahead in AI, we will increase our investments in front-end sales, data and AI capabilities and solutions for the rest of the year, and our adjusted operating margin will be lower in the second half of the year compared to the first half.

We expect a foreign exchange gain of approximately $3 million, net interest expense of approximately $16 million to $18 million and our full year effective tax rate to be in the range of 21% to 22%. We expect capital expenditures to be in the range of $58 million to $62 million. We anticipate our adjusted EPS to be in the range of $2.25 to $2.29, representing year-over-year growth of 16% to 18%, up from our previous guidance of $2.18 to $2.23. Our adjusted EPS guidance includes a marginal dilutive impact of $0.01 from the iMerit acquisition.

To conclude, we had a strong first half, underscoring our differentiated competitive position and exposure to attractive high-growth market segments. Our leading indicators remain positive, and our resilient, adaptable business model positions us well for a solid performance in 2026. With that, Rohit and I would be happy to take your questions.

[Operator Instructions] Our first question is from Bryan Bergin from TD Cowen.

I'd like to start on the data and AI strength and the durability there. So above 30% growth in the quarter, but even above 20% EXL digital solutions and data and AI-led ops. Curious if you could help kind of parse how much of that growth is coming from new AI-native programs versus traditional kind of analytics modernizations? And what gives you the confidence that data and AI perhaps can retain a 20% plus grower for the balance of '26?

Bryan, so yes, we saw a tremendous amount of strength in our data and AI-led business, and that growth rate has been accelerating for us for the last 4 quarters. The reason why it's kind of showing this kind of strength is because each of the service lines within our data and AI-led business are actually performing really well. So Payment Integrity continues to grow very nicely. The data management part of our business is accelerating. The analytics and AI services part continues to grow nicely.

So in terms of your question in terms of new work that we are undertaking here versus existing work that we're undertaking, it really is a mix of both. We do have a stable business within our payment integrity service line as well as within our analytics business. But the newer areas of data management, AI services and AI solutions, those are growing very, very rapidly on a small base, but becoming much more pronounced and big for us.

So -- going forward, this area of data and AI-led services and solutions for us is going to remain a high growth driver even after excluding data and AI-led operations. And we think we are very well positioned with this portfolio of services and the capabilities that we are demonstrating to our clients is giving them the confidence to engage with us in much more strategic ways.

Okay. Okay. That's clear. And then, Rohit, you had interesting comments on kind of tokenomics and the optimization potential there. Just given your data and process expertise, it seems to be a major opportunity for you and just understanding there are some highly valued third-party routing platforms in the market. I'm curious how you're approaching this vector. Is this an area where you're developing IP that can kind of better serve vertical-specific applications? And can you also use such a solution as kind of a tip of the spear to accelerate new opportunities out there?

Yes, Bryan. So look, I think token economics is going to become a very important and integral part of any AI modernization program for an enterprise client. And there seems to be a tremendous amount of value that can be created for clients just in terms of the design, the engineering and the way in which the implementation is done for AI models and the way in which agentic AI is used on the operating workflows.

We are obviously in a much more advantageous position, having a deep knowledge of our clients' industry and their workflow and their data. And we are building up capabilities that will give us advanced signals about the use of tokens and the cost of tokens, and we can make that transparent to our clients and be able to help them reduce their cost on token economics. This is going to be one of the principal areas of spend.

And I think clients will also start to look at more complex solutions out here where they might even think about having their own hosted environments for the infrastructure and being able to deploy the AI models on their own GPUs and in their own environments. So frankly, this is an area that's going to continue to become more complex and expand rapidly. And there's a lot of value that needs to be delivered to clients on that, and we are in a great position to do that.

Our next question is from Surinder Thind from Jefferies.

Rohit, can you maybe talk a bit more on just kind of the dynamics of what's going on in the international growth markets. Obviously, the back half of 2025 growth was kind of flat quarter-over-quarter. And obviously, you've seen a material acceleration there. Can you talk about can that continue and how we should think about that segment on a go-forward basis?

Sure, Surinder. So look, our view is that the international growth markets is a huge opportunity for EXL. The adoption of AI by clients in the international growth markets is actually trying to keep up pace with the adoption of AI from our U.S. clients. And actually, what we are seeing is given the fact that we are in a much more connected world right now, everybody is moving on to the adoption of AI very, very rapidly and equally. So we see that to be a tremendous opportunity for us to help and engage with clients internationally and be able to support them along these journeys.

Now in the past, we've had a limited presence internationally, but we've been investing quite aggressively in terms of building up more talent capability and solutioning capability in the international growth markets so that we can serve our international clients much more directly and much more near their point of execution. So this for us remains an important strategic focus area. It's a huge demand vector that is literally untapped by us. And we think we can play in this space and create a significant amount of revenue volume out here.

Helpful. And then as a follow-up on the investment spend or the incremental investment spend, you talked about investing maybe a bit more in front-end sales as well as data and AI capabilities. Any additional color you can provide there? Is front-end sales maybe more in international growth markets, certain segments? And then on the data and AI capabilities, is that more product build-out? Or is that more services capability build-out?

Yes, Surinder. So for us, the data and AI capabilities is much more building out solutions and product capability. And as AI continues to evolve, this is something which we will continue to invest in. And then the front-end investment is also essential because the skill set required to make a sale on data and AI is quite different from traditional digital operations. And that's something which we are investing in.

One of the things which we have noticed is that the velocity of decision-making of deals by clients is actually coming -- the velocity is increasing and the cycle time is coming down. So we need to have a greater amount of sales and front-end capability to be able to deal with this higher velocity and a much faster cycle time as well as much deeper product knowledge about our data and AI services and capabilities and the way in which we can enable AI for the clients.

Our next question is from Puneet Jain from JPMorgan.

Strong results. Rohit, are you seeing any changes in clients' conversations or their willingness to outsource given increasing AI complexity and the news flow around AI and also like the low enterprise value creation or ROI that some of those AI projects have created so far?

Thanks, Puneet. So yes, look, as we've kind of shared previously, the changes that we are seeing are clients are moving away from pilots and they want to go into production. So they want to be able to deploy AI in production. But still, we are in the early stages, and this deployment into production is use case by use case. So it's still at initial nascent phase.

The second part is enterprise clients are building out their foundations for the AI enablement of the enterprise. So what that means is they're getting their data estates in order. They are putting together platforms that will allow them to develop, deploy and activate agentic AI. They are putting together AI harnesses that will allow them to be able to iterate and modify their AI models very, very rapidly and be able to govern and be able to have adequate security and compliance with regulatory requirements and have that in place.

But the effort that is required to enable AI for any enterprise is massive, and it's really an enormous opportunity. I think this is one area which is significantly underestimated by the market. And what we are seeing is clients are really struggling with this change, and we are in a great position to actually help them with this. So we feel fortunate that we've got the right kind of capabilities, the right kind of skill sets and the right kind of relationships and the know-how to be able to help them move on this journey.

That's great to hear. And my second question is similar to Bryan's question, but focused on AI and data work that's embedded within digital ops. Can you double-click on the -- specifically what type of work you do in that practice within digital ops? That's like 15% of total revenue growing -- obviously growing at a very high clip. Directionally, how much of that growth in revenue stems from AI-driven agentic operations versus data analytics work with -- and again, I'm talking about data AI within digital ops.

Right. So when we talk about embedding data and AI into digital operations, think about some of the common processes that we run for clients. So let's take insurance where we run claims processes for clients or we run underwriting processes for clients. We are embedding agentic AI into claims and into underwriting. So what that means is, we are pulling together pieces of that process and AI enabling that and allowing the LLMs to be able to take the decisions and to be able to automate some of the workflows that we are working on with our clients.

Now we have to do this in a responsible manner, which is also got the right kind of guardrails for a regulated industry and for a regulated workflow. So what that means is that in some cases, we use deterministic models, which are very much well suited for following policies, procedures and regulations. And in some cases, we are using probabilistic models which is -- where judgment is required and where decision-making is required. And it's really the art of combining both of these 2 deterministic models and probabilistic models and integrating that into the workflow. That's the effort required to embed data and AI into these digital operations processes that we're running.

Now keep in mind, every time we do this, if you're doing this for a particular carrier, a particular business line, a particular geography, each one of this is a unique intervention that needs to be undertaken. It's got a common harness. It's got a common component pieces that can be deployed. But every single time, the enablement requires a very high level of customization. And that level of customization is also iterative, and it requires deep knowledge of the workflow, deep understanding of the data sets and a deep knowledge of being able to integrate and orchestrate across the various technology platforms of our clients.

So it's a slow and gradual process, but we are in the best position to be able to deploy this for our clients, because of our knowledge and understanding of the workflow and the fact that we already do this work for our clients. And that's why we're seeing greater adoption and greater traction and greater confidence by our clients to allow us to do this work.

Our next question is from Maggie Nolan from William Blair.

So there are a lot of moving parts on the margin, the acquisition, the India Labor Code tokenomics, you outlined some investments in the prepared remarks. Are you still hoping to drive gross margin up and then also modestly improve operating margin annually? Or is the priority for the business for the next couple of years really to invest for and drive growth on the top line?

Maggie, thanks for the question. I don't think anything has really changed in our thinking around gross margins and also AOPM. You saw gross margins come down about 90 basis points from Q1. The big driver there was our increments came into effect as of April 1 globally. So that always creates a lower gross margin in the second quarter, which is no different than the prior year. You'll see the same effect in the prior year.

And so we continue to make improvements marginally every year to gross margin as we drive more value overall from embedding more data and AI into our client workflows. And we've talked about that pretty significantly in the past, and nothing has changed there.

Now when it comes to our overall margin, we continue to see our overall margin in 2026 to be comparable to 2025. Now we had a very good first half of the year in terms of our adjusted operating margin, and we will continue to invest in the second half of the year, which will help us really drive overall top line growth, both for the second half of this year and 2027. And again, that comes into the -- that involves the investments that we're going to be making in front-end sales and also to build out capabilities in both data management and also in our AI capability area.

So we continue to make progress on gross margins. You will see them go up and down slightly when you look at it quarterly, but we will continue to make progress there. And you will see us invest a bit more in the second half of the year, but our overall margin for the year will be comparable to the prior year.

Got it. And then can you talk about are you -- have you seen success in penetrating the mid-market opportunity? Do you think that cohort is in perhaps greater need of a partnership and services from you all than the enterprise? Or how are you thinking about that?

Yes, Maggie. I think the mid-market is trying to catch up on AI as quickly as possible. And frankly, the mid-market needs a lot more help than the large enterprises. So we are seeing a fair amount of traction out there. And we are in a great position to help out the mid-market clients because of the kind of attention that we can provide to them. And these mid-market clients are very meaningful and wholesome client relationships for us that are developing quite nicely. And so it plays to the nice fitment between us and the mid-market clients and the value that we can deliver for them.

And the last piece I would say is everything has obviously got to be done with speed being at the center of the value equation. And again, our ability to be able to engage with the mid-market clients, deliver the value to them at speed and give them the focus and attention is really helping us.

Our next question is from David Grossman from Stifel.

So just looking at the kind of cadence of growth over the last couple of quarters, it looks like growth has accelerated on an organic constant basis. And just looking at the pace of new client adds in the back half of last year, it looks like you had a pretty dramatic uptick. And just curious, is the acceleration that we're seeing the pace of adds, new client adds over the last 3 quarters or so? Or was there something about the second quarter? Because the second quarter had a much steeper acceleration of growth. And just curious if there was anything else that may have landed in the second quarter that drove that.

Yes. Thanks, David. So look, I think the second quarter for us was a unique quarter. Every single service line actually delivered with strength. And we saw particularly good momentum in our data and AI-led business. And that grew very, very rapidly. We are also fortunate that our client portfolio is actually very broad-based, and their confidence in our ability to provide them these services that seems to be increasing.

So you're absolutely right. The wins that we had in the second half of last year, combined with all of our service lines actually seeing good traction resulted in a very strong outperformance in the second quarter. And that's what gives us confidence and much greater visibility into the second half of 2026. And therefore, we've increased our guidance for the full year.

Yes. I guess what's a little confusing, Rohit, is that despite easier compares, the guidance implies growth decelerating in the back half of the year, and that's really not your business model, right? It's fairly consistent and stable. So just trying to kind of reconcile what sounds like incredibly strong momentum and the guide that implies somewhat decelerating growth in the back half of the year.

Right, right. So David, look, I think for us, number one, the macroeconomic environment continues to be a little bit unsettled. We do need to continue to win the hearts and minds of the CIOs in our enterprise client organizations. And this enablement of AI and this change is hard. So it's something which needs to be worked upon, and we need to be able to demonstrate the ROI to our clients on a daily basis to be able to continue to build and grow our business.

So there are a number of challenges and risks that we have in our business. We are also doing a major acquisition with iMerit, and we are going to be integrating that and building up new capabilities on model evaluation, reinforcement learning, and we have to focus our attention out there. So yes, I think this is something which we are kind of -- this is our best guess in terms of a prudent way to be able to build and grow out the organization.

Sure. And just one quick question for you, Maurizio, on the share count. I know you had the ASR in place, right? So you brought the share count down in the second quarter. Does that -- can you give us some kind of insight into what the share count looks like in the back half of the year? Are we pretty stable now? Or does it go down some more in the back half?

Well, we'll continue to see benefit from the share repurchases we did in the first half and the second half of the year, obviously, in the share count. But we'll continue our share repurchase program in the second half of the year. I think given where our share price was in the first half of the year, we saw it to be prudent to spend a bit more on share repurchases in the first half of the year. And that's -- and again, we spent $179 million in the first half of the year. So we'll continue to be buying back shares throughout the year, just most likely not at the accelerated pace of the first half.

Our next question is from Vincent Colicchio from Barrington Research.

Yes, Rohit, I'm curious, can you -- can we think of this -- I'm trying to think of today's AI revenue and how much represents entirely new spending versus existing work being modernized. Can you sort of give us an idea of how that looks?

Right. So Vincent, for us, the AI revenue for us, which is stand-alone AI services and solutions is still a very small portion of our overall revenue. And we think there's a tremendous amount of growth that we would see in that service line going forward. The AI enablement of digital operations, again, the penetration of that remains quite low. And there's a lot more work to be done in terms of extending that to the entire existing portfolio. Keep in mind that the work that we do in digital operations is spread across 2,000 unique processes. It's spread across multiple hundreds of clients and enabling that for each use case is -- it's very difficult and hard and time-consuming and can only be done in areas where the economics justify it.

So frankly, this is going to be a much longer-term change that's going to take place. And our goal is to do this as rapidly as we possibly can. And frankly, the faster we can do this, the more client confidence we gain and the clients are willing to give us much larger pieces of their business so that we can do this across their much bigger operations estate.

And are you getting better at generating international revenue from cross-selling to U.S. clients? I know that's a large opportunity for you.

Right. Actually, that's one area that we need to invest more in, and we have not been able to do a good job of that. Bhupender, who's just come in as our President and Head of International Growth Markets, One of the key areas that he wants to drive and focus on is cross-selling to our U.S. customer base internationally. And that is something which we will focus on. It quite candidly has not been something that we have deliberately done in the past few years, and we do need to focus on that a lot more.

Our final question is from David Koning from Baird.

Great job. When we think about the acceleration, I was kind of thinking of a few buckets that it seems like the spending might fall into. First of all, business spending had been kind of slow in general. And so maybe some of that's unfreezing that slowness. Secondly, is there a reallocation of AI spend to the established IT services players like yourselves? And then third, is it just you gaining market share? And maybe all 3 are driving it right now. But is there a way to kind of bucket -- why you think this acceleration is happening, where it's coming from?

Thanks, Dave. Yes. Look, I think you're right in terms of these 3 categories. Our sense, if you think about the total spend on AI over the last 12 months, -- the largest part of that spend has gone towards AI infrastructure and then to the AI models. And actually, very little spend has gone towards the app layer and the AI enablement layer. So going forward, we think that, that is going to change, and there will be much more spend as a percentage of the total aggregate AI spend that's going to be on apps and on AI enablement. And we obviously hope to benefit from that.

The reallocation of spend that is there, that's a little bit difficult to estimate because clients will spend on the areas that provide them with the highest ROI. And I think we -- our goal is to be able to demonstrate transparently the evidence of delivering greater ROI and to be able to be part of that reallocation spend.

And then from a market share perspective, clearly, our growth rate suggests that, that's something that is happening. And I would attribute it to 2 things. One is the speed of play and the value that we are delivering. So the speed and the value that we deliver, both of them are going to be differentiators. And it's just that our knowledge of the workflow and our knowledge and understanding of the data set just puts us in a slightly better position as compared to our competition.

Great. And then just one follow-up. The iMerit acquisition, you gave the revenue contribution. Is it pretty split across all verticals? Or are there 1 or 2 verticals we should kind of allocate that more toward?

So the iMerit revenue is actually split up with some of the frontier and foundational model companies. So that would really fall into our diversified industries bucket. But they also do a fair amount of work within health care and life sciences. So that's going to be meaningful. There's very little work that iMerit does in the international growth markets. So that's not going to be a meaningful add. But I would say the majority of that really will fall into the diversified industries category, and that's where most of the revenue would come in.

We have no further questions at this time. This concludes our call. Thank you, and have a good day.

Good day, everyone. My name is Abigail, and I will be your conference operator today. At this time, I would like to welcome you to the ExlService Holdings, Inc. June announcement conference call. We ask that you please hold all questions until the completion of the formal remarks at which time you will be given instructions for the question-and-answer session. Also, as a reminder, this conference is being recorded today. If you have any objections, please disconnect at this time.

I will now turn the call over to Andrew Thut, Head of Investor Relations and Capital Markets.

Thanks, Abigail. Hello, and thank you for joining us to discuss this morning's announcement of EXL's proposed acquisition of iMerit. On the call with me today are Rohit Kapoor, Chairman and Chief Executive Officer of EXL; Radha Basu, Chief Executive Officer of iMerit; and Maurizio Nicolelli, Chief Financial Officer of EXL. We hope you've had a chance to review the press release we issued this morning. It is also posted to our company website.

As a reminder, some of the matters we'll discuss this afternoon are forward looking. Please keep in mind that these forward-looking statements are subject to known and unknown risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements. Such risks and uncertainties include, but are not limited to, those factors set forth in today's press release and in EXL's filings with the Securities and Exchange Commission from time to time. EXL assumes no obligation to update the information presented on this conference call today.

So with that, I'll turn the call over to Rohit. Rohit?

Good morning, and thank you all for joining us today. I'm pleased to announce that EXL has entered into a definitive agreement to acquire iMerit a recognized leader in AI model training, evaluation and reinforcement learning. iMerit is focused on helping its clients train large language and multimodal models for accuracy, precision and effectiveness. We expect to close in the third quarter of this year, subject to satisfaction of customary closing conditions.

This deal marks a significant milestone in EXL's data and AI strategy, further strengthening our position as a strategic partner for enterprise AI clients and now foundation model companies. This acquisition will bolster EXL's ability to help enterprises achieve important business outcomes from by combining EXL's deep data context and AI capabilities with iMerit's technology platform, expert-led solutions, and generative AI experience.

As I shared at Investor Day last month, what is becoming abundantly clear is that every model deployed in an enterprise environment must be fine-tuned, evaluated and continuously improved for the specific use case it serves. And that only happens through model evaluation, reinforcement learning and expert human feedback. It is now a critical capability for any forward-leaning organization serving enterprise clients.

iMerit and EXL together deliver exactly that, helping clients build AI systems that are trusted, accountable and built to perform in the enterprise. iMerit has a proven track record of delivering strong growth across multiple long-tenured clients and innovating new AI technologies. We will connect their capabilities to our enterprise client base and broader data and AI platform to expand these solutions across larger, more complex client environments. We look forward to welcoming iMerit's CEO, Radha Basu, and her talented team to EXL.

Let me now highlight 3 important reasons why this acquisition matters and why the timing is particularly compelling. Number one, iMerit's relationships with leading foundation model companies. Number two, it deepens EXL's vertically specialized AI capabilities; and number three, it expands EXL's total addressable market into high-growth AI tech sectors.

First, iMerit's relationships with leading foundation model companies will place EXL at the center of how next-generation AI is being built. The next wave of models will need expert-curated multimodal data that does not exist in the public domain. This growing demand has fueled iMerit's strong momentum and is expected to drive growth in the years ahead. Additionally, Foundation models are moving deeper into domain-specific applications.

EXL and its clients will benefit from early insight into how AI is trained and improved. As the focus shifts towards token efficiency, latency and accuracy, purpose-built small language models as well as fine-tuned models will become the standard particularly in regulated industries like health care, insurance, banking and capital markets, where EXL is a trusted partner.

Second, EXL's vertically integrated end-to-end AI capabilities when combined with iMerit's expertise, deepen the ability to deliver trusted AI at enterprise scale. As enterprises increasingly demand agentic AI systems that are reliable, explainable and continually improving, iMerit's capabilities become critical. EXL is already a leader in helping clients reimagine workflows powering enterprise data infrastructures and applying domain guardrails to enable AI-driven operating models.

With iMerit, we'll further strengthen resilience, reliability and trust in Agentic systems. iMerit enhances EXL's platform and human intelligence capabilities through its Ango Platform and Scholars Network. Ango powers sophisticated data interactions with GenAI models, enabling chain of thought reasoning, red teaming and multimodal evaluations. Scholars Network expands EXL's domain expertise through iMerit's global network of specialists, including physicians, scientists, engineers, linguists and other subject matter experts. We will integrate Ango with EXL's Agentic technologies, creating an end-to-end AI platform that helps enterprises accelerate AI adoption.

Lastly, this acquisition broadens EXL's total addressable market into high-growth AI sectors including high-tech, mobility and physical AI. iMerit's expertises across complex data types gives EXL the capabilities to build and scale specialized AI solutions for these adjacent markets now and into the future. We believe this combination strengthens our position in the fastest-growing areas of AI and creates a powerful platform for long-term growth.

I will now turn it over to Radha to say a few words, after which our CFO, Maurizio Nicolelli, will give a brief overview of the deal terms. Radha?

Thanks, Rohit, and thank you all for attending. It's a great moment for iMerit. And I'm excited about joining hands with EXL to realize our joint vision around data and AI. iMerit has been lucky to have a ringside view of the AI high-tech industry over several years. The markets we are in, high tech, autonomous mobility, health care and physical AI evolving fast, certainly the fastest that I've ever seen in my career.

We have learned to be agile, responsive and tech-driven in our approach. This has leveled up even higher in our work with the foundation and frontier AI companies. The big AI firms that want to send their foundation models into the enterprise need domain knowledge to build specialized AI systems and trusted relationships to sell them. The Frontier Labs are largely product companies. The question is, how do you move these models into the enterprise where trust, culture and long-term customer relationships matter to embed AI into the business workflows. EXL plus iMerit provides that answer.

What is exciting about joining EXL is the opportunity to take iMerit's experience and capabilities into a much broader enterprise market. EXL's relationships with large enterprises gives us a pathway into regulated data-driven industries. To achieve high-quality data for model training, clients need both domain experts and a technology platform that captures it evaluates it and governs it. This is even more impactful as the models become more powerful and the business workflows become more demanding. And that's where the differentiation comes in for us, putting together solutions like prompting, chain of thought reasoning and multimodal evals has taught us a completely new language of technology.

EXL and iMerit, we envision a whole ecosystem that connects the fantastic work being done by our clients and foundation models to trusted AI deployments in large companies and extend that into long-term production oversight and improvement. I'm very proud of the iMerit team, which has led innovation and established trust and leadership with customers over many years. And I look forward to continuing this energy as part of EXL on a much larger scale.

I will now turn the call over to Maurizio. Maurizio?

Thanks, Radha. I'd like to give you a few highlights of the deal and how it fits into our capital allocation strategy. The $310 million purchase price includes $170 million of upfront consideration and up to $140 million in incentives and earn-outs over 2 years, contingent on the achievement of specified milestones. Preliminary unaudited revenues for iMerit were approximately $59 million the 12 months ended March 31, 2026, driven by high growth in Gen AI.

We expect the acquisition to accelerate our growth rate and to not have a material impact on near-term profitability. We will update our guidance in the customary manner on our second quarter earnings call at the end of July. We entered this acquisition from a position of strength. The robust business momentum we started the year with has continued through the first half of 2026. The transaction is fully consistent with the capital allocation strategy we articulated at our May Investor Day deploying our flexible balance sheet and strong cash flow generation to build capabilities and extend our competitive advantage. That means continuing to execute on our $500 million buyback program while also investing in intellectual property and capabilities that best serve our clients' evolving needs. We look forward to updating you on all aspects of our business during the second quarter earnings call.

Finally, I would like to join Rohit in welcoming Radha and the entire iMerit team to EXL. We are incredibly excited about the opportunities ahead.

With that, I'll turn the call back to Rohit.

Thank you, Maurizio. In summary, iMerit will supercharge EXL's AI offerings and deepen our differentiation in the market. EXL is setting the standard for AI that is reliable, accountable and built to perform at scale in the enterprise.

We'll now open the call for questions.

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

I think the strategic rationale is very clear here. So maybe my first question will be on the financial implications. Maybe digging in a little bit more here just so I understand. Can you just comment on the growth trajectory that iMerit has shown maybe over more than a 1-year time frame? Just trying to understand if this has been a consistent type of an expansion or accelerating. And then just as it relates to maybe like the EPS implications, accretion, dilution in year 1, anything like that or synergies? Anything more that just help us with the model a bit.

Bryan, it's Maurizio. So when you look at iMerit's business and the way they've built up the business, there has been very good growth, particularly over the last, I would say, 3 to 5 years. They've done very, very a great job in really pushing forward into the GenAI foundation model work that's really started to propel the business. And that will be the big synergistic capability that we're going to embed into our enterprise workflows going forward. So overall, you've seen a very good trajectory over the last 3 to 5 years. And now it's really the opportunity for us to really embed it into our business and get that full synergy out.

Bryan, let me just add to what Maurizio said. So the financial trajectory of iMerit, number one, it's going to be a high-growth business for us, and it's a profitable business. So it's a business which is set up on good, solid financial fundamentals. The opportunity set of the combination of iMerit and EXL. We believe that is tremendous because it allows us to be able to serve foundation model companies, which are obviously growing very, very rapidly, work with some of the large high-tech companies that are deploying AI in a very aggressive manner. And it opens up the TAM for us very, very meaningfully.

So frankly, the opportunity set going forward into the future is very significant. And you can see the way in which this deal has been structured with a very strong earn-out component. It allows for the right kind of incentives and the right kind of alignment to be created between the 2 organizations.

Okay. Okay. Very good. And then my follow-up is just on the nature of iMerit's contracting. So can you comment on -- is this broadly kind of T&M FTE model or a non-FTE model. I'm trying to think about the forward business visibility as it relates to kind of annuity type versus kind of episodic swings depending on how kind of model training ramps and ramp downs go.

Yes. So the business model is pretty much got both elements to it. It's got an ongoing nature of work, which acts like an annuity business model. And then there are elements which will be a lot more project-based as such. I think when we looked at iMerit and we looked at the tenure of the customer relationships and the trajectory of growth with the clients that they've had for several years, that was very appealing because keep in mind that the work that iMerit does, it's all focused in high-growth companies which need these capabilities in a pretty meaningful way.

So we are actually quite comfortable with the kind of revenue model that they have in place and how this will evolve over a period of time.

Our next question comes from Puneet Jain with JPMorgan.

It seems like a nice asset so congratulations there. Can you share like if there is any customer concentration, it seems like health care is one of the large verticals. So maybe if you can talk about synergy opportunities you see with EXL health care business?

Sure, Puneet. Thanks for that comment. So like Maurizio said, the revenue of iMerit on an unaudited basis for the period ending March 31, '26 was $59 million. They work with several clients across the foundation model companies across the Mag Seven across the autonomous mobility companies in health care and in a number of different areas. They don't have any 1 single client, which is the predominant part of their business. Actually, the business is well distributed and the growth of the business is coming in from a number of newer frontier model companies that iMerit is signing up independently as well as from some of the existing clients that have relied on the quality of work and the kind of value that iMerit is delivering to them already. So we think it's very broad-based as such.

And in terms of the synergies, yes, there are significant synergies in terms of the revenue growth across our industry verticals. So health care is clearly an industry vertical that is likely to leverage AI in a much more meaningful manner going forward. iMerit already does work with a number of clients in the health care area. And with our health care payers and with our clients, we think that there will be a fairly meaningful synergy there.

No, that's great. And can you also quickly talk about like the deal structure like $170 million and $140 million now portion seems high for typical deals. So can you talk about like the goals, the targets that will drive that earn-out over in future?

Yes, Puneet, so at this point of time, we just have a definitive agreement, and we've shared the broad structure of the deal. We will -- as we get to closing and as we get to providing you with updated guidance on EXL and iMerit together, we will provide you with additional color on those details going forward. The part that you referenced, which is the earnout component being pretty strong, it's directly tied to the kind of growth and the value creation that we think that the asset can generate. And it basically aligns both sides to be able to realize that revenue synergy and profit synergy as such.

So it's tied to both revenue and profit metrics, and it's over the next 2-year period, and we believe that in that 2-year period, the integration will be complete.

Our next question comes from Matt Dezort with William Blair.

Team, this is Matt on for Maggie. Congrats on the acquisition. I guess, can you double-click on iMerit's head count and pyramid model. It seems pretty intriguing across full-time employees and active resources and contractors, I guess, and almost -- and also how many fully deployed engineers does iMerit have on staff?

Sure. So Matt, the total number of full-time employees that iMerit has that will transition over to EXL upon closing is going to be approximately 3,600. In addition to that, there is a very strong network of experts that are part of the Scholars Network and the Scholars program that iMerit has built and curated over the last couple of years. Those experts which are on the Scholars Network, they are independent contractors and they are not full-time employees of iMerit. iMerit uses them to get expert data and model validation and model evaluations done for being able to do a number of things to improve the accuracy, precision and effectiveness of large language models.

In terms of the engineering head count and in terms of the forward deployed engineers, iMerit has been building up that capability over the last couple of years. We've been actually very impressed with the kind of talent base that iMerit has put together, and we have been spending time with their teams. And we think that their knowledge, their expertise in this area of model evaluation, red teaming, chain of thought reasoning, putting together solutions for both foundation model companies and enterprise clients is actually very strong and very well established. And therefore, we are excited to partner with them and take this forward.

We don't have a number to share with you on the forward deployed engineers because that's a team that kind of keeps increasing, and we will be adding some of our staff alongside with iMerit to take this capability forward to the enterprise clients.

Appreciate that color. And then, I guess, as a follow-up, how concerned are you about coopetition versus competition with the Frontier model providers. Obviously, they're expanding their offerings and opportunity sets that they're going after. I'm just wondering how insulated iMerit is from competition with the Anthropics and the open AIs of the world? What's keeping the model providers from doing more of this upstream data work.

Actually, this is a very strong partnership that aligns the goals of some of these foundation model companies and iMerit. iMerit helps these model companies become better and these foundation companies use iMerit's skills capabilities in terms of improving on their models, going into more vertically specialized models and going into more domain-specific models. So frankly, there is no real element of competition that should be there. It's much more of a strategic partnership.

Now Matt, one thing we should keep in mind is all the foundation model companies at this point of time seem to be devoting a lot of their energy, effort and resources to taking these foundation models to the enterprise. And that's a skill set and a capability where actually EXL can help these foundation models succeed in effectively deploying these foundation models in the enterprise. And that's the opportunity set that we see in terms of this combination.

The enterprises, on the other hand, some of them want to build their own small language models or specialized language models and be able to take the contextual knowledge and data that they have sitting within their organizations and be able to fine-tune and train existing models. So frankly, we see this as an activity that can work both ways, which is helping the foundation model companies go into the enterprise and get adopted there effectively as well as for the enterprises to be able to deploy their own models effectively within the enterprise and own that intellectual property.

Our next question comes from Surinder Thind with Jefferies.

Just following up on the last question. This is just kind of a broader question about this idea of the success of the foundation models versus enterprises moving towards the more small language models. Does the shift in market share between large models, maybe if we move towards 1 or 2 large models large foundational models. Does that impact the growth rate of the business or the strategy for iMerit? And then maybe how much of this is also a strategic bet on what I would call the proliferation of small language models at this point. I'm just trying to understand this scenario or the conditions under which iMerit is successful and maybe where the risks are in the transaction.

That's a great question, Surinder. So let me try and address that comprehensively. So first off, if we just step back and you think about AI it really at the very core boils down to data and the model that being kind of being in place to be able to make AI effective. You know that EXL has been investing in helping clients on their data readiness and we've got a very strong capability on helping clients get their data ready for AI and that component is in place.

The AI model piece is what we are now investing in alongside with iMerit. And to your question as to whether this is going to be something which the foundation model companies, how will they think about it and how might this evolve and how would the enterprises think about it and how that might evolve. Our view is that this is actually going to happen in a bidirectional way, and it's not going to be 1 model versus the other model but rather using the right combination for the right use case and then therefore, there'll be some level of orchestration that's needed where you can use a base-level model for base level reasoning.

