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

Ladies and gentlemen, please welcome to the stage, John Kristoff, Head of EXL Investor Relations.

Thank you. Good morning, everyone. We are really excited to have you here today as we share our data- and AI-led strategy with you. We've got a really good program, informative program put together. Rohit will kick things off talking about our overall strategy. Then we'll turn it over to Vivek, and he's going to talk about how we're using new solutions, our new data- and AI-based solutions to penetrate new buying centers. Vikas will talk about the power of domain and data and AI combined and how we're using that to expand in existing buying centers. And then Pam will come up and talk about our talent strategy and how we're differentiating ourselves in the market with our talent, and Maurizio will conclude the event with a look at our financials. Of course, our presentation today does include forward-looking statements, and they're are subject to risks that are known and unknown, and we would refer you to the more detailed information that's been filed with the SEC to understand what those risk factors are. Just a few notes before we kick off, a few housekeeping items. Our formal presentation today is scheduled to last approximately 2 hours. At the end of the formal presentations, we will have a Q&A session. We will bring everybody up on stage, and our live audience will be able to ask questions. We are webcasting this event live today as well, so welcome to our web audience. Upon concluding the Q&A session, we will be serving lunch right over here in the cafe area. So we would invite you to stick around and have lunch with us. It's a good time to interact with not only the speakers, but our other EC members that we have here in attendance today. And then finally, the replay of this entire webcast will be available on our website later today, and a PDF of all the slides will be posted at approximately 11:30 Eastern Time at the conclusion of our program today. So without further ado, please join me in welcoming our Chairman and CEO, Rohit Kapoor.

Good morning, everyone, and welcome. I've got to say that this venue is much better than where the Met Gala was held last night. Look at these shades coming down, isn't that cool? And it's got all high tech and AI and everything built in. So it's perfect for us to be kind of getting here today. I also want to welcome everybody who's joining us online. Hopefully, you can hear us and see us and the message is going to be crystal clear. We're looking forward to sharing with you our Investor Day Strategy Update. We wanted to talk to you about how we've built our business, what we think the opportunities for the future for us are and why we think we will succeed. So with that, let me get started. Number one, we're going to share with you the secret of our outperformance, how we have been able to consistently execute and deliver above-average returns? Number two, we're going to talk to you about AI as a massive opportunity for EXL. We think this is something that will allow us to be able to continue to power ahead in the years ahead. And EXL is really, really well positioned for this. And finally, we want to talk to you about our unique positioning that allows us to take advantage of this market opportunity. My colleagues are going to share with you a number of things around how to create value from domain, data and AI, how to leverage AI, how we bring in the right kind of talent to be able to execute and implement. And finally, Maurizio is going to share with you our financial numbers and also provide some guidance in terms of our forward-looking numbers as to what that might look like. So let's get started. So 2024 for us is the year when we complete 25 years of being in existence. We started out in 1999, which was the height of the dot-com boom days. And this year, in June, we completed 25 years of being in business. So let me share with you and just to a look back of the past 25 years as to how we've performed in this time period. We started out in the early years basically focusing on helping our clients drive cost efficiency. And we did this with two levers. Number one was a labor cost arbitrage, and number two was applying the principles of Lean Six Sigma for getting to process efficiency. So we would run the process really well, and we would optimize the process, making sure that we could deliver cost efficiency to our clients. We then decided to add a new service line and expand our portfolio, and we made an acquisition of a small company called Inductis, which was the foundation for our Data Analytics business. And we started to offer BPO and Data Analytics to our clients. And the [indiscernible] did is to help them not only improve their cost efficiency but allow our clients to take much better business decisions that were backed and grounded by Data Analytics. Around 2015 or so, we started to invest in digital. What that means is we started to invest in a lot more automation, a lot more intelligence. And the goal here was not only to drive better cost efficiency and decision-making but also to enhance and improve the customer experience of our clients and customers. Post the pandemic, we made another pivot, and we started to focus in on data. We felt data was the foundation to create value for our clients. And we really needed to have expertise around managing data in addition to our ability to continue to manage the operation and the process and be able to help our clients make better decisions. What this allowed us to do is not only focused on the cost side of the equation for our clients and impact their cost efficiency parameters, but also allowed us to be able to help our clients be a lot more competitive in the marketplace and be able to grow their business and help them launch new products, help them get into new service offering, new geographies and be competitive in the marketplace. And finally, this year, going forward, we want to be a data- and AI-led company. Our entire focus is how can we help our clients with data and AI, we want to be the strategic transformation partner for our clients that enables them to modernize their platforms and take advantage of a foundation of data and leverage AI in everything that we do. We think as we deploy this business model, we'll help our clients with much better business outcomes, and we can do this at speed. I guess what I would say is if you take a look at our 25-year journey, every single step of the way, we were proactive in terms of the choice of the business model that we adopted in advance of these things becoming routine and normal. And therefore, we were a bit early in terms of the adoption of data analytics, digital, data and data and AI. And number two, we were very consistent in our execution. And because of that consistency in execution, we've actually been able to grow our top line numbers over the last decade. From 2015 to 2019, our top line was growing at a 9% compounded annual growth rate. And we were able to accelerate that in the last 3 years to about 19% organic constant currency growth rate. This is something which is a result of our ability to execute as well as our ability to continue to evolve in terms of our business model and bring together all the elements and the ingredients that are necessary for our clients to choose us as their trusted partner in terms of the enablement of these capabilities. And not only were we able to grow the revenue and the top line and accelerate that, we also grew our profitability in that same time period. One of the mantras that we have at EXL is that we'd like to be able to grow our EPS at a slightly faster rate than our revenue growth rate. And for the last 10 years, we've been able to consistently demonstrate a high growth rate of revenue and an ability to be able to improve our EPS at a slightly faster rate. Going forward, we're really, really excited about the opportunity with data and AI. And let me talk to you a little bit about what the opportunity set for us is. But before I go there, I want to just share with you two fundamental changes that have taken place in EXL over the last 3 to 4 years. The first change is that our business mix has changed. Not only have we increased in size, we've grown our revenues to about $1.63 billion. We've grown our Digital Operations business from approximately $600 million to about $800 million in this time frame. But our Data Analytics business has grown a staggering amount from $363 million to $729 million. And on top of that, our investment in digital way back in 2020, where we just had a nascent business of $3 million digital, last year, we did $106 million with our digital business on a stand-alone basis. This is something which you all recognize that when EXL -- when we report out our numbers, even our Data Analytics numbers, our stand-alone numbers; which means our clients are paying for those services separately. Anything that is embedded into our Digital Operations & Solutions business, whether that be analytics or digital, that's not reported out in these numbers. So what this has done is our share of Data Analytics and digital, which in 2020 was 38%, has grown at a staggering compounded annual growth rate of 32% per annum for the last 3 years. And today, more than half the company represents revenue from Data Analytics and digital alone. And again, this does not include all the Data Analytics and digital that is embedded in that Digital Operations business. So this is a big fundamental change and shift that has taken place in our portfolio and our business mix, and we think this positions us really, really well for the future. There's a second shift that took place. Our Digital Operations & Solutions business, as you know, is an annuity business. It's a very stable, growth-oriented business. But in the last 5 years, we were growing this business at about 6% compounded annual growth. With the ability to leverage Data Analytics, digital, we have been able to accelerate this growth rate very, very significantly. In the last 3 years, we've moved this number up from 6% per annum to 15% per annum. This is industry-leading growth. The reason why our clients choose us is not only because we can deliver much better business outcomes to them, we can do it consistently, and we can continue to modernize their operating systems so that we are a strategic relevant partner for them today and for tomorrow. What's also happened is this growth rate has been driven by the fact that we are now actually working with larger deal sizes, and our win rate has improved. Vikas is going to talk to you a little bit more in detail about the acceleration of our Digital Operations & Solutions business and how we've been able to impact a much larger deal size and have a much greater win rate. So with that, let's talk a little bit about the future. So we all know we are now in the AI era. And a few things have changed. Actually, these changes are very, very significant and very, very fundamental. First and foremost, in the last 18 months, with the advent of Gen AI, the way in which clients are transforming their businesses has completely changed. It is no longer the sole mandate of technology to drive the change. Actually, the change is being driven by the business, and it is not only technology led, it needs technology, but it also has a very strong lens of leveraging data. So we actually think that the way in which business transformation now will take place is a lot different than what it was taking place for the last 10 or 15 years. Number two, most of the business transformation involved companies taking their large ERP on-premise or on-cloud platforms and completely changing them. So what they would do is they would do a 3- to 5-year modernization program of their entire technology architecture. And that's how they would think about business transformation. Today, the way in which business transformation is being enabled is a lot more with applications and AI solutions being embedded into the workflow and being applied on the same platforms that have existed. You don't need necessarily more all your underlying technology platforms to take advantage of AI. And what this finally means is that all of this change is no longer centrally controlled. Actually, the change is taking place in a very agile format. And it's taking place in a decentralized format, where every single business unit owner can implement this AI change and be able to have a transformation. What this means is that there is a great propensity of our client decision makers to engage with us and to be able to drive this business transformation forward. In terms of our total addressable market, when we met with you in November of '22, we shared with you that we think that the total size of the market in 2026, as per IDC, is about $3.4 trillion. For us, the addressable market of that $3.4 trillion was approximately $350 billion. That was our work that we did on Operations Management and Digital Operations as well as on Data Analytics. In the AI era, the total size of the market has expanded to about $3.9 trillion, and this is a number for 2027, but our addressable market has almost tripled in size to $950 billion. And the big reason for our addressable market, expanding very, very significantly is because of AI services that has come in. And that number is a $450 billion number, which we think we can take advantage of and we can help our clients with. So for us, this represents a massive opportunity to be able to continue to build and grow our business and be able to help out our clients. So we all know that the AI opportunity is large and huge, but most of our clients and most of the participants struggle to derive value out of AI. And the reason is that there are a number of critical success factors, which we think are important for a successful execution of AI implementation. The first thing is you can't deploy AI on a stand-alone basis. And if you understand the technology, that's not good enough. AI needs to be deployed into the workflow. And because you need to deploy AI into the workflow, the knowledge about that industry and the domain expertise about how to apply AI into the workflow becomes a critical success factor. For EXL, we've been investing in terms of our domain knowledge and subject matter expertise for the last 25 years. And when we serve our clients in insurance or in banking or in health care or in travel or transportation or retail, these are industries that we understand really well. We understand the ontology of these industries. We understand the workflow and therefore, for us to be able to embed AI into the workflow is a lot easier and a lot better. Second, you need to have a very, very strong understanding of data. AI only works if you can apply the correct data to it. And the data has to be -- you have to leverage unstructured data, structured data. You have to use proprietary sources of data, combine it with our clients' data. There is a real-time data. This is a very, very critical ingredient to ensuring success with AI. And finally, the deployment of AI alone does not guarantee success, even if you have the domain and the data part of it. What we have learned over the last 15 years working with machine learning in our Data Analytics practice and with AI is when you first deploy AI, the accuracy rates and the productivity rates are actually pretty low. It's like almost 65%. And at 65%, AI is useless. What you need to do is to fine-tune that AI model over and over again in a very agile and quick manner and be get that accuracy rate up to 95% or so. So what it requires is a very strong understanding of how to apply and deploy AI and use it correctly and fine-tune that model so that you get to accuracy levels of 90%, 95% or more, and that's when AI truly becomes effective. That knowledge and that skill set and that ability to apply that and scale, that's something which EXL has and we're able to that for our clients. So for us, our business model is quite simple. We have a foundational layer of domain plus data and AI, which we think are the critical ingredients for success. We help our clients in Digital Operations, Data Analytics stand-alone and digital transformation. But also keep in mind that the value comes in when we integrate in all of these services together. And what we can do for them is we can reinvent their business models completely. So the way in which our clients serve their customers, we can change that equation for them completely. We can allow them to unlock greater value and be able to offer new service offerings, reach the right kind of prospect customers and be able to acquire the right kind of business and then finally, make sure that every single action that they take is driven by a foundational knowledge of data, domain and AI. All of this is great. But again, the ability to execute it is only if you have an A-class team. The one thing I'm really, really proud of at EXL is the team that we have to lead EXL and lead EXL in 2024 and beyond. It's an A-class team. We all have been together for a number of years. We all work really, really well. And this is the team that is responsible for guiding the organization forward. And I think we stand out in the marketplace in terms of our tenure, in terms of our commitment, in terms of our passion and in terms of the way in which we think about building up this business. So I'm really excited about the future, and looking forward to EXL continuing to build and grow. I'm going to stop here and pass it on to Vivek Jetley to talk and talk about how do we leverage AI and how do we create value from here. Vivek?

