Cognizant Technology Solutions Corporation ($CTSH)

I think we're ready to get started here. So I'm Surinder Thind, the technology and information services analyst here at Jefferies. And for our fireside chat today, we have Jatin Dalal, Chief Financial Officer of Cognizant. So, welcome, Jatin.

Thank you very much for having me.

Fantastic. So for today, I've prepared a series of questions where I think the first half, we'll probably focus on AI, the AI debate. And then in the second half, I think we'll move into more of the questions around demand, near-term trends and kind of the shifts that are going on in the business.

Sure.

So I think where I'd like to start is just -- there is a lot of investor debate around AI and people are viewing this as potentially deflationary impact on IT services broadly. But my sense is there's an oversimplification here for a business such as yours, which you -- there's a broad range of capabilities and services and products and offerings that you have. And that provides different value pools, I would argue, for all the different clients that you have. So as you kind of think about this strategically, like how should investors disaggregate your portfolio and better understand where the AI opportunity is and maybe where the AI risk is in the business and how you kind of go about thinking about this.

Sure. So I'll start by saying that there is a -- there has been a sort of narrative around AI deflation, which is well known. And this is something that the industry has seen it for last, I would say, 18 months, where we have seen that the traditional IT services work that we do, we are able to do it more efficiently, more effectively with AI. And therefore, there is a deflation on the individual contract size, although there is very little deflation at an aggregate industry and company level because even with that deflation at individual project level, we are seeing that last year, we grew 6% plus. This year, we have guided for a similar growth rate for a range of outcomes possible. But we are -- the core part of that guidance is that organically, we will grow again 3% to 4% this year. So even with the deflation there is very little contraction in the total revenue of the company or the industry. But what is really exciting for Cognizant is that AI is opportunity -- AI is opening up opportunities which are either do not available to industry in 2 ways. One, it is opening up new -- opening up old work, which you can perform in new ways. And second, you can do new work in new ways. And let me talk about both. What do I mean by old work in new places? If you -- if all of us have heard about mainframe modernization and that was the work that we used to do 15 years back. And now it's a tiny trail or small stream of revenue there. But certainly, with AI, the mainframe modernization has become a proposition which is very real, which can get done in 24 to 36 months. It's no longer a 4- to 5-year journey. Certain upgrades of ERP is far faster to get deployed than what it would have been before. So we just recently won a large opportunity on mainframe modernization, which is really not something that would have come as part of our pipeline anytime before because of AI. So that's really old work, which is mainframe modernization, but now being done in a new way. But let me speak about the new work and that we do and the new opportunity that we face -- we're getting. And the easiest is to think about opportunity like [ MATOS ]. It didn't exist 4 months back. It is now here. But if you start with MATOS and then you discover, let's say, potential 200 critical vulnerabilities in your environment in application database in network, you will have to go out there and fix it, and that opportunity was never existing when AI was not there. The second is whole agent development life cycle. Traditionally, we have all lived around the ecosystem that was built around microprocessors. So we had microprocessor in the middle, and then we had compute. We had storage. We had software. We had IT services, and that created the value for our large customers. So for 30 years, we have lived in a world which is -- which had only single direction, which was ecosystem around microprocessor. Now we are opening up the whole ecosystem around LLMs, which is LLM in the middle and there is a new compute of, let's say, NVIDIA then there is a -- there are a few LLM providers. There's a few agents provider and then there are branded agents, there are nonbranded agents, then there is a whole training of agents and then eventual deployment of agent and then agent monitoring for potential drift that can happen. So if you envision that a large Fortune 500 company will work with 70% of the deterministic systems that it built over 30 years and 30% of probably stick systems that will be built around LLMs, the whole runway for IT services company is around building and deploying those systems for large companies. So it's a new work to be delivered in a new way. And therefore, we remain very, very optimistic and bullish for what we can do with AI as new opportunity. So in summary, there is a pressure of deflation on traditional services. But if you see that is in our traditional space and that also we are able to overcome it by new work. And that's a $1 trillion of TAM. But we believe this probabilistic system and deployment around it is another $5 trillion, $6 trillion of TAM that we are opening up with the advent of AI. So it's a great -- I would think it's a great opportunity for the industry and for Cognizant.