You can use a specialized model for specialized work that needs to happen. It can be a contextually driven model that the enterprise owns for their own proprietary data sets and for their own proprietary work that they undertake. But just like you have an ability to kind of manage infrastructure on the cloud or on on-premise, whether the private cloud or a hybrid cloud, you're going to see model development take place in a similar pattern which is going to be large language models, open source models, open weight models.

There are going to be some specialized models, then there are going to be some proprietary models that are going to get created. But there's going to be a proliferation of these models and these capabilities across the board. And the right combination and the orchestration of the right combination is going to be critical.

Now one other piece that I'd add to this, the entire world in AI is now moving towards agenetic AI. The same elements that are needed for reasoning and improving the accuracy and efficiency of a large language model or an AI model, that same reasoning capability and evaluation capability is also going to be applied to Agentic AI. So frankly, we are at the very early stages of how these models are being developed and how these will evolve and how they will progress and then how it might be used for Agentic AI as well.

And our sense is at the end of -- as this thing evolves over the next 1 year, 2 years and 3 years, you'll have a proliferation of models and it's going to be using the right set of models for the right use case that's going to be critical. And that's again something which we would have expert knowledge because we would be helping some of these leading-edge foundation model companies deploy this capability. We'd be helping the enterprises deploy this capability and we are in the best position to be able to advise these kinds as to how they can create these models and use these models.

That's actually very helpful. And then just to make sure I fully appreciate the revenue model here for iMerit. Is this primarily services at the end of the day or do you also are building IP in the process that you can monetize on a non-FTE basis here?

Yes. So first off, iMerit owns 2 platforms. One is the Ango platform and the Ango platform makes model evaluation much easier, quicker and cheaper to execute. And that's a capability that continues to be developed and that allows for iMerit to be much more effective when it partners with a foundation model company or an enterprise.

The second is the Scholars Network that's been created on the platform that's been created, because the ability to curate expert experts to be able to compensate them to be able to evaluate them for their quality of work and to be able to choose the right set of experts, that's very well established. And therefore, the ability to scale up and scale down, that's very meaningful.

Now in terms of the work and the revenue model for iMerit, that's something which we think there's going to be an enormous need for this to be deployed. As you know, so far, most of the model training has largely been on public data sets and things that have been available on the Internet. But now as you kind of go into much more unstructured data as you go into much more of images, video, and contextual data sets that reside inside of client organizations or inside the heads of experts. And also as you go into the physical world and you go into physical AI, all of those data sets need to kind of be brought together and be used to train models.

So frankly, the need for this is really, really high and really strong. And we think that there's a tremendous amount of work that needs to be done here. The question is, can you do that work smartly, cheaply and at high quality. And that's the foundation that iMerit has built, and that's what EXL plans to leverage and scale that up in a very meaningful way. Does that help address your question, Surinder?

Sir, it was actually very helpful. Thank you.

[Operator Instructions] Our next question comes from Jacob Haggarty with Baird.

Yes, this is pretty interesting here. Just one question. It seems like it's going to be maybe dilutive to your revenue per employee numbers in the first year. How quickly can you improve that? And should we expect that to get back to growth pretty quickly? Or is this going to be a headwind for a little bit?

Yes. So look, I think, first of all, we are focused in on the strategic capability that we are building out here and the potential opportunity set for us which we think is enormous. Second, to your immediate question, yes, this will be dilutive to the revenue per head count initially but keep in mind that this is a small piece of EXL's overall revenue base and overall employee head count. As we move towards more complex work associated with this model reasoning, as we do more work with data experts, the revenue per head count actually will be higher than the average revenue per head count that we have at EXL.

So the ability to kind of use this for the dilution of the revenue per head count, that's going to be a very short-term phenomenon. And I think this will fall in line with the overall EXL revenue per head count metrics very, very quickly. And frankly, as we go into more complex work and as we go into more work that requires more expertise, this should be accretive to the revenue per head count numbers.

No, that's helpful. And then just as a quick follow-up here. Just thinking through these 2 sides of helping the enterprises with the contextual models versus helping the foundational models, is there a side of that, that's larger now. And then is there a side of that, that has a larger opportunity going forward? And then I guess within that, is either of those 2 options more at risk or less at risk of being automated through further use of AI, like will the foundational models be able to use AI to do this in the future? Or could the companies do it in the future? Just if you guys could touch on that a little bit.

Yes. Our sense is, over the next few years, the demand set for this kind of work is, frankly, unlimited. The enterprises are just getting started on thinking about their small language models or their specialized language models. But they -- neither do they have the capability of creating this nor have they built anything which is meaningful in this area. So it's really being at ground 0 with the enterprises.

The foundational model folks are obviously ahead on this game, and they've been kind of building up this capability very rapidly over the last year or so. And our sense is just by the volume of data sets that exist outside, which haven't yet been used to train their models, this is an enormous opportunity that's likely to continue for a while. The use of AI for model reasoning, model evaluation and model testing and red teaming, I think that's bound to happen and that's likely to kind of play out.

But at the same time, you're going to have more edge cases and more places where you'll be looking at how do you triage this and how do you kind of get to a space where it can be much more effective. So it's going to require a combination of both reinforcement learning through a human feedback as well as it's going to require some level of automation that can be built in as well.

We have no further questions at this time. This concludes our call. Thank you, and have a good day.

Good morning, and welcome, everyone. Welcome to EXL's 2026 Investor Day. For those that don't know me, my name is Andrew Thut, I am the new-ish, Head of Investor Relations and Capital Markets here at EXL. I see a lot of old friends and old faces here, and I see some new that I'm really looking forward to getting to know better. I was going to start with talking for a second about the moment that we're in. When a new technology is introduced there's a lot of excitement. There's a lot of opportunity that's involved with that, but also with that comes a lot of noise. Our hope this morning is to cut through some of that noise and leave you with a clear picture of three things: it's how we view the opportunities in the market, EXL's strong positioning to help our clients, and how we plan to sustain durable long-term growth. So now more than ever, we're really excited to have you here this morning.

So a few logistics. We are live webcasting today. So the slides will be posted to our website today. We're going to hold questions until the end. So after all the formal presentations, we'll have the whole group up here on stage, and we'll do a group Q&A. Coffee, as you know, is in the back, and we have bathrooms behind. Feel free to step out at any point as we're going to -- the presentation has no scheduled breaks. So we're going to go all the way through until lunch.

After Q&A, I would very much encourage you, lunch is going to be served through here, and we have the demos in the rooms over there of our IP and solutions. I would very much encourage you to take a look at those. Those are very important investments for EXL, which we'd love to get how you have a live look at. Lunch will be served in the next room. And you'll have a great opportunity to spend some time with the presenters, and we have a lot of EXL leadership here.

So safe harbor, as you'd expect, today's discussion will include some forward-looking statements regarding our strategy, outlook and financial performance. Actual results may differ materially and we refer you to the risk factors in our SEC filings for more information.

And then today's agenda. I'll hand it over in a moment. But here's how the morning will flow. So Rohit will come up and kick us off with what EXL is seeing in the market, our vision and strategy. He'll be followed by Vikas Bhalla, who will discuss EXL's competitive advantage in data and AI. Then Andy will come up and talk about where we're investing in our data and AI-led architecture to create value for our shareholders and our clients. Vivek Jetley will then come up and we'll bring it all to life. He'll give real world examples of making AI real for our clients and expanding those relationships and driving business outcomes. And then finally, Maurizio, our CFO, will come up and we'll close and talk about our strong and evolving financial model and the continuation of our long-term growth.

So without further ado, I'll turn it over to Rohit Kapoor, our CEO. Rohit?

Thanks, Andrew, and good morning, everyone. Good morning. Thank you so much for joining us today this morning. We really appreciate it. My god, this room is full. It's an exciting time for all of us and we'd love to be able to share with you what we are seeing in the marketplace, how we are thinking about playing the game and what it means for us, our clients, our shareholders and our employees. But first, our ambition is to be the strategic trusted partner for our enterprise clients helping them adopt and implement AI in their businesses. That's our simple goal.

Our promise to our shareholders is that we'd like to be able to deliver sustained market-leading growth of revenue and profit. We are in a fortunate position that we've been able to accomplish both of these objectives. And a large part of this might be a bit of luck. But a little part of this is some of the strategy and some of the thinking, and some of the steps that we've taken to position ourselves to be in that kind of a place. So I'd love to be able to share how we are thinking about our business and driving it.

There are 3 key messages that I'd like to focus on today. Number one, what is the AI opportunity that we see with our clients and how do we see this evolving? Two, what is it that it takes to be able to be successful in this environment and with all the change that's taking place. And finally, I'd like to close by how is EXL specifically positioned in the marketplace to address this opportunity with this capability set and take this forward.

Now, the one thing, which AI has done is it's created a lot of excitement, but it's also created a lot of uncertainty, because every single day or every single minute, there is a new headline news that comes up. And it basically is something which all of us read simultaneously. The natural human instinct is to think about that headline and extrapolate that headline indefinitely and make the assumption that everything will work seamlessly from there on.

The reality is that AI is a very, very powerful change that is coming about. The functionality is very real the new capabilities that are being added on are really impactful. But at the same time, in order to be able to get to the real business outcome you need execution, and that is complex. It is difficult, and that is something which only a trusted partner can help solve for an enterprise client.

The other thing we hear about a lot is, let's throw a lot of compute at the problem. And if you throw a lot of compute then by brute force one will be able to solve no matter what the problem is. All the other aspect is, let's take the foundational models, and there will be foundational models that will be able to do everything for every industry, every client, every use case, and it can be applied across the board. And therefore, the power of the model and the power of compute will solve the problem. Our viewpoint is you need that compute capacity, you need the power of the model. But unless and until you apply the knowledge and mastery on data and you bring together the data and make it ready for AI, unless and until you take all of the knowledge about the business and apply the business context in the modeling of the data and the AI, and unless and until you use your expertise to fine-tune the AI and you deliver trusted execution, you cannot get to the outcome.

There is talk about everything is going to get disrupted by new native AI entrants, and that's happening. And the startup ecosystem is absolutely galvanized. There are a number of start-ups that are now approaching the problem of how can you leverage AI in the workplace and be able to make a difference in terms of business operations. I think what the new entrants bring is a lot of creativity. They bring a lot of energy and they act as a catalyst. And I think that's very, very real. And the change that they're making is very, very profound. But unless and until you can take that creativity, and actually mitigate the risk associated, particularly with a regulated enterprise and be able to do both, which is to take the creativity of the new technology, take an existing business model and disrupt it, and be able to manage the risk and mitigate that risk, you cannot really get to the outcome.

And our viewpoint is, for the enterprise, risk mitigation is not an option. It is not that I'll try things out. And if things break so be it, it is about how do you move down that pathway and that journey in a manner that mitigates the risk simultaneously while delivering the profit and the benefit.

And finally, this is my favorite, which is autonomous AI will result in complete elimination of roles and jobs, and therefore, there will be no work left to be done by humans. Well, we all know that there are two sides to this. There is an elimination part of it, there's an augmentation part of it. Our viewpoint is that, yes, a number of jobs will get eliminated and that will result in compression. But AI fundamentally is going to be augmenting humans, and therefore, what we will see is actually a much bigger TAM, a much bigger marketplace for us to be able to play in. And the relevance of human intervention is going to get elevated towards more complex tasks, higher value added work and more judgmental capability.

The other thing to keep in mind is the body of work in the first case -- in the hypothesis that autonomous AI will eliminate all roles makes a very simple assumption, and that is, that the body of work is a fixed quantity and a fixed amount of work. That is absolutely incorrect, because as pricing drops and as the intervention becomes a lot more capable, the body of work actually expands significantly and moves towards higher value and higher complexity. And that's where we would hope to play it.

So I share these 4 signals with you because at EXL, again, if we have to drive sustained long-term growth. For us, it's really, really critical to understand what is a fad, what is noise. How do we distill that noise and come up with a signal and a direction in which we want to take the company and what is a secular trend that is taking place. Our goal is to position ourselves to be in the right place for that secular trend and to be able to read the signal correctly. If we can do that, we will be successful for our clients and our shareholders simultaneously.

So at the end of the day, our conclusion really is AI is an extremely powerful secular change that's taking place. It's going to play out over the next several years. This is the most important secular trend that's taking place at this point of time. But at the same time, the only thing the client is concerned about is being delivered value and being delivered a business outcome. And the only way to deliver that is through trusted execution. That's what EXL stands for today, and that's what we hope to be able to leverage.

So let me talk a little bit now on how we intend to deliver the value to our clients, and provide you with a little bit of deeper understanding of our approach towards delivering value to our clients.

First, in our viewpoint, there is no application of AI without transformation that can result in extreme value being delivered to the client. If you simply try and do a plug-and-play of AI, actually, the value is very, very limited, even if you get it right. You have to apply AI and transform at the same time to be able to deliver outsized value.

Second, in the past, if you think about outsourcing, that used to be done on the basis of a task. Task and parts of a process would get outsourced. If you think about the application of technology, technology was applied towards elimination of friction points. So in both the situations, it was a part of the process that was being impacted upon, whether by humans or by technology. In the world of AI, well, the first thing you've got to start with is you've got to have your data foundation correct. That's something which most, if not all organizations do not have in place. We think that, that's going to be a huge activity that needs to be undertaken just getting the data organized so that AI can be applied is a critical first step that every organization has to undertake.

So for us, what that really means is helping our clients manage both their structured data, their unstructured data, bringing it together from disparate systems, being able to work on any data that's existing in the enterprise and being ready for AI.

Second part of it is the algorithm and the AI and the model. Again, the first time that you apply AI, the accuracy level is actually very low. In our experience set, the accuracy level of a first-time application of the latest AI model is 60% to 65% accuracy. You just cannot operate at that level. We need to be able to elevate that accuracy level and take it into the mid-90s at a minimum for it to be effective. That requires fine-tuning that requires iteration. And by the way, that AI model needs to be continuously monitored. It needs to be continuously supervised because otherwise, it results in a drift taking place. Otherwise, you end up going outside of the guardrails and your governance, your security, your privacy, the risk associated with the accuracy of that model continues to fade away. So that's a skill set and a mastery of how do you really leverage AI. That's really, really critical.

And we are fortunate that we invested in analytics way back in 2006, and it's given us this tremendous capability of being able to be masters and be experts in AI. And finally, none of this works. You can be an expert on data, you can be an expert on AI. If you do not have the contextual knowledge to apply to both to data and to AI, this doesn't work. And what that means is years and years of investing in the knowledge about our clients' business, knowing about their operating processes and their businesses, and that becomes critical.

We believe that with AI, for the first time, the change has to be driven from the business side. It is not a technology-driven change alone. It is a business-driven change that uses technology that uses data and AI and brings in the context and that's what creates the magic. And finally, if you are going to transform the entire journey, you need to have ownership of the full stack. So you need to have capability of being able to impact data, AI, have the knowledge of the context and be able to transform that entire journey. So what's happening with this is clients are now engaging with EXL, not for a task, not for a friction point, not for a piece of process, but they're saying take the entire end-to-end journey and transform it, change it and provide me with the business outcome. That results in a significant expansion of TAM, a significant expansion of value, and that's where we are playing.

So we've been presenting to you for the last 3 years. Every single time we've come to you, the TAM has increased. And the pace at which the TAM is increasing is actually really much, much faster than what we were seeing previously. There are 3 fundamental reasons why we believe that the TAM is increasing.

Number one, our clients are spending a lot more on data and AI. I think there's a report by McKinsey, which talks about the spend on data and AI as a proportion of the spend on IT has gone up from 4% to 16% in 2 years. So it's a massive change that's taking place, and it will continue to increase. So that spend is increasing rapidly and that's the space in which we play in. When we talk about AI services, AI solutions on data, on AI platforms, that's the space that we play in.

Second, there are a number of adjacent capabilities that are kind of coming in together. And just like I spoke to you previously, managing the full stack means that we are actually addressing a much larger business opportunity. So that's expanding it. And for us, in the past, we would deal with the Chief Operating Officer, and we would deal with the business processes. Today, we deal with the Chief Operating Officer, but we also deal with the Chief Information Officer, the Chief Data Officer, the Chief AI Officer, and by the way, the Chief Risk Officer, the Chief Marketing Officer and the Chief Executive Officer. So our buying centers and our relationships have expanded very, very meaningfully.

And the third piece is, it's no longer just the large clients that are adopting AI. Today, if you're a mid-sized client, this is the best opportunity for you to be able to leverage AI and play in a democratized playing field where you can compete against the larger players. And the midsized players in the past didn't have adequate size and scale to outsource or to work with partners.

Today, actually, that's become even more relevant. If you are a startup, you again need to work with partners because as you scale up your business, you need all of that support and all of that change management that needs to be undertaken alongside with you. So what we are seeing is that the customer set is expanding very, very significantly and meaningfully for EXL. And we're going to talk to you about a few examples a little bit later.

So why is it that EXL is one of the few players that's been able to demonstrate this sustained market-leading growth for several years now. Our viewpoint is we've delivered and proven the performance. And the one goal that we've always kept for ourselves is we are going to grow our profit slightly faster than our revenue, and we're going to grow our revenues at a market-leading pace. We've delivered that for the last 5 years, and we've also delivered that in the first quarter of 2026.

Our portfolio today is in the goldilocks zone. Having this mix of 60% of our portfolio being in exclusively on data, analytics and AI allows us to be able to position ourselves for high-growth, high-value pools, and that business for us is growing very rapidly. And at the same time, the digital operations part of our business is 40%, and that allows us to learn, allows us to develop context and allows us to be able to invest on the data, analytics and AI side. And Vikas will talk a little bit more about our portfolio as to how this is resonating in terms of actual operations with our business.

I don't know if you realize this or not, because for us, we've been investing in our own intellectual property and proprietary assets of our own. And today, 25% of our revenue is based on EXL proprietary assets. So just think about it. When clients engage with us today, 1/4 of that business is being done on EXL proprietary systems, EXL proprietary technology and assets. This creates stickiness, it creates higher value. It creates an ability to expand margins, and it allows us to be able to build and grow our business, at the same time, as our ability to make change and make transformation.

And finally, we have a delightful set of clients. Our clients have an extremely high NPS score, and that's very, very fundamental and foundational. Over the last 27 years, one of the things that we focused on, is we wanted to be a highly customer-centric organization. And our viewpoint was, if we can make our clients succeed in the marketplace, we will automatically succeed. And that's that extremely loyal, strong franchise that we have in place. It's in highly regulated industries, which is complex. So we like the portfolio of clients that we've got, and there's a tremendous room for expansion for us.

We made a number of investments, and we are kind of increasing our investments. So we've taken up our investments by almost 4x. We've got a number of patents. We've got a number of technologies that we own. So we will continue to innovate in that. Some of the acquisitions that we have done in the past have been highly strategic. We still keep talking about the acquisition that we made in 2006, because in 2006, nobody was thinking about data analytics and we acquired Inductis and it's become the foundational capability of our data analytics and AI business today. We've invested deliberately in data management. We did 3 acquisitions in data management. And we think data management is going to be huge. We are literally just scratching the surface of the work that we are doing on data management.

And then our payment integrity business was another business that we invested in, which kind of brought everything together and that's been growing really rapidly. So some of these acquisitions that we have done are highly strategic, and they are really creating the right kind of capability set for us to drive our business going forward. And I will say this that we will continue to do strategic acquisitions as we go forward and continue to build up capability.

Finally, AI actually becomes even more relevant and talent is a critical ingredient for ensuring that. And we have always invested in talent and we continue to invest in our talent, and that's very, very important for us. We're making a slight change to the way we think about talent at the front end. We now think about this as being human on the loop as opposed to human in the loop. And for us, the difference is the human is providing judgment. The human is eliminating risk. The human is ensuring that everything is going as it should go, and there is adult human supervision in every single journey that we undertake. So that's become really important.

The second hypothesis that we have is enterprises are going to move towards creating large language models for themselves. And therefore, there is going to be this need for having RLHF, where we will train these models based on human learning and be able to do red teaming exercises and improve the models, do model evaluation, be able to put in context into the models, train the models, and therefore, there's going to be a whole body of work that needs to be undertaken that applies that human knowledge and applies it to an enterprise model. So that reinforcement learning through human feedback is going to become extremely important.

Third, you all saw OpenAI's announcement the day before yesterday, right, OpenAI deployment. So you're going to have forward deployed engineers. Even OpenAI is doing that. Anthropic is doing that. Everybody is doing that, which means even if you've got great technology, you need somebody to go in and help implement that. And that's something which we are investing on our own and building up that capability. And finally, I just want to clarify AI engineers versus software coders or software development talent is very different. Software development and coding can be done by AI in a very, very efficient manner. AI engineering and orchestration is not possible to be done by AI, at least not today. And we are investing very significantly on that engineering talent to be able to stitch all of these capabilities together and deliver the outcome to our clients.

Finally, in terms of our ecosystem, we've got a great partnership ecosystem that's been established 2 years ago, this was very limited. But today, these are very deep and strategic partnerships that we've created. I'll just highlight one of them, which is with NVIDIA, the world's most valuable company. The world's most valuable company, this year in March steps up and calls EXL, the advanced technology partner of the year. We are really, really proud of that recognition. And NVIDIA, by the way, sees everybody, sees all the players out there, and they yet they choose EXL as the advanced technology partner of the year. That's only because they see how we understand the business context, how we understand and apply data and AI into the business, and we deliver real outcomes to our clients.

Nothing happens by chance. I think you have to be very deliberate about how you build an organization. For us, creating EXL over the last 27 years, has been a very fulfilling journey because we've always believed that if we have the right leadership talent, what that will do for us is it will create the right culture in the organization, and it will allow us to handle the most change most volatility, most uncertainty in a very certain way. So I am really, really proud of this team because this team is experienced. This team collaborates. This team works together. And what we've got is such a high talent density that we can undertake any obstacle that comes our way, which is not known to us even today. So I think we are really well positioned for the future with this team.

I would like to call out, we've just made one addition to the team just this week, and that's Bhupender Singh, who's joined us as the President and Head of International Growth Markets. It should just show you, number one, that we continue to add to our talent density and bring on strong leaders in the space and be able to kind of execute and drive our business forward. But it also should indicate to you that our emphasis on the international growth markets is a key priority for us. We've taken that business up quite significantly over the last few years, but our ambition is to continue to drive that much faster.

So with that, our blueprint. I'd just like to summarize, is very simple. Number one, we believe data, context and AI is what allows us to create enormous value for our clients. What is needed is trusted execution and EXL has been able to demonstrate that repeatedly with its customer base. What that trusted execution results in is a very high level of business outcome on a sustained basis for our clients. And if we deliver that outcome to our clients, I think we will continue to have durable growth and profitability. So as long as we focus in on these basics, we'll be all good.

So thank you very much for my session. I'm going to pass it on to Vikas, and he's going to take you into more detail on how we apply data context and AI in our business. And how we apply execution in our business. Thank you.

Thank you, Rohit, and good morning, everyone. It's wonderful to be here today. So the 4 key messages that I will be focusing on over the next 20-odd minutes. Number one, a little bit more detail of what Rohit spoke about. That true value for AI and the enterprise comes from data context and AI with trusted execution. And how EXL brings those components very nicely together for the enterprise. The second is that to be able to accelerate these for the enterprise, we have created intellectual property and agentic platforms so that we can do this thing at speed and scale. We'll also talk about our two businesses, which is data and AI and operations and how they are now working in a very symbiotic way, from a viewpoint of data context in AI and how that will allow both the businesses to grow nicely together. And then finally, we're going to talk about the 4 key demand vectors that we're actually seeing from our clients.

But before we get to a little bit more detail on data context and AI, let's take a step back, I look at the evolution of AI over the last 10-odd quarters. So quarter 4 2022, that is the time that generative AI was unleaded. And then we have seen progression of that over the last 2.5 years. So initially, it was the first time that we could actually have AI, which apart from just analyzing could synthesize. So could actually talk to us, create summaries, create content, create output, which was human-like, which is very exciting. And then progressively, we learned as to how to make that more intelligent. Sometimes, we're infusing that intelligence in the model, but many times by creating systems and structures around the model, for example, rag model so that they get -- you can become more intelligent.

We also started moving into coding, and we found that the ability for AI to write and to edit code actually is pretty good. And I'm sure that you know that's one of the most talked about and relevant use cases. And finally, late last year and early this year, we started thinking about taking AI from just recent making to action, and there was this thing about bringing that to the desktop. However, when you look at the enterprises, over the last same time period, their journey has been a little bit different.

So when the excitement started with AI that could actually articulate and create content, there was a lot of excitement and everyone started jumping into use cases. And we found multiple use cases coming up. Everyone was experimenting a lot of lab work was happening. But we did not see almost any production-grade scaled up use case. So lots of experimentation, but no real production. Then the enterprises realize that one of the things that can be done is to take these and give it to the colleagues to the employees and tell them improve personal productivity. So desktop applications started coming in, summarize e-mails, help draft e-mails, organized calendar that phase went through.

And that is because every time you were taking AI to try and change a core workflow, every discussion on AI was very soon becoming a discussion on data, because everyone realized that data is not in a place. It is not fit enough to be used for AI. So we saw this period where most enterprises started working extensively in modernizing their data stacks. And making sure the data has more meaning, which is very important for AI to be effective. And it is only very recently that we have actually started seeing that organizations now are beginning to ask the question that we need to move from experimentation to production, which is in select but in core business operations, how do we infuse AI but infuse AI at production grade and at scale. So clearly, that shift is happening.

But there are challenges, significant challenges. This is not a drop in technology. You can't actually take an AM model and say, let's just drop this into an operation and it's going to become AI. What are the enterprises looking for? What are our clients looking for? The first thing they look for is, is it creating customer and business impact. Now the customer impact metrics have not changed. They're still the same. Customer satisfaction, NPS, time-to-market, responsiveness and so on and so forth. And the P&L metrics are very clear. Using these can I actually make better market impact? Can I grow faster? Can I make more money? So these questions still remain, because no point just having a fancy tool unless it can create a real customer impact and the business impact.

The second thing that's happening is that because we have seen a lot of experimentation happening. It is time that we start seeing some scale up. We start seeing scale up and not everywhere, but select but core business operations. So for if a health care payer, a core business operation is claims, it is member services. Can AI create that impact there, but clear that impact at scale. And finally, trust. So one of the things we have seen is that the decent making is moving from deterministic to probabilistic, which means that there's not a formula which is driving the can-making anymore. So ability to create audit trails and make sure that you can give evidence of why the same was taken, the way it was taken becomes very important.

And so to be able to do that, what we are finding is that 3 important things need to happen. First, data needs to have meaning and needs to have access. Second, context needs to come in so that it is relevant. And third, we need to scale up with speed. So 3 critical things that need to happen. So let me just talk about how these things are brought by EXL and how we are able to create this value for AI in the enterprise.

The first element is data. Now there are 3 important things with respect to data. The first is you need speed to access. And second, you need to have the confidence that it has the ability to manage multiple kinds of data. So data today is not only structured data, destructured data and unstructure data, internal, external industry, multimodal data and multimodal data sometimes with high velocity, video feeds come in. So when you manage all of these things, the old infrastructure, the old archaic infrastructure is no longer good enough. So organizations are working towards modernizing their data stacks. And Rohit spoke about the data management capabilities that we have built up over the last many years, including the work we do in analytics and the data management assets that we acquired.

We are doing extensive work with our clients to help modernize their data structure. So for example, if a large insurer today has ambition of actually converting their claims and underwriting to agentic. We have a massive engagement going on with them, just to fix their data and take that to a modern stack.

The second element about data is data has to have meaning so that AI can be effective. Now for data to have, meaning there are two connections that need to be made. And it's really important that those connections are made. So first is that you need to have the lineage, which is understand how the data is flowing in the organization. Where is it starting from and where is it ending and what journey is it taking? So that connection is important. The second connection which is important is that data element has to be related to other data elements. So for example, if there is a claim, the claim needs to be connected to a medical record, a customer, customer profile, a contract, a policy document. So that graph, which is called a knowledge graph is extremely important. So this lineage and knowledge graph is what gives meaning to the data. So when AI is deployed, it can be effective.

And the last one is that data has to have trust, because if these are going to be made based on purely data, then you've got to make sure that it's trustworthy so governance and quality, which is not, by the way, a onetime activity. It's an ongoing activity is important.

So EXL through its rich understanding of these domains is able to bring this context. We understand insurance. We understand health care. We understand banking. So we bring this context and we bring the trust, and we have created a platform that Andy is actually going to be demonstrating of how we're bringing all of these things together for our client.

The second one is context. And sometimes this question is asked, oh, is context what was being called domain earlier? Yes, but context has more elements to it. So the first element context has is the industry domain. It's about customer segments, it's about markets, it's about products, it's about dynamics, it's about regulation. Everything which is around an industry, is context. But what is also context is how an enterprise works, which is very, very specific to that enterprise, their workflow, their systems, the way that they're organized, their policies, their procedures, their customer preferences. Their strategies, all of that is also context.

And if you think about EXL's ability to bring context both at an industry level because of the sheer focus we've had over the last many years as well as at an entry level. So a deep long relationships with these clients, allows us to have an understanding. I mean just to give you an example, for some of our clients that they have been to technology changes over the last 10 years, we as an organization are the ones who have been through change management. So our understanding of their workflows and policies is far richer than even what they have internally. So our ability to bring industry context and client-specific context is very, very high.

And finally, to be able to give speed to it, you've got to bring some accelerators. Now these could be spot accelerators like extraction engines or customer assist stations, but they're also deeper and broader like fine-tuned LLM like genetic platforms. So this combination of ability to modernize data to bring meaning to data, to bring trust to data, to bring the context, which is at an industry level as is a client-specific level. And then to bring accelerators is what is allowing EXL to be able to create this value for the enterprise in the field of AI.

But nothing gets done. If you do not have the right talent. Now in the 65,000-odd colleagues we have at EXL, we have over 17,000 who are data scientists, data architects, AI engineers, solution engineers, business architects. These are people who understand -- and by the way, they've been working on these domains -- these industry domains for a very long time. So their ability to bring all of these things together at speed, to drive a trustworthy execution is extremely high. So one element of actually bringing success to the table is talent.

The other element is that are you all the time bringing a pure services model or are you building some IP and platforms to make it more successful do it with speed. And that is where we have invested in creating agentic platforms, which allows us to work on AI in the enterprise at speed. The 3 platforms and my colleague, Andy, is going to be walking through a case example for each of these 3 platforms for you to get a little bit better understanding of how this actually works.

The first one is all around data. So the mechanism of modernizing, creating meaning of the data, the lineage, the knowledge graphs, the trust. We have created an agentic platform so we can do this thing at speed at scale. So now all the data management work we are doing for our clients is all happening on this platform. And it gets us a significant advantage of creating this value for clients at a rapid pace.

The second is around is decisions. There are certain decisions that you cannot leave to AI or at least not right now and not in the foreseeable future. Now these are critical business decisions. I mean let give you an example. If a critical claim is declined by the use of AI, we may not be ready for that today. We may not be ready for that tomorrow. So you still need to bring in models that you actually created using data and analytics, but to be able to deploy those models we've created an agentic platform. So we have agentic platform, which now allows us to create decisions models and embed them as part of the processes very, very quickly.

And finally, EXLdata.ai. AI to rapidly deploy agenetic platforms. Now it is one thing to create a technology platform because you could argue that if you bring significant technology capability with the ability of actually working with agentic systems, you could do that. But what you'll find here is that this also comes with 27 years of deep domain expertise in these industries, 20 years of data analytics and data management capabilities, and 3 to 4 years of core investments in AI. So as a result of which these platforms come with prebuilt agents on the domain, they come with ontologies for the domain. They can bring in context crafts. So for example, today, in a claims cycle, we already have prebuilt context crafts, which are available which can be deployed very, very quickly. So in a sense, the platform is a combination of our domain expertise, our data expertise, as well as our ability to create agentic platform. So you'll find this demo very interesting that we will do in a few minutes.

Now let's just change the subject and talk a little bit about how the two businesses, which is operations and data and AI working symbiotically. For that, I'm going to use an example, and yes, let's use claims because I think it's a very effective example to explain that. So on the operations side, we work on claims processes for clients. So we do claims lodgment. We do claims processing, we do exception management, we do claims customer service. We also do post claims works like reserving. We do subrogation work. So we basically manage the complete claims operations. And then on the data analytics and AI side, we do customer segmentation work, customer value work, retention work, fraud models, catastrophe models. And we actually help clients in working on their data modernization, data semantics, all of that work actually happens there. Now we have discussed context is important. But remember, context cannot come only from 1 of these 2 elements. It has to come from both because they are a bit different.

