ExlService Holdings, Inc. (EXLS) Earnings Call Transcript & Summary

We'll get started. We're excited to have EXL and Rohit Kapoor, who is Chairman and CEO, 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 this age a little bit, and we were talking prior to getting up here a little bit about the evolution of EXL and business and IT services is 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 completed 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 Merit, 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 group. 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 of 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 lead 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 budget 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 category 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 1 of them is a unique use case, each 1 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 1 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. And that means we are able to continuously keep adding on to the quantum of work that we're doing and this is a growing pie for us.

And how is 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 game that you're able to deliver to us. So that 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's 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? Could it stay at those growth rates? Does it accelerate, decelerate the kind of law of larger 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 1 of these has got a very healthy growth rate. The TAM on each 1 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 leg room 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 a 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 extract and that allows us to be able to use AI to extract data from 1 platform and embedded it into another platform and do that autonomously, which previously would have been done by humans. Then we built up an agent 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 1 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 are building up now capabilities in insurance in 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 million to $12 million and head count growth, I think, was 12%, so slightly below that raise and 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?