So from a messaging perspective, what I'm hearing is, yes, there's deflationary impact in the traditional business. But with everything that's coming down the pipeline, you've got a much bigger TAM to go after, and that's why you're able to kind of keep your growth rate positive at this point. Is that essentially the messaging that there's a lot more coming down the pipeline?

I would say there's a lot more coming down in the pipeline, but that a lot more coming down in the pipeline is being pulled forward on traditional IT services side. It is still not the new services revenue around LLM, which is yet to be seen in its full force by the industry. But when it comes, then you will have sort of a dual engine growth, one being driven by the traditional business and the one being driven by the new business.

Got it. And then as part of this kind of what I would call, you've talked about the shift away from the traditional services space towards more of a platform or an IP-enabled model. So at a strategic level, what does that evolution actually look like for the firm? Like how are you thinking about the forward business model? And what is this role at proprietary IP these reasonable assets that you talk about that you guys are building? So is it more a software-like revenue stream that you're moving towards?

Yes. So traditionally, we have said that we have offerings that we deliver through skills, which is the pyramid of human intellect that delivers that services. Going forward, we believe that the world lives in a place where you will deliver the outcome in both the streams on classic IT systems and new AI systems, both you will cater to through 3 constituents. One is the skill. The second is influencer agents and the third is platforms. And we believe that you need all 3 to deliver most optimized outcome to a customer. And anyone is not going to be sufficient. And therefore, beginning of this year, we called out platforms as a separate feeder of delivery to our customer. And within that, TriZetto is a flagship IP on health care side where 2/3 of the claims in U.S. are processed through TriZetto, and that means it's a great IP because it has sort of thousands of type of claims on one side and another thousands of payers on the other side and it has sort of a residual knowledge of processing the most complex to most simple medical claims in U.S. Now where does it help us is that defined point A to point B journeys, help us deliver far faster outcomes to our customers. And I'm using example of TriZetto, but that is why we [indiscernible] platforms is -- similar to that, we can -- we have IP in multiple other places, which can be leveraged to deliver an end-to-end outcome. And one additional factor why we see TriZetto as a great IP is it is suddenly with AI, the surface of monetization is multiplied significantly. Traditionally, I sold the IP on what I call a PMPM model that's per member per month model. And there are only so many members I can onboard on the IP every year in terms of growth. But we announced that we have now made TriZetto headless, meaning that any agent or any virtual sort of -- any virtual agent can come, access their IP and get the work done and go out, which means now I have a monetization for the same IP with some incremental investments, but not significant. We have made it open to all the sort of agent workflows, which needs to be [indiscernible] at that IP, which means I have additional monetization every time some agent comes and touches our IP. So that's an additional monetization reason. So that for platform is a big priority for Cognizant.

And the impact on margins, I think at the AI forum last week, the idea was as you pursue this strategy, margin should be biased higher? Or how do we think about the impact on the business model?

These platforms by design, have a higher investment and therefore, a higher risk, and therefore, you anticipate a superior return in terms of gross margins. So therefore, yes, definitely, as the platform becomes part of a -- larger part of the portfolio, it should have a favorable impact on the operating -- gross margins and operating margins.

And then when we think about the next part of this platform or software strategy, like you've also highlighted other examples that you've had like a digital nurse or you've built like a wealth management agent. Now is the idea there that -- when I think about you highlighting this, the strategic significance of that, is there a lot more of that to come? Like how do we actually think about where you guys evolve to in terms of kind of this IP strategy versus a small supplement versus pushing really hard on that front here.