So from the data side, the context which comes in is insights, the context which comes is customer segments. The context, which comes in is the response to certain variables and how that entire thing works from a claims perspective. So all of those insights and intelligence actually comes from data and AI, which feeds our operations and makes that smarter. But then there's also a context which goes from operations to data and AI. And that context is around ontologies, semantics, understanding of customer personas, the way that the workflow is working, the way that the controls work, the guardrails, the regulation, all of that context exactly flows from operations.

So if you think about context, these 2 businesses actually are working beautifully together. They're feeding each other. And what we are finding is that this engine is working very nicely for us. Our ability to be effective in data and AI is being helped by operations, and our ability in operations is being helped by data and AI. I gave you a claims example, but the same example is relevant because remember, we do data science, data analytics, AI work for clients and for the same clients who have run their operations, and it's basically a very symbiotic relationship.

As a result of which, we are finding that both our businesses are growing, and I'm sure you have tracked results for the last few quarters, including the most recent quarter that we actually announced, the data and AI business is growing. We spoke about the reasons expanding TAM. We bring a differentiated value proposition of data context in AI. We have the talent pool, which is large, diverse and expanding and we have the platforms to bring speed and scale.

But if you look at the operations business, our viewpoint is that there is significant headroom for growth based on this differentiated value proposition. First, the penetration levels are still low. I mean different numbers float around. But in my assessment and happy to have this conversation offline, I think it's about 20% to 25% right now, max, that's the level of penetration, which means that's the level of outsourcing, which has been done. So significant headroom for growth.

Number two, most of the work we do is in complex, regulated and very, very sensitive operations, which are deep in the domain. And you need expertise to manage that. And more importantly, if you have to transform them, you need this expertise to be able to transform. So EXL working on those operations make sense for the clients. Third, both large enterprises and medium-sized enterprises actually are finding that it makes sense to outsource. Large enterprises traditionally have a more mature outsourcing engagement. But there are certain parts that were never outsourcing because they thought that they were too close to them. There was some uniqueness or there were some complexity. But now that they know that outsourcing to a company like EXL would automatically help infuse AI, they're much more open to these functions being outsourced.

And then medium-sized enterprises who are not necessarily the most mature outsource earlier are realizing if they have to basically be competitive in the world of AI, they may not have the ability to effectively infusing their business with AI. So working with a partner like EXL and having them run these operations, actually, which help them with this journey. So we are finding increased demand coming from both large enterprises as well as small and medium enterprises. And our viewpoint is that this will help in a symbiotic relationship that I just spoke about on the previous slide for us to continue to grow.

My final slide in terms of the demand vectors. Now there are 4 kinds of demands that we really seeing from clients and each of them are on increasing. The first one is data for AI, which is, like I said, every conversation on AI becomes a conversation on data very quickly. There are 2 kinds of demand we see. One is a use case-driven demand. That is, if I'm ably working on my underwriting system and I want to make it agentic, I need to fix everything in the data around it, be it modernization, be it semantics, be it trust. So EXL, can you please work with us and help us do that. And I think Vivek is going to be talking about one use case as an example of that.

The second demand we're seeing is at a foundational level. This is where enterprises are saying, okay, not for a specific use case, but we want to totally modernize the stack at an enterprise level? So EXL can actually work with you and can you help do us that? So 2 kinds of demands, use case drive demand and foundational demand. The second is AI services. Now AI services is from helping clients understand this very complicated provider landscape in terms of different technologies and tools, but helping them from there to deploy a genetic in their workflows and deploy that bringing in the components of data context and AI. Then there is demand, which is continuing saying, can you run my operations, we just spoke about the operations business. Can you run my operations, but can you run it AI first.

Now this has been on for the last 3 years, but it is changing a little bit. Earlier, it was AI-enabled, which is run by operation, but then you infuse some AI in it. That infusion is put an extraction engine here, let's put a customer agent assist there. So it was, to some extent, a bit tactical and transactional, but infusion of AI. But from AI-enabled, it is now becoming AI-led, which means can you please work on creating an genetic workflow, but then there has to be a human on the loop. And human on the loop means that somebody has to still manage exceptions monitor model drift, do reinforcement learning. So there always will be a human workforce, which will be working with that, but it has to be agentic-led.

And finally, these solutions, integrated solutions One of the examples we talk about a lot is Payment Integrity business. This is where we bring all the components. We bring in the data, the AI, the technology platform. And because we own the entire stack. Most of these businesses are typically on an outcome-based pricing, which means there is value to be created for the client, and there's value to be captured for EXL. Rohit gave us a number of about 25%, which is on EXL IP. So this is broadly that segment, which is all of these solutions, which bring everything.

Another interesting way of looking at it is the first one is data. The second one is data plus AI. The third is data plus AI plus operations. And the fourth one is data plus AI plus operations plus proprietary technology. So what you'll find is that data and progressively AI is basically becoming present in everything that we are offering to our clients, and that is where the demand is coming.

So Vivek is going to be talking about these 4 demand vectors with some specific client case studies in a bit. And I think you'll find it very interesting in terms of what we are seeing.

With that, I'm going to give it to Andy, and Andy is going to talk about how our data and architecture is helping this accelerate for our clients, and that platform that I spoke about how that platform of EXLerate, EXLdata and decision making actually is working with the use case. Thank you very much.

All right. Thank you, Vikas. Good morning. By the way, there's a side bet going on that I'll not finish on time. So please give me verbal notes, be attentive. If you're not, then I'll be making more stress in explaining that will take more time. Vikas talked about -- actually, let me just go back a second. Vikas talked about data context and AI and the trusted execution part. Rohit also talked about our patents and differentiation, both highlighted the platforms that we are building. What I'm going to do, firstly, maybe take an attempt as to why enterprise AI adoption is complex. Number two, most importantly, how are we making that easy and helping customers deliver value. And three, most importantly, just bring it to life with the demo and bring all these concepts together. So if some of these terminologies you didn't follow, hopefully, the demos will make it clear. So that's a simple plan.

Just one caveat. It's architecture conversation, as much as I try to make it nontechnical, I may not be very successful, so just bear with me, but that's the world we live in today. So you heard data context, trust, reinforcement learning, AI engineering, all of those also became priority for us for capability investments. And what we did is think about the enterprise value and one spectrum was how do we make production-grade enterprise way of delivering this value. And two, where are we differentiated that gives us a leg and perhaps customers can see that value more visibly and it cuts across different clients and different areas we deploy. So Rohit talked about patents. Let me just double-click a little bit on how these patents just come about.

Firstly, you may or may not know this, EXL has a large team of PhDs, researchers that actually come from Google, Stanford Labs and other reputable organizations plus with EXL engineers and domain experts. And this R&D group is essentially always looking out for 3 things, and I just want to contextualize this with an example.

First, when Claude Code came. A lot of people realize Claude Code is not just the Claude model. It's the Claude Code, it's the permissioning, it's the sandboxing, it's the tooling. And we realized what happened to code can actually happen to enterprise workflow. And that gave us the thinking to build agent harness for enterprise workflows. And that was one area of patent for us, and it's applying now everywhere where we deploy these.

Second thing, Vikas talked about knowledge graphs. Knowledge graphs, as Vikas articulated, is a living graph of these decisions, interconnected relationships and meanings. And what we realize is that organizations are using knowledge graph only for connecting the meaning, and just looking at how different data sets are connected. What people don't have is every time I decide where do I capture that? And if you don't, downstream agentic, an agent is not self-learning, that gave the room for our context graph patent. So we are always looking at the latest and greatest research. We're always looking at what's happening, and then we are applying it to enterprise context.

So I just wanted to double-click on the patterns a little bit. By the way, this is actually a very dangerous slide, and I'll tell you why. Most CEOs and boards go to conferences, and they look at a 5-layer cake or a 6-layer cake. They come back in the enterprise and they'll say, why can't I make and deploy AI real in my enterprise. All you need is infra, data, model applies some guardrails and governance, build agents and just there you go. That's what people go back with and then teams are struggling, oh my god, somebody wants something next week, and I can't deliver that.

Let me just take a few slides to explain what it really takes to make this real. And I'm just going to -- I don't know if it's a cake or a bakery. I'm just going to try and explain it to you why it gets complex, but how are we helping the clients. Firstly, let's double-click on the infra layer. EXL, as Rohit mentioned, has partnered with Google, NVIDIA, Microsoft, AWS, because clients are not yet ready to give their keys to the kingdom to one provider. They need flexibility.

Number two, even more importantly, think about sovereign needs, think about health care, HIPAA and HITRUST and think about GDPR when you think international. So it needs to be secure and flexible. So we partnered and made sure that this is dependable for clients to scale. I'm not going to spend too much time on it, Vikas talked about EXLdata.ai. There is one element that I'll highlight. He covered 125 agents that we are using for faster data processing, making it ready for identic workloads. By the way, per Gartner study, this is still 50%, 60% effort.

But let me give you another dimension. A newer problem. World's most data, 90% got created in the last 2 years. So many companies have data, they don't know what to do with data. And believe it or not, that's a 70% to 80% problem, and that's what explains the context part of it, which we will double-click in the demo, and Vikas talked about knowledge graph, I talked a bit about context graph.

Here is a third very important element for regulated industries, which doesn't get talked about, and that's the symbolic -- the neuro-symbolic AI. You will use neural network for things that neural networks put at. But in the enterprise, there are policies, there are rules, there are guidelines and that deterministic logic, you have HITRUST. It does not hallucinate, you will still depend on it. So by design, this is neuro-symbolic AI for us. And this is not just EXLdata.ai, our entire architecture follows that principle because it's very important for regulated industries.

Let me just next click at very quickly at the AI model layer. EXL today accesses 44, 45 models by the account yesterday, maybe 2 more got added this morning, which I don't know of. But we access 45 models, think about Grok, think about Nemotron family from NVIDIA, think about Anthropic, think about OpenAI. You name it. EXL has also created some specific domain LLMs, and a couple of them have got patented. Now one might ask why would you create domain when models keep getting created left, right and center, very simple reason. Sometimes model accuracy is not a factor of 89% versus 94%. Adjusters, underwriters will reject at 88%. And for higher accuracy, sometimes faster speed and better token economics, one other thing that I'm really obsessed with is token economics that may keep coming up because enterprise need to manage costs.

We, at times, have to create smaller models because we believe they have a purpose and they have a need. And Rohit articulated, we see that need will keep coming up. And by the way, it's 1/10 of the cost. I'm going to click next on the agent harness. By the way, we could just spend 1.5 hours on these tens of boxes. But if I were to just speak, let's say, one thing that just highlights how we've thought about these things. When open claw was launched, OpenClaw. Nobody will put this in the enterprise because of safety concerns and issue that OpenClaw brings, understandable. What people didn't see is OpenClaw is a marvel in engineering in the way it handles memory. It has 10 patents of memory, and we learn from it. The R&D team went to the all the papers, everything just took that code and just bifurcated it in any possible detail, and we apply those memory principles into our agent harness.

So agent harness for us is the agent orchestration in a persistent state, where it doesn't lose memory, why large complex workflows don't run for minutes or hours, they run for days and weeks. And in our memory principle, first, we make it accessible. You can audit it. It's not a black box. Many platforms have that problem. Secondly, you may or may not know this, AI has an amnesia problem. When the context gets full, it deletes, and we do context flush by design, we manage our long-term and short-term memory really well.

Thirdly, I don't know how to put it, but deduplication, which is we get super lean for token economics, how we don't want agents to learn what they've already learned once. So those are just 3 principles I'm talking. Each of these is like how engineering depth has gone into it to make sure agents can reason they can run they can execute, but they don't lose the state.

EXLdecision.ai. This is something that you would not typically see in a reference architecture, but there's a reason why we do this. Vikas alluded to our analytics practice, 18 years of building machine learning models, pricing, underwriting, reserving, actuarial, clinical adjudications. And these 800-plus algorithms that we've built over so many years that expertise, we actually applied agentics to accelerate the model building. So if you want to use a machine learning model, which may be the right thing for you because of the reasons I mentioned earlier, election control, you trust it, it's governed absolutely. So we've brought all of that into EXLdecision.ai. And this, believe it or not, sells as much has probabilistic models today because the value is still immense.

Governance and guardrails, I just want to mention one thing. The newer world has newer nuances. And one of the big differentiation that hopefully will come out later in the demo is how we are applying very domain-specific guardrails. There are guardrails for inputs how you manage outputs, which most technologies do. There are some very specific nuances which I'll bring up.

And last but not the least, this is where the magic happens. You bring everything together, you charge on outcomes and you take a portion of value. Subrogation, payment integrity, bank transaction fraud underwriting, payment or which is collections and Vivek will make some of this very real. Essentially, if you think about it, we've taken all experience, AI depth, data, and brought them in a manner where we can charge on outcomes to our customers in most cases as a percentage of value. So as AI scales, all the good work we do the benefit still sort of accrues to us.

Second thing, which gets forgotten, speed. In today's world, if there is one thing, every CEO has on their mind is speed, and this brings speed, which is something you can go 60% and rest of it perhaps tweaking is required. So these are -- we are looking at more and more inspiration of such ideas that we can create more agentic workflows. I'm doing good on time plan.

All right. I'm just going to tee up the demo, and then I'll show you what the demo does very quickly. We picked claim for a reason, as an example because one, everybody can relate to it. Number two, when you hear stories claims is often talked about as you can automate the workflows very, very fast, right? And the key point I just wanted to make here is that in this context, a demo that I'm going to show you, let's look at 3 or 4 problems.

Number one, 30-plus just systems. You look at third-party sources, unstructured data, medical records, police reports, all of that, this is a very, very complex data environment. Number two, fragmentation. Data is sitting in silos and in different places and they're not connected. The problem of that is sometimes you can miss an important context that can have an impact on your liability recovery or a fraud. Every claim has its own nuance. For auditors for -- your compliance teams, they need a decision trail of every decision you're making and why. And then as you all know, claims have regulations. There are workflows that don't run for a day, claims runs for weeks and months, right? So it's important in any agentic design, you don't lose the state. What did I escalate? When did I speak to Andy, what was the context, what was told to the customer. So it needs to be in a state where it can remember that context and not lose its state.

And then lastly, in all the claim design, you'll be looking at multi-agents and they have to interact with each other and at various places, you'll be doing human hand off. So I just want to articulate that you think of that simple layer of the cake and you think about what goes into it, this is sort of the reality. As Vikas mentioned, this is where I'm just going to bring the platforms together, and I'm just going to do a quick demo after this. What you'll see is EXLdata.ai will help create -- bring the data, establish the lineage and build a context foundation, decision.AI and between AI model layer, there is a routing logic that based on the context accuracy need cost will route the right context to the right model. That's one of the other differentiators we've created a routing logic because otherwise, you have 45 models, how do you choose what to do when.

Third, I talked about harness, which will come to life. Governance and guardrails and then EXLerate will bring this whole workflow together. With that, I'll just take a liberty to do the live demo, and I hope it works. And I leave my glasses too.

So this is -- actually, let's just start with data.ai. All sources, you can see how the pipelines came together, what am I ingesting, what am I parsing what transformations and what validations, right? So I'm not going to go through all the things I'll just highlight one important thing, which is our patent and differentiation. This is what Vikas was talking about bit lineage. You know why this is different? Most platforms -- by the way, this is compatible with data unity catalog. You can feed into patent or foundry, you can feed it to Snowflake, not an issue. You can open source it to Calibre, whatever you like, you can do it.

The beauty of this is, most companies will make this transparent to you in their platform. EXL will make the lineage transparent to regardless of the platform. We'll bring the entire meaning flow state of the data, two big advantages: one, the speed; two, 40% of data estate in the enterprise is unusable, but they still go ahead and migrate everything because they just don't know what to leave behind.

So that's just one thing I want to quickly highlight on lineage. Let's pick the one other thing. Actually, let's pick a use case so that you can see how this all works together. We talked about context. Think about ontologies as the relationships in the business context. This is where the first time data marries context. Think about knowledge graph, a living graph like because articulated of how everything is connected, a claims to a policy to a record to a call to a transaction and what that really means because this is all the facts that you will need where agent will start to make informed reasoning and not just look up data. That's the big thing about it. And this is one that I'm really, really proud of, the context graph, which is one of the latest ones.

What we've done is, in addition to knowledge graphs, we capture every decision trace. By the way, this was a research paper. Now there are companies that are formed on it, but this is in our stack for the last 3 months. And one of the most deployed things with the customer. I'm going to make this live. Let's just look at what happened here.

Let's take a case. So I went to EXLdecision.ai. Based on the client recommendation, the model routed and said, pay the claim, but do not send it for recovery because the model -- the threshold is 97%. The police record is 99% certain. You don't need a recovery, but the case is okay to be paid, right? But it fell in the queue of the agent. I'm just going to go to the context graph, and I'm going to ask why claim [ 1847 ] hours recommended for some probation and give me the evidence. Now look at the beauty of this.

This is natural language, and this is for geeks, whichever you like. So it got approved and look at the magic of the context, knowledge graph suggests there is a potential recovery based on the case laws and comparative negligence. The cloudy weather and low visibility incident suggest there's a potential for a liability split. There is a Texas guidelines and arbitration ruling, which otherwise we would have missed. Perhaps you can even pursue a split liability or you could go for the entire thing. So this just broad -- by the way, how did it -- how this all got applied?

Just very quickly, this is my agent studio. I looked at my subrogation agent. I looked at the guardrail. This is the guardrail where I have all the details of the domain. Remember, symbolic verification. I have Texas subrogation statute of limitations and all the other states. You may or may not know this, EXL handles 12% of subrogation of entire U.S. We collect $25 billion in recoveries for the companies. So the knowledge of subrogation married with AI. So essentially now I go to the trust and governance layer. I can see, yes, liability validated, subrogation done.

Let me review the case, human on the loop and not in the loop. Everything else is provided to you. I'm like Waymo. If car is now getting out of control, I just take the control of the car remotely. So I'm just looking at what was -- what went into in. Why did I do this and, okay, makes sense. Let's just approve the subrogation. So the beauty of ontology, knowledge graph and context graph is, ontology gives me the schema and the meaning and the domain blueprint, knowledge graph, then brings the relationship and living graph. And decision 3, real time applies the brain and the context for the decision. And then everything is traceable,we could spend hours on this demo looking at observability, token economics, we can do any kind of concession and ask questions and all of that is available in the system.

So that just gives you an example of what we've been able to build. And one homework for all of you, please find me a technology, and maybe I'm a little proud here, but please find me a technology that can do all of this in the way I just demonstrated. You'll probably find 8, 9 different things will have to come together for this to happen. It can happen. But many things will have to come together.

So why? So what, right. The important question here. You heard data context and AI. But what we've been doing is we are creating IP. We are charging on outcomes. And obviously, every time we are delivering trusted outcomes and trusted better outcomes to our customers. So this keeps improving. This flywheel, I learn every time. I trace every agent decision. The human pushes to the agent. No, don't give this work to me next time. This workshop on the loop means what can I give to the agent. In the loop means every time give it to me and I'll just keep looking at it, right? But here is a bigger story of this. If I can charge on value, think about repeatability, think about every time I'm able to take it to the customer, and think about every time at the speed of deployment, how I can scale and multiply this over many customers on all those chosen workflows, right? And I think that's what I just want to leave you with. Thank you.

Okay. Thanks, Andy. I hope you guys got a sense of why did we win that award from NVIDIA for technology partner of the unit. It has to be this complex, because if you don't make it this complex, you're not getting that award, right? But we're very, very proud of what we showed you just now. So for those of you who are getting restless, we are into the home stretch now. So it's going to go faster and then going to be no more architecture charge from this point forward. So it will speed up.

Look, my job is a relatively easy one. I just have to show you how it all comes together. And how we're supposed to make AR real. So I took a cop out. Instead of me talking to you about what I believe I'm going to show you client examples. I'm going to show you real life examples. No pilots, no proof of concept, real-life examples of clients that are using our AI and our data capabilities in production and that are driving phenomenal value through that. So real outcomes.

You're going to get a sense from this section about number one, why do our clients pick us? Why does EXL win? What is it that we deliver for them? And what is it that is going to create that sustainable value advantage for us? So a couple of key takeaways. Number one, I just want to reiterate, deeply embedded within our clients. Rohit talked about this. We work with regulated industries and have a phenomenal client base. It's probably our strongest asset.

What you've seen from the presentations that you heard from Vikas and from Andy, is we now have that full spectrum of capabilities across their entire data and AI needs, and we show you how that's translating into wins. And then I wanted to end with is basically the value equation for EXL, where we bring that together with the TAM, our capabilities, our IP all comes together to create sustainable value for us.

So let's start off with who do we work with. And this is a page that we really like to brag about because take a look at it. It's got 115 clients that are in the Fortune 2000. We've got 400-plus clients that we're doing deep work with on data and AI. And this wasn't just a cursory check the box. We actually went back and checked what's the scope of the work that we do and what's it delivering for the clients.

And the next part, our average client tenure is 10-plus years. And take a look at the quality of what we are doing for them. You've heard all of these reports about MIT talking about how many AI projects fail. Take a look at our score, 94% AI deployment success. So what does that translate into? It translates into phenomenal deep relationships with the biggest and the best companies.

Take a look at what we've got going on, 10 out of 10 on insurance, 8 out of 10 on banking, the top health care providers. This really becomes our reference client set. When these guys, the executives here move from one company to another, when they are reached out to by the peers to talk about EXL, you get that comes up. And this really becomes our strongest pull.

I mean the logos here are the best of the best, and these are built on years of relationships working with these clients and kind of building that reputation for EXL. So let's go a little bit deeper into, okay, where do we play and what is it that we do for these clients.

Now Rohit alluded to this as well. The work that we do across these industries is in highly complex workflows. These are not easy, and they are highly regulated. So you have to be very precise in terms of how do you deploy the AI and what is it that we are doing for them and the customer-centric it's really, really focused on what is it that they're doing with the customer. So with these needs, why is it that they pick EXL? Number one, it's the trusted execution. It's because what they understand that core to the EXL DNA is customer obsession. We are phenomenally customer-centric and that trusted relationship of saying these are the guys that are going to deliver for me is one of the first reasons that, that conversation opens up.

But once you get that opportunity, the execution of it really comes back to the ability to bring that data context and AI together. The context that we've built up over years of operation with these companies about understanding that ontology, understanding how is it that they work together, how the data pieces come together, how do judgments made what are the guardrails.

And finally, the last piece of it, the ability to bring EXL proprietary IP solutions, the ability to start bringing in our IP to say, here's an AI that I have built out. And here's something that can get deployed to drive that outcome. So the combination of those factors is why EXL gets picked. And it's something that we kind of keep building on, keep delivering and keep furnishing our reputation.

Now I'm going to go a little bit deeper into these examples, and I'm going to go back to the themes that Vikas talked to you about. So our themes right now, and this is what's really driving the really high levels of data and AI demand EXL.

Number one, we get called in when a customer says, I need to start bringing in AI solutions, my data is not ready, come make my data ready. It's probably the first gate check on saying, I need help, and I need someone to fix it for me. Two, is customers that are talking about saying, okay, I need help with both getting my data ready, but I also want to understand how do I reimagine my workflow and bring in some AI capabilities to make that workflow better.

The third archetype is one of our existing operations clients, who comes in and says, okay, I've been running this business with you EXL, how can we make it better together and how can we share in the benefits of it, right? And the fourth, is where it all comes together for us because here's where we've been able to take our data, the context, the AI, our technology and bring it all together into an industry solution that we run and we run on an outcome basis. So it's the ability to actually say, I'm going to be putting all of this together for you, Mr. and Ms. client, and here's what you're going to get as a benefit. It's the highest bar and it's one that we are increasingly moving towards. So I'll get you examples of each 1 of them, and they're going to start coming to life.

So let's start off with the first one, which is how do we help our customers get the data ready for AI. So the example here is a top 20 global insurer. Now these are guys that are in commercial insurance, and what they needed to do was to say, I need to move to a model where I'm taking my underwriting and my claims processes, and making them AI first, okay. Problem is easy. Lots of companies have taken a stab at it. But the challenge was that the data wasn't ready for it at all. The data was all over the globe in different places, different products, different lines of businesses. In fact, they couldn't even agree on what a definition of premium was. Premium was defined differently across all the instances.

So what we ended up doing for them is using our capabilities in EXLdata.ai to build out data pipelines for them. The ability to ingest this data from these various sources, the ability to parse it to validate it and then start feeding it into a new data lake that they were then going to use for driving their downstream work. Now the benefit for them is pretty clear. What we were able to do is massively reduce the amount of time and the effort that it took for them to build these data pipelines. And what ultimately that did for them is once they created the underwriting algorithm with that new data, they were able to massively reduce the cycle time for it.

The benefit for EXL is probably even bigger because what we were able to do was take a relationship with this client. That was 10 years old, but it was largely with the operations side and expanded to doing work for the CIO, CDO. We took a relationship that was old, that was mature and we more than tripled it because of the new work that we are now doing with the CIO side of the business. And when I talk to the CIO to say, why did you choose us? This answer was very simple. You guys were the best at bringing together the engineering skill set with the domain knowledge. You guys knew insurance and you guys knew the data engineering. And that's the value prop that really works for us.

Now that same value prop because of the work that we've done here that value prop is something that we are now taking to all of the other insurers. Be they commercial, be they personal lines, be they L&A. And that value prop is now really working in terms of just the momentum that we've been able to create in our data management business with this particular use case. So as you can see, it has like a long tail for us in terms of what we've been able to drive.

Let's go to the next case. This one is actually one that really excites us. Rohit talked to you about how is it that we think the midsized market is going to become an ideal use case for EXL's capabilities in terms of bringing together data and AI. And this is a really interesting case where a midsized client came to us. They were in an RFP mode. And they came to us and said, okay, what is it that you can help us with? Because we think our processes are fragmented. We think those processes, we need to basically take cost out of those processes.

Now in the old world, this would have gone down the RFP route. They would have basically said, okay, let's talk to different vendors. We are going to select a vendor, and we're going to give some of the outsourced work to a vendor. So it would be a small piece. We'd probably end up doing a little bit of work for them and claims, a little bit of work for them on underwriting, probably do it offshore.

What we ended up doing here though was something dramatically different, because what we did was said, why don't we just design how we are going to have an AI-first transformation for your work processes. And we won't just stop at designing the AI-first transformation map for you we'll go back to your data assets, and we'll tell you what you need to do with your data to make that data AI ready, and we'll put it all together for you and we'll design it.

As a consequence, what we'd be able to do is as for the client side, we've been able to create a massive bottom line impact for them. And this is work that's still undergoing, but a massive bottom line impact for them because we've taken the scope really high instead of being at a tiny piece that we were doing on outsourcing, which would have probably created a couple of million dollars for them. We've made it now something that's really material to the CEO of the business. And we've been able to improve their success rate in terms of grabbing new business as well. So this is a virtuous cycle for them.

The benefit for EXL is phenomenal, because what we've done is taken something that was going to be a small outsourcing piece and now it's become part of a multiyear program to transform the business for them and then to operate that transformed business, because we are going to be running the data ops going forward. We're going to be running the AI-first ops for them. And what it's given us is the playbook, because this playbook is now something that we're going to take to all of our midsized customers across businesses, across verticals. And that's the playbook that we are going to be able to say choose us because we can actually help you through our transform operate framework, we can help you really AI-enable your business, all aspects of it, and it's something that we can continue to run. So we are really excited about what this can mean in terms of the potential upside.

Let's keep going. I talked to you about a business where -- we were already doing operations work and how do we bring AI into that existing business. So one of the biggest things that everyone talks about is the cannibalization of revenue. It's what's going on with work that you're doing on the CX side, on call centers, does that all go away?

Well, first of all, I should point out and going back to the Goldilocks comment. EXL has a really small component of work that we do on CX. We've never been built as a CX company. But even with the tiny amount of work that we do on CX, we've actually managed to create a tailwind out of it, because what we've been doing is actually winning on CX modernization. And here's an example of how we made that happen.

So the example is for a large U.K. retailer, where we were doing some work with them and it was manual, it was mostly human-centric. And it was a simple conversation about saying what is it that you can do in terms of deploying AI to make -- to bring in the improvements. We deployed our proprietary smart agent assist for them. We were running it on our own platform. And what they decided was it was working so well for them in terms of the output because it was -- I'm sorry, I am skipping ahead, I think. It's one remote control with 2 buttons. You would think I would manage it. Yes, there we are success. So we -- what we were able to do with them is -- I don't think it's me. Thank you. Thanks for the assist.

What we were able to do by deploying our smart agent assist is dramatically increased agent productivity. What this does is actually gives every agent real-time nudges in terms of what should they be talking about to the customer next? What is it that they need to be able to do to resolve that query in the most efficient manner. Clearly, a big improvement. But what really was surprising to the client was they had multiple vendors at this point. They saw how our AI was performing. And they saw what we were able to deliver in terms of the productivity improvements. And EXL's AI then got adopted by our client across the board.

So now they -- our client has a end-to-end AI cycle that has EXL's AI in front, and that has a number of their -- all of their scope, internal, external, everything kind of following it. And here's the interesting part for us. So as you can imagine, when we took that human work, we embedded the AI, there was a little bit of revenue that dropped, because that work is now the number of hours that you're spending is fewer for the same number of calls.

But our revenue for this client actually went up 20%. And the reason it went up 20% is now we are responsible for a larger scope of work. Now we are responsible for more calls. And it's our AI that's actually embedded within everything that they're doing. So think about it. We delivered a revenue -- we delivered savings back to the client. But because of that scope expansion, we are up 20% and our margin has gone up because now we've taken away T&M work and replaced it with AI. It's a really powerful use case for us. I will now know what to do. There you go.

Let's move on to the last two examples. These are both examples where EXL owns the entire platform. These are run on EXL platform where we are providing a service to a client on an outcome-based model. So the way it works is all the data is -- comes to us -- we own the AI, we own the model improvements, we own the execution and most importantly, beyond the feedback loop, which allows a model to keep getting continuously better.

The first one I wanted to talk to you is about collections. Now collections is something our analytics business has done for years. We've solved every single collections problem that there is. We've done it for the large banks, the midsized, you name it. But the collections industry had a huge problem, which was nobody would pick up land lines anymore. So the way you have to do collections now is through text. The way you have to do collections is through basically reaching people out in different ways.

So what we did is build an end-to-end platform that married the analytics that married the know-how in terms of which risk tiers to contact, what should be the treatment for each risk deal. And we married that with the pipes, the digital pipes of saying, how is it that I'm going to reach out to someone via a text message? How do I make that equation work? And it was really, really powerful because it brought together 2 discrete elements within the industry and put it together into one platform.

And what that's done for our client is actually a phenomenal lift in terms of the customer contact rates, because customers are now getting the text on the phone, they're able to respond very clearly. And because we are still marrying it with all the analytics on who to contact, how to contact what kind of a structured plan to offer them. It's actually led to a massive reduction in the charge-offs. Now put this in context, a 20% charge-off reduction is huge when you think about the scale of some of these players. It's a huge benefit for them.

And as a consequence, we've been selected as the global collection transformation partner. We are now getting a huge amount of scope going through our system. But here's where the value really gets even better for EXL because our collections platform now has so much volume going through it, so much data. We've been able to now just keep improving the algorithm, keep improving the collections efficiency. And today, we are with 20-plus customers, 20-plus clients.

Let's play this forward. We know that there's -- the credit environment in the U.S. is going to get -- you're already seeing that on the private credit side. As that credit environment deteriorates, we expect to start seeing much more volume churn through our system, and we expect to kind of keep adding to the value that we are providing to our customers. So this is to us, it's really the start of something that is going to be a really, really strong integrated solution.

Now let me end with the last example, and I probably save the best for last, which is from EXL's payment integrity solution. Now as you know, with payment integrity, we are a market leader. We work with 4 out of the top 5 national payers in the U.S., and we're churning an enormous amount of claims volume through our payment integrity system. Last year, we did -- we identified $3.2 billion of claims for our clients. I'm going to talk to you about a use case, which is an example for one of our largest clients. Who actually came to us and said, look, EXL, we know you're doing very well with the collections. But what we want to do is try and reduce the amount of time and effort that we are putting on collections that are post pay. So post pay, just as a quick explanation is I've already paid the provider or the hospital. And now I identify something wrong with the claim, so I'm trying to reclaim some money back.

As you can imagine, a lot of friction in that conversation, nobody likes to give money back. And there's a lot of work involved in terms of the back and forth. So wouldn't it be nice if instead of paying them an incorrect amount. I found that error upfront and never paid them I didn't correct them up. That's what prepay is. And what they wanted us to do is to say, take all of your logic, bring it to before the provider gets paid. And fix the arrow before it happens. Easier said than done because if you just move it there, then what happens if you're not efficient enough at catching the errors.

That's where I think EXL really outperformed. So what we've been able to do is take our algorithms, modify them, bring them up into the prepay cycle and maintain that efficacy of the models. So what we -- we are -- in certain categories, we are performing about 50% better than our competition when it comes to the categories that we are looking at. And that outperformance married with the fact that now it's before the payment gets made has really been a huge unlock for our client.