Thank you. That's going to be a tough act to follow. Look, my job today is a relatively simple one. Rohit has already talked to you about the massive AI opportunity that exists. We've talked to you about the fact that our TAM is going to be almost triple, we're going to be almost just shy of $1 trillion of an addressable market for us. And most of that is driven by this once-in-a-generation AI opportunity that exists out here. My task today is to tell you a little bit about why we believe that EXL is incredibly well positioned to actually capitalize on this particular opportunity. I'm going to talk to you about the capabilities and the value prop that we've already developed to attack the opportunity, what's really working with for us in terms of the traction. And I'm also going to share some highlights of where we are in terms of our pipeline, what does -- how is that translating into actual business momentum for us. We've got a short demo, where we'll give you an example of one of the solutions that we've created and how that solution is actually helping us attack a market opportunity that EXL already couldn't earlier. It's actually creating a new wallet share and a new spend area for us to target. The other part Rohit talked about was the fact that while everyone is focused on AI, a lot of that AI is actually driven by the data. Without the data being AI-ready, none of our clients can actually create those workflow interventions, can create the AI interventions. It's actually creating a massive opportunity in terms of data modernization of everyone actually accelerating the shift to the cloud and taking their data legacy infrastructures and modernizing them. Now EXL's, again, proactive invested in that space earlier. We were early in terms of building up our capabilities in that space. And today, we are benefiting from that transition happening. And there's a huge opportunity for us, untapped opportunity as of yet, in terms of progressing that. And I'm going to give you some color on that and show you where we are with it. And finally, as we are creating all of this innovation, we are also innovating our go-to-market model. We are innovating our value-delivery structure, revisiting how is it that we create value for our client and how does that translate into value for EXL. So I'm going to show you a little bit of that in action and show you an example of an instance where we are actually taking on a much larger scope for a client than before and how that translates into a better outcome for them and a better outcome for us. So I'll walk through some of these things. So let's, first of all, start off with the AI opportunity. Now, with AI over the last 18 months, we've done hundreds of different conversations with clients and prospects about how is it that they want to think about doing implementations, what they're focused on. And when you go out and read what's reported in the press, the bulk of the attention is about the foundation models. It's who's come out with the foundation model, what's the latest version, what's the new -- what's the token size limit and all of that. But for us, what we've realized is that the value creation doesn't really happen with the foundation models. The key to the value creation of the bookends. It's how do you make clients' data ready for AI, how do you break those silos, how do you bring in a lot of the unstructured data into the equation. And then once you've developed the actual -- the algorithm or the intervention, how do you go about implementing it on to the workflow? So the effort in terms of actually implementing something at scale for a client tends to be on the two bookends, and that's where EXL really brings in our firepower. Coming back to the foundation models, Gartner actually released a survey very recently, where they talked about the fact that even though today, all of the foundation models tend to be industry-agnostic, they predict that by 2027, 90% of enterprise AI is actually going to be driven by domain-centric models, which are going to be developed on top of these existing models and are actually going to be refining and bringing in the domain knowledge. Now that is an area that's actually perfectly suited to EXL's capabilities because we have the ability of combining that domain expertise, combining that knowledge, bringing in proprietary data and helping our clients develop those. So it actually sets up very well in terms of the way we deliver value. So I wanted to do just a little bit of a deep dive and tell you a little bit about how we set ourselves up, so how to think about our capabilities? Core to our capabilities, when it comes to the AI ecosystem, is our domain knowledge. As with everything else in the past, we are not approaching it as a horizontal. We are focusing in on saying let's bring in the right set of domain expertise, let's make sure that when we are talking to a P&C insurance client and we are looking at a claims process, we actually bring in the claims expertise, we bring in the claims workflow, the data the ability of saying "Here are the right interventions that need to happen at each specific point." So that allow us to actually start developing very industry-specific use cases. When my colleagues and I go talk to our clients, we don't go to them with "Here's a roster list of everything EXL has done." It's very specifically tailored to saying, "Here's the business problem, here's how AI will work for you, and here's what we've developed." And that's getting traction in the market. We are working with our clients right now in -- for certain folks who are early in the journey. We're helping them with their vision, with their strategy, thinking through what use cases to implement, which ones not to. But for the majority of our clients right now, we've moved on from the POC stage. So we are right now in the stage where we are actually helping implement use cases that have gone through the prioritization and that are now getting to a point where they want to kind of start taking them to scale. And that's actually been very, very interesting work for us. That's where our AI experts, our data experts are kind of working alongside clients and making those implementations. Now the next level over is, okay, you're designing this, but where does this sit? How do you integrate it with the data platforms and data structures? And this has been one of the fundamental shifts that EXL has made in the last 2 years because now what we've done is we've gone out and created these hooks into the partner ecosystem. The EXL of the old used to go to a client and say, "Tell me what platform you're on, I'll do the work for you." The new EXL actually goes in and talks to them about saying, "I know all the partners in the space, I can advise you which one is best suited, and I can work with them and bring that in and own the whole end-to-end implementation." It just dramatically increases the scope of what we do. From being just the workforce, it's now the solution provider who comes in with a viewpoint. So that partner ecosystem for us has worked very well. It also requires for us to kind of get in the right hooks, work with those partners, make the right investments, but it's something that a client really values. And finally, all of this now is on the cloud with the infrastructure base and foundation models, and that's something we've worked on as well. So I was just talking to you about the partner ecosystem. Let me just give you an illustration of who are the types of players that we've partnered with. We've done extensive partnerships with the [ data platforms ]. We are going to market jointly with them, going to customers together with solutions. We've obviously got very large partnerships now with the hyperscalers. Our most recent one was with NVIDIA, which we announced at our AI symposium recently. Now within these partners, we've also gotten to the higher levels. So Google, for example, with [ GCP ], we are a premier partner. We're an advanced partner with AWS. And what that means is it allows them to fund projects with us. So a lot of these players have actually funded new projects with us, where they pay us to go show an AI capability to our client. A lot of the data platform players have gone jointly to market with us, where we go to our client together and win a project together. We just won something very recently with one of those data implies. And this is now the new way that we're going to market. And it's -- again, it goes back to kind of expanding the market size and getting us to a much different and a new way of effectively taking our solutions. So what does that all mean? What -- how does that translate into actual business traction? Now these are numbers we haven't shared with you before. We haven't really broken out our pipeline for you and talk to you about how does the pipeline translate. So it's new information. Our data and AI-led pipeline, which is what Rohit was talking about, that kind of backs up the 51% of our revenue, is right now at about $2 billion plus in terms of TCV. So it stacks up very well with where our revenue is going to go, stacks up very well with our projected growth rates. That includes our health care business, which is growing incredibly well. It includes data management, which is one of our fastest-growing businesses. And it includes digital, which as you saw from Rohit's presentation, is driving a huge part of that top line growth for us. Within that pipeline is also our Gen AI and industry solutions pipeline, which is now pretty healthy at about $150 million. Now this is admittedly not something that either us or anyone else in the industry is telling you that, "Hey, this is clicking and this is converting very, very quickly." But we are very happy with where that pipeline is. And what we're very happy with is this number right here, 30-plus client use cases, already signed, already in the process of getting implemented. And that means these are EXL solutions that are now getting deployed, a completely different model than what we used to have earlier, where we were just [ T&M ]. These are solutions, solutions that we are taking to market. We've become a partner of choice for some of our largest clients. A top 4 bank has us -- chosen EXL as a preferred partner for the Gen AI initiatives across the consumer bank. We are now implementing for a leading Australian insurer, a number of different AI use cases across the company. And right now, we've become an exclusive partner for one of the largest global asset management companies in the world, showing us the traction that EXL now has in this particular space. I want to now just show you an example of something that we've developed, which just goes to show you how all of this translates. Now this is something that we shared in our AI symposium. The problem that this addresses is one of code conversion. I mean everyone has old code lying around, which they need converted. Historically, it was a problem that EXL would never step into. And the reason for that was our folks were -- the price point was never right for us. It was always something that used to go to the large IT majors with huge [ bench ] strength that could do this at a very different price point, and the conversion was almost entirely manual. Now what we've been able to do with AI is actually make it AI first and human second. And when you create that approach, you suddenly come up with a very different solution. And that solution actually starts becoming, first of all, very viable for the client. It allows them to do stuff that they couldn't earlier. It creates a business case for the [ trunk ] conversion that wasn't there earlier. And two, it makes it very viable for EXL now to make money off of this. So I'm just going to show you how that solution works. Let's start first by selecting code conversion. I am we're going to upload a file with the code already existing in it, and we're going to choose the conversion. And in this case, it's from SaaS, and the destination code is going to be Python. So at this point, the court is getting read in. Now once the code gets read in, the first step that happens is we generate pseudo code, which is effectively understanding the code and creating a logical structure for it in plain English. And that's what you see on the right-hand side of the screen. The pseudo code is now the core of everything that happens from this point forward, and it's understanding the code at its context and converting it into English effectively. The second step is now converting the code. And what you see on the right-hand side is the new output code that's already gotten generated in Python. Now once the code gets generated, the first task is to actually test whether it's working or not. And that's what the module does next. So what it does is it understands the data dependencies within the code, and it starts to generating test data for that. Now we're going to run that test data through and see whether the code has been executing efficiently or not. And that's the first step really of testing whether things work. Next, I want to show you how the debugging module works. So we've inserted a couple of errors in the code that got generated. And now the system is going to use the synthetic data to be able to test the code, find the errors and then use the log file of the errors and iteratively fix it. What you're seeing now is the errors got caught, and now the new code is getting generated without the errors and getting tested again. The next module is our module for optimizing the code. This allows us to actually read through code, improve its readability, improve its syntax and perhaps more importantly, enhance its run time and enhance the way it actually uses resources. So optimization of code is a very powerful feature in terms of not converting it from one language to the other, but making it more effective and more efficient to run and to maintain. To show you how this feature works. I'm going to upload a big query code module. And what we do then is run the syntax -- run the optimization on it. So on the left-hand side, you'll see the old code, and then the system is going to generate a new optimized version of the code on the right. Now we made quite a few changes here. There have been changes made in terms of comments, in terms of the sequencing, the sequence of [ joints ] and in the overall syntax. And all of those are recorded at the bottom in terms of a log to show you what the changes have been made. And now that we've generated the new code already, lets show you the difference in the performance. So what we've got next is the compression where the new code is performing 20% to 30% more efficient in terms of run time and memory usage. The next module is perhaps one of the most important things in terms of controlling how data gets used. What it does is it reads through the code and creates a data dictionary as well as a lineage of all of the data variables that are used. And it does -- again, the machine generates this by looking at -- by reading the code and understanding how that data is used. Let me show you how this works. We're going to upload a Python code and then the solution will run through it to create -- to understand the data and generate a data dictionary. And here's what the solution has been able to generate, a list of all of the data variables and the definitions and the ranges for the data that's incorporated within the solution. Next, what we're going to show you is the variable lineage. So this shows you the lineage of each one of the variables that's being used. And we've also produced the same lineage for tables. This creates a very, very accurate log of all the data that's used. Our code [ harbor ] solution effectively transforms the way code migration gets done or code modernization gets done. In terms of the results that we've seen so far, we've been able to migrate code about 3 to 4x as fast as previous. We've been able to dramatically reduce the errors by about 50% in terms of the code that actually gets generated. The run time of the new code, because of the optimization module, is about 20% to 30% faster. And finally, the level of documentation and the richness of the understanding, especially for old legacy code, is dramatically improved. So this is something that actually really brings in a number of different benefits into the way our clients are approaching code migration and transform the way that they approach it and they think about it. So this was the example of one of the solutions. We've got a number of different use cases driven by AI. This is one of those that's actually got one of the largest proportions now in that pipeline that we were talking about, the $150 million. And the key factor here is it's the EXL-owned solution that implements for a client, either as EXL labor plus solution or in certain cases, something that gets deployed on a client side, where we're charging them a license and then our resources are actually also working on it together. So let's move on to talking a little bit about the other opportunity that Rohit talked, which is what's going on with data. Now we've chosen the iceberg analogy here on purpose. While everyone talks about the stuff that's up top about saying, "Hey, what's going on with Gen AI or what's going on with Insights and so on?" The effort, 90-plus percent of the effort as with the iceberg, is really in terms of what's going on with your data. Is your data available? Are the silos broken down? Are you -- do you have a way of updating it and cleansing it in a regular manner? A couple of things that are changing dramatically with AI. First of all, I think when you think about the amount of new data that's getting created, specifically the unstructured data that most Gen AI algorithms require, that's going through just a massive swing up and explosion. There's a huge effort that gets spent on data governance and ensuring security and privacy. And now, there's a huge amount of effort that is spent in terms of the data operation shifting the data onto the cloud because almost all Gen AI implementations that we've seen require cloud, not very, very few, a handful of companies are actually implementing Gen AI on on-prem. Almost 95%, 99% even, are on the cloud. So this is where -- I think, for us, this has become a very, very good capability set because what we'd done was at EXL, we'd actually proactively invested in building out our data management and our data engineering capabilities. We started as early in [ 2017 ]. We've built out our own internal team. We made a couple of acquisitions. Most significantly, in 2021, we added Clairvoyant with its strengthened data engineering and with the strength in terms of migrating data to the cloud. And that's turned out to be a very good bet for us because it's placed us now perfectly at the spot where we are benefiting from all of the new work that is happening in terms of data modernization. It's helped us really target some of these new higher-value buying centers. The amount of work that we are doing for a CIO, for a Chief Data Officer, for a CTO today is dramatically higher than what we were even 2 or 3 years ago. And it's those new higher-value buying centers that are driving a pretty large amount of the growth when we were looking at the overall pipeline. Now I'm going to give you a little bit more example of what we are doing with some of these higher-value buying centers. And Vikas, in his presentation later on, is going to talk to you how we've evolved the offering even for our traditional buying centers with data and with AI. But let me do a click-down into what we've done with the higher-value buying centers or the new ones. One of the big things that we've done is really go after the CIO/CDO space with our new data engineering and data management capabilities. Now this has required to a approach, what we've shown you here are examples of where we've gone after existing EXL customers. And for those existing EXL customers, we've gone through this process saying, "Here's our capability, here's what we bring to the table." In certain cases, we've told them, "Test our capability. Why don't you pilot us out?" And at the end of all of that, we've been able to get them to switch from their incumbents on to EXL for some very, very high-value projects and very high-value assignments, where what they're doing is using us specifically for the purpose of saying, "I want you to help me with the data management journey. I want you to help me with how platforms are getting modernized." So we've taken a very specific skill set. Today, we have about 1,500 data engineers in the company taken that skill set, and we've been able to kind of get our existing customers to switch over. Now, that's where we are today. And by the way, that opportunity is not just a project, there's an data operations opportunity that continues from there as well. But take a look at the right-hand side. While we've done that, only about 20% of existing EXL customers have switched over to using us for that. The total number of customers we have today is about 800. So 80% of that 800 customer set is basically a captive opportunity of saying, "Here's something where we can take our new capabilities and get them to switch similarly." So to me, it represents a pretty massive opportunity in terms of what we can do and where we can take the data management business over and above the secular growth rate that's there. This is from within EXL. Let me now kind of move on to the last part, which is talking to you about how we've evolved our offerings and what is it that we've done in terms of creating more value for clients and more value for us. This is basically a chart that shows you the evolution of our offerings. We started off effectively with a simple offering, which is use us for our services, use us for consulting. EXL wouldn't take on any risk. We would paid for the people that we would bring the experience that we had with our resources, and we'd basically get paid on time and material type of pricing. Now over the years, as we've developed our own IP, we've evolved that model. And today, the model that we are focusing on, specifically for our AI and analytics solutions, is one where we bring a solution to the table. We've designed a solution using our proprietary data or we've designed a solution using our experience across multiple clients. That solution becomes a core of what we do for delivery. And now we have the ability of charging a client in a model where we have -- we charge you for the IP, and we'll charge you for labor on top of that. Or we can actually take on the risk of doing the end-to-end execution of a particular type, and we'll charge you for either a transaction-based price or, in certain cases, an outcome-based price. Now when we do a transaction- or an outcome-based price, EXL is taking on more risk, but we are also accountable now for a bigger scope. And the fact that we are taking on more risk allows us to do two things: One, it makes that whole process and that holds engagement a lot more stickier. It's very, very difficult to switch away when someone is doing that entire scope for you and even taking on the risk of actually getting that executed. And two, if we do this right, it helps get a much higher margin because now we have -- we know how that works, and we know how to kind of generate a margin from it. I'm going to show you an example of a solution which we've developed internally, where we've been able to transition that model. This is our digital collections approach, where previously what we used to do was effectively just provide resources to do the collections. And today, where we are, it is a fully EXL-owned solution that we deploy for clients. [Presentation]