Yes. So this is what I what I talked about the new work in new ways, that is what we call Vector 2 and Vector 3 opportunities, which is coming because of AI, where we -- Vector 2 is making organization ready for AI and Vector 3 actually deploying agentic workflows for our customers. So either having wealth manager agent or an agentic nurse is a classic agentic deployments with agent development life cycle and monitoring of that deployment over a period of time. And that's absolutely a new opportunity that we never had before. And it is, therefore, a big focus area for us. It's a tiny revenue stream today. But if the growth that I spoke about, which is from $1 trillion to $6 trillion, the large mass of that growth is the new ops that we never did before, which we can do now with the agentic workforce is through opportunities like this.

That's helpful. And then just -- in the very recent, there's been a lot of talk about tokens, token economics, the spend and all the challenges that clients are having. But as you yourself consume more AI to deliver your services more compute. Can you help us understand the implications for your revenue model and how that -- how you work with clients on that? I mean, is this an idea where you will -- just the client buys the tokens and you build on top of that? Is this a situation where maybe you guys buy the token and they cost plus it or would you just go down the whole path to fixed price where the client just -- they don't care, they want a certain service for a certain price. How do you think about those revenue models and where you end up in this?

Yes. So I think we are at a point where our customers have just begun consuming tokens materially. Of course, there are cases where people have over consumed tokens. I'm sure many of you know about this. And therefore, there is a question now as to the throughput or the value created by broken or inference, right? We work with our customers on all 3 models that you mentioned, meaning, I create the Tier 1, which is agent development life cycle, and I actually create an application or agentic deployment for customers, but I only charge for human effort that was there to deploy it. But that's a smaller component. What we are increasingly seeing is customer is asking us to blend the influence as part of offering. But still, it's very transparent with customers, how much influence is getting consumed. They're willing to let us make margin on that, but they would like to have visibility on that. Eventually, we see a fixed price project that you -- type of deal or outcome-based type of deal where customers say, I don't care how much of platform you are using, how much of inference you are using, how much of skills you are using. You tell me if you can deliver this outcome at this price to me, then I will buy it. So that could be -- that's a sort of, I think, somewhere in future, that model will start getting traction. Right now, we are at a point where we are beginning to embed inference in the rate cards in the fixed price project. It is visible to customers today. It's not very tight fixed price. But we -- the evolution has already started.

Got it. And I've also heard you mention in some very recent conversations that the fact that clients are questioning their token usage, their budgets, that is perhaps a leading indicator of change or demand. Can you talk a little bit about that and how were you might help clients cross that bridge?

Yes, absolutely. I think there is a broad realization that there is a cost associated with token and therefore, there has to be -- like any other cost, there has to be some amount of prudence on how you -- token cost gets consumed. Where Cognizant comes in place just the way we have traditionally built a pyramid of human intellect and skills. we build a pyramid of LLMs and SLMs, where not every query needs to go to the most expensive token. You could essentially build a small language model for your accounts payable process in your office, which caters to 70% of the users' queries or agents' queries, and agent can freely work with that. For the 30% of the queries, just need to filter out, we'll go to any of these models. That is fine. You don't need to send every possible query to the -- to an LLM. In a very industry-specific domain, you don't have to work with the large language model, you can work with a narrow language model. So you could really optimize your intellect usage depending upon the query that comes in, and that's where we add value. And that's where we become relevant when somebody feels that they are overspending on tokens versus the value that they are generating from it.

Got it. And you mentioned something interesting here, which is this idea of introducing a lot of -- it sounds like proprietary models. Can you talk about that versus this idea that maybe there's 1 or 2 big winners and how do you see that evolving and maybe the investment that you're making in building your own industry-specific or workflow-specific models?

Yes. I think there is a certain amount of prominence that a few models, large language models have got because of the universal applicability, but there is even today, availability of narrow language models or what we build for our small language models for our customers for the limited domain that we do. Typically, it's their IP because it's very relevant for that customer. But it makes the whole the ROI question, very favorable for customers versus the question mark that sometimes comes when for simple queries you are using, certain things. Also, you must remember that there is always a most optimal way of doing a few things. Meaning if you want to add up 15 numbers, you're going to go to your Excel or your calculator, you are not going to go to an agent and ask the agent to compute. And so it's a very simple example. You could extrapolate that to everything that you do in an enterprise where you don't have to go to agentic answer for everything, you will have 70% of work being done by today's systems and 30% by, let's say, agentic outcomes. And that's where we add value where we marry the 2. You are not solely relying on one type of delivery to get to an optimal answer. So I think the question of toekn cost and token usage is relevant. And I think that's where companies like Cognizant can add value.