So what we've been able to do is actually massively shift their efforts into the prepay side. which reduces the friction in the whole system. And take a look at the scale that we are running at right now. So that prepay program, the total program that we run for this client is delivering more than $600 million of annual sales. The benefit for EXL is really, really clear. We took that account, one of our largest accounts to begin with, and we've doubled it, more than doubled it over the last 3 years. And now the increased volumes that we are seeing both on the prepay side and the post pay side through our system, through our model has allowed us to actually keep strengthening the advantage that EXL has on PI.

So now when we go to a client and we talk to them or a prospect and we go talk to them, it's really, really easy for them to see the output that EXL brings to the table, the efficiency of the model, the effectiveness of the outcome and say, yes, straightforward. I'm going to move you to the top of the line, and I'm going to give you more market share. You're seeing some of that kind of flow through in our health care results as well. So it's really an instance of all of those components coming together and really delivering that outperformance.

So let me end with what does that mean for our overall value creation story. Our target addressable model -- market is expanding. We keep adding to new areas that we are reaching out into. We keep adding to new places where EXL previously wasn't a big player, but now is our demand vectors are extremely strong our clients keep bringing us in as their data and AI partner of choice. And we remain customer obsessed, as always, about those particular areas. But now we have the ability of actually capturing that with all the IP and the solutions that we've built. It's the stuff that Andy showed you in terms of the platforms that we've built out. It's the vertical AI solutions that we have within PI, within payment integrity. And the other tools that we have. And all of that really comes together to create the virtuous cycle for EXL, the sustainable value advantage.

The simplest way I think about it is this is why we've won in the past. This is why we had that massive delta against the industry peers. And this is why I believe we'll continue to win into the future.

So with that, I'm going to invite Maurizio because at the end of the day, you want to hear about how all of this translates into EXL's financials. So Maurizio, over to you.

All right. Thank you, Vivek, and good morning, everyone. Thank you for coming to Investor and Analyst Day today. It's really a pleasure to see everyone here. And you've heard a lot about our discussions around the market and also around our TAM that continues to build over time. You've heard a bit about our advanced capabilities and also how we differentiate in the market and also how we go to market now and really drive value for our clients. So I'm going to try to bring that all together into our financial model and also talk about our performance historical and then also our momentum going forward, which is really important.

So a few key messages that I'm going to focus on in my presentation. One is our industry-leading performance, and I'll show a bit of a comparison between us and our peers. I'll talk a bit about data and AI. It's our pivot to really drive our sustained growth quarter-over-quarter year after year. And then I'll also talk a bit about our strong balance sheet and also our capital allocation really going forward.

So first off, let's talk a bit about our market-leading growth. When you look at this chart, this is the last 9 quarters of our year-over-year quarterly growth versus our industry peers. And you see that we are growing each of these quarters, at least 2x versus our peers, right? In every quarter, including the most recent quarter, the first quarter, we grew at almost 14%. Our peers are right around 6% overall.

And so what's driving this at the end of the day? And I'm going to go back to what Vivek talked about in his slide on the right side, said why does EXL win at the end of the day. And it's really those 3 vectors that we talked about. One is data context and AI. We have been performing extremely well operationally over the years, and that sets us up extremely well to implement and adopt AI into our client workflows. Two, is our investment in AI IP solutions over the years in our targeted segments that we operate in, that really helps us continually win quarter after quarter.

And then lastly, which is really important, is our trusted client relationships with so many industry leaders that we have developed over the years. Our average client tenure is over 10 years, and Vivek just talked about that, right? When you have clients that are -- that you have for so many years, you developed deep trusted relationships and they trust you. And in doing so, we're able to implement AI to have much better and continue to grow the business overall.

So this market-leading growth and our growth rate just overall in terms of our top line growth, really helps us really drive the whole financial model overall. And if you look at the execution of our financial performance over the last 5 years, it's really driven right off that top line growth. We -- and Rohit talked about it, we want to drive EPS faster than revenues, right? And you see that year after year, including the first quarter where we grew EPS at 20% overall versus 14% revenue growth.

But even more importantly, when you dig into our financial model and you look at the different levers, you continue to see growth over that 5-year period. Our gross margin during that period, and I'm going to talk about that even later in terms of investments, grew 350 basis points during that 5-year period. Our adjusted operating margin similarly grew 360 basis points during that 5-year period, and our return on invested capital has gone up significantly over that 5-year period, well over 1,100 basis points between 2020 and 2025.

So what's driving our growth overall in our business over the last 5 years? If you look at our data and AI-led business without any revenue from AI-embedded operations, it grew 21% over that 5-year period overall. And more -- and even more importantly, our total operations business overall and includes data and AI-led operations and nondata and AI-led operations grew 14% during that period. And so you see a very healthy mix between both sides of our business. Now as we embed more data and AI into our operations business, you're seeing that total overall data and AI-led of our total business continue to climb.

That -- and when we look at total operations, overall, in 2025, almost 20% of our revenue in operations was data and AI-led overall. And that's almost from -- that's a line from almost 0 back in 2020. And so why is that really important on why we embed data and AI into our client operations? Because it becomes IP-led, it becomes extremely sticky with the client, right? Overall, and we end up owning much more of the overall workflow going forward, which is really important as it -- and on top of all that, Vivek pointed out in one of our examples, and that happens in many different examples, we end up with more revenue on top of that once we embed data and AI, which is really important at the end of the day. And I'll get into another metric later on that really highlights that.

But that's really important for us to really continue to build the business overall and become that much stickier with our clients. And you're seeing a transformation in our business, whereby so much more of our business overall is becoming more data and AI led. When you add the overall data and AI-led operational revenue on top of our data and AI-led revenue, you get to 55% of our business in 2025 was data and AI led overall versus 38% back in 2020. That just shows the transformation of our business over that 5-year period. And you'll continue to see that because the amount of investments, and I'll talk about that in a little bit, that we're putting to data and AI is significant now going forward, and it's going to continue to drive our overall business and become much more of a bigger part of our overall revenue base.

And you could see that increased data and AI-led penetration in Q1 overall this year. I just showed you in 2025, 55% of overall data and AI-led revenue was 55%. In the first quarter of '26, you saw an increase to 60% overall, which is really important. You're seeing that continually every quarter. And even more importantly, you're also seeing in the first quarter the health of the overall business. Not only did our total data and AI-led business without any AI embedded operations revenue increased 18%, but you see our total operations business grow 10% overall in the first quarter.

And you can continue to see overall, and Vikas talked about it a bit why we're going to -- we continue to see very good growth momentum overall in our total operations business. And that will continue going forward. We have a nice balance between the 2. And what we're -- and on top of that, we're seeing much more of our revenue become data and AI-led and that's really going to help us really transform the company in the years to come.

Now when we look at revenue by industry vertical over the last 5 years, you see us continually drive the overall business in each 1 of our segments. We've had very good growth in every 1 of our segments. We don't have a segment that is slow growth or hurting our overall revenue growth or top line growth overall. You see insurance growing 14% over the last 5 years. Health care driven by payment integrity in a few other areas at 18%, and then banking and capital markets and diversified growing 20% during that time period. So each 1 of our segments is growing extremely well in driving the overall top line growth. And then overall, you're seeing us continually get more diversified globally.

14% of our revenue in 2020 was driven by our International segment. It's up over 17% now in 2025. And we do see our international area to be -- that should be growing at or above the overall growth rate. Now as we've seen over the past, and that should be the case going forward also.

So let's talk a little bit about our overall business model. And this is a little bit of a reminder and also a new metric that a number of investors have asked us about. First off, over 3/4 of our revenue continues to be reoccurring. We have a very annuity-like revenue base. And when we talk about reoccurring, we talk about revenue that is contracted 1 year or more overall. And so we continue to see that. This has been our historical trend, and it continues to be our trend.

What we've also seen is our net revenue retention be greater than [ 1.1 ], particularly in the first quarter of this year and also in 2025, which means -- of our current recurring revenue, we continue to increase that overall on a quarter-to-quarter annual basis overall. And that's really important in this new AI era, where a lot of concerns are about cannibalization and revenue coming down from existing clients. That's not the case in our revenue base. We are embedding data and AI, but what we're seeing is getting a bigger moat within the client and also driving the overall total revenue for that client. And that's reflected in that metric. And it's one that we're very proud about. And one we'll keep display now going forward because it's an important one. And it's also been a concern for investors, that has been brought up a number of times.

I talked a little bit about our gross margin expansion over the last 5 years. And you can see it grew 350 basis points over the last 5 years. What that has done has given us the ability to increase our investments more than 4x in the last 5 years. AI needs investment. If we're going to build -- continually build AI solutions and continue to really work with our clients to embed that into their workflows, it needs investment, not only in the solutions, but also in R&D.

And the way to fund that investment is to drive gross margin, right? And we've talked about that a lot in our earnings call, whereby we are looking to drive gross margin, but we're also going to be spent on investments to drive AI solutioning and also in R&D. And that's what that $81 million now is really comprised of. It's us building AI solutions now going forward and also spending a considerable amount of money now on R&D to really find those solutions, work with clients, whether it's a POC or something that we are building ourselves to really drive the overall top line growth. And all of that is going to help us continue to drive that percentage of data and AI in our business in our total top line growth to 60% and beyond that.

Now let's talk a little bit about our capital allocation. And we really have been working on our capital allocation to really drive shareholder value. Our return on invested capital has gone up materially since 2020. And back in 2020, we were less than 9% in ROIC, and we're well above 20% in 2025. And actually, the first quarter was even higher than that. So what you're seeing is a meaningful increase in our overall return on invested capital. And how have we done this over the last 5 years. It's really driven by 2 things.

One, it's us increasing profitability overall over the last 5 years. You saw the increase in AOPM during the last 5 years and the increase in EPS. That's helped us drive ROIC and then also being disciplined with our overall balance sheet. We've been fairly prudent, I would say, with our balance sheet and our capital allocation over the last 5 years where we've done a number of tuck-in acquisitions or smaller acquisitions and also allocated a considerable amount of capital to share repurchase over the years. And that really has helped us drive our overall ROIC.

And then when you look at kind of going forward, the level of capital that we can allocate, you're seeing some very strong metrics. First off, our free cash flow now in 2025 was virtually $300 million during that period. And it's up 34% from the prior year period. So our business is generating a significant amount of free cash flow that gives us the ability to allocate and deploy going forward. What you'll also see in our balance sheet is a fairly modest overall leverage balance on our balance sheet.

Our leverage as of the end of the first quarter was less than 1x overall, which puts us in a great position now going forward to be able to allocate capital. And if we -- and if you think about a conservative 2x leverage for the overall business and you add this and you add our free cash flow that we generate on an annual basis, that gives us plenty of capital now to really allocate to that 2 big levers going forward. It's -- and those 2 levers are M&A overall and then also allocating capital stock buyback.

What you've seen in the last 3, 4 years is us being a bit more tiered toward allocating capital towards stock buyback, which has done well for us over the years. But now you'll probably see a little bit more of a balanced approach. We talked a bit about the capabilities that we need going forward to really drive top line growth, particularly in data and AI. And that will result in us allocating our capital allocation a bit more balanced between M&A and stock buyback going forward.

Now let's talk a little bit about our metrics really going forward. Now here is our 2026 guidance. So when we released our first quarter, we increased our guidance from what we started with at the beginning of the year. We started the year at 9% to 11% in terms of revenue growth for the year. And in the end of the first quarter, with our outperformance, we increased the 10% to 12%. What we do see going forward in our medium-term target. And when we talk about medium-term target, we're talking about 2026 and 2027. We continue to see double-digit year-over-year growth in our business. We have the momentum in our business today with all the investments that we made.

When you look at our pipeline, it's still very robust and very healthy. And that gives us the confidence to tell you that we continue to see this double-digit momentum well into our medium-term target, which is the -- into 2027 and the end of 2027.

When we talk about -- when we think about AOPM, we do think about a fairly flat AOPM for 2026 when you compare it to '25, but we do see the opportunity for incremental improvement in our AOPM in our medium-term target going into the end of 2027. And lastly, our adjusted EPS guidance today is 12% to 14%. That's up from 10% to 12% that we started the year at. Again, we had a very good first quarter, and that puts us in a great position to increase our guidance for the year. And we'll continue to revisit that, by the way, on a quarterly basis now going forward. But for us, really going forward, we continue to look to drive EPS faster than revenue growth. And you saw that in my prior slide, whereby over the last 5 years, we've virtually done that every year. And we continue to focus on that. And we do that through an incremental improvement on AOPM but then also optimizing everything below AOPM to really drive that now going forward.

So in summary, Rohit talked about that we're well positioned or well suited to thrive in this AI environment. If you look at both sides of our business, both our data and AI and total operations business, they're both symbiotic and they are both growing very healthy now going forward. We've made all the -- we made a significant amount of investments in AI, particularly in AI IP solutions that gives us the ability to really sell into the market and really capture more market share now going forward and continue that double-digit revenue growth versus our peers overall.

And then overall, Vivek talked about our growing TAM and our ability to really succeed in really going to market with an increasing TAM and also those trusted client relationships. So overall, we believe that there is a very solid bedrock there for us to really continue to grow double digits now going forward.

So with that, we'll head into a Q&A.

Thank you all. I think the way that we're going to -- we're going to do Q&A, is we're going to get some chairs up on the stage and have the presenters up here, and then we're going to have some microphones. And because we're being livestream, please wait for someone to get you the microphone before you ask your questions, so people can hear it on the webcast.

Why don't you guys just sit and I'll grab a seat, and then we'll start taking questions. First question over to David.

Dave Koning at Baird. Thanks so much for this. This is great. And congrats on good growth. I guess my question is around the cost of AI and the tokens that seems to be ramping. We get a lot of questions on that. Can you buy those in bulk or do you pay for those? Do your clients pay, could you buy them in bulk and then resell and take a little profit. Maybe talk about all the costs and benefits of the tokens.

I could take that. So firstly, you're right, sort of company reached a certain point and then the reasoning model started -- was introduced as Vikas was talking about in that AI spectrum. And the cost suddenly went up more. So the more you reason and you're using it, it just sort of keeps consuming more tokens. Firstly, by design in our solutions and how we are using the harness there, et cetera, we are always looking at how to manage the token cost better. That's the first lever, because in enterprise, you can't scale. Second lever, I talked about the deterministic logic use wherever you don't need the LLM, you actually don't use the LLM. And you use the deterministic logic, which is the other thing, as I mentioned when I was presenting,

Thirdly, the other important thing is that, you're also making the choices for use the right model for the right context. Not everything needs a Ferrari. So don't do a Ferrari. So first, big focus is the token economics because without that enterprise value can scale. Now to specifically to your question, absolutely, we have relationships in which we have preferential rates for tokens. We have our own LLM, which actually, like I mentioned to you, are 1/10 of the cost because they are like a $7 billion parameter model versus a $70 billion or $1 trillion billion parameter model, so that helps. That's one.

Two, in some instances, what clients will say that, look, we will just -- we will manage this token part of it because we have a broader enterprise arrangement. And for that, we will make sure that we consume that. But for all our solutions, we have actually procured that compute space. That's why we have -- also have the partnership with all the hyperscalers. And we have made sure that we do that all the time.

Here is the other amazing thing. Every time we keep engineering well and keep using it well, and we save it, if you're charging on value, that sort of accrues back to you. So that's sort of how we've been approaching this.

I just wanted to add to that last point that Andy made. So we showed a certain outcome-based client stories. 30-plus percent our revenue today is outcome-based. So when you're charging outcome-based when you've got end-to-end accountability, you absolutely have the ability to actually say, I'm going to choose how I'm going to manage the tokens, but my customer is going to pay me on outcomes. So you have that delta there.

This is Puneet from JPMorgan. It's really helpful. So from the presentation, it's clear like there are a lot of changes happening in the industry and how you deliver services to clients like the change management will be very important for EXL as well as much as it is for your clients. So talk to us like how are you measuring like your success in executing against those your internal change management challenges like making sure that like the services that you deliver that it's really AI-first services rather than just adding AI to people-based services, like at the foot soldier, like the folks who are engaging with customers that they are executing against that agenda.

Makes sense. Yes, go ahead.

Yes. So you're right. moving towards here is as much a change management challenge as it is a technical challenge. And many times, the change management turns out to be more difficult than the technical challenge. So I think there are 3 or 4 key things we are doing in EXL. One is for the operations business that we are really running. We have a very clear metric of progressively moving those operations to more AI-enabled and potentially more AI-led. So all of our operations leaders actually have this target that they have to move it. But remember, for us to move to categorize and operation as AI-led, not only the AI has to be infused with effectiveness, but the contract needs to be modified because the client needs to acknowledge contractually that it has moved.

So that is one clear metric, which is driving operations more towards the all. Then as far as the data and AI business is concerned, that's a very clear metric, more services, more solutions, revenue is something that drives us to what kind of growth. You saw the 18% growth that we're actually driving in that business. We are also realizing that we need to drive innovation to support this at multiple levels. So we have 3 levels of innovation. There is what we call the democratized experimentation, wherein we are inviting all our colleagues to bring in ideas and prototypes and solutions to the table. We run an annual event, for example, last year in the idea tank event that we actually ran. We've got 11,000 ideas with prototypes. So that's another metric which using us towards innovation happening. And then, of course, we have ideas progressing from there to R&D and to then funded projects that we're actually doing.

There, the metric is all around how much of investment is actually happening in creating those solutions. And Maurizio spoke about the investment they're actually deploying into that. So that becomes the second metric. The third metric is how the talent pivot is happening. And given the business that we are actually running clearly, there are 2 kinds of colleagues. One is people who are working on AI. So these are the people who are likely creating solutions, the data structures. I spoke about those 17,000-plus people we have, which are data scientists, engineers, engineers, architects and so on and so forth. So the whole thing there is creating more capacity and more capability and diversifying that talent pool. But then the rest of the organization also needs to participate because while they may not be directly working on AI, they are the ones who are actually bringing in the context, the that I spoke about as well as have to eventually work with AI. So we call them as colleagues we're working with AI.

So they also have to go through that pivot. So these are the 3 or 4 things that we're actually doing. And then finally, it's also treating EXL as the client zero, which is to say how much of AI infusing in our own operations. So I can give you an idea, for example, last time, we had the earnings call. We actually had an AI agent available to Maurizio and Rohit who were basically querying whatever information and the agent was actually returning based on all the data sources we have within the EXL on the specific responses to some of the questions that you guys were asking. So just to give you an idea of that.

Vikas, I'm just going to add 1 thing, Puneet, you also mentioned the customer part. I'm just going to -- any time technology moves too fast, People are always behind. And what happens is customers struggle, right? With due respect because things are changing so fast. So for us, when you saw that EXLerate thing, it's not just in the lab, we encourage our teams to actually deploy production use cases and make it real. Because unless you're working with a customer, that's like a durable moat because if you can bridge that gap and create that value, Rohit also talked about investments in those skills, call it, forward deployed engineers to work with customers so that you can bridge that gap between how fast technology is evolving, where are you. And then what you need to do to deliver value. So I just wanted to highlight that part as well.

Surinder Thind with Jefferies. As a management team, how concerned are you with the pace of chain at this point? In terms of the services that you provide and the disruption risk that you face? Like there's been instances where maybe planning last year for services, you fast forward to this year, and maybe it doesn't make sense to offer those services? And what you're doing to kind of stay ahead of the curve? Is it -- are you planning on a 3-year horizon? Are you looking at certain capabilities that don't exist today and you're figuring that's where the models are going to be 2 years from now? Like how are you working through all of this?

So Surinder , first of all, I have to acknowledge that the pace of change in this industry is quite -- it's very, very fast. So if I were to sit here and tell you that we have a crystal ball that tells you where our revenue is going to be by line of business with recision 3 years from now, I don't think you believe us. So what we've done is we are actually planning for a whole range of scenarios. And what we are doing is in our planning, we are building out specific scenario range, especially as you go further out. In terms of adoption rates and what that scenario means for us. And the scenarios are really about enterprise adoption rates of AI and how quickly do enterprises make that shift versus not.

Now the way we build our planning is because of that Goldilocks scenario, we actually win in all of those scenarios, irrespective of what that pace of change is, but the drivers of the growth will tend to vary between our different lines. So that's one aspect of how we've looked at it.

Your second question was, are there new things that we are planning to do? We absolutely are. And that's where I think the strategic planning focus has become that much greater for us. So there are new capabilities that are getting formulated as we speak. What is EXL's role in creating that capability. What is EXL's role in kind of delivering that going out into the future that's absolutely a core tenet of our planning right now. And we are investing in that and we're kind of building for it.

I'll just add a couple of things. Number one, the planning cycle for us, which used to be 3 years and reviewed every year, now it's become quarterly. So the planning cycle has actually become a lot faster given how quickly the changes are happening because the environment change is very rapid. And number two, our focus on where we are going to be making investments and where we'll be looking at doing acquisitions, that's changed significantly, because we need to be investing ahead of time before some of these trends become reality. And our ability to be able to kind of create the right kind of foundation and the right kind of capability set that becomes really important.

So we've increased, obviously, the magnitude of investment but we are also being a lot more deliberate about where we are investing so that we can position ourselves for this uncertain and ever-changing world in a rapid pace.

[ Inge Kao with IDC ]. My question is with the whole proliferation of identic agents, AI agents. This is going to result or is already resulting in AI sprawl, which means we need to somehow keep track of them, govern them, identify them register them. Can you speak a little bit about agent registries and if and how you're approaching that?

Sure. I can take that. So firstly, we just didn't have the time to go to the full breadth of the demo. But what we've essentially done in our agent studio is, firstly, we've made it completely compatible to true AI, [ laser ] some of the other new technology that are coming up, so you can use any foundational technologies. What we've also allowed is within the company, you have to our innovation to happen so that everybody can contribute to innovation, right? That's the point Vikas was making.

However, what's important is that what problem you're solving for and your ability to register those agents and making sure that. And I talked about the governance, the state the traceability, the auditability, that is super critical because without that most people will stand up on stage and they'll say this year, we want 100,000 agents. That's not what the message Rohit will give. His point is, where is value and work backwards from value to see where it makes sense. And by the way, now there are identic designs that you don't need to have like 20 agents for something.

Part of the problem is People have worked in a manner where they're taking the human work and giving to an agent in some of the new orchestration design, you should be collapsing workflows. You should not be replicating the old world. So a, start from use case to make sure you have the technology that allows you to innovate; three, register that technology in the right manner and make sure at all times, it's governed it's managed, it's observed and that you're able to monitor the performance. That's how we've been going about it.

And that's an important point because what we don't want to do is to curtail innovation in the organization. So we ask our people that free to extent, but remember 2 things. One, do it within the guardrails and do it using the standard technology stack, which is being available to you. And then once you think that you're actually getting to a point which -- where your experiment can get into production use, it has to go through a compliance check. And it has to be registered in the standard platform, so it can be monitored and maintained going forward, right? So put those guardrails of development and ongoing monitoring, but then allow the innovation to happen because you don't want to tell people that don't do it because of these concerns.

Gates Schwarzmann with TD Cowen. Obviously, there's -- it feels like there's a new AI event every week that every investor wants to talk about. It feels like the past week now has been talking about the OpenAI and Anthropic joint ventures that they've been discussing. We believe open AI raised about $4 billion aiming to more directly tackle enterprise AI services. Curious what your thoughts are on that. Does that pose any indirect tailwind to you guys or maybe a headwind in the future? Just curious about what your thoughts are there.

I'll let Vikas and Vivek talk about it first and then I'll add to it.

So one of the, I guess, one of the narratives that we've been hearing over the last many months is that AI is going to become so strong or is already so strong that you do not need any switching around it. You don't need to put any context in it. It's basically just drop in technology, right? So take the technology, drop it into a workflow and will start working. That's generally what we've been hearing. But what we have been seeing and what we've been working on, and that's where we see the opportunity for us is that no, edit, it's very complex because you can have the core model, but then to bring the data the context, the stitching together creating the solution, deploying the solution, monitoring it, deploy and then the change management around it.

All of that requires an immense amount of solutions around the core model as well as services that need to be provided on a onetime basis on an ongoing basis, right? That's our thinking. And that's where we're seeing the demand. So in a way that you actually have these foundational model and frontier model companies creating these services businesses to be able to create this in the enterprise is validating point of view that you cannot just deploy a model, you need to do that. So that's the first thing that I think this is validating that free -- for AI to become real in an enterprise core workflow, a lot more needs to happen. And frankly, that is where we bring our expertise. We bring in the.

Now the second question is, will we end up competing with them? Yes, we will, to some extent because as they start getting into this area, we will compete. But I think we have an advantage, which comes in from years of experience on data on context on specific industry domains, which I think is something -- and established client relationships, enterprises where I think we're like give us a bit of a head start. And I think we feel confident we should be able to continue with that advantage.

Vivek, something you want to add?

Yes. I just want to add two quick bullet points to that. So first of all, we've been talking to you in our presentations about the fact that you need data, context and AI altogether to win. Now for the most part, OpenAI and Anthropic, so far, we're talking just about AI. And what you've heard is, as Vikas pointed out, is that approach doesn't work. You need all 3.

When it comes to all 3, we've really got a massive advantage over them on the data and the context side because that's where the decades of work with those regulated industries comes to 4. I would like love to hear someone who walks in and says, I've just learned about insurance yesterday. Now let me tell you how claims ontology needs to be designed. We are going to win that battle every day.

The second part I wanted to give you was an example. So I talked to you about the top 20 commercial insurer, which was where we won the data work with the CDO. They actually did a hackathon. They brought in 10 different providers from within the industry all of whom had experience at that client site and said, okay, let's all design data pipelines together and let's see who can design that data pipeline the fastest, the best. And EXL came #1. The reason we came #1 was not because our engineering skills were superior to everyone else. It's because we knew the data, we knew the ontology. We knew the context. So I think, yes, we welcome the entrants. We welcome entrants who are like validating our hypothesis. And in those industries, in those clients, I think we'll continue to win. Yes. So it's a validation of our hypothesis and frankly, the opportunity for us.

No, I think you guys covered it. It was great. We have an internal WhatsApp group for the management team, and [ Anita ] posted this in our WhatsApp group and everybody went to 2 thumbs up, because -- this is fantastic. I mean OpenAI is doing it. Anthropic is doing it. Sierra is doing it. Palantir has forward-deployed engineers. Every single new technology application, if it requires FTEs is just validating our business model. And by the way, they can either try and hire these FTEs which are super expensive and try and build it, which is super tough or they can partner with us, and we'll do it. So it's a great opportunity for us. And is because remember, we bring this from multiple directions. We bring it from the operations side. We're bringing from the data side. We bring it from the analytics side, the end model side. And you need all of these components to come together to build that right [indiscernible].

Just to add to that point, if you think about Google or AWS or Microsoft, they all had professional services arms because that's the way they serve some of the strategic clients. With OpenAI and Tropic, as they're going into the market and the enterprise reach is increasing, they're realizing they can't service it. So they are going to have a professional services arm like the partners or the other hyperscalers have, but they cannot scale it.

So as the partners have an ecosystem of partners, where we are very much entrenched, I think that opens up that further opportunity as you partner with Anthropic and open the eye they are using the same model. They're not going to serve 10,000 customers themselves. So I think they are just doing what hyperscalers have already done. So in some ways, it's a huge opportunity for us. in terms of how we partner with them and realization that it can be done is the validation of our whole thesis.

From Evec. Great presentation. We have seen outcome-based pricing model tried in the past several times, and they have really failed to scale as expected. We talked about outcome-based pricing model today. How are you looking to approach it differently this time so that you are able to scale it and execute it well?

So can I go ahead? So first of all, we are already at scale. So the outcome-based component for EXL today is more than 30% of our revenue. So when you put that into perspective, right, that's already a massive amount of business that's flowing through those outcome-based models. Now what we've done is we've got a two-pronged approach. One is in industry areas where the client is already used to an outcome-based kind of an approach, we've really gone forward and we're driving further ahead and increasing our volume through there. Payment integrity is an example, collections is an example. The industries are already used to outcome-based pricing.

What we are doing is we are just grabbing more market share and we're kind of building a better product and winning more. The second part of it is taking our existing business where the client wasn't really outsourcing on an outcome-based way, and saying, okay, we are going to take on more end-to-end accountability. And by taking on more end-to-end accountability, we are going to kind of flip this into an outcome-based model, and there's going to be a win-win. And we are actually seeing some very nice traction on that side as well. And the traction on that side is actually coming from AI.

Rohit talked about the midsized market for us. If you're a midsized company, and you have to compete with the large guys who already have AI, you have to kind of adopt it very quickly. You don't have the means of being able to do it internally because you don't have the size and the scale. So you want to go to someone who can say, I have the rails built out, I have the technology built out, I have the AI built out. You don't have to worry about it, just pay me on results. That's where our wins are coming.

So I think you're going to see both of those motions happen. You're going to see for the large industries like collections and payment integrity, will continue to drive volume. And increasingly, we're going to switch to outcome-based for the midsized.

[ Prajeet, ] I was just going to add one more thing to what Vivek said. Where the client can't define value or the outcome and the value can't be attributed to us, then we won't do it, because otherwise, what's going to happen is everything you do, you're going to pass on, right? And two, it's going to be difficult to baseline and assess.

So to Vivek's point, we've been actually very careful, look at subrogation, look at Payment Integrity, look at claims. Companies have been used for -- are used to over the years to send those data feeds out and get the value back. And we can control that entire stack wherever it's definable and you can attribute value and you can charge on value. So we've been very prudent. Places it makes sense, sign up for it. Places that doesn't make sense, don't sign up for it.

Lars Goransson with IDC. Very curious if you could expand a little bit about your growth strategy on complex domain-specific industry problems versus your international expansion. Would you consider potentially expanding into other industries that exhibit similar characteristics to the type of problems you're solving in BFSI and health?

Well, first of all, we have one of our IMUs, which is called banking and diversified industries, right? So the intent was that we do want to use that to expand into newer areas and do that in North America and then as part of our international growth markets, do it internationally as well. So we do think of that as an area where we can start expanding.

Now the areas that we are looking at expanding into across the board -- we are very, very focused on the large technology players. The tech space is something that is of interest to us. Telecom and communications is one where we've already built up a pretty substantial presence. We want to expand that further. And then mobility is an area that we're looking at very closely. So I think if you think about us, there is a plan and a strategy for going after more of the high-tech players and the digital native players that have a very strong spend profile, and that's an area we're looking at, doing it both North America as well as international.

We'll take our last one over here.

Chandak Biswas from Everest Group. So in line with Rohit's keynote, I think the noise around AI is creating -- is also creating an AI fatigue in the industry. And we have seen a large number of clients with failures as well in the last couple of years, either through internal experimentation or through other incumbents, right? So what's your strategy of engaging with those clients? And what's EXL's way to differentiate yourself from others when you engage with such clients whether bad taste in their mouth?

I can take that. So remember, we spoke about today that enterprises are actually moving from experimentation to production grade. And you can't get into production grade all over the place. What means by production grade is that you select a few but core business workflows and make agentic AI real in that. So our approach with our clients is first to work with them to identify what those areas are, what makes the biggest impact, where the technology maturity is the point where meaningful value can be created. How we can actually build this whole concept of data context and AI to create a solution, and then how do we deploy and drive user adoption in that. So it's taking them through this journey.

But the conversations have changed into this production-grade AI deployment with our clients. But they do need help on that, right? Because experimentation is a different thing, but then taking it to actually deploying it at scale and making it effective and outcome actually is very -- but you're right.

The question that where is the return and when do I start seeing it in my customer metrics or in my P&L, I think that conversation is very real. And it wasn't happening about 6 months ago because it was still -- let's -- it was a lot around data management, but it wasn't so much about agentic AI, but now the conversation is happening a lot.

I'll just add a couple of things to what Vikas said that one, I think you're exactly right, good advice in this time is priceless because there is so much fatigue and clients are getting things left right and center. And so there is one very interesting metric we measure. How many agentic AI conversations you actually say no to. 58% of the conversation we'll advise the client don't pursue this. This is not the right one. That's the reason we have 93% success rate because you have to say no to a lot of things because, like I said, people look at that 5 layers of cake, and they think, let me just put this in the enterprise networks.

Secondly, demonstrable evidence where you've done the work and you've had those one-on-one battle combat, you've learned from it and you can bring it back. So that sort of really is valuable for clients. And third, like Rohit mentioned, there's also a significant investment in that talent to be able to have that conversation, because it's not just that you're advising them what you can bring to the table, you're also advising them what not to do and how to go about it. So I just want to add that context to what Vikas said.

Okay. Last one.

Great presentation, great conversation. So I'm Sudarshana Bhattacharya from Gartner. I have two questions. You talked about human on the loop, right? So moving from human in the loop to human on the loop. How do you see industry reacting to that position, especially FSIs? And how do you see the diffusion of roles happening on the end user side, like the CISOs, CIOs, how is that happening? So that would -- I'll be interested to hear that.