So in addition to what you saw about how a client can use this to collect more, faster, better, cheaper, take a look at what it does for EXL. We've now got something that is modular, that actually improves with every single transaction that we run because it's our algorithm and we learn, and we kind of keep improving that. And it's actually producing much higher gross margins for us than we would have if this were just a pure services kind of a construct. So it's something that is actually driving -- that solution is actually driving faster growth and is driving a higher profitability. I want to end with just one page that summarizes why we believe that we are kind of perfectly positioned for capturing the AI opportunity. This is a chart that Everest just produced. It's part of their latest 2024 assessment of all analytics and AI service providers. And what they've done is, they've reviewed everyone in terms of their capabilities, in terms of the strategy and also in terms of the market impact. And what you would see is EXL's rated as the top performer on both of those metrics. I actually believe that the chart doesn't fully do us justice because to take a look at the scale that we have in the analytics business, where we have 51% of EXL's revenue today with data and AI, that scale is actually much larger, several times larger than some of the other players out here. So that scale, the depth and the extent of our capabilities, in my mind, actually gives us an enormous leg up in terms of how we can actually capitalize on this opportunity and how we can go and target that 3x addressable market that Rohit talked about. I'm going to stop now, and I'm going to invite Vikas to come over. And Vikas is going to talk about how we are going to use data, domain and AI to generate value for us and for our customers. Vikas?