Got it. And so maybe just putting together the last few questions in kind of one big wrapper here. When I -- I think the idea is -- I'm trying to get to is what does Cognizant maybe look like 3 or 5 years from now, right? I mean if enterprises start to move meaningfully from kind of what we have is deterministic systems with human wrappers, right, that's the existing workflows, towards more of these autonomous agents that operate across the enterprise. How does this -- how do you look at -- where are you in that vision 3 or 4 years from now?

Well, our vision is -- comes from what customer requires. And we believe a large Fortune 500 customer would be using maybe 70% deterministic systems that we use today and maybe 30% would be probably a system built around LLM. And we will have a meaningful role to play on both sides. We have sales capability for probably [indiscernible] system, we will have sales capability for traditional deterministic system. We will have delivery, as I mentioned, with a combination of human skills, influence or agents and platforms. We will have a model of Cognizant behind it, which would be far more in our assessment, flatter and wider where multiple middle layers of the organization may not -- may be merged in a way that it creates more value for our customer. That's what we see as a model of future for Cognizant.

Got it. And then I think the final thing here, just maybe I want to talk about the pace of change, right? And I think the idea here is how quickly it is. If we go back to just entropic blog post last week about how things like the software engineering capabilities of the models are advancing maybe faster than people are going to be able to keep up, the length of the tasks, everything that's going on, how do you actually plan and commit to your investments when the world around you is changing so fast? I mean, let's say you were working on something 6 months ago, and all of a sudden there's new capabilities introduced. How do you work through that and plan around all of that?

So we have -- so I'll answer it in 2 parts. One is how we keep up with that. I think we have probably the best in the industry lab for AI and probably some top minds researchers bought for us. Our Chief AI Officer is probably one of the most respected names in the Valley. So that's how we keep up with the pace of what's going on. But the more relevant question for all of us in the room is there is a technology potential, which is, let's say, 100, and every day, it's growing. So in 10 days, it will be 110. I mean I think there is a new model which has been put up by Anthropic this morning. But if you see the value that enterprises have delivered, there is nowhere close to 100. I mean you all may have a different number, somebody will say 10, somebody is 15, somebody is at 20, but it's nowhere close to 100. So I think there's a big value gap that needs to get bridged because eventually, every technology is only as useful as the eventual value delivered by the end user. And I think there is that gap that is visible today and organizations and companies like Cognizant, who are primary advocates for our customers to stitch the ecosystem well and deliver that value. Because if technology continues to progress that's great. But I think today, the bigger and burning question is how do you bridge that gap between technology potential and value to the enterprise.

And so I think that will lead us to the next section here, which just the broader demand, demand environment. So when you talk to clients today, right, I think there's this concern about what I would call mounting like legacy complexity. There's a lot of technical debt. Things are changing fast. How do you characterize that conversation that you're having with clients today, right? It seems like there's a lot of demand, but at the same time, there's a lot of concern from a client perspective. So can you help us understand what's going on.

Yes. I think there is -- and that brings us to a little bit on short term. I think we definitely see that on one hand, there is a lot to do for our customers, but at the same time, the world around us is changing so fast that is creating a certain amount of indecision or delayed decision because nobody wants to jump into something which can be later called a technology of yesterday. So there is that pause or delay that we have definitely observed. And so while currently, the traditional work is being done far more efficiently with use of AI. The new work related with AI will take its scores as it continues to build momentum around itself.

And then I guess, at this point, like what would it take to get clients to engage maybe a bit more aggressively or invest a bit more aggressively? Like discretionary spend has been weak, broadly speaking, for a number of years now. Can we talk a little bit about what's going on there? And do we need to see maybe some stabilization in the technology to kind of get clients over that cliff? Or like -- are we just kind of stuck here for a little bit?