And the second one, you mentioned about 40% reduction in effort for data pipeline. So if you can add a little more color to that, what type of use cases where you are seeing 40% reduction? Does it include both unstructured and structured data, especially -- and also, how are you tackling the fragmentation that's already there? Again, focus with FSIs.

Yes, I'll take that and then Vikas and Vivek can chime in as well. So to your first question -- sorry, what was your first question?

Human on the loop.

Human on the loop. Yes. Sorry. That's okay. I was just thinking about the second question. So on your first question, see, that's sometimes terminologies and how people use them. If you saw in the demo, I think the pace at which agents process information is much faster. If you're going to put human at every step, the problem is you're going to create more bottlenecks, right? However, it doesn't mean that every important decision, every aspect of governance, every audit trail, everything where you need that traceability, you can't lose on that. So if I just take you back to demo quickly, one of the things that we are very careful of is that let some of the things be informed to an agent to make that decision, human agent I'm talking about. And they don't have to intervene at every step. They are observing what's going on.

So in that example, you were able to see that while I got the approval to go ahead with this, but I'm getting a sense from the context that this is something that may be an opportunity for recovery, which otherwise was not. So it's still human, but the profile of the human changes. Think about the old days, you had work divided like this complexity, this complexity, this talent.

Now that human's role is elevated and they're making judgment about things. So technology is making things available. Human is still making the judgment versus getting everywhere in the process, they're just sitting in the loop and observing this end-to-end. So I don't take this as you just want to completely ignore the traceability, the evidence, the governance and decision traces and then you'll make the decision, you're pressing the approved button. So that's sort of the first part.

Let me just illustrate that with an example. So we talked to you about claims today, right? So let's talk about how claims was built, how we would have done AI for AI-led claims earlier. There would be a claims adjuster. That claims adjuster would basically get a claims form. The AI was just focused on extraction. That's it. We were just focused on extraction. We were bringing the information out. The information was still getting served to the claims adjuster. The claims adjuster was saying, okay, all that manual work of kind of taking the work out, that's been saved, right? So the saving was more manual, but the role of the person was still making the judgment.

Today, in an AI-first model, you're actually trying to say, no, I want the AI to make the judgment as well in most of the cases. And in some cases, I'm going to get the adjuster involved. So now for the AI to become good at basically making judgment in all of those cases, you don't really need a claims adjuster sitting on every transaction, but you still need some element of RLHF, reinforcement learning. Now that reinforcement learning from a human is now a different type of a human. And that is now human kind of working on the loop rather than in the loop.

The output here is that the AI accuracy goes up, the number of cases that the AI is solving for goes up dramatically and your overall cost and the cycle time into this process comes down dramatically. So we think this is how AI-first operations is going to play out. There's going to be a huge amount of work for the RLHF human factors in there, and there's going to be some amount of work still for the expert.

I will just take the second question also, last comment on this one. It's iteration, iteration, iteration. Sometimes it never lands perfectly. So like Vivek said, I have now confidence on this part. I can hand it over to the human because accuracy is much higher. By the way, our subrogation human machine combination today is performing way better than what we had as a human performance before, way better. It's 4 percentage point difference that we're observing now. But it went through iterations, never just lands perfectly the first time. But once you master the solution, then you know how to replicate and rinse and repeat it.

To your second question, see, this is where -- and I think this goes back to the token question as well. The problem is most companies will advise you when they come with platforms to move the entire data estate because it's driven by consumption. When we run these unstructured data, structured data logs, metadata, go read from COBOL, go read from mainframe. And we've been using Claude Code because we saw great efficiency, but it's years of working on SaaS. It's -- we are working on COBOL.

So when you look at that data estate, these things got built 40, 50, 60 years back. Invariably, you find 30% to 40% of those logs, port sets are redundant. They're not even used. They're not even feeding to the downstream pipeline. You want to cache it, you want to keep it in some data sets, fair, but you don't need to migrate and spend on that consumption and bring that data because that's not needed. And for enterprise transformation or the use case you are driving, actually, that has no relevance or value at all.

So typically, everybody was approaching that let me take the as-is state and take it to-be state because you just did not know what lies in your estate. We just made that whole thing transparent. And what that does is all these excessive logs that you didn't need, all those pipelines that were created that you didn't need. Downstream 1,000 reports that we were using was feeding from some data source, nobody is using them. We are able to clean up all that log. And that in itself saves 30% to 40% effort, but more importantly, downstream consumption because you're not just bringing garbage that you don't need.

Well, thank you, everyone. It's a terrific day. Again, I'm going to remind everyone that the demonstrations are in the next room, next to lunch, and we hope that you'd stick around for lunch, be able to speak with the presenters, and we've got lots of EXL management around the space. So thank you so much for joining us. It's a terrific morning. Have a great day.

Hello, and welcome to the ExlService Holdings, Inc. First Quarter 2026 Earnings Conference Call. [Operator Instructions] Also, as a reminder, this conference is being recorded today. If you have any objections, please disconnect at this time.

I will now turn the call over to Andrew Thut, Head of Investor Relations and Capital Markets.

Thanks, Jenny. Hello, and thank you for joining EXL's First Quarter 2026 Financial Results Conference Call. On the call with me today are Rohit Kapoor, Chairman and Chief Executive Officer; and Maurizio Nicolelli, Chief Financial Officer. We hope you've had an opportunity to review the first quarter earnings press release we issued yesterday afternoon. We have also posted a slide deck and investor fact sheet on our Investor Relations website.

As a reminder, some of the matters we'll discuss this morning are forward-looking. Please keep in mind that these forward-looking statements are subject to known and unknown risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements.

Such risks and uncertainties include, but are not limited to, those factors set forth in yesterday's press release and in EXL's filings with the Securities and Exchange Commission from time to time. EXL assumes no obligation to update the information presented on this conference call today.

During our call, we may reference certain non-GAAP financial measures, which we believe provide useful information for investors. Reconciliation of these measures to GAAP can be found in our press release, slide deck and investor section. With that, I'll turn the call over to Rohit. Rohit?

Thank you, Andrew, and good morning, everyone. We entered 2026 with strong momentum. In the first quarter, EXL generated revenue of $570 million, up 14% year-over-year and adjusted earnings of $0.58 per share, an increase of 20% year-over-year. Our sustained double-digit growth demonstrates the strength of our competitive position as well as strong execution against our data and AI strategy.

EXL's recognized industry expertise and leadership in helping clients adopt AI throughout their enterprise is resonating strongly with the market and fueling our growth with new and existing clients. Demand is being driven by scaled deployments of AI inside core client workflows where EXL delivers measurable productivity, increased effectiveness and superior risk-based outcomes.

Underpinning this growth is a combination of capabilities that has taken over 2 decades to build. Helping our clients adopt AI in complex regulated industries requires more than technology. It requires deep familiarity with the operational workflows, regulatory frameworks and data ecosystems that define how our clients actually operate. This is where our unique combination of domain, data and AI expertise differentiates EXL and drives superior client outcomes.

EXL's proprietary data assets, domain-specific AI models and orchestration capabilities allow us to embed intelligence directly into how work gets done, not as an overlay, but as an integrated part of the process. It is one of the key reasons our renewal rates remain high, and we continue to grow at market-leading rates.

In addition to our segments, we also provide revenue information across 2 categories: data and AI-led and digital operations. Data and AI-led revenues grew 28% year-over-year in Q1 and now represents 60% of the company total. We are seeing strong momentum across our full portfolio of data and AI-led offerings as clients are stepping up the pace of AI adoption and need help with data for AI, design of their Agentic AI systems and reimagining business processes.

Most of our clients across verticals need to improve the way that they capture, enrich and utilize their structured and unstructured data to drive AI outcomes. We are seeing strong market interest in our EXLdata.ai platform, which helps clients preserve domain-specific semantic context as they build new AI-ready data foundations.

And we are continuing to leverage AI in solutions that we manage, which is both driving greater efficiencies and creating new value for clients by increasing precision and enabling improved outcomes. We are embedding AI both in our data and AI-led solutions as well as the operations that we manage for our clients.

This last point is important and worth stressing. When we successfully embed AI into an existing client workflow, the nature of that engagement changes. It becomes more intelligent, more IP-led and more value added. The revenue associated with it moves from our digital operations category into our data and AI-led category.

As I communicated to you last quarter, in order to provide greater transparency, we share our investor fact sheet with a total operations view, which combines digital operations and data and AI-led operations that have migrated into our data and AI-led category.

In Q1, total operations grew 10% year-over-year and remains a growth driver for our company's revenue. The reported digital operations revenue after that migration was down 2% year-on-year. This is by design. We expect this deliberate and planned shift to continue going forward. We saw strong performance across each of our 4 operating segments to start the year.

Insurance grew 13% year-over-year, representing over 1/3 of our revenues. I'm particularly pleased to see it return to double-digit growth. Insurers are accelerating adoption of AI to improve underwriting, claims and customer experience. We are seeing strong deal activity across all market segments. Healthcare and Life Sciences grew 21% year-over-year, representing over 1/4 of our revenues.

Payers and providers are facing rising cost pressures, regulatory complexity and margin strain. They are turning to EXL to apply AI at scale to improve productivity and outcomes. Payment integrity continues to be a significant driver of growth, along with broad-based strength in analytics, AI services and solutions and operations.

Banking, capital markets and diversified industries grew 8% year-over-year and represented 1/4 of revenue. The quarter saw very high deal activity, and we remain confident in continued progress as the year unfolds. International growth markets grew 13% year-over-year, reflecting successful AI-led expansions in new and existing clients.

International markets are an important driver of our long-term growth and global expansion strategy, and we continue to invest in talent and partnerships to expand our footprint. During the quarter, we hosted our annual AI in Action flagship event, bringing together senior business and technology leaders from across our client and partner ecosystem.

The focus this year was on what it takes to make Agentic AI real inside enterprise operations from building the right data foundations to orchestrating AI across complex workflows. The level of engagement and the participation reinforced what we are seeing in our pipeline. Enterprises are moving from AI curiosity to AI in production, and we are the partners who can help them execute.

We are also seeing co-innovation with our technology partners continuing to resonate and earn us industry recognition. EXL was recently named Advanced Technology Partner of the Year by NVIDIA, Best New Partner of the Year by Genesys and AI and Machine Learning Market Disruptor of the Year by AWS. These partnerships are not only enabling our differentiated solutions, they are becoming meaningful go-to-market and pipeline contributors.

In summary, EXL entered 2026 with strong momentum, and we have excellent visibility for the remainder of the year. Demand for our data and AI-led services and solutions remains robust, continuing the momentum we saw at the end of 2025. We continue to strengthen our competitive position through investments in capabilities, partnerships and talent.

Our portfolio is well balanced. Our pipeline is strong, and we have high renewal rates. More than 75% of our revenue is recurring or annuity-like, providing revenue stability and a great line of sight for the year. For full year 2026, we are increasing our revenue guidance to a range of $2.3 billion to $2.33 billion, representing 10% to 12% constant currency organic growth.

We are also increasing our adjusted diluted EPS to $2.18 to $2.23, representing 12% to 14% year-over-year growth. As always, I want to thank our clients, partners and employees for their trust and commitment and to our shareholders for their continued support.

Before I hand it over to Maurizio, I'd like to remind you that we will be hosting our Investor and Analyst Day on May 13 in New York. We will share our multiyear growth framework, AI monetization model and client case studies that bring our AI strategy to life. For those of you looking to understand the EXL growth story, this is the event to attend. Please reach out to Andrew for details. I look forward to seeing you there.

I will now turn the call over to Maurizio to provide more details on our financial performance.

Thank you, Rohit, and thanks, everyone, for joining us this morning. I will provide insights into our financial performance for the first quarter and our revised outlook for 2026. We delivered a strong first quarter with revenue of $570.4 million, up 13.8% year-over-year on a reported basis and 13.4% on a constant currency basis. Sequentially, we grew 5.1% on a constant currency basis. Adjusted EPS was $0.58, a year-over-year increase of 20.2%.

All revenue growth percentages mentioned hereafter are on a constant currency basis, unless otherwise stated. Now turning to segment revenue for the first quarter. The Insurance segment grew 12.6% year-over-year with revenue of $193.9 million. This growth was driven by expansion and higher volumes in existing client relationships and new wins.

Sequentially, Insurance grew 4.4%. The Insurance vertical, including revenue from international growth markets, grew 12.2% year-over-year with revenue of $226.1 million. The Healthcare and Life Sciences segment reported revenue of $151.9 million, representing growth of 21% year-over-year and 6.8% sequentially. The year-over-year growth was driven by higher volumes in our payment services business and expansion in existing client relationships with other health care services we provide.

The Health Care and Life Sciences vertical, including revenue from international growth markets, grew 20.9% year-over-year with revenue of $152.1 million. In the Banking, Capital Markets and Diversified Industries segment, we reported revenue of $127.4 million, representing growth of 8.1% year-over-year and 4% sequentially. This growth was driven by new client wins and expansion of existing client relationships.

Banking, Capital Markets and Diversified Industries vertical, including revenue from International Growth Markets, grew 9.4% year-over-year with revenue of $192.2 million. In the International Growth Markets segment, we generated revenue of $97.1 million, up 10.9% year-over-year and 5.4% sequentially. This growth was driven by ramp-ups and higher volumes with existing clients and new wins across Banking, Capital Markets and Diversified Industries and Insurance.

SG&A expenses as a percentage of revenue increased 20 basis points year-over-year to 20.4%, driven by investments in data and AI-led solutions. Our adjusted operating margin for the quarter was 20.5%, up 40 basis points year-over-year, driven primarily by improved gross margins. Our effective tax rate for the quarter was 21.9%, down 40 basis points year-over-year, driven by higher profits in lower tax jurisdictions.

Our adjusted EPS for the quarter was $0.58, up 20.2% year-over-year on a reported basis. Our balance sheet remains strong. Our cash, including short- and long-term investments as of March 31 was $266 million, and our revolver debt was $417 million for a net debt position of $151 million.

During the quarter, we spent $13 million on capital expenditures and repurchased 4.4 million shares at an average price of $31 per share, totaling $136 million. This includes 3.35 million shares received upfront as part of the settlement of our previously announced $125 million accelerated share repurchase. We expect to receive the remaining shares in the second quarter.

Now moving on to our outlook for 2026. While we remain cautious about the current macroeconomic climate and geopolitical uncertainties, we are increasing our guidance for the year based on our current growth momentum and our strong pipeline. We now anticipate 2026 revenue to be in the range of $2.3 billion to $2.33 billion. This represents year-over-year growth of 10% to 12% on a reported and constant currency basis.

This also represents an increase of $20 million at the midpoint, which includes a $2 million foreign exchange headwind from our previous guidance. We anticipate increased investments in data and AI capabilities and solutions for the rest of the year to expand our competitive advantage and continue to drive top-line revenue growth.

We expect a foreign exchange gain of approximately $2 million to $3 million, net interest expense of approximately $6 million to $8 million and our full year effective tax rate to be in the range of 21% to 22%. We expect capital expenditures to be in the range of $50 million to $55 million. We anticipate our adjusted EPS to be in the range of $2.18 to $2.23, representing year-over-year growth of 12% to 14%, up from our previous guidance of $2.14 to $2.19.

To conclude, we had a strong start to the year, demonstrating our unique competitive position and participation in high-growth market segments. Despite the current geopolitical uncertainty, our leading indicators remain positive, and we have a highly adaptable and resilient business model, setting us up well for a solid 2026.

With that, Rohit and I would be happy to take your questions.

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

I wanted to ask here on the growth guide. So good to see the raise. Can you just dig in on the key assumptions for data and AI-led versus digital ops growth and maybe how your views on the industries may shape up? And then Maurizio, just despite the strong commentary here, it doesn't suggest any demand impact to you, but would you still say this feels like a prudent outlook for the balance of the year?

Bryan, so our growth outlook, we've increased our guidance for the full year. As you all know, our first quarter is typically a strong quarter, and we had a great first quarter this time. What we've seen is that we've been able to kind of outperform our own expectations in the first quarter. We continue to see good pipeline and good demand for our services. And therefore, we've increased our guidance for the balance of the year.

The data and AI-led part of our business is actually resonating very nicely in the marketplace. It now represents 60% of our total portfolio, and it's growing up very nicely. Even for digital operations, as we've shared with you, our total operations is actually growing quite nicely as well, and we continue to see demand out there.

If you talk about industries, typically, we continue to see good momentum in insurance, in banking and in health care. Some of the industries where we see a little bit of softness is on retail and on communication. But a majority of our portfolio is really made up of banking, financial services, insurance and health care, and those are all very, very strong pipelines and demand for us.

We don't really provide a breakup, Bryan, as you know, between data and AI-led and digital operations. But it would be fair to say that our digital operations will grow slightly below the company average, and our data and AI-led piece will be powering the growth of the overall company.

I'll pass it on to Maurizio to talk about the prudent guidance that we've given.

Yes. Thank you, Rohit. And Bryan, as Rohit talked about, we're seeing very good momentum coming into the calendar year. So Q1 is normally a strong quarter for us to really start out the year, and we saw that again this year. And we continue to see that momentum going into the rest of the year.

One thing to highlight is we did raise our guidance at the midpoint by $20 million more than our beat in the first quarter, and that does include a $2 million FX headwind from the last time we gave guidance. And then lastly, our guidance is going to take -- is going to be a bit prudent and take into the account what's happening in the current macro environment and also the geopolitical uncertainties that are out there.

And we have 3 more quarters remaining for the rest of the year. So look, we have very good momentum going into the second quarter and the rest of the year. And our guide is we've increased it, and we're still early in the year, and we'll continue to revisit our guide as we go forward. But the big positive here is that it's -- we've got very positive momentum going into the rest of the year from Q1.

Okay. That's helpful. That's clear. Maybe on margins. So it looks like you outperformed there as well. Can you just comment any change in the expectation on adjusted op margin for the year? And just is it investment timing, any cadence expectations just to help?

Yes. Sure. So you saw our adjusted operating margin come in at 20.5% in Q1, and that's up 40 basis points from Q1 of last year. We always see Q1 being a very strong quarter, both on revenue and profitability. And that sets us up very well for the rest of the year in terms of investing to continue to drive double-digit growth for the rest of the year and also going into 2027.

So you will see us, as you saw last year, start to make additional investments, particularly into our data and AI capabilities during the rest of the year. And our AOPM forecast for the rest of the year will be similar to what we have talked about in that mid-19% range.

Our next question comes from David Koning with Baird.

Great job again. I guess one question. We kind of hear your clients, just all the companies in the environment right now are really looking for AI savings. Do you get some of them pushing you on price a little bit just saying, "Hey, we need to find ways to save to show our CEO, our Board, et cetera" that we're saving money. Do you see that as a price headwind at all or more of a demand tailwind?

Dave. So let me just kind of provide a little bit of context around what we are seeing around the adoption of AI. #1, I think what we are seeing is our clients are switching over from AI pilots and AI POCs to AI in production. So that's a big change, and that started out early in this year. And frankly, that's playing to our strengths and the value that we can add to these relationships.

The second thing that we are seeing is as clients think about AI in production, they are quite willing to open up access to their technology systems, to their databases and allow us to be able to make changes to the end-to-end workflow. As you know, the application of AI has to be driven in conjunction with the transformation of the workflow, and we are in the best position to be able to drive that.

And the third piece is the commercial model is also changing. And what we are seeing is as clients come to us with the adoption of AI to be implemented and enabled, the commercial model is changing much more towards a fixed fee and a milestone-based payment and an outcome-based model. So that is something which allows us to be able to manage the pricing and the margins and be able to add value to the customer relationship.

So the negotiation and the conversations are much more about providing our clients with deterministic benefits associated with AI adoption and for us to be able to do it in a way that allows us to be able to earn a respectable margin associated with that. We are not really seeing clients come to us just asking for price reduction. The price reduction is alongside the transformation and alongside with the value creation for them.

Yes. And just one follow-up. In the International segment, I know you called out a little uncertainty with the conflict. 17% of revenue, it actually accelerated pretty nicely in the quarter. Would you expect to see a little deceleration there? Maybe describe kind of what the impacts you think can happen?

Yes. So Dave, firstly, our international growth market is highly underpenetrated. So frankly, the opportunity set out there is enormous. Second, we have very little and very limited exposure to the Middle East. Most of our revenue from clients really comes in from U.K., from Europe, from Australia and New Zealand. And we are seeing healthy adoption of AI in these geographies.

Our goal will be to continue to drive a greater and a faster adoption of our services in the international growth markets. So we are not really seeing any direct impact due to the conflict as such. There may be some downstream second degree or third degree kind of impacts associated with that with our clients. But frankly, it's actually very fertile ground for us in the international growth markets.

So we're going to continue to invest in that space by adding on more talent, bringing on more capabilities. And we think we should be able to grow our international growth markets business quite nicely, and it should grow at the same level or if not higher than the company average growth rate.

Our next question comes from Maggie Nolan with William Blair.

I'm curious if you can share any perspective on net revenue retention at some of your largest accounts to kind of help us get at the question of volume versus some of this work migration between types of offerings?

Yes. Maggie, that's a great question and something that we've been paying our close attention to. So like I said earlier, one of the things which is happening with our more mature clients is as they ask us to help them adopt AI into their enterprise workflows, what we are able to do is to actually work on much larger pieces of operations for them as compared to the past and also work on a lot of work associated with building the right kind of data foundation and new service lines, which we would not have kind of engaged with them on previously.

So frankly, the landscape at which we are operating, that's expanding. Our TAM is expanding, and it's becoming a much bigger playing field for us. And at the same time, we are able to deploy AI and eliminate and reduce the amount of manual effort that's required to be able to do some of these processes and pass on this productivity benefit to our clients.

So if you talk about net revenue retention, actually, it still is a growth story for us because on a net basis, we are seeing a much wider landscape to play in, and we are seeing the revenue size and the size of the operation actually increase despite providing them with a benefit associated with the manual work -- manual portion of the work that was being done previously.

And then I noticed in the prepared remarks a little bit of an emphasis on partnerships. And I'm wondering, if there's anything you can share with us to give us a sense of how that's progressing, like what the partner source pipeline looks like or co-selling metrics? And then any sort of variance in things like the deal cycle when you have partnership involvement?

Sure. So we've been actually very pleased with the progression of our partner relationships. And as you saw, our partners are recognizing our effort, and they are recognizing our differentiated capabilities as compared to some of the other players that they might be dealing with.

The one unique thing about EXL is that we come at the transformation and the adoption of AI from a process and a workflow and a knowledge of our clients' business and operations lens. And our partners are finding that to be a unique value proposition because that knowledge of the domain, that ability to apply contextual understanding of our clients' business alongside with the technologies that our partners are providing, that's creating a huge amount of value uplift for our clients.

So frankly, these partnerships are resonating. The motion is becoming a lot easier and smoother in terms of our go-to-market strategies and our partners are recognizing us and giving us these awards as compared to other players. The go-to-market is actually the more exciting part because now when we go in and we interact with clients, we are able to take our partners there. And our partners are also bringing us in into deals which they are participating in. So frankly, the activity and the deal flow has increased substantially. And I think that's something which we would foresee going into the future as well.

Our next question comes from Surinder Thind with Jefferies LLC.

Rohit, can you help me understand the step down in the digital ops segment as we kind of take a look forward. Over the past couple of years, that was a high single-digit grower. I think the expectation is more muted. Is the idea here that, that correlates with the advancement in Agentic and bottle capabilities? And should we expect to maybe a year or 2 from now, see a further step down in that segment as the models further advance? And then ultimately, does all of it get recaptured in the data and AI-led segment?

Yes, Surinder. So let me try and go through this in a step-by-step basis. Firstly, if you take a look at total operations, that continues to grow and that continues to expand. And in the first quarter, total operations grew 10% year-on-year.

Within total operations, you could split it up into 2 buckets. One is digital operations and the other is data and AI-led operations. As the adoption of AI increases, we are going to see there being a bigger shift towards data and AI-led operations. And frankly, that is a very good thing from our perspective because as the operations shift towards data and AI-led -- we are putting in more IP, more proprietary assets of EXL and creating more value for our clients as such. And that business becomes much stickier, much bigger in size, and we control the outcome end-to-end.

So going forward for the remainder of this year, digital operations will likely continue to have the same kind of deceleration of growth that has happened in the first quarter. But the shift towards data and AI-led operations, that's the critical piece. And that's something that is positioning the company to be a future-forward company for our clients.

And that's something which other clients are looking at, our prospects are looking at, and they're engaging with us in an even more determined manner. And that's why we're seeing our pipeline being extremely full and the level of activity is very high, and we feel very confident about continuing to grow our overall business in this double-digit kind of a range going forward.

And then turning to headcount. You continue to see a strong uptick there. Is that how we should expect the model to evolve over the next couple of years where there's a spread between revenues and headcount? Or should that spread expand in the coming years when we think about getting to a more revenue per headcount model? -- to build out your IP?

Yes. So if you take a look at Q1, our revenues have increased by 14%, and our headcount has increased by about 11%. If you take a look at previous quarters and previous years, typically, that's been the trend where the headcount increase is lower than the revenue increase. And we would expect that to continue on our business model as such.

Now going forward, it depends upon the type of service mix that we are providing to our clients and the kind of activities that we are undertaking. As we move from digital operations to data and AI-led operations, that is definitely going to result in a lower headcount addition and a much higher revenue uptake. But if we get into newer service lines, there, it will depend upon what the dynamics of those new service lines are and the revenue per headcount will be determined by the characteristics of that particular service line.

So I think on a steady-state basis, as this transition takes place, you would expect the delta between the revenue headcount and the revenue headcount to be 3% or so, which is what the case is right now. But as we go forward, that can shift one way or the other.

Our next question comes from Abbey Hochreiner with JPMorgan.

It's Abbey on for Puneet. I was wondering if you could talk about the specific drivers on such strong traction in AI and data services you provide to operations management clients. Was it in any way related to AI model evolution or just clients embracing AI with new budgets?

Yes, sure. So our data and AI-led portion of our business has multiple elements within that, right? It's got our data management business. It's got our analytical model and services and solutions business. It's got our payment integrity business as part of that. It's got our data and AI-led operations.

So frankly, we are seeing broad-based traction and growth across all of these different service lines that we have in that category. I would say that the data management part is foundational, and that's something which we are seeing, frankly, a huge amount of demand in. And the challenge for us is actually being able to hire talent quick enough to be able to fulfill that demand.

In other areas, we are seeing a pivot take place. So we are seeing some of our analytical services switch over to AI services. And that's a very strong pivot that's being made associated with that service line. We are also seeing a very sharp increase in the data and AI-led operations. And that conversion, when it's moving into production, that's something which is actually driving a faster growth rate of our data and AI-led category.

So we are very pleased with the fact that we've got multiple service lines in that category. And each of these service lines has got a tremendous amount of headroom, and they're all growing very nicely, and it's very broad-based. So it's not one particular service line that's driving that growth. It's actually multiple service lines driving growth. And that's what gives us confidence in terms of the sustainability and the durability of our business in terms of our business growth.

Got it. It seems very broad-based. And maybe as my follow-up, so our checks are showing that AI-driven automation of business processes from 50% to 80% is 10x harder than going from 0 to 50%. So could you touch on what you're seeing in your clients in embracing this next milestone? And is there any progress within the quarter?

I'm sorry, the 50% to 80% refers to what metric?

AI-driven automation of business processes.

Right. So what our sense is when clients want to adopt AI into their operations, it's not just simply taking an LLM model and pasting it on top of that operation. There's a whole series of work that needs to be undertaken. #1, the data foundation has to be correct and the ability to use structured and unstructured data and make that be readily usable, that's a key foundational step.

Secondly, the application of the LLM or the AI model, that needs to be iterated upon and refined as we go forward. Third, there's a very big piece associated with the knowledge and understanding of context. And therefore, bringing together all of the policies and rules and regulations and how a particular transaction needs to be processed, that knowledge needs to be clearly kind of defined with the use and the application of the AI model.

And finally, the semantic layer, which is the key for creating value for any enterprise AI adoption. That needs to be combined using both probabilistic elements of an LLM, but also combining it with deterministic elements, particularly for regulated industries. So bringing together all of these things and then putting together guardrails, putting together security, putting together a number of elements associated with token economics, this is all very, very complex.

And I think we are in a fortunate position that we have done this several times over, and we can deliver the business outcomes to our clients and our clients trust our ability to execute, and that's what's driving the growth there.

Our next question comes from David Grossman with Stifel.

So I think, Rohit, you had mentioned in an earlier question that the NRR is above 100%. We can clearly see that in the numbers. So perhaps you could help us understand how that number has trended over the past couple of years as well as perhaps compare and contrast that with what the IT service companies are seeing who are struggling and kind of what's making you different there as well as maybe talk about the backlog and just how far out you can see that dynamic continuing.

Yes, David. So the NRR for us is actually quite strong and positive. And I think the big reason is that with the adoption of AI clients are actually getting more comfortable outsourcing more work and outsourcing more end-to-end process journeys. In the past, they were just comfortable outsourcing tasks and pieces of it. But now they're quite comfortable actually allowing a partner like EXL to manage that journey end-to-end. And the reason for that is that's the only way to transform the journey, to take control of the data assets, to be able to deploy AI across the workflow and to be able to be held accountable for the outcome.

So frankly, the business model change with AI is allowing this to be a very, very favorable sort of a shift that is taking place as compared to previously. Previously, with the adoption of any other technology, whether it was some of the -- some of the bots that were being put into place, it always used to be about whether more work can be outsourced. But now it is the full end-to-end life cycle that can be outsourced. So actually, it's a lot better in this AI adoption wave.

And how long, Rohit, does it take to go from kind of the beginning to more of a steady state?

It takes a while, David. I think what we are seeing is the setting up the data foundation that itself takes a fair amount of time because most of our clients do not have very mature data estates and putting that in place does take a lot of heavy lifting. And then iterating on the model and making sure that it's working along with the contextual pieces of our clients' business, that's also something which requires a fair amount of effort, a fair amount of complexity and a fair amount of time.

And also, the last thing I would tell you is this is not a once-and-done piece. This is -- once you do implement AI into the workflow, you have to constantly maintain upkeep and there is a managed service portion to it that needs to be in place because these models will drift over a period of time and you need to apply new context to these models, and that needs to be done on an ongoing basis.

So frankly, it's a fairly complex piece of work that needs to be done to deliver the outcome. And then to maintain it and to upkeep it, there is an ongoing piece of work that needs to be there as such.

So can the NRR stay above 1 when you go into the maintenance mode?

I don't know. We'll have to see how that progresses. It's certainly -- one of the things which we are seeing is as we deploy AI into the workflow, our clients are now starting to offer newer feature sets to their customers in their offerings. They're also willing to offer newer service lines. So frankly, there's more being added to the existing piece of work. And if that continues, yes, I think the NRR will continue to remain above 1.

Great. And just one quick one on margins. I know you're guiding Maurizio to flattish margins year-over-year, or at least that's what it would appear in the mid-19 range. And that's despite kind of what looks like favorable mix shift. So is there some dynamic when you migrate from kind of a person-based billing model to more of an outcomes-based model on a short-term basis where there are just some transition costs when you're going through that? Or is there something else going on that would drive kind of result in flattish margins on such strong revenue growth?

Yes, David, we had a very good quarter just overall on profitability. And so you continue to see kind of that uptick in gross margins overall. If you look at the second quarter -- second quarter of last year, we were at 37.7% this quarter, we're at 38.9%. So you're seeing -- and each quarter since then just continues to rise.

What you're seeing is just us being able to drive profitability there. But the offset there is continued investment. If you look at our investment line and you look at the level of investment we're making, it's exponentially higher than revenue growth overall. And so we're going to have to -- we need to continue to invest, particularly in R&D now going forward as we develop more and more AI capabilities now going forward.

So that's what really leads us back to a mid-19% range overall in margins. And if you just look at our just overall guidance, we still are driving EPS slightly higher than overall revenue, which is one of our stated goals.

Our last question comes from Vincent Colicchio with Barrington Research.

Yes, Rohit, you had mentioned the commercial model has changed, and it often incorporates on an outcome-based pricing on AI deals. I'm curious what portion of new AI deals involve outcome-based pricing?

Yes. So look, I think that's an area where we are seeing some of the AI deals have outcome-based pricing models, particularly those where the clients are allowing us to transform their end-to-end processes. That's where they're holding us accountable for the outcomes and the pricing model is switching over to that.

Keep in mind that the adoption of AI is gradual and then that's a shift that's happening over a period of time. There are portions of our business which are already outcome-based. So the payment integrity work that we do is completely outcome-based driven. So that's -- as that business continues to grow and use a lot more AI in its service line, that portion continues to increase.