Thank you, Vivek, and good morning, everyone. Rohit started by saying that next month, we'll complete 25 years as a company. And I'm very happy to say that next week, I will be completing 23 years in this company. It has been a wonderful, wonderful journey, and I'm so glad to be talking to you guys today. What I'm going to talk about are three key things. Vivek spoke about the opportunity that AI is creating for EXL and how we are so well-positioned to take advantage of. What I'm looking about are three key things: one, how the combination of domain, data and AI creates true value and differentiated value for clients and what it brings to EXL; second, how this contactual is going to help us to continue to be the leader and the disruptor; and third, how this combination is helping us win. And I'm going to be using specific client examples just to give you a little bit sense of how these conversations with clients are going in, what we're doing for them. Let's start with the first client example. This is a large U.S. life insurer. We've been working with this life insurer for the last 8 years. Like Rohit said, over the last few years, we have been evolving, continuously moving from domain to domain plus data and now domain plus data plus AI, which is what we have done with this particular customer. But let me just point out as to how the conversations have evolved. So when you are focusing on domain, the conversations tend to be around process-flow optimization. This is about saying we understand the business, we understand the domain, we understand the process flows and what is it that we can do using the traditional techniques of Lean Six Sigma or looking at other ways of optimizing value-, nonvalue-added activities and create improvement. When you start bringing in data along with domain, then you can actually shift your focus from process flows to data flows. This is where you start looking at how the data is flowing between systems, how is the data flowing between the customer and the organization, where are the problems, where absence of data is creating an issue and where data is not being used to create intelligence into the operations. So the conversation moves up a few notches. And then when you add AI to it, you have then the ability of actually driving automation in these operations, but automation which is technology enabled, but led by domain and data. So as we've gone through the journey with this particular client, there are two things I think are important to note. The first is the productivity improvement that we've been able to give to this client. So as you can see, we have moved up the value continuum and built in this more sophistication of offerings, the productivity that we're able to drive for this client actually has gone up. But the second and more important thing is that with the kind of value that we are actually now able to generate using domain plus data plus AI is not only just high productivity, but a much higher value set with a significant impact on customers. Let me just talk to you in detail about one of them, which is this 3x self-serve using conversational AI. Now self-service is simple, somebody calls into the life insurer and wants to have a conversation about anything, about the policy, about the claim or so on and so forth. And organizations have been trying for a very long time to divert that and have conversational AI manage that and only the exception should go to humans. But we've seen over the years that adoption rates are very low. And the reason adoption have been low is because customers are not happy with their conversation. They find it difficult, it's clunky, they don't get the right information, they would rather talk to a human on the other side of the loop. When we started working, we brought in the power of domain, data and AI to help solve for this. And we brought our own conversational AI, which is AI led, called EXELIA. And let's just talk about how domain, data and AI helped it. First of all, domain. We brought a deep understanding of insurance for this particular client. So the conversational AI that we brought in was insurance pretrained. It was trained on insurance taxonomy. We had many years of experience of working through the nuances, the scenarios. And a lot of that information was already prebuilt into this thing before it was deployed. Second, data. We had a very deep understanding of data flows, the client system, the technology ecosystem. And we knew what all data sources we had to tap to bring in that correct combination of data so the conversational AI can be affected. And finally, generative AI to create human-like interfaces and responses so that the customer can actually feel comfortable that this is a good conversation to have. And so what we were able to do was to improve the adoption to 3x. As we have created value for this particular client, what has happened is that this engagement, which is already a large and an 8-year old engagement, we've been able to grow revenues with this client by 100% over the last 3 years. And this is a great example that as you raise the threshold of what you can actually do for the client, even for large and relatively old engagements, you can significantly grow. Let's talk about another example from a different industry in a different market. So this is the U.K. and the industry here is energy. Exactly as in the previous example, we have moved from domain to domain plus data to domain plus data plus AI. The conversation mechanism has changed. And you can see here also that productivity that we are able to deliver to this particular client has gone up significantly. But then again, as in the previous example, let me just double click on creating a higher value set, which is what we have done using Agent assist. Now Agent assist is the call is still being handled by a human being, but there is an Agent assist, which is listening to the call on the side powered by AI and providing nudges to the human, the agent, to facilitate the conversation with the customer. So the customer is actually not seeing the AI, but the agent is seeing the AI. And again, this can be done only if you have deep understanding of domain data and AI. So let me play a scenario for you. So call comes in. And a lady is calling in to basically find a resolution for paying her energy bills. She's calling in and she's saying, she has a problem. She does not have the finances right now, go through the whole thing. The agent starts working on it. And somewhere in the conversation, the lady refers to that her delivery is due. The AI, which is listening to this [indiscernible] and says, "Oh, this is a pregnant lady and starts providing nudges to the agent to basically say that there is a vulnerability here, this call needs to be handled differently, the empathy, which is coming out is not correct, and by the way goes and searches for what regulation provides for providing protection to such vulnerable cases." I just think about it. All these AI-led nudges is helping the agent handle this very complex situation in a much better way. But again, you can see the deep understanding of the domain, which is the regulation, what data sources to go in, what kind of vulnerably assessments needs to be done. And then again, the use of generative AI to have this conversation. In this case, what has happened is that through the success we've seen with this client, we've been able to open new buying centers, particularly in the CIO area and the product area. And frankly, this capability has helped us create multiple new opportunities in other organizations in the U.K., which includes energy as well as retail. So you saw two examples, different industries, different markets about how domain plus data plus AI is not only creating a higher value set for clients, but helping us win much better and increase the threshold. Now the question is, okay, so these are two client examples. But do we have any proof points of what it is doing to EXL as a whole? So I want to talk to you about two things. The first is that it is helping us win better and grow better. As we have been able to create more value for our clients, which is a combination of higher productivity and you saw that productivity go up significantly as we move from domain to domain plus data plus AI. And we've been able to create more value by impacting customer outcomes. We have increased our pipeline. In fact, we've seen a 2x increase in our pipeline in digital operations. And second, our win rates have gone up by at least 20%. The biggest proof point of that is, if you look at the -- the clients we have, the number of clients we have in EXL with over $10 million of annual revenue. In 2020, that number was 22. And in 2023, that number was 38. In fact, the first client example that I spoke about actually isn't that list, which went from here to there. Now what opportunity does it provide to EXL. To me, it's huge. Because, even when we have taken these 38 clients to above the $10 million number, it is still underpenetrated. So there's still a significant room for us to grow in these clients. And then you extrapolate this opportunity now to the hundreds of other clients we have in EXL. Our belief is that this value proposition of domain plus data plus AI is helping us increase the threshold and grow much more rapidly to much higher scale with our client set. The second is integrated deals. Now, both Rohit and Vivek referred to this. But if you look at our offerings, you can look at the offerings in terms of domain-led digital operations. You can look at the offerings in terms of analytics and data management and AI. Each of these 3 offering sets have significant demand in the marketplace, and they're all growing businesses for us. But what has changed a lot in the last 3 to 4 years is the demand for integrated deals. And integrated deals means that rather than the clients looking at buying these services from you on a stand-alone basis, they want to engage with EXL and say, "Well, we want you to do the whole thing, build all of these capabilities in there and give us an outcome price." And so these integrated deals will include running operations, but embedding data analytics and AI interventions, the scope tends to be more end-to-end because the more end-to-end the scope is, the better is the opportunity of embedding these and creating impact. These have where we typically have outcome ownership, so which means the pricing models are a little bit more towards outcomes rather than time and material. And these are large deal sizes and typically very sticky. So in many ways, the digital operations business with all of these embedded into it, is sort of becoming a new consumption model for data analytics and AI. It is a new way of consuming data and AI by our clients by just asking us to embed this in these operations. Let me give you an example of one of such deals. So this is a U.S. regional insurer, property and casualty, and as I'm sure you know that most of the relatively smaller players have been struggling over the last 2 to 3 years because claims cost has been high. They've had -- they found rate increases difficult to come. Operations are still old-school archaic systems, broken, haven't really invested in data and AI, and they are all struggling. So we work with this client and first, we help them create a target operating model. This is what a new operating model should look like, and this is what the transformation journey is. This is typically a consulting engagement. This is what a consultant will do and then basically just walk away and expect the organization to implement. What we do, because we are practitioners and not consultants, is that we actually execute on it. So we are now setting up a new operation for them. But apart from just running the complete operation from new business to policy administration and claims and customer interaction, what we are doing is building all of data and AI interventions is part of that. So this includes looking at their data architecture and figuring out what the data flows need to be worked on. This includes embedding generative AI and AI in their operations to make it a little bit more seamless. This includes helping create a new digital channel their customers rather than customers speaking to the insurer. They have a digital channel and the digital channel is a dynamic channel. It is not a static old hard code system. It's a dynamic channel, which is using AI to make sure that the experience is more natural. And we are implementing our state-of-the-art smart data signals as part of the claims process. Now, what is smart data signals? It's an example I'm going to be using multiple times. But before I actually get to that, let me take you through a quick demo of smart data signals. [Presentation] So this is our smart data signals claims solution. As you would anticipate, this has got a global demand. We are implementing it for insurers in the U.S., in the U.K. and in Australia. We're doing it for personal lines provider. We're doing it for commercial lines provider. We offer this thing as a stand-alone platform. But every time we manage claims for an insurer, this comes built along with that. So we don't charge for it separate. We build it as part of that. We commit to outcomes. And that has -- gives the confidence to the insurer to allow us to run complete claims operations with this embedded. Here's an example of integrated deal where the consumption model for this technology becomes us running their operation. I am going to use smart data signals a couple of times more as an example, particularly when I double-click on domain and data. Coming to the second thing I wanted to talk about. With all of this, we think we are positioned extremely well to be the leader and the disruptor. Now many times, we get asked this question, and I'm sure this is a question which is top of mind for you also that, is our business prone to disruption by AI? We strongly believe that the kind of work we do, both in digital operations as well as analytics, you need a domain and data expert to be able to drive the disruption. Vivek referred to this data point where Gartner is saying that by 2027, 90% of all the solutions using AI will be domain-centric. We have a firm view that the kind of complex work we do in digital operations, which is very deep in the domain, whether it is in terms of managing customer interactions, customer operations, finance and accounting, everything associated with how we run operations. We are deep in the domain and complex that it is not really prone to disruption using just pure horizontal AI. And you have to create a domain-led AI solution to be able to create disruption. So let me just use this example of smart data signals again. What you saw in smart data signals was that we were actually looking at the complete data set and claims and the policy admin engine, almost on an online basis, well, it could be once, it could be twice a day, it could be absolutely online, pulling all of that data together and then using our analytical experience we have around leakage, fraud, compliance, complaints, we're able to convert that into a signal, a signal, which is a propensity of fraud, propensity of leakage, propensity of complaint, propensity of customer attrition and feed that signal to the right point in the process. So when the claim is still in progress, when the progression of the claim is happening to the workflow, you have an advanced signal which allows you to do something rather than a post facto analysis, which is typically what used to get done. Now to be able to do that, you need a deep understanding of the domain. So, why? Because you first unify the signals. You need to understand how these signals will actually create value? Where are these signals coming from? At what point of the process do they need to be fit in? What is the important problem to be solved? What is the nuance associated with solving that problem? You have to have a good understanding of the ecosystem, which is being used by the insurer and the insurance industry to be able to do that. You also need to have tons of experience in creating these models as part of our analytics business, over the last so many years, we have created hundreds of fraud models, leakage models, complaint models, all of that in claims. By the way, we have identified 27 signals that an insurance company could use. Right now, what we've built are only 6 and all 6 are actually built on claims, but we're building out the rest. This level of domain expertise is required to be able to create a solution which will actually create an impact of claims and not necessarily horizontal solution. So smart data signals is an example of one of those solutions. But over the last 3 years, we've built 50 such solutions. We have created data and AI-led CX transformation offerings. I spoke to you about a couple of examples of, one, a complete virtual agent; and second, a virtual agent assist or digital channels, all of them deep in the domain and embedding AI into our platforms in the results. Here are few data sets as results. So for example, our digital operations business has grown by 50% over the last 3 years. And like Rohit said, it's a significantly higher number than what it was in the previous times, and it is market leading. For example, and this is one of the specific client examples for a particular retail client in the U.K., we have virtual agent assist, which is running on a 1,000 people operation, where each of their agents now actually has this AI running along with them, and giving them those nudges to make sure the customer experience is good. And finally, we have an ability today to launch new products, end-to-end, and I will talk a little bit more about that in a couple of slides. So from a digital operations perspective, yes, AI will disrupt, but it is the domain-led AI, and EXL is extremely well positioned to be that leader and disruptor. When you look at data analytics. Now this has a business which has got a significant tailwind for us when it comes to AI. Vivek spoke about this in quite length. So I will not spend as much time on it, but I'm going to again use that example of smart data signals. The second time I'm going to use that. And this time, I'm going to focus on the data. Now when you want to create a smart data signals kind of a solution for an insurer, you have to have a very deep understanding of the data structures and you need to have the ability of helping create the right data asset and have the right access to their data asset. Every time we have tried to implement smart data signals, we found the single biggest problem is that the data is not in place. Now, think of smart data signals. So what do you need? You need all the internal data. You need the data, which is in the claims engine. You need the data, which is in the policy admin engine because the cross-referencing of the two is where you need to have that value. So for example, there might be a claim coming in. And you may find that in the notes of the policy document, there is a coverage sealing. So you need to basically create those cross references. You need to have structured data. You need to have unstructured data. So unstructured data, documents, faxes, call transcriptions, claims notes. You need to have internal and external data, put all this internal data together, but you need to have access to external data, which includes demographic data. It includes topographical data in terms of claims. So creating that data asset, enabling that movement to the cloud so that it has the right access and then the ability to tap that data asset and bring those relevant elements to the transaction is what data management for us is all about. So, data management actually has got a huge windfall benefit as we are implementing AI. Now, we have made investments in data management. You know of the two acquisitions we made, which is Clairvoyant and DataSource. But apart from that, we've been growing it beautifully organically and our growth rate in data management -- annual growth rate, the CAGR is 110% over the last 3 years. And the reason is deployment of AI needs this data management capability. The second part is all around talent. So deployment of AI needs talent. But talent, which understands AI, understands data, but very importantly, in the right domain. Because generic AI talent has limited application in the kind of work that organizations need to be done. I won't spend a lot of time on that because Pam who's going to speak after me on the talent advantage is going to talk a lot about that, but it creates that opportunity. Coming to the -- last thing that I wanted to talk about, and Vivek did refer to this, what has happened is we've moved to domain data and AI. We have created new buying centers as well as started selling new services to existing buying centers. And I won't go through all the detail on this slide, but this gives you a snapshot of new buying centers and existing buying centers and some examples of new offerings that we've had. So let me just talk about two of them. The first one I'm going to start right at the bottom, which is insurance product launch, which is for the CTO organization, but it is also for the product organization. This is something that we launched late 2022, early 2023. So we actually launched a new product for an insurance company. So think about it. The insurance company wanted to launch a new life insurance product. They design the product. They wrote the underwriting rules for it, they wrote the pricing rules for it. We did everything else. So first, we used our analytics engine to be able to identify leads and feed those leads into the hopper. We have a digital new business engine, which is highly configurable. It's a low-code no-code platform. But the important thing is that the journeys are not static. They're dynamic. So there is AI, which is enabling it, which means when the potential policyholder comes in, the AI is able to configure the journey based on what we think is the least resistance path towards buying a policy. Then, we have a policy admin engine, and we configure the product into the policy admin engine. And finally, we have an operational workforce across using our global delivery model, which supports that. So for this particular insurer, we actually launched the product, and we run it in an outcome-based model, which means every time they sell a new policy, we get a fee. Here's an interesting thing. Because insurance company systems are very clunky, it typically takes them between 6 months to 1 year to be able to launch a new product once they have finalized their design. We were able to launch this product in 3 months. And that's because we have a very nimble system. We could deploy it faster, and we could move faster. So here's a great example of EXL launching new products, frankly, something which is not so well known, but something that we've started doing more and more. The second example is around digital lending platform, which is an offering to an existing buying center. And as you guys would probably know, this is about the point of purchase loan mechanism, right? Online retailer, somebody goes into buy, wants to get an access to debt there itself. So there are 3 parties involved here. Apart from the consumer, there is the online retailer, there is the bank who's going to provide the financing, and then you need somebody to orchestrate this whole thing, somebody who's going to basically make sure that the 2 get connected, but how fast can you deploy it? What you're going to be seeing right now is a demo of the lending platform. But as you go through this demo, just keep 2 things in mind. One, this is just not a simple platform, a simple tool that has been deployed. It comes in preconfigured journeys, again using AI to keep it dynamic. And we have the underwriting rules set built into it. So this is a sort of a ready-to-deploy model, and we are deploying it for multiple retailers. So let's just do a quick demo of lending platform. [Presentation] Coming to my last slide, and this is something similar to what Vivek showed earlier. So what Vivek showed to us was how EXL is rated by analysts in the area of AI and analytics. This is from the same organization, but the dimension is a bit different. This is about the ability and the impact of embedding AI and data and digital in operations. So it is digital platform and augmentation in operations. Now we've used insurance as an example, and insurance, as you know, is a very large part of our overall business portfolio because these metrics are actually created like that. But this gives you an example that how the industry analysts see us not only as the leader in analytics and AI, but also as the leader in embedding these in running operations to be able to create value for our clients. So with that, I will end here, and I'm going to request Pam to come and join us. She is going to talk to us about the true differentiator, which creates all the other differentiators, which is a talent advantage. Pam?