So I would think there are multiple data points which point towards saying that we are taking maybe not fast enough steps, but we are taking firm steps towards AI adoption. If you see some of the revenues of large LLM players, they are growing very rapidly, which means that large enterprises are consuming that. And when large enterprises are consuming that, they are all smart buyers. At some point, they would start investing effort on how do we get ROI on that. So that will come. So I certainly don't see -- I don't think that you need to do something different to land there. I think we are taking a slower step but firmer step towards AI adopted world. Whether it is -- and a result of that discretionary business for IT services industry is 2 quarters away, 3 quarters away, 4 quarters away, difficult to call, but I definitely see ourselves walking towards that.

And then if I could maybe ask for a bit more color on that. You've conceptualized it in this concept of Vector 1, Vector 2 and Vector 3, right, where Vector 1 is productivity led, Vector 2 is more about infrastructure and the build-out of all the capabilities so that you can use AI. And Vector 3 is where you effectively redesign workflows or you go AI native. When you look at the mix of demand right now, where are we there? Like are we seeing enough sign that we're moving between the vectors at this point or...

Yes, I think it's a great question. I think we definitely see still a large component of our pipeline is Vector 1, which is productivity-led IT work. But we are beginning to see a meaningful pipeline for Vector 2, which is making enterprises ready, especially on data side which is vector to work. We are seeing first implementations of agentic workforce which we spoke about in our AI forum on Friday, where customers came and spoke about it. So that's also beginning to take. But still, that is relatively small and some way to go. But Vector 2, I certainly -- I'm beginning to see a good traction in our Vector 2.

And does it all have to move in sequence, meaning you got to do Vector 1 first? Be [indiscernible], I would say, dissatisfied with the level of productivity that you're getting. So you're like, I got to rebuild my infrastructure, then you go and rebuild it, get your data cleaned up. And then you finally kind of move to Vector 3? Or like who's Vector 3 today?

So it's again, a great question. I don't think you need to go sequentially. It depends on the architecture of your enterprise whether you need to go sequentially. AI is very powerful even with a slightly suboptimal data structure, I think agentic deployment will work well. But it may be a little bit more expensive because now inference has to work through navigation of complex data structure. So it is not sequential in some form. But if you want to optimize each aspect of your agentic development, then you would want to optimize your data structure and then go for the inference cost rather than inference do extra work on data side too. Also, there could be companies which have invested 2 years back on a great data structure. They don't have to go to Vector 2. The companies which are which are using genetic workloads effectively are simpler organization, what I'll call single geography, a large line of 1 or 2 businesses, they are effectively deploying agentic workforce as of today. It is not a conversation or future. They have replaced human efforts with 40%, 50% of virtual effort, and they continue to do so. If your business is a little more complex, you have 300 products, operating in 50 countries, that is taking a little more time before they deploy Vector 3.

Got it. And then for the, what I would call the native company -- or the ones that have kind of tried to make the Vector 3 type of transformations, how are they measuring the benefits? Like are they seeing what they're supposed to be seeing? Because there's this mix narrative of it's really hard to right now realize the benefits of AI. Are you beginning to see signs where we can start to measure some of those benefits? And that ultimately becomes one of those situations where when we look about past cycles, it's the success of one client. They build a competitive advantage and then all of a sudden, their competitor says, wait a minute, if they did it, I also need to do it. And so you kind of see this acceleration.

Yes. I think where you agentic deployment, the answer is very comprehensive and very clear, it's a question -- I mean it's a night and day difference. I mean one of the customers who spoke in our AI forum spoke about saying he had a few hundred agents who are performing a service. He has moved to agentic model. The number has come down sharply by -- I mean he's left with maybe 10s or 20s of those. And effectively been replaced by virtual agents, the turnaround time has come down from a few days to a few minutes of the process that they were doing. And what he takes -- talks very proudly is that of his few hundred agents, the virtual agents he has trained are the -- he picked top 5 agents from those few hundred. And he trained the virtual agents with those best performing agents. So now he has almost entire throughput going through virtual agents who are trained from his best agents. So his quality of outcome has increased significantly. Number of days of turnaround has become a few minutes. So it's a comprehensive comprehensively superior outcome.