Anytime we are adopting more AI into the workflow, that's something which is kind of kicking in. But the biggest barrier to this is clients don't have good metrics associated with how to define that outcome and how to be able to attribute the responsibility for the outcome. So some of this tends to be a portion of it being on a fixed fee basis. And then over and above that, there is some sharing of the gain of productivity that we can provide to our clients. And that model is something that works well for -- particularly for newer clients that are kind of going into this and wanting to see the benefit of that outcome.

And could you update us on your -- how the robustness of your acquisition pipeline and where your priorities may lie?

Yes. So look, I think in this environment, we are seeing a fairly strong pipeline of assets. We want to be very careful in terms of the choice of these assets and make sure that these are something which will further our ambitions in terms of being able to be the AI strategic partner of choice for our enterprise clients.

There are capability sets within the AI enablement work stream that we want to kind of add to. And so we've consciously picked and chosen the areas, where we'd like to kind of add more capability, and we're looking at acquisitions on a fairly active and a fairly regular basis. But as you know, the acquisitions are, I guess, only when you consummate an acquisition, can you really be sure about doing an acquisition.

And so we hope that we'd be able to kind of close an acquisition soon, but we can't really comment on the timing of that as of now.

We have no further questions at this time. This concludes our call. Thank you, and have a good day.

Hello, and welcome to the EXLService Holdings, Inc. Fourth Quarter 2025 Earnings Conference Call. [Operator Instructions] Also as a reminder, this conference is being recorded today. If you have any objections, please disconnect at this time. I will now turn the call over to Andrew Tout, SVP, Investor Relations and Capital Markets.

Thanks, Mariana. hello, and thank you for joining EXL's Fourth Quarter and Full Year 2025 Financial Results Conference Call. My name is Andrew [indiscernible], and I'm the new SVP of Investor Relations and Capital Markets for EXL. On the call with me today are Rohit Kapoor, Chairman and Chief Executive Officer; and Vivek Jetley, President and Head of Insurance, Healthcare and Life Sciences. Maurizio Nicolelli, Chief Financial Officer, will not be on today's call as he is tending to a family matter. We hope you've had an opportunity to review the fourth quarter earnings press release we issued yesterday afternoon.

We have also posted a slide deck and investor fact sheet on our Investor Relations website. As a reminder, some of the matters we'll discuss this morning are forward looking. Please keep in mind that these forward-looking statements are subject to known and unknown risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements. Such risks and uncertainties include, but are not limited to, general economic conditions, those factors set forth in yesterday's press release, discussed in the company's periodic reports and other documents filed with the SEC from time to time. EXL assumes no obligation to update the information presented on this conference call today. During our call, we may reference certain non-GAAP financial measures, which we believe provide useful information for investors. Reconciliation of these measures to GAAP can be found in our press release slide deck and investor fact sheet. With that, I'll turn the call over to Rohit. Rohit?

Thank you, Andrew, and good morning, everyone. Welcome to EXL's Fourth Quarter and Full Year 2025 Earnings Call. I'm pleased to be with you this morning to share our financial results. We delivered another strong quarter, exceeding expectations for both revenue and EPS for Q4 and the full year. This reflects sustained double-digit growth momentum and strong execution of our data and AI strategy. For the full year, revenue increased 14% to nearly $2.1 billion, and adjusted EPS grew 18% year-over-year to $1.95 per share. These results reflect strong market demand for our data and AI services and solutions and reinforce client confidence in EXL as the partner of choice to embed AI directly into mission-critical workflows. In the fourth quarter, revenue reached $543 million representing 13% year-over-year organic growth. Our dollar volume of wins in the quarter was more than double that of any other quarter in 2025. While Q4 is seasonally strong from a client activity perspective, we saw accelerated decision-making to advanced transformation initiatives planned for 2026. Increasingly, clients are selecting EXL as an outcome-focused partner that can modernize data foundations and operationalize AI end-to-end at scale.

Our revenue is split across 2 categories: data and AI-led and digital operations. Our data and AI revenue includes data, analytics, AI solutions and services, and it also includes data and AI-led operations. In the quarter, our data and AI led revenue grew 21% in year-over-year and now represents 57% of total revenue. Digital operations, which represents 43% of our business, grew 4% year-over-year. As previously shared, our digital operations revenue excludes data and AI-led operations revenue. In order to provide greater transparency, we've enhanced our investor fact sheet with our total operations view. The total operations revenue includes data and AI-led operations and digital operations revenue. In Q4, total operations grew 11% year-over-year and 14% for the full year. Our deep domain expertise and proven ability to embed AI in the workflow continues to be a strategic advantage as clients modernize operations using an integrated approach to data, AI and human in the loop solutions.

I'll now walk through our fourth quarter performance across each of our 4 operating segments along with key wins. First, insurance. The insurance segment grew 7% year-over-year and 3% sequentially. The Insurance is our largest vertical representing 1/3 of our revenue, and we see good momentum in the growth rate going forward. Insurance carriers are accelerating adoption of AI-powered solutions to drive growth, optimize costs and improve customer experience. A notable Q4 win was with a large North American insurance carrier that selected EXL as its enterprise transformation, data and AI partner. As part of this multiyear initiative, we will deploy a genic AI directly into operational workflows, build a comprehensive data strategy powered by EXL data and deliver end-to-end customer experience transformation.

Second, Healthcare and Life Sciences. This segment represented approximately a quarter of Q4 revenue and was once again our fastest-growing segment with 26% year-over-year growth. This growth was broad-based and was driven by strong demand for data and AI solutions continued growth in payment services, data analytics and expanded digital operations across both new and existing clients. Our solutions are well positioned to help health care organizations manage rising costs, navigate regulatory complexity and improve outcomes. One of our largest wins in Q4 was with a top 5 health care payer. This client has been with EXL for many years already embracing our technology platform and AI-powered payment integrity analytics. In a significant expansion of that relationship, the client selected EXL's AI-powered payment integrity solution to reengineer its clinical auditing processes to improve yield, productivity and operational alignment.

Third, our banking, capital markets and diversified industry segment grew 11% year-over-year, representing nearly 1/4 of Q4 revenue. Clients in this segment are turning to EXL to deliver measurable business outcomes by applying data and AI across the value chain in areas such as credit risk fraud, collections and customer experience. In Q4, we renewed and expanded a multiyear engagement with a leading financial services company with an expanded scope of AI services that spans risk strategy, regulatory modeling, forecasting, collections and fraud. In addition, EXL will design and deliver the company's first-ever governance framework for generative AI models, setting a new benchmark for responsible AI adoption within a global financial institution.

And fourth, our International Growth Markets segment grew 8% year-over-year representing 17% of our total revenue. International markets are an important driver of our long-term growth and global expansion strategy. During the quarter, we won several new deals across insurance, banking and capital markets and energy in this segment. Next, I'd like to highlight the market opportunity we see in and our strategy for growth. Enterprises are under intense pressure to extract real value from AI and our challenge to successfully applied across the enterprise. The gap between AI's technical promise and real-world impact is significant. This gap is where EXL stands out. Through our mastery of domain processes, understanding of complex regulatory environments and expertise in data and AI, we are seen as a trusted partner and orchestrates enterprise workflows and makes AI real.

Let me share how we are executing on this strategy across 3 areas: first, deepening our data, AI and services capabilities; second, expanding our partner ecosystem; and third, developing AI talent and scale. 2025 was a milestone year in advancing our data and AI capabilities. We drove rapid innovation with new Agentic industry solutions embedded AI directly into our core platforms and grew our AI services capabilities. Launched in Q4, exldata.ai our Agentic Data Solutions suite is resonating very strongly in the market. Clients recognize that as an AI-led enterprise starts with getting the data foundation right. We help clients move from data to context to AI by governing and managing enterprise data capturing business context and then activating AI use cases on top of that foundation.

One recent exldata.aI win was with a leading consumer lending fintech, where EXL modernized the full technology stack from legacy on-prem systems to a cloud-native platform. and operationalize the new solution in just 4 months. Another win was with a large health care payer where exldata.ai is being used to create a centralized covered contract repository spanning structured and unstructured data. This enables stronger alignment between contract terms and claims adjudication reducing manual effort, minimizing payment discrepancies and improving speed and accuracy. Importantly, this is broadly applicable, well beyond health care. Anywhere where contracts and policies drive downstream operational decisions from supplier and pricing agreements to customer and partner terms. In addition to building innovative new data and AI solutions like exldata.ai, we continue embedding AI into our core platforms.

In Q4, we introduced a new set of AI agents on our industry-leading life and annuities platform, enabling insurers to automate complex tasks such as product setup, correspondence and data mapping and launch innovative new products in weeks instead of months. Finally, we are seeing accelerating demand for AI services. This represents a large addressable market and an important growth engine for EXL. AI integration is a fundamentally new technology challenge particularly for complex data-intensive industries. Our data and analytics capabilities and our investments in AI give us clear advantage for winning AI services contracts. We support clients across the full AI life cycle from AI strategy and adoption to models orchestration and making data AI ready. Our partner ecosystem is a critical enabler of scale. In 2025, we accelerated co-innovation with AWS, Databricks, Google, Microsoft, NVIDIA and Genesis. This included partnering with NVIDIA on its new AI blueprint for fraud detection, integrating our AI capabilities for CX into Genesis and completing the migration of our life and annuities platform to AWS.

Core selling momentum has increased with 16 EXL solutions now on marketplaces with AWS, Microsoft, data bricks and Genesis. For example, we collaborated with AWS to deploy agentic AI for Sonos' IT service management workflows aiming to create a new benchmark for efficiency, operational intelligence and risk mitigation. These efforts earned industry recognition including becoming a globally managed Google Cloud strategic services partner and being named AWS' 2025 AI ML market disruptor of the year.

Finally, our growth strategy is powered by our talent. We are building an AI-native workforce with deep expertise across engineering, generative AI, agentic AI, partner technologies and cloud platforms. Our sustained focus on embedding AI into workflows is driving rapid employee innovation resulting in 10 new U.S. patents awarded over the past 12 months. Innovation is central to the EXL culture. We continue to invest in training, certifications and tools such as our AI tray ground, enabling colleagues to explore, experiment and build with agentic technologies. Our second Annual Idea Tank competition generated more than 11,000 employee submitted ideas, a sevenfold increase from last year. From these 200 ideas were shortlisted for development on our sandbox with winning ideas receiving development resources to launch new capabilities.

In summary, while AI is reshaping the services industry, we view it as a clear opportunity for EXL. Our integrated approach to AI is creating better business outcomes and growth for our clients thereby resulting in new revenue streams for EXL. AI is driving revenue expansion for our clients and has become a growth engine for EXL enters 2026 with strong momentum and clear strategic focus. Our data and AI pivot is well underway representing 57% of our revenue. Demand for our data and AI-led services and solutions remains robust, and we continue to strengthen our competitive position through investments and capabilities, partnerships and talent. Our client base is diverse. Our pipeline is strong, and we have high renewal rates. More than 75% of our revenue is recurring or annuity like. This provides revenue stability and predictability. We have excellent visibility into 2026.

Turning to our outlook for 2026. We expect revenue to be in the range of $2.275 billion to $2.315 billion representing 9% to 11% in constant currency organic growth. Adjusted diluted EPS is expected to be in the range of $2.14 to $2.19, representing a 10% to 12% increase over 2025. I want to thank our clients for their trust our partners for their collaboration, our employees for their continued innovation and commitment and our shareholders for their ongoing support of EXL's vision. Lastly, I'd like to call your attention to 2 upcoming events. We look forward to hosting our Annual AI In Action virtual event on March 11, followed by our investor strategy update on May 13 in New York. With that, I'll turn it over to Vivek, who is stepping in for Maurizio.

Thank you, Rohit, and thank you, everyone, for joining us this morning. I will provide insights into our financial performance for the fourth quarter and for the full year 2025, followed by our outlook for 2026. We continued our growth momentum in the fourth quarter with revenue of $542.6 million, up 12.7% year-over-year on our reported and 12.6% on an organic constant currency basis. This increase was driven by double-digit growth in our data and AI-led services, which grew 20.7% year-over-year on a constant currency basis. Our adjusted EPS was $0.50 and a year-over-year increase of 15%. All revenue growth percentages mentioned hereafter are on a constant currency basis.

Now turning to the fourth quarter revenue by segment. The insurance segment grew 7.2% year-over-year and 2.9% sequentially with revenue of $185.8 million. This growth was primarily driven by expansion in existing client relationships. The insurance vertical, which includes revenue from international growth markets, grew 6.7% year-over-year with revenue of $215.2 million. The Healthcare and Life Sciences segment reported revenue of $142.2 million representing growth of 26.2% year-over-year and 5.1% sequentially. The year-over-year growth was broad-based, driven by higher volumes in our Payment Services business and expansion in existing client relationships. The Healthcare and Life Sciences vertical, including revenue from international growth markets grew 26.1% year-over-year with revenue of $142.5 million.

In the banking capital markets and Diversified Industries segment, we reported revenue of $122.6 million, representing growth of 10.8% year-over-year and sequentially 1.3%. This growth was driven by the expansion of existing client relationships, primarily in banking capital markets and new client wins. The banking capital markets and diversified industries vertical, including revenue from international growth markets grew 10.6% year-over-year with revenue of $185 million. In the International Growth Markets segment, we generated revenue of $92 million, up 8.1% year-over-year. This growth was primarily driven by higher volumes with existing clients in banking, capital markets and diversified industries and new client wins.

SG&A expenses as a percentage of revenue increased by 130 basis points year-over-year to 21.2% driven by investments in sales and marketing. As expected, our adjusted operating margin for the quarter was 18.8%. This was flat year-over-year. Our adjusted EPS for the quarter was $0.50, up 15% year-over-year on a reported basis.

Turning to our full year performance. Our revenue for the period was $2.09 billion, up 13.6% year-over-year on a reported and constant currency basis. This increase was driven by double-digit growth in our data and AI-led services, which grew 18% year-over-year on a constant currency basis. The adjusted operating margin for the period was 19.5%, up 10 basis points year-over-year. Our effective tax rate for the year was 21.6%, down 70 basis points year-over-year, driven by higher profitability in lower tax jurisdictions. Our adjusted EPS for the year was $1.95 and up 18% year-over-year. Our balance sheet remains strong. Our cash, including short- and long-term investments as of the 31st of December was $331 million, and our revolver debt was $299 million for a net cash position of $32 million. We generated cash flow from operations of $351 million in 2025. And up 30.6% year-over-year. This improvement was primarily driven by higher profitability and better working capital management.

During the year, we spent $53 million on capital expenditures and repurchased approximately 7.5 million shares at an average cost of $42.3 per share. for a total of $317 million. Now turning to our outlook for 2026. Supported by strong momentum, our current visibility and a robust pipeline, we expect 2026 revenue to be in the range of $2.275 billion to $2.35 billion. This represents a year-over-year growth of 9% to 11% and on a reported and constant currency basis. In November, the government of India consolidated multiple existing legislations into a unified framework referred to as the new labor cohort. These changes did not have a material impact on the income statement for the quarter. However, they resulted in a onetime increase of $10.3 million in our defined benefit liability in the balance sheet with a corresponding increase in accumulated other comprehensive income. We expect a prospective increase in employee costs for the year, resulting in an approximately $0.02 to $0.03 dilution to adjusted EPS, which is incorporated in our guidance.

We expect a foreign exchange gain of approximately $2 million net interest expense of approximately $1 million and our full year effective tax rate to be in the range of 21% to 22%. We expect capital expenditures to be in the range of $50 million to $55 million. Our Board of Directors authorized a $500 million common stock repurchase program, effective the 28th of February 2026 for a 2-year period. This is in line with our capital allocation strategy. This new authorization of $500 million represents confidence in our ability to continue our growth trajectory and generate significant free cash flow. We anticipate our adjusted EPS to be in the range of $2.14 to $2.19, representing year-over-year growth of 10% to 12%. This includes a 100 basis point impact due to the implementation of the Indian labor court.

To conclude, we delivered industry-leading financial performance in 2025. And demonstrating our strong competitive position in embedding AI across client businesses. Our leading indicators remain positive and our robust pipeline visibility positions us for a strong start to 2026. With that, Rohit and I would be happy to take your questions.

[Operator Instructions] Our first question comes from Puneet Jain at JPMorgan.

So I wanted to ask about like all the recent news flow around Entropic and Agentic solutions. Like Rohit, you also talked about like how you are seeing like the accelerated decision-making at your clients. Could that accelerated decision-making be a result of all that news flow, which might be causing more urgency on clients to act and to move forward with AI? Or if not that, what would you attribute that accelerated decision-making to.

Sure, Puneet. So we saw accelerated resection making in the fourth quarter. And frankly, the client conversations in the first quarter of 2026 continue to be very active. I think what we are seeing is a greater propensity from enterprise clients to adopt and leverage AI and I think that fits in really well with EXL's capabilities and our engagements with our clients. As you know, in 2025, most of the engagements ended up being proof of concepts, and there wasn't really an enterprise and a scaled up adoption of AI. That seems to be changing at this point of time. There's also a change in terms of shifting the focus from a cost takeout to growth. And I think using AI for growth allows companies to be a lot more competitive and to be able to build up their businesses. So frankly, from our perspective, the environment has become a lot more active and it allows us to engage with our clients even more strategically to help them in these implementations and to help them in the adoption of AI.

And thanks for sharing additional disclosures around data and AI-LED within operations. Could you share like when -- like a traditional operations management client when they decide to implement AI in their processes. What happens to the overall revenue, including like the typical range of efficiency gains that you pass on to the client and incremental work that might come your way in form of maybe data services or managing additional workflows or servicing more processes to that client. What happens when an operations management client decides to implement AI with the EXL to revenue?

Sure. So first of all, we've now disclosed our total operations revenue and shared with you the growth of total operations that we are seeing quarter-on-quarter with our clients and we think that, that growth is very positive and very supportive of the total company's growth rate. There are a few points that I'd like to kind of just highlight for you and everybody else around operations management. Number one, the penetration rate of outsourcing of operation management by clients, in general, continues to remain low at about 15% to 20%. So what that means is that there's a lot more that clients can outsource and particularly with the engagement of AI and technology coming in, a number of more complex processes and more intertwined processes can now be outsourced and that creates a huge amount of opportunity for us.

The second part of this is that as AI and intelligence is being put into the operations, what clients are looking for is a trusted AI-enabled operator of their business and EXL is uniquely positioned to serve as a trusted AI operator for our clients, and therefore, their confidence in entrusting us with more work is being reflected in our revenues and in our financial statements. The AI certainly is able to provide productivity benefits associated with the use of that technology in operations. And the question really is can a partner make that productivity benefit real for the clients and result in tangible business outcomes being delivered to the client and get them the ROI. EXL has been in the fortunate position of actually executing to that and making good on that promise, which clients themselves on their own struggle and other providers have struggled as well. We understand the domain, we understand the data. We have expertise in applying AI into the workflow and therefore, our ability to deliver value to the customer and real tangible outcomes is what is creating EXL to be able to grow at a much more differentiated pace than all of our other peers and competition. I hope that helps you?

Yes, it does. And I totally appreciate like that AI within operations is growing at 40%, 50% rate, much faster than the overall industry and EXL overall. So I appreciate it.

Our next question comes from Bryan Bergin at TD Cowen.

I wanted to ask on the 2026 growth guide. And really, I'm just trying to reconcile a '26 versus what you did -- you're obviously bringing more AI IP to the market. I hear the strength of the AI-related services and the strong pipeline, and you guys have grown well ahead of comps but you are guiding annual growth 2 points lower than you initially did last year. So I'm trying to reconcile that. What would you say is the biggest difference now versus 1 year ago? Is it parts of the client budgets or programs that are just seeing some more pronounced pause or pressure because of the AI initiatives? Is it potentially factoring some added uncertainty in the forecast? Just help us reconcile that, please.

Sure Bryan. Let me clarify for you. In 2025, when we gave guidance that included in organic growth. We had done an acquisition for ITI Group and our guidance included inorganic growth. From our perspective, the guidance that we are giving you today for 2026 is exactly the same as the guidance that we gave to you in 2025 on an organic constant currency basis. Now in terms of our visibility and our backlog associated with this, the visibility is about the same as what we had last year. The backlog is actually a little bit stronger. What I will tell you is a difference for 2026 revenue guidance is we are exiting Q4 of 2025 actually very strong. And you can see that being reflected in a slight uptick in our growth rate.

We've also shared with you that we had client wins in Q4, which are more than twice the pace at which we signed up clients in all of 2025. So frankly, our expectation is that we're going to have a much stronger start to the year in 2026 than we had in '25. And so we feel very good about our guidance, and we feel very good about the visibility associated with our guidance.

Okay. That's good to hear. If we go a layer deeper here, can you give us a sense on how you're thinking about data and AI-led growth potential versus digital ops and any important considerations as you move through the year from a standpoint of growth as you go through the quarters

Yes, absolutely. As you know, our data and AI-led business is 57% of our portfolio, and it's likely to grow much faster than the corporate average. Our digital operations business continues to grow, and we continue to see demand for that from our clients, but it's going to grow at a pace which is lower than the corporate average. And I think the portfolio mix that we have is actually really, really foundationally strong because it allows us to be able to learn from the operations business, build new AI services and solutions for our clients and be able to accelerate the growth rate of our data and AI led business. We're seeing a tremendous amount of strength on data management. We're seeing a tremendous amount of strength on AI services. We are seeing our analytics business be the leading edge to get converted into AI services. So frankly, the engagement with clients is very, very good, and we are very actively engaged in conversations with them as to how they can deploy AI.

Our next question comes from David Grossman at Stifel Europe.

Rohit, I think you already gave some pretty good color on visibility and kind of growth in 2026. I'm just curious, how should we think about the cadence of growth over the course of the year? Do you expect it to be relatively even throughout the 4 quarters? Or are there some things that we should be attentive to that may skew one quarter over another.

Sure, David. So at this point of time, clearly, the visibility into the first half of '26 is much better than the second half of '26 just because of the timing perspective. And what we've already shared is that we're going to be starting out strong. You can see what our growth rate was in Q4 of '25. We think Q1 of '25 is going to be a good growth rate for us. So frankly, with the current guidance, the way it kind of sets up, we're going to start off strong, and we're going to wait and see how the visibility develops in the second half of the year. And based on that, we will update our guidance accordingly.

Got it. And you mentioned some of the things that differentiate you versus some of the IT services companies as well as other peers in the space and why you're growing faster. I'm just curious, you mentioned that you had a large win with an existing client, one of the top 5 payers in the U.S. for Payment Integrity during the quarter. So if I got that right, Payment Integrity is actually one of the areas that people thought would be most vulnerable, right, to some of the innovations coming with AI. So can you just give us any insight into kind of that process, what they were thinking, why that re-upped with you or an expanded scope given some of the newer technologies that are out there?

Thanks, David. This is Vivek. I'll take that question. So as you can see, health care was a very strong growth driver for us, all the way through '25 and including in the fourth quarter. Now what distinguishes health care for us is that we're seeing broad-based growth in health care across our multiple offerings. So if I were to call it out, there's really 3 pillars for growth in health care right now, one of which is our AI-led operations. The other is the work that we do in AI services and analytics and the third one is payment integrity. Now we've got good visibility for all 3 and we expect to see growth for all 3 as we go through the year. Your point about the cost pressures for payers and what does that mean for payers actually points to a tailwind for payment integrity. Because what happens is right now is the payers start facing more cost on their medical loss ratios as well as on their admin costs, they're going to be forced to become more efficient and manage those costs in a better way and EXL's payment integrity offerings is one of those things that they will return to in order to optimize that.

Our win in Q4 is actually an illustration of that because it's someone who's looking to say, "Look, I want to kind of use the AI, bring it into what I'm doing with my payments and try and optimize my overall cost base. And we see that trend continuing.

Right. I guess the question though Vivek is, is there any consideration at the payers we've got fairly substantial IT budgets and operations to try to start doing this themselves, given the availability of some of the newer technology and if not, some gating items to them doing that.

So we are not seeing any of those trends right now. In fact, what we are seeing is that the payers are actively talking to us and to other partners in terms of looking at what is it that they need to do to kind of refine their algorithms and what is it that they need to do to kind of create more sales. So we are not seeing any initiatives at this point and trying to take it in-house.

So David, let me just add to Vivek's comment on that. Clients at this point of time are concerned with business outcomes. And if you can drive superior business outcomes than what they can get with other providers or by their own internal teams, they're going to allocate the business to that provider that is delivering better business outcomes. You can see in the same example that we've quoted, this was a client that has capability, of course, of doing this work in-house. We have been working with us for several years. But based on our capability, which is demonstrated and proven. They decided to award us a single largest win for us and give us even more business because they want to get the better business outcome. And that's what is going to be, I think, quite different in this AI world where business outcomes will matter a lot more than a promise or any other statement because everybody will be making statements and claims. It is which entity can actually deliver that business outcome, and that's what we are seeing is reflecting in our growth rate being much higher than others.

Our next question comes from Elanor Dick at William Blair.

This is Eli Dick on for Maggie Nolan. I wanted to ask about the win in the fourth quarter with the large North American insurance carrier. Can you talk about the nuances of that process? Like was it competitively bid, was it new or existing? And what were like the pricing dynamics there? I'm just wondering if you had any takeaways from the process about pricing or revenue cannibalization or accretion?

Sure. So first of all, this was a brand-new customer for us. This was a new client that we acquired. So there is no existing revenue and therefore, no cannibalization. But what really stood out for us in this deal was that the client actually is engaging with us on a complete enterprise transformation. The work that we are taking on is a data and AI-led transformation of their overall data infrastructure. We're using exldata.ai for that. And then using our AI capabilities and accelerate AI capabilities to go in, transform what they're doing with their CX, transform what they're doing with their back office and transform what they're doing in terms of the overall end-to-end processes that they are running. The deal really stands out for us because what we've done is chosen us as the enterprise AI partner that's going to come in, do all of the transformation and then pick up the operations and operate it in an AI plus a human manner, bringing in our outsourced capabilities. So it's a really interesting deal for us.

The other part of your question was in terms of pricing. And in this deal, what really stands out for me is the fact that DXL is able to start charging for our IP. So we've built in a component here, which is an explicit pricing for the capabilities that we're bringing in and deploying and that's over and above what we've got in terms of time and material. And I imagine you're going to start seeing deals -- more deals of this type going forward.

And then also I just was wondering, are you seeing a shift in client priorities between cost takeout mandates versus long-term digital transformation, including AI? And how can you pivot to capture either?

So I think AI right now actually works across the spectrum, right? So you could actually walk into every functional group within a client and they're looking at saying, how can I deploy AI in a better way? And what is it that I can do right now with agentic AI and bring that into my business. So it's across the board. It's on the revenue line, it's on customer management, it's on cost, it's on audit and controls, what have you. So from our perspective, we have the capabilities right now within our verticals of talking to CXOs across those different areas and bring to them the right AI-led value propositions and that's, I think, what you're seeing a little bit in terms of the pipeline and in terms of the wins that we've talked about. It's that value proposition kind of resonating in the marketplace.

Our next question comes from Robby Bamberger with Baird.

Yes. So just thinking about types of employees being hired, like how should we think about which employees are being hired? Are they higher revenue per employee AI trained? And then maybe also the expected cadence of employee growth through 2026. Any color on that revenue per employee through the year as well?

Sure. So I think the first point I'd like to make is that our employee headcount growth rate is much lower than the growth rate of our revenue. And we think that, that trend will continue. So we will see a differential between our revenue growth rate and our employee headcount growth rate. Second, in terms of the skill sets, our goal is to make sure that every single employee of EXL can be provided the opportunity to get trained, certified and practically apply AI as confidently and as comfortably as one needs to be in this new age of AI. So that's something which we are investing a huge amount of effort and resources to be able to train and skill our employees to be proficient with AI and work with AI as an AI-enabled operator as well as create new solutions leveraging AI.

Lastly, we are hiring, of course, a lot of people, particularly around data management, around working with AI services and creating a lot more of engineering talent that can deploy AI solutions in the enterprise. I will tell you this that our data and AI-led business today is constrained for growth due to talent. And that's something which we are working very actively on to make sure that we have adequate talent resources to be able to leverage the full potential of the data and AI piece.

Just to add to Rohit's comments, I'll just substantiate that with the data. So it's a part of our fact sheet, but the headcount growth for the full year in '25 was less than 10%. It was 9.8% in -- and you see that the revenue growth that we delivered against that was closer to 14%. So that's where we're kind of creating that leverage in terms of revenue per headcount.

Yes. Super helpful. And just in terms of guidance, just wondering what operating and gross margins you have embedded in 2026 guidance and maybe the cadence through the year as well and how that Indian labor code impacts margins throughout the year as well.

Sure. So I mean, you had already heard from Mauricio about our viewpoint in terms of how to manage adjusting operating margin going forward. Our adjusted operating margin for Q4 was 18.8%, which was flat to what we were in Q4 of '24. This is something that we'd already talked to you about, and this was because of investments in the front-end sales and support and in solutions and services. For the full year, our adjusted operating margin actually went up to 19.5%. Our expectation is that we are going to keep it flat for next year and which includes the impact of what's going on with the new labor courts in India. So the number would have gone up were it not for the new labor court. But net of that, we're going to keep it flat.

Our last question comes from Vincent Colicchio of Barrington Research Associates.

Can you you hear me now?

Yes.

Rod, I'm interested in an update on the competitive landscape on the AI side. Are you seeing any new competitors? And if so, is there any impact on your win rates?

Yes, Vincent. We are seeing new competitors come in, and that's something which we've been seeing for a while now. So it's no longer just the traditional services companies that we compete with. We are seeing some of the hyperscalers kind of get in here. We're seeing some of the technology providers getting into the space. And there certainly are a number of other consulting firms who are kind of trying to kind of go into this space. So the set of competition has certainly changed and it's changed for a while. Our advantage really is the fact that we have this integrated approach to helping our clients embed and use AI across the enterprise in a very disciplined way that delivers much better business outcomes.

So our knowledge and domain expertise about the industry and our clients business, our mastery of data and applying AI and ML techniques to the adoption of AI and our understanding of the workflow. These all make it very, very easy for clients to kind of adopt AI. And then finally, Keep in mind that a large percentage of our client portfolio is in regulated industries and our knowledge and understanding of regulations and the ability to keep our clients fully compliant with regulatory requirements. That's a big standout, and that's why they trust us for being that AI-enabled operator and the AI implementation partner for them. And so we stand out, though others are certainly coming into this space, and they're certainly coming at it from a standpoint of either having the technology or having the foundational model and trying to leverage that capability and bringing it to enterprise clients.

And can you update us on your acquisition priorities?

Sure So one of the good things about EXL is that we've got a very strong balance sheet, and we've got a tremendous amount of capital available to do acquisitions. And at this point of time, some of the valuations and some of the assets are becoming quite attractive. And then therefore, for us to be active in the M&A space, that's something which is something that you should expect. The prioritization for us is going to be to continue to further our strategy around helping clients with AI. And so what that means is investing in capabilities around data and making data ready for AI. What that means is having the engineering skill sets to apply AI into enterprise workflows, what that means is for us to be acquiring new capabilities that we can take to our clients. And then finally, geographic diversification. So those are some of the areas that are important priorities for us from an M&A perspective. And in this environment, I think the targets are becoming a lot more accessible, approachable and hopefully, affordable. So that's something which we are hoping that we can take advantage of.

We have no further questions at this time. This concludes our call. Thank you, and have a good day.

Good afternoon. My name is Puneet. I'm from JPMorgan's Payment Processing and IT services team. Glad to have here with us Maurizio, CFO of EXL. You all know him well. Last presentation of the day, but saving best for the last. So Maurizio, thanks for joining us.

Format of this presentation is going to be fireside chat. I'll start with a few questions, and then we'll open up the floor from questions from audience. So thank you again, Maurizio.

So maybe for benefit of investors who may be new to the story, could you talk about EXLS' positioning, your value proposition and how you are unique relative to your peers?

Sure. And thank you for having me, Puneet. So why don't we talk a little bit about EXL's value proposition? When we think of the value that we propose to our clients, it's really in three different areas that are core to the overall business.

The first is domain expertise, in that we understand the segments of our clients' workflows significantly because we've been at operations since essentially the early 2000s. We manage many, many workflows for many different of our clients within different industries, whether it's insurance, health care, banking, many different areas. We've built that domain expertise over many, many years.

Secondly, data management. Back in 2006, we made the acquisition of a small Indian company called Inductis, and we got into data analytics. And since then, we have built up our data management business. And now that's becoming very significant for us, especially in this AI world.

Everything starts -- when you start to build AI, it's on client data. And in many cases, we need to work on the client's data in order to make it ready and structured in order to build AI on top of.

And if you notice, about a month, 1.5 months ago, we had a press release that we came out with EXLdata.ai. So what is that? That is 65 agentic AI agents that we use, and it's an accelerator to be able to use that functionality to structure and manage the clients' data to get it AI ready at the end of the day. And so that data management piece is really core overall to our proposition.