Thank you. Now we're all awake, right? Our colleagues are the ones who are creating all and innovating, all the products and solutions that Vivek and Vikas have been talking to you about. So they're really core to us. And what I want to talk to you today is about how we make sure that we're bringing on the right skills, knowledge and attributes and how are we attracting the right talent to serve our clients for the future. And then once we attract that talent, we have to continue to develop that talent. Rohit talked about the 25-year history of EXL and its continual evolution. Well, that's because we've got colleagues who continually develop their skills, knowledge and attributes to meet the needs of our clients for the future. And then once we attract them, we develop them, it is so important to make sure that they have a sense of belonging. They feel that they have a sense of purpose at EXL, they have interesting work to do and that we retain them so that we have continuity for our colleagues and our client experience. Vivek and Vikas talked a lot about our clients. The colleagues of EXL are actually my clients. So I have to treat our colleagues better than or equal to how we treat our clients. And that is so critically important. Now I am relatively new or maybe Rohit, I can't say that anymore. I'm coming up on my year anniversary. But EXL is a very complex, diverse business. So I have spent the past 11 months doing various listening tours around our 50-plus locations. I want to say I hit about 85% of them. And I've met with colleagues across the domains of insurance, retail, energy, utilities and health care, people with experience in data, digital, AI, generative AI, our clinicians, our digital operations experts. And I really made sure that I listen to them, and there's a couple of things that I did. I made sure that I asked in all my town halls and focus groups which were extensive. I asked everybody 3 questions. If you've been at EXL for a long period of time, tell me why you're here? Tell me why you've stayed? If you left EXL and came back a boomerang as I called it, tell me what brought you back to EXL? And if you're new to EXL, tell me what's different from the company that you came from? So to a session, to a person, to a focus group, one word kept coming back to me, the word culture. Now culture is a huge word, right? It means so many different things to so many different people. So I asked everybody to find what culture means to you. Take me through that. So here's what I heard. Somebody cares about me. I said, well, what does that mean somebody cares about me? -- There, my manager listens to me. My manager cares about my career. I know what's happening in my department. I know what's happening in my industry, and I know what's happening at EXL. The next thing that I heard was that as EXL has grown, I've had the ability to grow my career because the company has invested in my development and my growth, and since the company continues to grow, my career continues to grow. Super important. The other thing that I heard is if I have a good idea and it makes a difference for a client, we are able to make that happen for the client. I am able to continue to do interesting work and focus on interesting work. Now, I also interview -- have interviewed a lot of people, who want to come to EXL in my 11 months here. I'm not going to tell you all the interview questions that I ask them, but there's one question that I always ask. I always say, so you've met a number of people at EXL. What made you decide to spend another 45 minutes or an hour talking to EXL today in meeting with me. And it's very interesting what I hear. What they say is I've met a really a lot of smart people here at EXL. I like the work that you're doing. I like the investment that you're doing. It's a leading edge. It's cutting edge. It's an opportunity that in my current organization whether it's the size, whether it be too large or too small, I don't have, and that's really what is making me want to spend more time talking to you with EXL. And to me, that means smart people want to work with other smart people. So the other thing that people ask me is how would you describe the colleagues at EXL. So I say there's kind of 3 words how I would describe them. We have smart people who are intellectually curious with a bias to action. And to me, that really shows the domain expertise that we have, how we've been able to drive to a digital data in AI-led organization and how we're continually innovating for our clients. So -- let's talk a little -- I almost hit the wrong button. Let's talk a little bit more about how we develop our colleagues because people who are intellectually curious want to continue to learn. And as the company grows, investing in their development is also how they grow their career. So domain knowledge is so important. You heard Vikas and Vivek talk about that. That's kind of the pillar of what we have and how we coupled data and AI expertise with that. Now we continually are running Domain academies for our clients and our colleagues so that they understand what's happening in the industry? What trends are happening, and so that they're always looking ahead. We've also certified about 47% of our colleagues in data and AI skills. We have over 100 learning paths focused on data and AI that are easily accessible to all of our colleagues. And 55% of our VP population has developed data and AI skills so that they can talk intelligently with their clients, but they also know how to work with or within data and AI, which is so important today. We have to also continue to develop our colleagues because there is a limited supply of people with this knowledge in the marketplace right now based upon how rapidly technology is advancing. So investing in our colleagues with that domain knowledge is so important to us. And we give them opportunities to work in sandboxes. We also treat ourselves like client zero. So I mentioned that our colleagues are my clients. Trading ourselves like client Zero also enables our colleagues to say, if we were a client of EXL, and we look at our processes internally, what would we recommend to ourselves to do. So therefore, our colleagues have the opportunity to deploy data and AI projects within the organization, learn that implementation, but also then get the experience as an end user, and they can then bring that to the clients because then they have a more fulsome experience as they're working with our clients. So we also have tremendous retention of our leadership team. We have over 90% retention of our population of vice presidents and above. That is well above any benchmark. We also have 60% of our colleagues who are promoted internally to Vice President. So we have a nice balance of internal promotion and external hires for our senior leadership team. Again, that brings continuity, it speaks to the growth. It speaks to the ability and the investment of developing our colleagues so that they can continue to grow their careers with EXL, which is another reason that colleagues and candidates are interested to come to EXL because they hear about the retention and the growth capabilities and possibilities that we have. Engagement. Engagement is so important because we invest in our colleagues, we develop them. We want our clients to have continuity. So there's a number of things that we do to drive engagement. We have engagement surveys every year. We have over 85% participation historically. Anything better than 70% is considered exceptional. So we feel very good about our participation rates. And we typically score a rating of 4 or higher. Now engagement surveys historically have happened once a year. We've actually changed that this year at EXL. Because of the rapid pace of change in the world and in the industry, we have actually moved to quarterly surveys. So we are doing what is known as, "moments that matter". We're asking colleagues 5 questions that take 2 minutes to answer so that we get rapid results, and we can quickly address what they aren't happy about or what they need to know more about or what we want to make sure we keep doing. We're asking questions such as have you had a one-on-one with your manager? Do you have clear goals and expectations for your job? Have you focused on your development? Are you driving your data and AI skills? That is so important. So that's one part of it. But the other thing that is really important to colleagues is their career growth, a career path, knowing that they can develop as leaders or that they can develop a subject matter expert within their technical area of expertise and that there is dual path for people to be successful, recognized and rewarded. Also, our corporate social responsibility is so important at EXL. Our colleagues want to participate and things that matter to them in the communities where they live. And we have a very strong CSR movement within EXL. I talked a lot about our culture. Our culture is a key drive and element of retention at EXL and the transparency of our culture and the ability to reach out to anyone at any time is also so important here. We also have what is known as communities of practice. So we talked about the multiple domains that we have, right? So we have insurance. We have health care, we have technology. We have utilities. But within all of them, we have colleagues that have expertise in data, AI, data architecture and bringing them together as a community regardless of the domain so that they can focus on leading-edge and cutting-edge experiences becomes even more important to them. So driving our communities of practice is another way of engaging and retaining our colleagues. So our talent proposition is about driving attraction, retention, engagement and focusing on developing our data and AI skills within all the domains in which we operate at EXL. So with that, I'm going to turn it over to Maurizio, who is going to talk about how all of our colleagues actually deliver the results that you're interested in hearing.