Got it. And then maybe shifting a little bit to -- I don't want to get too near-term focused, but there's a lot going on in the current environment. When we think about it from a geopolitical perspective of where we started the year, there was a lot of excitement. Maybe can you talk about just the evolution of client behavior and maybe like the last 90 days. Just kind of where we sit in this macro geopolitical uncertainty and how -- like at least from your perspective, like -- what are the changes that you're seeing from your clients in terms of their decision-making cycles, maybe prioritization around projects? Or just maybe the visibility that you have to your client spend relative to historical norms and standards at this point?

Yes. So I mean, we spoke about in our quarter 1 earnings call that there is a near-term macro uncertainty, which we see in customer behavior. It is driven by geopolitical drivers. It is driven by a little bit of rapid change in technology that we spoke about earlier. And things have not changed since then, meaning we still don't see any acceleration on demand side from that situation. We have -- we have guided for this quarter, keeping in mind that environment, and that's what we think is playing out. We had also budgeted for within our guidance, a month's revenue from our recent acquisition, Astreya, but that is yet to close. And once it closes, we'll make an announcement around it.

Got it. And then when we think about just where -- are you seeing any geographic differences because there's also a concern about how the different economies are evolving, let's say, Europe versus the U.S. or even Asia Pac at this point.

In our largest geographies, which is both U.S. and Europe, we see a growth momentum, which is quite uniform. Especially in Europe, we -- when Ravi took over as the CEO of the company, U.S. was among the first to start showing the traction and the growth. Europe took a little while, but we have had some market wins in Europe in the last few months, and we remain quite optimistic for 2026 for Europe, too.

And then I guess the final kind of component here within the current environment, your BFSI segment doing really well, they're spending, they're ahead of the curve. Can you maybe talk about what's going on there versus is it just a macro issue? Or are they just kind of -- are they just leading the space in terms of the investment here versus maybe what's going on in like retail or health care at this point?

The BFSI is always the leader in appreciating sort of potential of new technology and its deployment. So we certainly see BFSI leading the whole investment on AI and on -- and therefore, the discretionary spend related with Vector 2 that is quite visible to us in BFSI space. I would say following that is health. Manufacturing is doing decently okay, but probably most impacted from the geopolitical uncertainties. And finally is communication and media space, which also is not in its prime in terms of growth, there are company-specific sort of priorities that our customers are tackling.

And broadly speaking, you've had a lot of success in winning a lot of large deals. That said, on the discretionary side, there's been a bit more softness on just discretionary spend, yourselves, industry-wide as well. Can you talk a little bit about that dynamic? In one of the narratives out there, there's concern that as these tools advance, the AI models advance, clients are trying to do more themselves. And so that's kind of keeping discretionary. How do you think about what might be going on there?

Yes. Yes. I mean I have not seen clients doing more of new work with them. It does tend to happen because any new technology client wants to have a comfort and feel of operating it before it gets outsourced. But I don't think that's been a driver this time around. Even we have had very large successes with setting up GCCs for our customers. So even customer GCCs, we are helping them run in India or elsewhere. I think it's just -- in -- outside BFSI, it's more macro and the fast pace of AI tech evolution that is probably pausing decision-making a little bit.

Got it. And is there any indicators that you're looking for, meaning more positive client conversations or commits or how do we think about -- somebody that maybe sits from the outside trying to understand when we kind of get over this hump, right? I'm not talking about predicting next quarter that we have, but just kind of how we think about it broadly.

I think it's essentially how it starts is the texture of the business changes from many large deals to multiple small deals every time there is a discretionary rhythm change. And I have a feeling that when it changes, it would be very clearly visible. We speak also about our ACV growth numbers every quarter. And when you see ACV growth maybe a couple of quarters inching up, I think that would be a great indicator that finally, we are seeing the discretionary back.