And then lastly, it's us building AI solutions for our clients. Now those can be AI solutions that we sell in a proprietary mode or it's AI solutions that we embed into existing or new client operations that we bring onboard into our overall workflow.

So between domain expertise, data management and AI capabilities, when you put all three of those together, that is an extremely valuable proposition to our clients because many clients will -- or many competitors will have one but not the other two, right?

They may have domain expertise because they run operations, but they may lack the level of capability in data management. Or AI or it could be the other way around. It could be an AI-specific business that builds AI solutions, but does not do much in data management and does not have domain expertise in certain industries.

No, very good. And clearly, like it reflects in the growth rates, you are clearly outperforming your peers, IT services companies. So let me -- so let's disaggregate data and AI revenue that you talk about.

How much of that data and AI-led business is data-driven, so like you said, like helping clients improve quality of data, helping them access data? So is there a way to think about like how much of that data versus, let's say, legacy analytics work that you've been doing for decades now? And also, maybe if you can also talk about marketing analytics, which has been an anchor to that segment's growth in the past. Is that still a headwind?

Sure, sure. So our Data and AI segment is the piece of our business that is really growing significantly now. We are embedding data and/or AI in so many of our clients' workflows. Every new opportunity is starting to have some form of data or AI embedded in that new opportunity. We're also cannibalizing and going after our existing Digital Operations business and embedding data and AI into that business overall.

When you look at the total, when we released our third quarter results, we now have 56% of our revenue as data and AI. So what's the makeup of that piece? The big piece of that is our historical data and analytics business. When we ended the year last year, about 44% of our business was data and analytics. So that is buried in that 56%. The other 12%-ish of that total is the remaining us selling our AI solutions into the marketplace.

And so as we move forward, you should see data and AI continue to grow between 15% and 20% going forward. And as Rohit talked about on the call, digital operations is started -- we're starting to really embed more and more data and AI into that revenue. So you're going to see some of that revenue actually move to data and AI. So that growth rate will probably be somewhere in the mid-single digits going forward.

When you aggregate it all together, you still get to low double-digit growth overall. And like this year, our organic growth rate will be right around 13% overall as a total consolidated growth rate, with data and AI being in the high double digits or high teens and Digital Operations being in the single digit.

Let's double-click on Digital Operations, like mid-single-digit growth that we expect there. Obviously, that segment also suffers from AI cannibalization, like that revenue moves into the other segment when you automate a process using AI, right?

So the segment was down like 2% sequentially in the last quarter that drove like a lot of concerns among investors about the long-term growth of that segment that if AI cannibalization could keep that segment from growing at mid-single digits. So talk to us about your confidence in that mid-single-digit growth in that segment. And what will drive that incremental revenue to offset cannibalization?

Yes. So there's still plenty of opportunities in Digital Operations. Even though the majority of our Digital Operations business will have an element of data and/or AI in it, we are now -- and you're starting to see that in our results, whereby we are actively going into our Digital Operations business.

And we used the example of our British energy client in the call in that we went in into one of their workflows, and we got a 30% efficiency from that overall workflow from embedding agentic AI into that process. And so essentially, the client got a 30% efficiency benefit.

If you think about it in terms of revenue, the revenue comes slightly down because of that. But what ends up happening is because we've embedded AI into that business, we're getting more of the work from that client because we have to handle the workflow from end to end. And because of that, nothing -- we did not see any material change in revenue. What we did see was profitability increased materially for that client because now a big portion of that is technology driven.

We also become a stronger strategic partner for the client because they've seen us embed AI into their workflow. Our success rate in embedding AI, particularly in operations, is 90% plus because we run the operation. And so we're able to embed AI successfully because we're already running that operation.

So what you end up having is an extremely positive effect overall with the client. It's much better revenue for us because it becomes technology IP driven at the end of the day and it gives us the opportunity to expand within that client.

And if you look at kind of the growth of our company over the last 4 or 5 years, and we have it in our investor deck, we continue to expand within existing clients, more workflows and increase in total average dollar amount that we're billing the clients because we're taking on more and more workflows. And that's been the way we have grown the company essentially since the beginning of time. It's really a land-and-expand approach.

Yes. So the new contracts that you're signing in Digital Ops, like I'm assuming like almost -- in almost all of them, there is some level of AI benefit that's baked in that. So contract comes up for renewal, when you renew, the client would expect some level of AI benefit if they're locking themselves in for 5 years, let's say.

So are you seeing like in those cases, some level of overpromising by your peers, like overpromising AI benefits? Because you are -- like AI is still evolving, right? So do you off-promise benefits of what AI can do today or what you think AI will be 5 years from now? And so could that create like a scenario where some of your peers might -- who are hungrier for growth, who are finding it difficult to grow, overpromising what AI-based benefits?

I think clients are pretty astute, right? I think with the evolution of Gen AI and now Agentic AI, you have many, many more competitors out there in the market. We traditionally have competed against the traditional BPOs. We've traditionally competed against someone like Accenture and a few others.

But now you're seeing consulting firms with their own AI practices. You're seeing some of the big 4 accounting firms with their own analytics AI practices. You're seeing a number of other types of players in the market trying to sell AI solutions. So when you -- when we go to -- when we start the process and bid for a piece of new business, you're seeing many competitors.

That funnel really starts to shrink as we get further into the bidding process. And you end up and the client really understands what each of the capabilities are, who they're willing to trust, who has the domain expertise, data management and AI capability for all three to be able to implement. And that really narrows the field down to 1, 2 or 3 competitors when you get down to it.

So I think clients are astute to it. And so I do think you start with many more competitors now, but the funnel comes down pretty significantly.

And do clients typically wait until like a contract renewal to renegotiate or to have like their vendor bake in AI gains? So if you -- let's say, if you signed something 2 years ago with 3 more years on that contract, would clients wait 3 more years? Or would they say, proactively go to the vendor and say, "Hey, let's figure out, let's see how you can include AI" and renegotiate the contract?

It really depends upon the client, right? So there's going to be clients whereby we implement AI and we determine to wait until the end of the contract. Some other clients, we will embed AI and we will repaper it and discuss the new terms on that contract.

If we're embedding AI in a contract that is transaction-based, we -- that waits until the end of the contract because the client is just paying on a transaction basis, whether there's AI or not. But for us, it's important because embedding AI means we will have a higher profitability because there's less people involved.

So it really depends on the client and the process and how it's getting built.

Let's talk about Agentic AI. Obviously, like a lot of hype, a lot of focus, energy there. How much of that is real in terms of like the actual use cases or actual dollars like where clients are replacing their current process with an Agentic solution? Like is it still like very, very early stage, more like POCs and pilots? Or are you seeing like that it is large enough that it's meaningful to move the needle for EXL?

I think it's getting more implemented. I think, in order to move the needle for us, it has to be pretty material, right? So we're not there yet. It's obviously, we're not there yet. But as we create more and more Agentic AI agents, you're seeing it have more an effect in different client operations. And as we go quarter after quarter, you're going to see kind of this effect going forward.

We had our first quarter in the third quarter where you saw Digital Operations actually decline in total dollars, right, because we embedded AI, and a lot of that is Agentic AI.

So you're seeing it start to move into client operations. Some clients are willing to implement it more quickly than others. And some are taking a little bit of a wait-and-see approach. But for us, we're using that technology not only to embed it into client operations, but also in other areas like data management, like EXLdata.ai is literally us creating 65 Agentic AI agents to use in transforming client data.

So we're using the technology in many different areas. I think clients' reception is a little bit mixed. But there are clients that are moving forward with it, and we tried to highlight that in the call.

Yes. AI readiness has been like a topic that has come up in almost every of meetings today. So talk to us like what are the constraints in a client organization that's keeping them from embracing AI.

It could be governance like the security, privacy, change management issues, it could be the data is not ready, like you talked about like you help clients transform data or it could be like the use cases like there is not enough conviction in those AI use cases, the benefits they might get, like if I go back to that MIT report that everyone talks about.

So talk to us like when you see your clients, like what are the bigger constraints that's keeping them from embracing AI? And how can EXLS help those clients overcome those barriers?

Yes. I think it's a little bit of all of that. I think, one, it's security clearance overall. I think that's an impediment for a lot of the highly regulated segments that we operate in, whether it's insurance, healthcare, banking, I think that's an impediment and that delays the process overall.

Data management is critical, right? So in most cases or in a lot of cases, the clients' data is not structured in a way to implement Agentic AI or an AI solution on top of. You got to keep in mind that some clients still have not moved their data from on-premise to the cloud, right?

And the cloud came out 20 years ago. So clients are still moving -- doing that work and then looking to do work such as Agentic AI on top of that to be able to become that much more efficient.

So I think there's a bit of -- still of a process that clients are going through. But there will be clients that will move very quickly to implement this technology. And that's why we want to ensure that we have the solutions for them to take advantage of that sooner than later.

At this time, are there any questions from audience?

I was curious if -- it's kind of like a higher-level macro question. If you just -- if you think about like your clients and how they're engaging with these types of projects, does it feel like there's a holdup around macro uncertainty still? Or has this been kind of like -- because I feel like we've been talking about this concept of uncertain -- macro uncertainty for like 3 years.

Is that behind us now? Does it feel like there's still an unlock that could be kind of achieved if a macro turns one way or the other? Do you get the kind of crux of the question?

Yes. Yes. I think clients are looking to assess the ROI on these solutions. I think the macro has changed a little bit. If the macro gets better, I think they'll be a little bit more aggressive. I think if the macro gets a little bit weaker, maybe they slow it down. If they see the ROI on implementing that solution, I think they will move forward.

I would say, probably 1.5 years ago, in the banking sector, we saw a little bit of a hold on budget spending. We don't see that today. We see our banking business doing very well today and a lot of our global banks implementing more of our AI solutions and doing more data management work with us. So if they are releasing their budget on the banking side, then I think the macro is actually not in a bad state today, right?

So I think it's less macro. It's more about them willing to implement and seeing the ROI so they can move forward and getting over that hurdle that we talked about just with -- in terms of impediments.

My question is about ROI on like AI and agentic solutions. Everybody is talking about the MIT report this year, and we've heard a lot that a lot of the preliminary POCs didn't really get off the ground, didn't generate a lot of tangible ROI.

So I think maybe the question -- the way I want to ask this question is like what are people doing wrong if they're not achieving results? And like what have you seen in the market where people have made misplaced investments on AI that didn't really pan out versus what you all are up to?

I think we've been a little bit more successful with POCs because we're doing POCs in segments we operate in. And so we understand the process a bit better. It also points towards the success rate that we have in implementing AI -- so we have been -- we've had a higher success rate with POCs. And a lot of those POCs have made it into production now going forward. So I think we've had that benefit.

If you're an AI firm building AI solutions but you don't have that domain expertise or you don't have that data management capability, your success rate, from everything that I've read, is around 50% overall. That's a dramatic difference from our success rate. And I think that gives us a very big leg up. And I think that contributes to the difference on the success rate on POCs between us and maybe the rest of the market that has a little bit less of the three capabilities that we have.

You mentioned that you like to implement AI in your projects because from [ EXL ]standpoint, it's accretive to you guys. What's the pushback you get from clients in implementing AI in those projects? And is there a specific sector where you're seeing more pushback than others?

I would say there are clients that want us to take over an operation. And they first want us to take over the operation and then subsequently, at some point, embed AI into that process. They don't want to do it right away. And I've seen that in a number of international clients that we brought onboard, whereby we take over the operation and the discussion with the client is we're going to embed AI transformation in years 2 or 3 later on. So that still occurs.

Now that means -- that also means it's good for us because we want that Digital Operations revenue also, right? Because we know at some point, that's going to get converted, right? And so once we have our tentacles into that operation, then we can embed that AI, but they're not doing it right away.

And they're not doing it for multiple different reasons, right? It's kind of the stuff we talked about, security, they're worried about their data being unstructured or not structured well to build AI on or just a reluctance internally to do everything -- to start everything all at once.

So let's take a step back like and focus on the entire industry, BPO industry rather than EXLS. So let's say, AI adoption picks up. And what that does is it reduces the price like, let's call, prices as price per unit transaction, right? Not the bill rates, but the price goes down because you're doing services, providing services using AI.

On the other hand, like the bullish we would be the quantity, the volume of work that will also increase to offset that reduction in price. Where will that increased volume come from? like -- because in BPO, like whatever the new volume, the new work that you will get, someone else is doing that work right now, right?

So for the overall industry, I can clearly see EXLS gaining share, taking share in that. But for the overall industry, where will that increased volume come from? Who's going -- who's providing that?

I think when you look at just the overall landscape, right, so think about our contracts historically. We're always guaranteeing a 3% to 5% efficiency every year to our clients in our contracts, right? So that still remains, even in this new era. But as we move forward -- and we have grown off that, right? So when the contract comes up, there's a level of discount we'll give to the client, but then we get new work.

Historically, that new work comes from client-run operations. And when we look at kind of where we continue to grow, there's still a huge opportunity in terms of managing more and more of our client-run operations.

I don't think we've gotten to the point where any one segment is completely penetrated. Even if you look at insurance in terms of what can be outsourced, and I think only about 30% of what can be outsourced has been outsourced, and that's our most significant segment. So I think the opportunity still exists materially in the marketplace, in the different segments that we can take on more and more of client-run operations.

Rohit talked about it on the call that the opportunity just in healthcare alone is enormous for us. We are extremely small in that area, particularly in operations. So that is just an example of many different areas within in our client set that we can grow into just gaining client run operations, forgetting competing with -- for operations that are run by our peers.

And are you seeing -- like you talked about earlier, like that you're seeing not just companies like Accenture, but also big 4 consulting companies trying to compete for the same opportunity. So are you seeing like -- how should we think about BPO companies' competitiveness?

Like obviously, you have the domain knowledge, you have the process expertise versus some of the IT services companies, which can bring like the technology capabilities they can help move -- help clients move to cloud, improve data and all that. So how do we assess like these two priorities or focus that BPO versus IT services companies bring to address this opportunity?

Look, I think all of our peers are trying to poke into different directions into each of our businesses, right, or our business models. I think, for us, we are very well positioned in terms of many different areas.

One, I think we're well positioned in Digital Operations with our domain expertise. I think that is core to us really continuing to build out the business. You're going to try -- you're going to see some of the peers or like the IT companies look to get into that space. And they've made -- some of them have made comments to try and do that organically. But again, it also comes down to who's the client going to be most comfortable with at the end of the day.

And a lot of these companies don't have the other capabilities that we have, particularly in data management. I think everyone is trying to build their AI services or capability. But I think the domain expertise and the data management capability are a bit unique. And they -- you can't just build that over a 12-month period, right? That is years of knowledge. And to a certain extent, it's almost like an IP to us.

So given like all those changes that are happening in your delivery structure model, how should we think about margins? Like in slight margin expansion at EBIT margin level is still the right goal?

So when we look at the trajectory of the business, right, and you look at 2026, we should be continually driving data and AI, the percentage overall. That should continue to drive higher the percentage of the overall business in data and AI.

As we do that, that should drive overall gross margin, right? We talk about driving gross margin so that we can invest more below gross margin. The net of all that should be an increase overall to adjusted margins, right? And we do that every year.

Now some years in the past, we've done a lot of increase in adjusted margins. But now you're going to see us invest a bit more in R&D. So we're going to continue to grow EPS faster than revenues. If you look at just at 2025, EPS growth is between 14% and 16% in our guidance and revenue growth is 13%.

We have a mandate internally to drive EPS faster in revenues. In order to do so, we have to drive margin. So you're going to continue to see margins increase on an annual basis. They may not be as high as you saw in '21 or '22, whereby in some years, we've increased it more than 100 basis points; but that's still going to be a goal of ours on an annual basis and a target.

And can you also quickly talk about use of cash priorities? You haven't done an acquisition in -- it's been almost a year, I think. So talk to us like how do you see -- like with valuation, especially with valuation for IT services or for some of those technology capabilities, public valuation at least coming down so much, how do you see like use of cash in terms of acquiring a strategic asset tuck-in deal versus other uses?

We're still very inquisitive on M&A. We still look at a lot of assets, I think. And the assets we look at are particularly in data and AI, capabilities in data management and also digital AI capabilities also are -- that we can build out horizontally in all our segments. It's very attractive to us. So still very inquisitive.

For us, it's more tuck-in acquisitions or medium-sized acquisitions that we're most interested in. And if we're not allocating capital to M&A, then we're buying back shares, particularly in this environment. When you look at the stock price today, that's very attractive to us in terms of our buyback. We were authorized for $0.5 billion in buybacks back in March of 2024. That will expire in March of '26. I think we did over $200 million last year. We'll do over $200 million this year.

So we'll continue to buy back shares at this price because when you look at the intrinsic value of the company, looking at the future profitability and cash flows, it's an attractive price today on where it is. And for us, the buyback shares today is a good use of -- and we believe it is a good use of our cash.

That's amazing. All right. Thank you so much.

Hello, and welcome to the EXLService Holdings, Inc. Third Quarter 2025 Earnings Conference Call. Also, as a reminder, this conference is being recorded today. If you have any objections, please disconnect at this time.

I will now turn the call over to Shirley McBeth, Chief Marketing Officer.

Hello, and thank you all for joining EXL's Third Quarter 2025 Financial Results Conference Call. On the call today with me are Rohit Kapoor, Chairman and Chief Executive Officer; and Maurizio Nicolelli, Chief Financial Officer.

We hope you've had an opportunity to review the third quarter press release we issued yesterday afternoon. We've also posted a slide deck and investor fact sheet on our Investor Relations website. As a reminder, some of the matters we'll be discussing this morning are forward looking. Please keep in mind that these forward-looking statements are subject to known and unknown risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements. Such risks and uncertainties include, but are not limited to, general economic conditions, those factors set forth in yesterday's press release, discussed in the company's periodic reports and other documents filed with the SEC from time to time.

EXL assumes no obligation to update the information presented on this conference call today. During our call, we may reference certain non-GAAP financial measures, which we believe provide useful information for investors. Reconciliation of these measures to GAAP can be found in our press release, slide deck and investor fact sheet.

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

Thanks, Shirley. Good morning, everyone. Welcome to EXL's Third Quarter 2025 Earnings Call. I'm pleased to report another strong quarter as we consistently executed on our data and AI growth strategy. In the third quarter, we generated revenue of $530 million, an increase of 12% year-over-year. And we grew adjusted EPS by 11% and to $0.48 per share.

In the quarter, our data and AI led revenue grew 18% year-over-year, reaching 56% of total revenue. Our data and AI-led revenue comes from EXL's AI-powered solutions and services, including those in which we embed data and AI into client workflows. This is the third consecutive quarter we have accelerated our data and AI led revenue growth, underscoring both the rising demand for AI-driven solutions and demonstrating our leadership in embedding AI directly into client workflows.

At the same time, our digital operations revenue grew 6% year-over-year. This is significant when you consider but as we embed AI into workflows, we manage, the revenue moves from digital operations to the data and AI-led revenue category.

Our third quarter results displayed sustained momentum across all operating segments. The insurance segment grew 9% year-over-year, which represented 1/3 of our revenue in the quarter. This growth was driven by our insurance clients evolving their operations to be more AI-powered. We believe the increased adoption of AI in the workflow in insurance is a long-term trend from which EXL is well positioned to benefit.

Healthcare and Life Sciences represented 1/4 of our revenue and was once again our fastest-growing segment at 22% growth. This performance was fueled by our demand for data and AI solutions. This included growth in our Payment Services business as well as expansion of digital operations and analytics services with new and existing clients. banking, capital markets and diversified industries grew 12%, representing nearly 1/4 of our revenue.

Looking ahead, we see significant opportunity to further increase value and improve business outcomes in this segment by leveraging our enhanced data and AI capabilities across the value chain.

In Q3, we drove 8% year-over-year growth in our international growth markets segment as we continue to diversify our business geographically. This segment represented 18% of our total revenue in the quarter. International markets represent meaningful potential for us to accelerate our long-term growth trajectory and expand our global footprint. We are encouraged by the overall demand environment, which remains positive. Our sales pipeline grew with the addition of several new data and AI-led opportunities.

As enterprises navigate ongoing economic uncertainty, their priorities are expanding beyond the traditional focus on cost efficiency. They are also looking to change their business models, expand their total addressable market and grow revenue. As clients adopt AI to help achieve these goals they need trusted partners to help them navigate change and deliver tangible business outcomes. With our proven track record and comprehensive set of innovative AI-led solutions. We are a natural partner for our clients on this journey.

For our existing contracts, we maintain exceptionally high renewal rates. More than 75% of our revenue is recurring or annuity like. This provides revenue stability and predictability. Combined with a healthy new business pipeline, we have momentum to sustain double-digit top line growth into 2026.

I'd like to highlight progress made in advancing our data and AI strategy to deliver differentiated value for our clients. I'll cover 3 areas: number one, the launch of our new exldata.ai solution; two, client momentum with the adoption of embedding AI in the workflow; and three, industry recognition of our domain, data and AI leadership.

Firstly, I'll cover the launch of our latest innovation. Earlier this month, we unveiled EXLdata.ai, the first its-kind agenetic AI suite of data solutions that help clients make their enterprise data AI ready. Data is the single biggest barrier to AI adoption. Our research shows only 30% of organizations can access their data enterprise-wide and most struggle with unifying data silos across legacy platforms. The challenge is especially acute with unstructured data, which now represents 85% of all enterprise data, especially in regulated industries.

To make unstructured data AI-ready, it needs to be annotated, labored and categorized within a structure. This is a manual, time-consuming and expensive process. We built exldata.ai to solve these challenges with exldata.ai, more than 65 AI agents autonomously managed data modernization, governance, quality, lineage and -- accessibility across the entire data life cycle.

This AI-first approach sharply reduces implementation time, which previously used to take months, down 2 weeks and even days, Built on a platform-agnostic architecture, exldata.ai integrates seamlessly with all leading data platforms, including our launch partner, Databricks, and Snowflake and [ Panter ] and can be deployed across all the major cloud providers, including Microsoft Azure, Google Cloud platform, Amazon Web Services, as well as with NVIDIA's accelerated computing infrastructure. We believe exldata.ai is a game changer, helping clients overcome the biggest hurdle to AI adoption.

Next, I'd like to highlight our success with embedding AI and client workflows. I'll share 3 examples that are illustrative of the scale of many projects underway and the client business value that we generate. The first use case is our multiagent powered solution for a U.K. insurer designed to improve and accelerate risk assessment for underwriters. EXL embedded AI into a new business submission workflow that processes thousands of e-mails and attachments each month. The AI agents extract the right information and assess risk in real time. Processing time has been reduced from a week to a few hours and court conversion has increased by 7%. Real-time insights for brokers also help improve the customer experience during client interactions.

The second client example is a large U.S. health care organization. The collaboration began with the successful delivery of an enterprise-wide gen AI platform for document processing which has become a foundational solution for managing unstructured data across the organization.

Building on that momentum, we are designing a next-generation agenetic ecosystem to power safe and secure solutions across their finance, pricing and supply chain functions. These AI-powered solutions are helping to reduce the cost of care accelerate speed to market for new solutions and improve end user experiences, work that used to take weeks can now be done in ours.

My third example demonstrates how EXL is using AI to transform digital operations for existing clients. EXL's in-depth knowledge of the client processes that we already run is a huge advantage in accelerating infusion of AI and driving faster outcomes. For the past 2 decades, EXL has been a strategic partner to one of U.K.'s largest energy and home services companies. We've helped them reimagine their end-to-end operating processes, including onboarding, meter to cash, consume to pay and customer exit.

By integrating data and AI throughout the front, middle and back office operations, over 35% of transactions that we run for this client are now AI-enabled. Our solutions have driven significant improvements in customer experience, including achieving 98% onboarding accuracy and a 10% upliftment in billing accuracy and timeliness. In addition, our initiatives leveraging intelligent automation and applied AI have improved productivity by over 30%.

Our revenue from this client has not declined as we were awarded additional work that grew the relationship. And we are positioned really well to begin implementing in genetic AI for this client and grow value-added revenue streams. These 3 client examples are representative of numerous successful EXL client AI deployments. While many enterprises struggle to generate returns from AI investments, EXL's unique strengths in domain data and AI are delivering meaningful ROI and transforming how businesses operate. This has resulted in a success rate of over 90% and for EXL's AI deployments.

Finally, I'm proud to share that in Q3, EXL received several recognitions of our AI services and solutions leadership across our core industry verticals. Here are a few highlights. In insurance, we were named a market leader in the HFS Research, Horizon Insurance Services 2025 report, which emphasized EXL's data-first approach, deep insurance expertise and AI-driven operational insights.

For health care, EXL was recognized as a leader in Evris Group's health care data, analytics and AI services Peak Matrix 2025 for our domain expertise, analytics focus and strong partner ecosystem. In banking, EXL was recognized as a category winner in the 2025 IDC FinTech Real Resorts program. We were recognized for building a financing solution that allowed First National Bank of Omaha to introduce new financing options quickly, integrate seamlessly with merchants and scale with agility.

These recognitions validate EXL's innovative data and AI expertise as well as our unique approach to helping clients deliver significant business outcomes at scale. In conclusion, we saw strong demand for our services and solutions across the markets we serve. We have bolstered EXL's competitive position by investing in next-generation data and AI capabilities with the launch of exldata.ai.

Our business portfolio is well balanced and stable and we have excellent visibility and confidence for the remainder of the year. As a result, we are raising our revenue and EPS guidance for the full year.

With that, I'll turn the call over to Maurizio to provide more details on our financial performance.

Thank you, Rohit, and thanks, everyone, for joining us this morning. I will provide insights into our financial performance for the third quarter and 9 months ended September 30, followed by our revised outlook for 2025. We delivered a strong third quarter with revenue of $529.6 million, up 12.2% year-over-year on a reported basis and 12.3% on a constant currency basis. Sequentially, we grew 3.1% on a constant currency basis. Adjusted EPS was $0.48 and a year-over-year increase of 10.8%. All revenue growth percentages mentioned hereafter are on a constant currency basis unless otherwise stated. Now.

Turning to the third quarter revenue by segment. The insurance segment grew 8.5% year-over-year with revenue of $180.5 million and 4.9% sequentially. This growth was primarily driven by expansion in existing client relationships and new client wins. The insurance vertical, including revenue from international growth markets grew 7.3% year-over-year with revenue of $211.1 million.

The Healthcare and Life Sciences segment reported revenue of $135.3 million, representing growth of 21.6% year-over-year and 4.5% sequentially. The year-over-year growth was driven by higher volumes in our Payment Services business expansion in existing client relationships and new client wins.

The health care and life sciences vertical, including revenue from international growth markets grew year-over-year with revenue of $135.5 million. In the banking, capital markets and Diversified Industries segment, we reported revenue of $121 million representing growth of 11.8% year-over-year. This growth was driven by the expansion of existing client relationships, primarily in banking, capital markets and new client wins. The banking, capital markets and diversified industries vertical, including revenue from international growth markets grew 12.1% year-over-year with revenue of $182.9 million.

In the International Growth Markets segment, we generated revenue of $92.8 million, up 8.4% year-over-year and 1.7% sequentially. This growth was primarily driven by higher volumes with existing clients in banking, capital markets and diversified industries and new client wins. SG&A expenses as a percentage of revenue increased by 120 basis points year-over-year to 21.3% driven by investments in front-end sales and marketing. Our adjusted operating margin for the quarter was 19.4%, down [ 50 ] basis points year-over-year, driven by investments in front-end sales and new solutions. Our effective tax rate for the quarter was 22.1%, down 70 basis points year-over-year, driven by higher profitability and lower tax realizations. Our adjusted EPS for the quarter was $0.48, up 10.8% year-over-year on a reported basis.

Turning to our 9 months performance. Our revenue for the period was $1.55 billion, up 14% year-over-year on a constant currency basis. This increase was driven by double-digit growth across both our data and AI-led and digital operation services. Our data and AI-led services grew 17.1% year-over-year on a constant currency basis. The adjusted operating margin for the period was 19.7%, up 10 basis points year-over-year. Our first 9 months adjusted EPS was $1.45, up 19% year-over-year. Our balance sheet remains strong.

Our cash, including short- and long-term investments as of September 30 was $393 million, and our revolver debt was $355 million, for net cash position of $38 million. We generated cash flow from operations of $233 million in the first 9 months of the year versus $163 million for the same period last year. This improvement was primarily driven by higher profitability and better working capital management.

During the first 9 months, we spent $42 million on capital expenditures and repurchased approximately 4.2 million shares at an average cost of $44 per share for a total of $183 million. This includes 2.3 million shares received upfront as part of the settlement of our previously announced $125 million accelerated share repurchase plan. We expect to receive the remaining shares in the fourth quarter.

Now moving on to our outlook for 2025. Based on our strong year-to-date performance, continued momentum and current visibility for the remainder of the year, we are raising our revenue and adjusted EPS guidance. We now anticipate 2025 revenue to be in the range of $2.07 billion to $2.08 billion, representing year-over-year growth of 13%, both on a reported and constant currency basis. This is an increase of $15 million at the midpoint of our previous guidance. We expect a foreign exchange gain of approximately $2 million to $3 million, net interest income of approximately $1 million and our full year effective tax rate to be in the range of 22% to 23%. We expect capital expenditures to be in the range of $50 million to $55 million. We anticipate our adjusted EPS to be in the range of $1.88 to $1.92, representing year-over-year growth of 14% to 16%.

To conclude, we delivered a strong third quarter, demonstrating our formidable competitive position in embedding AI into the workflow and our resilient business model and strong sales pipeline gives us confidence in our ability to maintain double-digit growth momentum in 2026.

With that, Rohit and I would be happy to take your questions now.

[Operator Instructions]. Our first question will come from Surinder Thind with Jefferies.

Rohit, can you maybe just talk a little bit about how you're thinking about the change in the overall demand environment? Would you characterize it as relatively unchanged or are clients maybe getting a little bit more positive when it comes to kind of some of the innovation spend that obviously, you guys sit on a different end of the spectrum versus some of your peers, but I just wanted to understand what you're seeing in your commentary on the sustainability of the double-digit organic revenue growth.

Sure, Surinder. So I think the way I would characterize it is that the overall demand continues to be very strong. And what we are seeing is that the TAM for our services and solutions has really expanded. But this is probably the first quarter in which the shift in demand is now visible in our financials. And you can see that our data and AI-led revenue has moved quite significantly up and become 56% of our total revenue. We can see the conversion of some traditional domain and F&A operations, businesses that we used to manage being converted to AI-led operations.

We can see Gen AI and Agentic AI move from POCs to production to actually going to enterprise scale. And there is a huge amount of demand that is building up around data enablement for AI. So frankly, the market overall demand in terms of innovation spend and sustainability is moving exactly in the direction in which we thought we should be strategically playing and building out our capabilities. And some of this is now becoming quite visible in our financials.

We are pleased with our ability to in new clients. We are pleased with our ability to win market share from other providers. And we're pleased to become the AI transformation partner for these clients and help them along these journeys. So when we think about sustained growth in double digits, we are very confident of our ability to be able to drive that because our data and AI led business, which is 56% and it grew at 18% in the third quarter, that alone will be able to command a double-digit growth rate for the full company. So frankly, all of these signs are very encouraging for us and the pivots that we have made seem to be playing out quite nicely.

That's helpful. And then I guess as a follow-up in the shift in demand and the shift in the underlying business. I think you pointed out some interesting things where work that may be used to take months may not be done in weeks and in a few instances or some instances, maybe it can be done in the course of ours or a week. That sounds very deflationary, right, optically.

Can you maybe help us understand how and where the makeup of this is that when you go to that client and you offer to do work that used to be 3 months, and now you're telling him, hey, we can do it in 3 weeks. Where is that rental revenue coming from? Are you now doing 3 or 4x times as much work at that client? Or what is going on in here -- to also understand the sustainability of the growth rate.

Yes, absolutely. And the best anecdotal example of that was what I shared in my prepared remarks about an existing client for which we have implemented AI-led operations. And now we have 35% of the transactions, which are AI-enabled, and that's generated 30% productivity benefit for this client. And yet our revenue for this client has remained the same.

And to your point, the reason the revenue has remained the same is -- this find obviously has great confidence in our ability to be able to apply AI and deliver that productivity benefit to them. And therefore, they're giving us more and more work which is being shifted over from what they were running themselves or what they might have been running with other providers and we are winning more business from them. And therefore, this deflationary piece that you talk about, we don't really see that because today -- even today, I mean, the penetration of the work that we do with our clients is still relatively low. And the opportunity set for us is enormous.

And then finally, there are new areas that this client would never have engaged with in the past with anybody, so things around a genetic things around bringing together their data together and bringing it in a manner that can be accessed, looking at data lineage, looking at data governance. These are things which were never necessary in the past because AI was not being used in these business operations. And now that it is these are areas that need to be kind of worked upon and we are the natural choice partner for them.