All right. Thank you, Pam. Good morning, everyone, and thank you for attending today. We'll get into some financial performance, some numbers in the presentation. So you've heard a lot about our pivot to data and AI and how it's driving our business now going forward. I'll talk a little bit about our strategy and how it's really driving our growth, both our past performance and our go-forward expectations. Rohit talked a little bit about the majority of our revenue now being data and AI. I'll get into that -- those metrics a little bit more. And then I'll talk a bit about our strong balance sheet and capital allocation. So when you look at our performance here, if we're back to 2015. Between 2015 and 2020, our total revenue growth rate was 9%, and it was 6% on a CAGR basis. In 2020, we pivoted to our data-led strategy, and that really propelled us in both data analytics and in digital operations. And what you saw is our CAGR growth rate grow 18% between 2020 and 2023. Overall, we grew 19%. And what it really did was propel our data in the AI business from 38% to 51% during that period. And so it really shows that we're becoming -- more than 50% of our business now is really driven by data and AI. And that will continue to grow because almost all of our new deals and our opportunities going forward really have an element of data and AI embedded in them. And what you saw during that period was a significant increase in profitability. You saw our AOPM go from 14 -- an average of 14.3% during the period 2015 to 2020 to 18.8%. That's a 450-point basis increase in our profitability, when you compare the 2 periods. And that is significant, and that's really helping us really grow the company even further going forward. And you see this growth in both our businesses, as I just explained. You saw data analytics grow significantly. Rohit had this up on his chart. We grew basically more than doubling -- or doubling our overall data analytics business, and we grew at a CAGR of 26% between 2020 and 2023. But you also saw Digital Operations Solutions business grow significantly well above the industry average at 15% overall. And that's really our ability to embed data into our overall digital operation solutions business. And when you look at a bit more at profitability, we grew in every one of these metrics significantly. Our gross margin went up 240 basis points between 2020 and 2023. That's really important, not only for profitability, but it also helps us invest in the future. And I'm going to get to our investments later on in a few more slides because this really gives us the ability to pour money back into investments, especially in this AI era where investing is going to be really critical going forward. We saw AOPM grow 340 basis points between 2020 and 2023, up to over 19% now in 2023. That is our peak adjusted AOPM, and basically in our lifetime as a company. And then you saw EPS more than double during this period. EPS grew at a 27% CAGR rate but it more than doubled during that period. So a significant increase just overall in profitability, which all leads to the one thing that Rohit talked about in that we're looking to grow EPS faster than revenues. Vivek talked a bit about our new solutions in cross-selling, helping us grow revenue overall for the company. And it's really reflective in our revenue per client. If you look at the growth in our revenue per client, particularly in our 2 lines of business that we service for clients are more we significantly grew between 2020 and 2023. And then if you look at our top 100 clients, we almost doubled the average revenue per client in our top 100, going from $8 million revenue per client in 2020 to $14 million in 2023. So what you're seeing is a significant increase overall in our top 100 clients, where we're really bringing on new clients, but also expanding existing clients significantly. And this leads to a bit about what Vikas was covering. It's our end-to-end capabilities that gives us a much bigger moat within our clients to really expand. And if you look at the total number of clients that we have that we generate $25 million or more in revenue on an annual basis, that's doubled in that period between 2020 and 2023. And what's even interesting here is when you look at 2023, you look at the number of clients that we generate $2 million -- approximately $2 million per client. That's 119 clients where we generate right now $2 million per client. That means we have a significant opportunity within that client base to really be able to drive revenue and really build that out. So those clients move up this chain really going forward. And it just gets back to just larger deal sizes, that we are entering into now going forward, that really helps us really drive total revenue per client. And here we get into what's helping us drive revenue. It's investment. And we really have focused on the amount of investment we pour back into the business between 2020 and 2023. We've tripled the amount of investment that we pour back into the business over this 3-year period to $60 million in 2023, which is almost 4% of our revenue now going forward. But this has enabled by that previous chart that I had on our gross margin. By driving gross margin and driving profitability, we're able to pour more money into investment, which really helps drive the top line growth. And that's really critical for us now, going forward in this new era of -- in this AI era now that we're all in, that level of investment is going to be really important for us. And this is going to be -- this has been reflective of our investment that's going to continue now going forward. And then all of this really translates back to a very healthy balance sheet. And a strong cash flow. If you look at our balance sheet, you look at the total debt that we have on our books has actually come down. And our debt-to-EBITDA ratio has been decreasing over this period, which really gives us a lot of ability to be able to allocate capital, right? Capital to many different areas: internal investments, M&A and share repurchase. Because now with our ability to generate a significant amount of cash flow out of the business, we generated $158 million of free cash flow in 2023. This year, we project at least $175 million or more in free cash flow, that gives us the ability to really allocate capital going forward and really gives us a -- it puts us in a position to have a very strong balance sheet. And when we allocate capital, One of the areas that we allocate capital is our share repurchase program. So we sent out a press release back in March. Our Board of Directors approved a $500 million authorization for our share repurchase program for the next 2 years. Now this is an authorization for most of 2024 and 2025. But that doubles, if you just split that between the 2-year period, that doubles from where we were in 2023, where we spent $125 million in share repurchases. And we've already enacted on that authorization. We repurchased about $20 million in open market purchases in Q1, and then we entered into a $125 million accelerated share repurchase program in March that we're effectuating today. And -- so that's in a symbol that we are really allocating more and more of our capital, both to M&A and you'll see more of that towards M&A, but also significantly to share repurchase because we do believe that we are opportunistic. We try to be opportunistic in where we allocate capital in share repurchase, depending on where the value is on our stock. And this -- and it's proved and we have the ability now to really take advantage of that. And then it gets -- then we take a look at -- if we look at our capital allocation strategy, just overall, if you look at our return on invested capital, it has significantly increased over this period of 2020 to 2023. We were sitting at 8.9% in ROIC back in 2020, and we've almost doubled it in 2023 to 17.5%. So how have we been able to do that over that 3-year period? It's really 2 reasons that ROIC has significantly increased. One is by becoming much more profitable, right? We have a 340 basis point increase in our AOPM during this period. So we're driving more profitability. We're driving more free cash flow in the business and more return just on our overall capital. And then secondly, we've very effectively managed our asset base essentially. And so by increasing profitability and managing that asset base, we've been able to significantly make a meaningful change in our return on invested capital overall. I want to take you back to November 2022, where we had Investor Day, and we talked about our medium-term targets, right? And so if you take a look at the economic environment back then, it was significantly different than where it is today, right? We had much better economic times in 2022. And the world kind of changed a little bit come 2023 and today. But what you're going to see is our business being very well balanced and very resilient no matter the economic environment that we're in. So if you take a look at what we said back in November of '22, and this was our medium-term target for 2023 and 2024, we said we would grow revenue 11% to 13% during that 2-year period. Our AOPM would be 18% plus and adjusted EPS would grow in line with revenue at 11% to 13%. If you take a look at what our projected results are against those 3 metrics, using the midpoint of this year's guidance, we will grow 13% on a year-over-year basis. So at the high end of that revenue, our AOPM will be higher than what we said back in November '22 for this 2-year period. And our EPS growth, more importantly, as we talked about, we want to drive EPS faster in revenues will grow at a 15% growth rate. And so this gets back to us having a very resilient, well-balanced business. We are able to grow both in very strong economic times, but also in more difficult economic times. And we saw that in this 2-year period, which really shows that we are able to grow that double digits going forward. And this leads me to our expectations now for 2024 and beyond. If we look at 2024 guidance, we talked about on our first -- on our most recent call for the first quarter. For the year, our guidance is 10% to 12% revenue growth for 2024, 19% plus AOPM for the year. That's what's embedded in our guidance. And our adjusted EPS growth is 10% to 13%. Based on our annual guidance that we just gave in our first quarter earnings release. We do believe going forward for the medium term, which is 2024 and 2025, we can continue that double-digit growth momentum. Based on our strategy and our pivot and our strategy. And we've seen that over the years, and you saw that from 2020 and beyond. We do believe that we can continue to make some incremental improvement on AOPM as we drive more higher-value services and solutions out of our business. And then lastly, as Rohit talked about, we do believe that we can continue to grow EPS faster than revenues overall. And when we look at this, it really is a continuation of where we are today. But the pivot to our strategy of data and AI led. And the opportunity that exists from what Rohit, Vikas and Vivek talked about is really important because it drives this medium term guidance that we're giving you. To summarize, we talked a lot about domain data and AI being at the core of our strategy. We talked about our evolving capabilities and tapping into new and existing buying centers. And, Pam talked a bit about our talent advantage. When you bring that all together, you have a business that's very well balanced, that's very resilient regardless of the economic environment. And based on our strategy, we can continue to drive double-digit growth. We can drive also profitability overall incrementally on an annual basis. And what that ends up doing is it allows us to invest because that's going to be critical for us going forward and it drives EPS faster than revenues. And then overall, what that drives is shareholder value. From there, we'll take maybe a quick pause, and we'll take your questions.