Got it. And then I want to -- there was an interesting comment that Ravi had made about just kind of right now, you have peer-leading growth, but then you talked about getting to like breakaway growth. Can we talk a little bit about that, the aspiration there and the genesis of that? Like when I think about it strategically, financially, like widely that goal out there right now, you're already peer-leading and now you've kind of set another aspirational goal about getting to breakaway growth.

I think the comment for breakaway growth is in light of the opportunity that AI builder or Vector 3 provides. And I think if we get to that zone, I think higher growth is definitely possible because we are going after a much larger TAM than what was visible before. So that's a statement of aspiration that as we move from a traditional $1 trillion TAM to a much larger TAM, you would accelerate your growth rate significantly.

Got it. And then just kind of wrapping up here, maybe something on just kind of capital allocation and M&A. Can you talk about just your capital allocation strategy at this point and how M&A fits into the broader strategy at this point? And then I've kind of got a couple of follow-ons about the strategic use of it.

Sure. So our capital allocation philosophy is 50% of the free cash flows for M&A, 50% for returning to shareholders. Within that 50%, half is for dividend and half is for buyback. Now last year, we did not have a large M&A. We were consolidating [indiscernible] that we had acquired a year before. So we had a large $1.4 billion of the 2.5%. So roughly 60% was on a buyback. This year, we started with slightly higher allocation for buyback because the share price was lower. But in month of May, when we realize the share price has dropped further, we went with another $1 billion of -- so collectively, $2 billion of buyback is what we have planned for 2026. And that, as you can see from -- it's nearly 80% of the $2.5 billion of free cash flow that we generate. But the way I see this buyback of this year is more a pull forward of our maybe future years because the time was so opportune that we had to act and we acted. But over a period of time, we will like to retain this balance of 50% of use for M&A or strategic use and 50% for returning cash to the shareholders.

And then in the current environment, I can understand the share repurchase component of it leaning into it. But what about this idea, given the amount of change, leaning a lot more heavily into the M&A, right, to find new ideas to kind of diversify against all of the different possible outcomes that there are? Like why not just go all in on M&A?

Yes. I mean one could go all in on M&A. And I agree with you, these are the times where you should lean into M&A for going out of new opportunity, both acquisitions we have done since beginning of this year, 3Cloud and Astreya really play in that whole AI super cycle. 3Cloud really works on the cloud deployment for AI workloads. And Astreya is right in making data center ready technologically. And we all know the kind of investments which are going in data center. So clearly, we are picking up businesses which help us gravitate faster and more substantively towards the AI opportunity. And we'll continue to look at that. I mean good thing about the current market is that good companies or good opportunities are available at reasonable prices. And we'll go -- we'll continue to invest there. But you also balance where you are in your share price and therefore, repurchase made imminent sense at that price. So we went all out. But that doesn't even as we made it, we were quite categorical to make a statement that, that doesn't restrict our M&A option. We have plenty of leverage opportunities or leverage bandwidth on the balance sheet if we found something big that we can't manage with our cash flows.

And then final question, I noticed we're down to the last minute or 2 here. Anything you want to lean into the portfolio from either an investment perspective or an M&A perspective where you'd like to add to something maybe -- like I feel like you've made some important product engineering acquisitions. Anything that sticks out to you? Or...

No, I think platforms is one. Operations is another because the whole hypothesis around targeting tech plus operations could be another area of investment or interest to us. And one last point I would make on investment is that we also announced Cognizant innovation network, which is really the investment in early-stage companies, which bring that additional innovation to Cognizant. And idea is that we invest in that IP and we bring that IP to our Fortune 200 customers. So there's an additional investment dollars are not that big. But it just provides a very different ledge of innovation to go to customers with. So yes, I think these are the few ideas to double down on.

Okay. Well, fantastic. I covered a lot of topics here. So I really appreciate the time.

Thank you very much. Thank you.