So frankly, what we are seeing is the more benefit we can provide to our clients and the quicker we can do it for them. the more they tend to rely on us and give us more work and we become an even more trusted partner in this journey.

Our next question will come from Bryan Bergin with TD Cowen.

First question is on digital app. So can you just unpack further your expectations for the fourth quarter for digital ops and really into fiscal '26 as well, just given the first half comps. And Rohit, just based on that demand shift noted here recently, where does the comp lie in digital ops longer term? Is it still kind of high single digits? Is it mid-single to high single? And then my follow-up, I'll ask both up front here. Just on the top client, what's driving that top line strength and what's the sustainability.

Sure. So Bryan, first of all, I just want to make sure that everybody understands that when we talk about digital operations, it includes 3 service lines below that. Number 1 is domain operations. Number 2 is finance and accounting operations and number 3 is platform services. So those are the 3 elements that constitute our digital operations business.

Now in terms of the growth rate of digital operations, Clearly, as we embed AI into domain operations, F&A operations and platform services, some of that revenue is moving from the digital operations bucket to the data and AI-led bucket. So that's very important to understand because we ourselves are AI-enabling a lot of digital operations and making it data and AI-led operations.

What you are seeing is the net growth of digital operations, which is at 6% for this quarter. What you are not seeing is that the overall growth rate of this business is much higher. And because the shift of digital operations to data and AI led is taking place, that's not visible to you. So that's something which I just want to preface the conversation in.

Now in terms of how we are seeing domain operations grow, finance and accounting operations grow, platform services growth. We're actually very pleased with how clients are engaging with us a lot more in this direction. And when they first engage with us, a lot of this is some of the traditional work that we have done with them. And then very quickly within the first 6 months to 1 year, we start to apply AI into this operation. So frankly, this engagement with a new client, starting out with the digital operations and then converting it to an AI-led operations is a very good pathway and we feel very good about the kind of growth that we are seeing, the kind of engagement that we are experiencing. And this seems to be working really, really well.

The top client question that you asked, for us, the top client grew very nicely year-on-year. And I will tell you this that our penetration rate with this top client still is extremely low. And we think there's an opportunity set for us to really expand this volume of business with the top client far, far more meaningfully. In fact, it can be multiples of the amount of business that we do with this client today. So there is no real limit to how much we can grow.

I think if you also look at our second largest client, that also grew very nicely. So frankly, as we get more engaged with clients across multiple service lines, which includes domain operations, finance and accounting operations platform services, analytics, data management, AI services, our ability to expand work and revenue with large clients is actually -- there's a tremendous amount of potential out there.

Our next question will come from Maggie Nolan with William Blair.

Maybe to build on an earlier question about your ability to win additional work in these clients, can you talk about how you're changing your client relationship management, your go-to-market motions and those types of things. And just in general, your confidence in your ability to win additional market share as these shifts happen?

Sure, Maggie. I think that's a really important attribute and you're touching upon something which we've been working on very proactively because Clearly, the nature of our conversation with our clients has changed. It's no longer about just providing them with cost efficiency. It's much more about innovation. It's much more about transformation of their business model a lot more about applying AI correctly in a sustainable way.

So what that means is 2 or 3 things. Number one, our account managers and our client executives and our sales hunters. They all need to be proficient in terms of being able to engage and talk to clients with the use of some of these highly complex and newer technologies. So they need to be conversant with data with AI. They need to know how to apply that into the client workflow. They need to understand what it takes in order to pull this whole ecosystem together and deliver that business outcome to them. So that's a big change, and we are training our front-end teams to learn, understand and be able to communicate this appropriately to our customers and our prospects.

The second thing that's happening is, we are no longer talking only to the Chief Operating Officer. We're talking to the CIO. We're talking to the CDO. We're talking to the business head. We are talking to the CEO. And therefore, the buying centers are much more integrated and much more spread across the organization. What that also means is these are much larger deal sizes, and these are much more strategic decisions that the client needs to make.

And the third part of it is the entry point for us is it starts off with providing them the confidence on a single use case and then expand that use case at scale for the enterprise and then actually expand and proliferate across the organization across multiple businesses, multiple geographies and multiple functions. So the go-to-market piece has changed quite significantly.

And then the third element of this is a large part of our go-to-market is now with partners. So the partners has got a number of the technology partners that we partner with. So we partner with Microsoft Azure, with GCP, we partner with AWS, we partner with the data platforms, Databricks, which is our launch partner for exldata.ai, with Snowflake, with [ patent ] and the go-to-market motion is jointly with these technology partners. We're also partnering with private equity firms, which are looking at applying this AI into their portfolio of clients a lot quicker. So again, the total market motion has changed very, very significantly.

That's great detail. My follow-up would be about the growth in revenue per employee. Can you talk about the puts and takes there, just given that your growth was led by the data in AI? I would have expected that to track a little more closely with that growth rate? Any incremental color would be great.

Right. So we are seeing there to be changed an upward improvement in terms of the revenue per employee across the company. So that's a trend we would expect to see going forward for the next several years as we apply AI and we get into more complex work for our clients. But keep in mind that we are also going to see this happen over a period of time.

So there may be quarters in which this will move in different directions. It all depends upon what work we are winning, what the business composition of that work is and how it correlates. You can see actually quite visibly that the number of employees that we've added year-on-year is at a much slower pace as compared to our revenue growth rate. And that's been trending for the last several quarters. And that's something which we would expect to see going forward. So we would expect to see our revenue grow double digit but we don't expect to add employee head count at a double-digit growth rate. It's going to be pretty much in a single digit, maybe a mid- to high single-digit kind of a growth rate.

Our next question will come from David Koning with RW Baird.

Thanks. Good quarter. I guess A couple of questions. My first question, health care has dramatically grown the last couple of years. It's about 50% bigger than 2 years ago. Maybe just talk a little bit about the outlook there. Can you keep growing this fast? What are you doing to kind of keep the pipeline going. But yes, the biggest question, just can you keep this growth rate up, it's been so good.

So Dave, for us, the way we think about it is that our health care business is really in its infancy because the health care market is so enormous and so huge. You also know that it's very data rich. It's got broken and fragmented processes. It is adopting AI and it's applying analytics in a much more aggressive way. And therefore, the opportunity set in health care is enormous.

We are pleased with how we've -- we are building up our health care business. If you talk to our clients in health care, they can clearly see the kind of value that we bring to them. Our Payment Integrity business continues to grow very nicely. But what we are also very pleased with is that our domain operations business in health care this year grew very nicely.

So frankly, there are multiple areas where we can help our clients as such. One of the biggest opportunities for health care is going to be able to help them around their data. And that's something which we can see again is growing nicely. So the headroom for us is enormous. These are enormous markets for us. And I think even if we've grown 50% it's just a fraction of where our potential is within these industry segments.

Yes. Okay. Great to hear that. And then a question for Maurizio. You're doing a really nice job executing to the full year plan on margins. But it's a little bit lumpy -- the way Q1 was so good with margins. And then now as you're kind of going through the year just as expected. But margins being down in Q3 and maybe flattish in Q4, it looks like is -- do you still expect growth next year, right, like growth in margin next year? I'm just wondering the cadence this year is next year going to be more kind of stable growth through the year?

So Dave, you are correct, right? So the first half of the year, our adjusted margin was 19.9%. We just closed the quarter at 19.4%. So it's a little bit lumpy this year, right? We started extremely high, just over 20% in Q1, and we're trending more towards what I've been guiding to is 10 to 20 basis points higher on a year-over-year basis, which would put us right around 19.5% for the year. So going forward, when we look at Q1 and also 2026, we continue to see margin improvement of 10 to 20 basis points a year, but a little bit more flatlined than what you've seen this year, meaning Q1 should be a little bit more reflective of the annual margin going forward. And you should see that for the rest of next year. So a little bit more balanced next year.

But right now, you're seeing us spend a bit more on front-end sales and also on capability development and that's where we're really putting our investment dollars in the second half of the year.

Our next question will come from Vincent Colicchio with Barrington Research Associates.

Rohit, the exldata.ai, the product sounds very promising. Just curious, what does the landscape look like there? Are there similar products out there?

So yes, Vincent, I think on the data side, a number of companies which are trying to build solutions and help clients manage their data and get their data AI-ready, we all know that, that's the #1 problem that clients face. But I think the way in which we have thought about being helpful to our clients is really to use AI to make data AI ready. And therefore, the large part of the effort and the heavy lifting is not manual, but actually it leverages AI itself for helping our clients have that data be here ready.

I would say that we differentiate ourselves in 2 ways. Number 1 is the use of AI for data being AI ready. And number 2 is we built this platform and the solution set, which is fully comprehensive end-to-end. And that means it can help in data lineage, data accessibility, data governance, master data management, having data being coordinated across different platforms and silos and really attacking unstructured data which is the heaviest piece of lifting that needs to happen and do that by leveraging AI itself.

So as of today, we think this is really a first of its kind -- when we talk to our launch partners and other partners, which have data platforms, they find this solution to be compelling. And we are seeing tremendous amount of excitement around this. So a lot of demos, a lot of use cases and a lot of activity associated with this at this point of time.

Thanks for that. The international segment looks should be a large opportunity for you given your penetration. What are you doing to accelerate that? Are you making investments in the marketing side, for example?

Yes, Vincent. You're right. The international piece for us should overall be growing at a faster pace. And that's something which we are consciously investing in and also making sure that we have senior executive talent closer to our customers out there. So we are bringing on additional talent out there. We are taking our solutions and capabilities that we've created our leverage with some of our U.S.-based clients and applying that into these international geographies. We are building up some local partnerships in these geographies. And we really do need to mature the business as such, but the opportunity and the potential is very, very strong here.

Next question will come from David Grossman with Stifel Europe.

I wonder if we could talk a little bit more about the requirements to really deploy enterprise or AI, if you will. And I think Rohit, you're talking quite a bit on this call about the amount of data preparation required to execute that and the new products that you have in the marketplace that automates a large part of that.

So when you're going to these clients, are you going to market offering this service, which is then converting into follow-on revenue? So in other words, is it being sold as a stand-alone business. And if it is, what is the typical multiplier effect that you're getting once you get in with the client on that type of engagement in terms of the following work.

That's a great question, David. You're right. We're doing this in -- 2 different motions. One is on a stand-alone motion. So we are taking exldata.ai as a stand-alone capability and whether or not our clients use us for embedding AI into the workflow, we're just helping them get that data estate in order and make sure that their data is AI ready. And so these are stand-alone engagements. They typically start off with demonstrating our ability with 1 particular use case, but it very quickly expands to kind of working across the enterprise and working across a number of their legacy data platforms and converting that and moving that to the cloud and moving that into a much more modern data platform. So that's 1 motion.

The second motion is where we engage with clients to help them embed AI into the workflow, and we have the responsibility of doing the end-to-end charter, which means we have to get the data estate in order. We have to apply AI to that data and we have to deliver a business outcome to the client. And there, it's in a much more integrated format that we bring in our capabilities and services. We are finding actually -- both of these seem to be resonating. And clearly, the need for this across our clients is very, very strong.

So what do you think the multiplier is stand-alone versus kind of the integrated sell?

At this point of time, David, the data management piece in itself is a large part. So the AI enablement is a much smaller piece, but the heavy lifting is much more around the data enablement. So the multiplier at this point of time is not that strong. But I think as this kind of progresses, it will become larger and larger. And so we'll see how that plays out.

And then just as you're thinking I know you've guided to double-digit growth as your target model here. And as you kind of formulated that double-digit target, how much of that is kind of net revenue retention or same-store growth, same client growth versus new bookings?

Yes, that's a very strong metric for us, David, because with existing clients, as we embed more AI as we deliver greater value to them, the renewal rates are north of 90%. We continue to win additional business from them and then we add on new clients. I think we've shared this metric before for us, adding new clients in any given year only contributes somewhere between less than 5% of the revenue for that year. So a large part of this is with existing clients that they're able to kind of build and grow.

Got it. And just if I could sneak one more in because I was a little confused by your response to an earlier question because I think you said that you were getting 30% productivity gains from a client, yet their client -- their revenues were remaining flat. So I think the context of the question was the deflationary -- or potential deflationary component of AI to the industry, not just for EXL. So did I hear that right that it's flat? And if I did, again, I guess I would ask the same question again. How should we think about this if we're just getting to flat off of a 30% productivity gain?

Yes. So think about it this way that if our revenue was 100, we were able to provide a productivity benefit of 30 and it dropped down to 70% we were given incremental revenue of another $30 that brought it back to 100. Now we came back to 100 with better margins, higher revenue per head count and an increased amount of value for the customer. So our penetration rate with that customer increased and the strategic relationship with that customer just got enhanced.

Got it. So then is that more of a I wouldn't call it a onetime event, but is it really just more of an upfront event. So if you can keep it flat, that's kind of a victory, and then you can grow off that base going forward? Is that the way to think about it at higher margin and higher value?

Yes. So that part of the business for us remained flat. But we then became a strategic partner for the same client on helping them use a genetic AI. And Agentic AI is a space that we would have never played with in the past and the client would never have kind of used us in the past. And therefore, it opened up newer revenue streams, which are much more higher value added and a much more strategic and much higher margin.

Okay. I appreciate that.

We have no further questions this time. This concludes our call. Thank you, and have a good day.

[Audio Gap] conference. I'm Bryan Keane, I cover IT services here at Citi. And we're excited to have ExlService with us. We have Maurizio Nicolelli, who is the EVP and CFO; and Vivek Jetley, who's the President and Head of Insurance, Healthcare and Life Sciences. So welcome, gentlemen.

Thank you.

Thank you.

When I think high level about EXL, I always think about the consistency in the growth rates, double-digit revenue growth and others in IT services have struggled. So I was hoping maybe you could help us understand what makes EXL different than a lot of your peers?

Well, first of all, thank you for coming here today, everyone, and it's been a great day just in terms of the interactions for us. But this is the one question that I think is something where we'd want to get the message out in terms of what we are seeing. I think the biggest difference for us today is that there's a very, very strong demand from enterprises that are adopting AI and shifting to AI

And for EXL, AI and the adoption of AI is a very, very strong tailwind. It's a bigger -- it's a bigger tailwind. It's a growth driver for us, and it's a growth driver across all lines of business for us. And I think that's the single biggest differentiation between us and what you would kind of take a look at the overall comparison set.

Now the reasons why it's such a strong tailwind for us is a little bit based on how strong we were in data and analytics to begin with. So we've been disclosing this publicly, but data and analytics until last year before we reorganized was 43% of our overall revenue, a large portion of our overall base in terms of what we did, large portion of our clients.

So the shift for those clients to kind of move towards using EXL for their AI needs was a shorter distance and a shorter jump.

And then the second part of it is, as clients have been trying to move towards adopting AI in the workflow for their operations needs and making their operations AI-led, that's where EXL has really become the premier partner for them because there's the ability from us to bring the data, the domain expertise and the AI skill sets together and stitch it all together in a meaningful offering for them. We're seeing a very strong demand for it from that side.

So I think just to summarize, it's the combination of a very strong AI demand and that AI demand translating in business momentum for EXL across all lines of business.

Now what do others in the industry when you look at their business models, what is it that they're lacking or why is it become almost deflationary to many?

So I can't speak to other business models in detail. But I can tell you what about our business model has been very, very strong. So there's a couple of different portions for it. One is you need to have the combination of the data, which is either access to proprietary data or the ability to help a client with using their own structured data in the right way. You need to understand the domain, which is to say, what is the problem that's getting solved for, what's the outcome that a customer is looking to solve for and then making sure that you understand how -- where that fits into the workflow.

And then the third portion is the AI capability, which is both leveraging your own proprietary AI or working with third-party partners to bring the AI in. So the combination -- and it needs to be a combination. If you're just unipolar on one, it doesn't work, you need to have all 3 of them together,

That and I think the ability that -- or the advantage that EXL had was we never had these large pieces of business that were getting cannibalized by the AI. So we have a very low concentration on low-value call centers, we were very low on some of the IT development, IT maintenance work. So we're not seeing that cannibalization impact, and we're benefiting from the pull-through from here.

So when AI, Gen AI first became real hot in the headlines, everybody pointed to BPO services as the place that it was going to cannibalize. Obviously, that's not quite happened that way. What maybe did people misread originally when they thought it was going to be BPO was going to be hit the hardest?

I think they -- I mean, from my estimation, they really underestimated the time and the amount of work that it takes to pivot away. And in the early days, the buzz about AI was all about I'm running these 50 different POCs, I'm running 100 different POCs. Those proof of concepts never really had meaningful impact behind them. So most of them haven't gotten implemented, not seen light of day. And that's -- I think it's a combination of those 2 things that's delayed the AI impact. I must call out the fact that what we are seeing today, though, is very different.

Today, the conversation with a client is, what can I do to use AI to help me solve for this meaningful outcome or how can AI drive a significant amount of change for me in my workflow. And that conversation is very different. So it's not 50 different ideas. It's 2 or 3 ideas. And it's ideas that are enterprise-based and ideas that are trying to drive to a meaningful outcome. And we feel that, that's a space that we fit in perfectly with.

So when you do the Gen AI solutions in your delivery, how prevalent is it in all the delivery mix with client engagements? And then how do you has pricing and contract terms changed at all with the push into Gen AI?

I'll answer the first part, and I'll let Maurizio take the second. I think on the first part, there's a very strong focus across the company on making sure that we bring in AI into pretty much everything that we do. So just this morning, we were looking at enterprise AI applications, which we are using right now to train our people better to kind of accelerate the curve that it takes for someone to get trained to work on a client process. And AI is doing that for us. AI is becoming the trainer for someone that's coming in. We have -- we've introduced AI into how we are doing data engineering work for our clients. We've introduced AI coders into how we're doing analytics, predictive modeling. So there's a huge body of work that we've done in terms of how we are adopting AI within the company to start driving meaningful outcomes for our customers. And that's been a pretty big driver of giving back benefits to clients, which we think in the long run, drives a pretty big business impact. I'll let Maurizio talk about the commercial model because that's been the second big change that we've made.

Yes. So when you start to look at how we've pivoted in AI, you start to see us really focusing on and clients focusing on the overall outcome that Vivek talked about. Before, it was more about reducing cost and reducing the number of employees. But now it's -- it's much more a conversation around outcomes. How can I get better outcomes in terms of claims to be processed better or faster? How can I get collections done in a much more efficient way and collect more? We, in that process, want to get paid more on an outcome transaction basis going forward. So when you look at the Gen AI solutions that we've put in place, you look at Agentic AI and a number of just the general AI use cases, we are pushing much more outcome transaction-based pricing, and that changes the commercial model.

Now does every client want to go down that path? Some do not. Some want more cost certainty. So we have to work with those clients. But that is a commercial model change for us going forward that we want to drive a higher portion of our revenue in that direction. And what that does, it increases the benefit or the opportunity for us going forward also. The client does better, and we participate more in that. And that also drives a higher gross margin for us going forward in the long run. And so that is how we're pivoting that commercial model now going forward.

Yes. I think people understand that the margin and some of the abilities of the synergies that you'd be able to get. I guess if you said you had a project you were doing claims processing for $10, I think the market assumed that you would do the same project and have to do it at $8 or $7 and then have to give $2 to $3 of those back to the client.

How is that -- have you been able to gain extra additional revenue to offset some of that lower pricing?

Yes. And that's where exactly we benefit from doing that activity, from doing that work in that, yes, we may end up pricing it slightly lower, but the volume and the additional opportunity for us going forward into additional workflows is the outcome that is really going to help us continue to drive revenue. If you look at our investor deck, there's a page in there that shows how deeper we've gotten into our client set, how many more clients that we have a much higher dollar amount and a much higher amount of workflows that we manage going forward. And that's how we've actually grown the company over the years in that we end up doing well in a certain process and then it's more of a land and expand, we end up getting more workflows from the client. But in that example, you're doing exactly that with more of an outcome transaction type commercial model.

To stay with the same example, just to add to what Maurizio said, you talked about claims processing $10 going to $8. We absolutely will go to $8. But I think in that same conversation, we are going to say, well, what about the unstructured data that you need to bring in into the claim, the intelligence. What about the analytics on saying, how does that claim exposure impact what you're doing in a feedback loop to underwriting. And we're going to start asking the client for those pieces along with the processing work. So when you start making it more end-to-end, what you're getting after is a bigger market size. And even though one component of it is probably becoming more cost effective for the client, net-net, because the scope of work that EXL gets is bigger, we grow.

Got it. Got it. I want to step back and just think about the overall IT services demand, how you guys saw it in '24 and then so far through year-to-date in '25. Maybe you can just talk about macro factors and other things you're seeing in the market that could be changing the demand for IT services.

So there's some portions of the IT services demand that have gone down or that have gotten cannibalized. I think we don't get exposed to it much, but we kind of see what's going on in IT -- custom IT development or custom IT maintenance. But the portion that impacts us is some of the dashboarding work or the business intelligence work that we were looking at as part of our data and analytics practice. Some of that is -- the demand for that is much lower. But what we are seeing is probably a bigger input into the demand for things where you're starting to bring AI models where you're starting to do AI engineering to be able to kind of bring data into the AI algorithms and push it back into the workflow.

And in certain cases, just doing things like data annotation, which is building out the data management engine to support that. So the demand has shifted. And depending on where you are and how receptive you are to the new demand that's coming in, that would determine what your overall demand size and your pipeline is. Now we've been very fortunate. We've been -- made the right investments. We've been in the right spaces. So for us, our pipeline continues to go up. And I think demand continues to go up as well. And you're seeing some of that reflected in the forecast that we put out.

And then, how are clients thinking about the difference between cost takeout versus growth priorities, especially short term to long term? And are you seeing any more discretionary spend come back?

So I think this is something that varies by almost industry to industry. So I'm responsible directly for insurance and for health care, 2 of our largest businesses. I can tell you right now in insurance, it's a question of investing in capability because the last couple of years, insurance has really been focused on cost takeout. So this year was the year on saying there's a lot of new risks that are coming in, how do we invest in capability to prepare for that risk. That has been the story on the P&C side.

On the life and annuity side, it's been more about modernization. Now the story is completely different for health care, where if you're a large payer right now, you are focused on cost takeout. So I think the priorities change depending on which industry you're in and what's going on with the overall picture. But I should add that the way EXL is constructed, and we've talked about this in our Investor Day as well, we have a very good balance between parts of the business that do very well in a cost takeout environment and parts of the business that are doing well in an invest and a grow environment. So there's a very nice balance to the business, which makes us actually, in certain cases, continue to grow irrespective of what the market environment is.

Do you see external factors or economic factors like the tariffs change decision-making or pipeline conversion?

So we have not seen a significant impact from the government activity, specifically the tariffs, to be quite honest. We've gone through a lot of the literature and also some of the communication coming out of -- from the federal government. We just have not seen that. We haven't seen that in insurance nor we have seen any effect on us within health care either. So we really have not -- we have not been affected at all. And we haven't seen a direct effect on our clients either to be quite honest.

And then can you talk a little bit about the revenue contribution from recurring and nonrecurring? And what kind of visibility do you have even on that nonrecurring piece if economic conditions worsen that kind of thing?

So when you look at our revenue base, 75% of our revenue is contracted for 1 year or more. So embedded in our revenue base is a very nice annuity stream of revenue, which makes us a little bit different than some of the more volatile IT services kind of companies that are out there right now. When you look at visibility, because of that, when you look at visibility, particularly right now, we're just at the start of September, what's our visibility for the rest of the year? It's 95% plus. That gives us a very high level of confidence that we're going to grow 12% to 13% for the year overall on an organic basis. When you get to January 1, traditionally, we have visibility into 80% to 85% of our revenue overall. So because we have these long extended contracts, we're able to have very high level of visibility into a period of time regardless of that period of time overall. And so we'll start the year with 80% plus visibility into that revenue stream and a large majority of that is contracted already.

Can you talk a little bit about the competition? Who are you guys seeing now the most of the marketplace and how rational are some of those competitors you hear sometimes some folks pricing aggressively, just to win business?

So I think in the marketplace that we are in, which is right now working with these insurance, health care companies, banks and so on and helping them drive outcomes through AI, that marketplace has a number of different entrants within it. You've got the IT companies that were traditionally working with the CIO that are working there. You've got the consulting companies that have built out their arms. You've got, in certain cases, the hyperscalers. And in others, you've got start-ups that are trying to compete for the same play.

So it is a competitive marketplace. I think what we keep going back to is when you start looking at companies that have that combination of the domain expertise, the data and AI capabilities and that huge strength and the ability to both create AI and implement that AI. When you start looking for companies that have all 3 of those in the combination, then it starts becoming a much smaller set. And that's the set that we think is the -- probably the most heads-on competition for us as opposed to the large number of entrants that are there.

Yes. I want to ask about the data and AI revenue component. How do you guys categorize that? And as you embed AI into all digital operations, you reclassify all that revenue. Just make sure we understand the data and AI.

Sure. So at the end of the second quarter, our total revenue in data and AI was 54% of our overall revenue. And that you're starting to see inch higher every quarter going forward because data and AI revenue is growing faster than the overall business. Now why is that? A couple of reasons. One is every new opportunity that comes our way, we introduce a component of either data and/or AI or both overall. So that means when we look at our pipeline, such a large portion of our pipeline are opportunities that contain data and AI. So when you look at the 2 different pieces of the business, that should drive a higher growth rate in data and AI.

We also are looking at our traditional digital operations business and those contracts that do not have an element of data and AI and also introducing data and AI to our clients in those contracts, to a certain extent, cannibalizing that revenue because at some point, someone will come to that -- to our client and look to introduce an AI solution or some component of that in that operation. So that will also -- we will transform that operation, but that will become data and AI revenue going forward. So when you look at the data and AI revenue of our business, that should be growing faster than the overall portion of -- or the overall company average for revenue. And that should be the case now for the next 2 to 3 years.

Got it. The popular question then to ask on that is the moat around data and AI. And you have open AI and Google and other AI models moving rapidly. Could they take a portion of that revenue?

I'm sure they'll want to market more to the enterprise. But so far, what we've seen is that they're more -- I mean, Google, in particular, is more a partner as opposed to a competitor. So we are going to market. We've got certain joint clients, customers that we are doing some transformation work for them together, Google and our teams. So I think there's an element of that going on. I think where there is a direct competition would be in terms of how do you want to build out certain enterprise AI algorithms or workflow solutions. And that's where I think the differentiation that we have is we have the domain knowledge and understanding the client's workflow and in a lot of places, the data associated with that workflow.

So you need to have those 2 elements in order to build an AI solution that's effectively solving for that -- for a problem, let's say, the claims problem. And that's somewhere -- that's something which we think that some of these larger players don't have. In fact, that's why they're partnering with us to bring that capability in. Think of them as a very, very strong horizontal capability and think of EXL as that vertical capability on top that is then helping to solve for an industry problem.

Okay. Great. I wanted to ask about to date, I think EXL has created 7 domain-specific LLMs. Can you walk us through what is needed to create a successful model and where it has been most promising so far?

Sure. So I'll talk about our first one, which is the insurance LLM. The way we did it is we have a platform. It's called MetConnect. And what that platform was doing was taking a look at these claims forms, which had injury reports onto them and then extracting out the medical procedures, which would then be fed into the workflow. Now what you have within that process is you have a ton of data coming in, unstructured data. And you had a very, very trained and a skilled operator extracting that, going through each page, coming up with the outcome and a doctor then going through the outcome and saying, okay, this is what it is. So we got the clients' permission to do this.

And what we ended up building was our LLM, which now has the capability of going through these unstructured injury report forms. And coming out with the precise information that is needed for the insurance claims form, 3 bullet points. And those 3 bullets have actually been trained on the data that we've been running for a very long period of time. So it's the golden data. Now when you do it that way and you train your algorithm that way, we found out that we are outperforming the rest of the industry. So we are outperforming in that case, GPT 4.0 because 4.0 was trained to create a paragraph and a description around the accident report, but not the 3 bullet points that the claims form needed. And therein lies the differentiation. It's the ability to say, I've got the data, I know the workflow. I know what the workflow needs in terms of the output. And I have the ability of kind of training my algorithm with the data that I have to get that workflow output. And that's -- I think that's the edge.

How do we think about head count growth and the talent you guys need to -- for data and AI to keep moving forward on that? Is it going to be less than the traditional models of the years past?

So as we drive more data and AI revenue, what you'll find is a little bit of a different type of headcount growth and type of person we're looking for now going forward. We're going to need more technology kind of data engineering type employees now going forward. So you're changing a little bit of the mix. You're going to a higher paid employee to a certain extent going forward. It also will start to reduce the growth rate of headcount. Now if you look at the second quarter, we grew revenue at 13% on a year-over-year basis, but headcount only grew 10%, right? And that you're starting to see now revenue per headcount start to grow off of that, right? And that's us embedding more AI into our client workflows, driving more price and revenue with a lower amount of employees now going forward.

So you'll see a little bit of the shift. We're still going to be growing headcount because we still have that operations piece to our business that has AI embedded in the operation. That's the human in the workflow component to our overall operations. But you should start to see headcount growth be lower than overall revenue growth, and you should start to see revenue per headcount grow on a consistent basis now going forward as we move our data and AI revenue higher from 54% from where it is today.

Is there a percentage like 3% to 5% on an annual basis is kind of where maybe revenue per headcount could grow?

So we -- when we do our modeling, in general, we look to grow that somewhere between 3% to 5%. I would say I think that's pretty -- I think it's pretty reasonable to be quite honest, on an annualized growth rate going forward. And that will move with that data and AI revenue percentage.

Yes. Vivek, I wanted to ask you about -- there's been obviously the Big Bill in passing through in D.C. for the health care impacts and then also just regulatory pressure. What are the impacts since you're in charge of both insurance, health care and life sciences, what are the impacts you're seeing from some of these financial and regulatory pressures?

Sure. So on -- the impact is more on the government programs. But specifically, there's a very large outsized impact on Medicaid as opposed to Medicare. So we went back and took a look at our portfolio and took a look for where do our clients have government program exposures and where do they specifically have Medicaid exposures.

We are very comfortable with the fact that our exposure to Medicaid through those government programs that our clients run is very small. So I think as far as our exposure to that, it's very limited.

But the bigger thing that's happening within health care is the increase in the medical costs that all of the large payers have experienced. I mean you saw United's numbers of 90.5% in terms of the medical cost ratio. That's a really, really high number. It's unsustainable.

Now that's created a huge amount of pressure on all of these payers to start reducing in certain cases, the unprofitable members. And in other cases, it's just taking cost out from the overall base in order to kind of make sure that the profitability comes back. So we are seeing the impact of that. But in certain cases, cost takeout is a positive for us.

Maurizio, I wanted to ask about the margins. I think you're guiding to 10 to 20 basis points of improvement. Can you just talk about some levers? What are some of the upside and downside factors in the margin ranges?

Yes. So when you -- when we -- we've done a lot on margins over the years. If you look at our margin trajectory from 2020 to 2025, we went from about 14%, 15% margins to now we're projecting 10 to 20 basis points higher than last year, and last year was at 19.4%. As we look -- and if you look at the first half of the year, we're actually higher than that. But when we project out the year, we do think of 10 to 20 basis points. And what are really the levers.

The first lever is us driving a higher gross margin. And as we talked about, as we drive more data and AI revenue as a percentage of our overall business, that should drive a higher gross margin. And we do believe that, that will be -- that we will see that not only this year but also going forward. And so as you see that higher gross margin, that should be driven in our overall P&L. The offset to that is investments. We need to invest more, and we have been. If you look at our level of investment over the last 3 to 4 years, it has more than doubled significantly over that time period. And so what is that? What that really -- what that is, is us investing more in building out AI solutions and doing more R&D work. And some of that R&D work is also proof of concepts that we're entering into more and more going forward with our clients. And so as we get deeper into building out AI solutions for our clients, we're going to have to spend more on R&D. So you're going to have a higher gross margin, offset by a higher level of investment and the net of that is an incrementally higher margin overall.

And what does that mean for free cash flow conversion?

So when you look at our cash flow, we are now driving north of about $200 million in free cash flow now going forward. So that is starting to become significant for us. And capital allocation is going to be much more important for us. And when we allocate capital, that's really going to be between looking at assets in the M&A market and also repurchasing our stock, especially when we think it's undervalued. And if you noticed, when we released our second quarter earnings, we entered into another ASR with Citi actually to buy back a significant amount of our stock at this lower level that we're sitting at a $43, $44 range. But that will flow through that free cash flow line.

And what would be some of the M&A type targets you guys would look at? Is it geographic? Is it service expansion? Or what would you be looking at?

So we've got a couple of different M&A thesis going on. All of them are for capability, not for scale. And they include going deeper in terms of building out our data and AI skill set, so very specific capability related. It's also further expansion within certain areas like within health care and life sciences. And finally, internationally, where we want to kind of increase our presence in certain key markets.

Okay. With that, gentlemen, I think we're about out of time. Thank you very much for being here.

Thank you, everyone. Thank you, Bryan.