Just give us a minute to set up the stage here for the Q&A. While we're waiting for that to happen, I would invite you to take the gifts that we have there for you, the Rocket books. That's a gift for you to take with you today. So jump to Q&A here in a sec.

Thank you for being so patient and listening to us for the last couple of hours. I took a bet with the team that you guys will ask a question before we got to the end of my session, and you guys disappointed. Really. So anyhow, now we can take your questions. And by the way, Moshe, we're going to go with you first. Maurizio and I are going to be backstage which means we are not going to be answering any of these questions. We're just going to give it to Vivek, Vikas and Pam because you don't see that very often. And we'd love for you to be able to kind of grill them a little bit out here. And you can -- you know whose idea that was. Go ahead, Moshe.

Okay. Thank you. So looking at the various pricing mechanisms that you had on one of your slides, you had both IP-led and outcome-based pricing. We've been talking about outcome-based pricing for some time in the industry and hasn't really scaled or materialize into anything significant? So how would you characterize a client's appetite for these kind of mechanisms, if you will? And then how does it translate into revenue growth part of the revenue mix down the road, profitability? Is it even factored in the guidance that you're talking about? Because it seems pretty significant if it really kind of gets that traction. And what does it mean for your head count growth down the road? ]

Okay. Great question. I think Vivek will take that. Vikas can add. And I'll chime in a little bit at the end.

So let me start off first with your first question, which was about the pricing, right? So at EXL, we've actually been very forward in terms of taking on our transaction-based pricing and outcome-based pricing both. We've actually got a substantial part of the overall analytics revenue that's not priced on an FTE basis, that's priced on either a transaction or outcome. And one of the fastest portion -- fastest-growing portions of our business, which is led by Anita's team in health care is payment integrity, which is basically completely outcome-based for us. So EXL gets a share of whatever we collect for the insurers, the overpayments that we identified, we get a share of that. Now we want to continue to grow that because -- I mean payment integrity is very high margin for us. Our other transaction-based models are very high margin for us. So we want to continue to grow that. And this is something where -- like I talked about earlier, we want to take the risk, but we feel that we get more than an adequate kind of return on that risk. So it is something that we want to lean forward in and do more of. On the adoption side, we tend to get better traction where the industry is already attuned to that kind of a model. So in collections, it works well. Because you're collecting the sum of money and the industry is attuned to that and saying "I'm going to pay your collection fee." Our attempt has been to try and find models such as those and then create an offering that kind of fits that, right? And that's where we think we'll be able to grow more of it. On the IP-based pricing, a lot of the AI work that we are doing right now are explicit kind of approach to a customer is, I'm going to write a statement of work for you. That statement of work is going to have one line, which basically says, my IP or what I'm bringing for the solution. And then the second portion is going to be how am I customizing that and how am I building that into your system? So it's getting to the IP plus the services-based pricing. And that's kind of the second part of it that we are trying to do. So on both those fronts, the IP as well as the outcome based, I think our intent is to grow that and effectively get the benefits of it.

Vikas anything you want to add?

I think Vivek covered it. So what I want to emphasize is where the industry is already on such outcome-based pricing, the traction is much more. So in our business, the payment integrity business, the collections business, there's also a subrogation business, where for every dollar that we're able to add to an insurer, we get paid a few cents for it. So those are relatively easier. And we focus a lot on using AI to improve our own operations because given that the pricing is fixed as outcome, we can actually create more margin for us. For the rest of the businesses, we're progressively working towards moving them. Remember, with outcome-based pricing, there is higher reward, but there is higher risk. And we have to be very comfortable that we've reached a point of maturity in a solution wherein we feel comfortable with that equation. I use the smart data signals example a lot today. So just sticking with that, if we can reduce indemnity cost by 100 basis points, that's a significant value that you're actually creating for an insurer. And ideally, we'd want to move to a point where basically it's purely outcome-based pricing. But today, if you ask me the question, are we an outcome-based pricing today, the answer to that would be no. Because I think we need to get a little bit more confidence before we actually get to that. So, we have a good momentum. There's more work to be done, but I see a significant upside as we start moving more and more of these solutions to outcome-based pricing.

Moshe, I'll just add a couple of things. One, today, if you take a look at our business, 35% of our revenue today comes from outcome-based or transaction-based pricing. 65% of it today is on an FTE-based pricing model. So there's room for us to be able to continue to expand that. In terms of revenue growth, the adoption of IP-based models or outcome-based models are going to have necessarily a much faster growth rate if the adoption is great and the value is being delivered. But at the same time, it also carries risk of obsolescence. And any time somebody else comes up with a better mousetrap of being able to deliver a better outcome, it has a susceptibility of declining your revenue growth very, very significantly. So there are 2 things which we want to be able to do. One, maintain a balance in our portfolio so that we can continue to be able to develop these new solutions, which are IP-based solutions and diversify that risk. And number two, we'll have to keep investing in making sure that the outcome that we are delivering to our clients is best-in-class because that is what's going to result in our ability to grow the top line and the bottom line for our business. All right, Puneet?

Puneet from JPMorgan. Great presentations. Is your competition changing as you pursue more data, AI-based contracts? Like are you competing more versus IT services companies? And then like those companies talk about we know technology, we know AI data, your positioning is, we know, client processes domain. So can you talk about like are you going after the same type of contract, same type of workflows or use cases? Or is that different?

Sure. So it certainly changed the competitive landscape. But Vivek, why don't you answer that?

I think, Puneet, the short answer is, yes. Absolutely, the competitive landscape has changed. I'm going to use one example, one of the asset managers that we are working with. We went in to kind of talk to them about AI solutions that we had. And they brought us into this bake off as it were. And what they're doing is testing our solution versus the others. Now, the others include in this case, Google, who has brought their own services team. It includes IBM and it includes McKinsey, right? And I use that as an illustration because it is one of each. There's like players from all different parts of the spectrum that have come in. Now -- for us, we have -- we believe that our ability to beat the others is going to come from domain. It's going to come from the fact that we understand that space better. We understand the workflow interventions better. And honestly, the foundation models are available to everyone. So we think that we are able to customize that better and make that work better for us. We won some of these big offs. We won 1 in the U.K. against a very similar competitive set. So we kind of like our chance is there. I think the other thing I wanted to highlight is, while I've talked to you about the competition, there's a lot of cooperation that's also going on, right? So going back to that ecosystem chart, we're also now partnering a lot more with some of these companies. I mean, our partnerships with the data platforms with SnapLogic, we've won a pretty large deal where they introduced us, and we kind of are not doing the implementation. We are in a place where AWS and GCP have funded different projects of ours with a client where they're paying us the money to prove the value and then we go implement it together. So it's a very exciting space because it's a question of -- for a client, it's a question of choosing the right combination. For us, it's a question of what's the best way that we can bring that value and who comes along with us. So pretty exciting from that point of view.

So let me add a couple of things to that. The competitive landscape is not the IT services guys, but actually a much broader set of competitors out there. And the business problem from a client perspective has changed. In the past, what they would have said is, I want to modernize my platform, and I'll go to an IT services provider. Or, I want to build a strategy for how my organization should compete in the future, and I'll go to one of the consulting firms or I need to move my stuff on to the cloud and let me talk to the hyperscalers. Today, they do not approach this as IT versus strategy versus movement to the cloud, et cetera. Today, they think about how do you deliver the right kind of value to your customer and how do you engage with that customer. And therefore, the solution is a holistic solution. And it's the player that understands how to solve that problem that's going to win. And I think for us, it's the natural way in which we would normally compete. For the others, they have to change. Think about a McKinsey, they would just come in and talk about strategy advice. Today for them to be able to compete with QuantumBlack and do all the things that they've got, they've got to be able to come in and talk about execution and implementation, which is new skill sets that they have to learn. The IT guys have to learn how to run operations, they have to learn how to embed AI into the workflow. So it's changing for everybody. It's changing for us as well. But I think for us the advantage is that it's much more coming now from the business side of it, which is where we normally would play. Maggie, go ahead.

Maggie Nolan with William Blair. Thanks for your time today. My question is about the medium-term expectations. When you disaggregate that revenue growth expectation and you think about digital ops and you think about analytics, are those going to trend at similar levels of growth as they have historically? Or does the cross-selling proposition change how those segments grow over time and the delta that we've seen between them?

You want them to answer? Or you want Maurizio to answer that?

Whatever you prefer.

Okay. Vivek will talk about data analytics and Vikas will talk about digital operations. So go ahead, you go first.

All right. So let me just talk about digital operations. So as you have seen that we have seen an acceleration of growth over the last 3 years. And like I said, the reason is because the threshold has gone up, our ability to do more work with clients has gone up. And frankly, I see the demand set to be very strong across all multiple business verticals across different markets. So yes, we do expect the digital operations to continue growing strongly. And like I said, the digital operations business in many ways is also becoming sort of a new consumption model for data and AI because embedded services as part of digital operations continue. Now one of the things about where our business is, even within digital operations, is extremely well balanced is that we work with clients, both on the growth side as well as on the cost side. And based on the industry-specific environment or the overall macroeconomic environment, the conversations may change a bit, but we still see a healthy pipeline. So you saw this example about new product launches. That's all about growth enablement. And then you saw the example about creating value and claims by arresting leakage, that's all about cost management. So I think this balance between enabling growth and managing costs for our clients is an extremely good one for us. And we do expect to see some secular growth continuing on this over the next few years. Vivek, do you want to talk about analytics?

Yes. So for analytics, I think the story is twofold. Right? First of all, I mean, you've seen obviously in '23, our growth rate slowed down. And that was because of the prevailing market conditions. We took a hit in terms of our marketing business, which is still tracking lower. But as Maurizio talked about in our most recent earnings call, we now expect to see sequential growth. We've already seen that happen from Q4 to Q1. We expect our annual growth rate percentages to kind of increase through the rest of the year. And if I were to kind of then start projecting that over the medium term, as the growth environment starts coming back or the current cost pressure environment starts easing up a bit, we expect to see that to translate into kind of growth momentum for that part of the business as well. I think the last thing that I wanted to emphasize was even in the current marketplace, a lot of the things that we've talked to you about today are very strong growth drivers. So data management for us, you saw the growth rate there. It continues to kind of boom and with the market opportunity, the way we think that AI will play out, that's going to continue to grow. Payment Integrity continues to grow stronger for us. We think that, that growth momentum is going to continue. And the digital business, we think that we are already seeing that traction kind of convert. So a lot of those underlying drivers will continue to be strong. And of course, if the market conditions change, then that's an add-on the company.

Brandon with [ Hoffman ] Capital. On the recent share buyback, if you just speak to -- if you see it being accretive in the first year and maybe how we should be thinking about debt pay down later in the year? And if we should start to see maybe just like the share count ticking down each quarter? Or how should we be thinking about that?

Yes. So we have accelerated our share repurchase program, as I talked about, right? We got half of -- $500 million authorization. And we've started with $144 million already this year. So this year, you're going to see us at least to most likely $250 million or possibly more than that in share repurchase. The benefit is lowering our share count will happen -- will start in Q2. We started really the ASR in the middle to late March. So you -- so the share count in absolute terms came down, but on the weighted average shares, it comes down significantly in Q2. But you'll start to see the share count come down throughout the year as we do more of this, but the real accretion will be in 2025 and thereafter, right? This year, you'll see minimal amount of accretion in EPS, but you're really going to see that benefit well after that.