I think we're ready to get started here. I'm Surinder Thind, the Technology and Information Services Analyst here at Jefferies. For our fireside chat today, we have Jatin Dalal, Chief Financial Officer of Cognizant. Welcome, Jatin.
Thanks very much for having me.
Fantastic. For today, I've prepared a series of questions where I think the first half will probably focus on AI, the AI debate. 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.
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. My sense is there's an oversimplification here for a business such as yours, which 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. As you think about this strategically, 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 go about thinking about this?
Sure. I'll start by saying that there has been a sort of narrative around AI deflation, which is well known. 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. Therefore, there is a deflation on the individual contract side, although there is very little deflation at aggregate industry and company level. Even with that deflation at individual project level, we are seeing that last year we grew 6%+. This year we have guided for similar growth rate for a range of outcomes possible. The core part of that guidance is that organically we will grow again 3%-4% this year.
Even with the deflation, there is very little contraction in the total revenue of the company or the industry. What is really exciting for Cognizant is that AI is opening up opportunities which are hitherto not available to industry in two ways. One, it is opening up old work which you can perform in new ways, and second, you can do new work in new ways. Let me talk about both. What do I mean by old work in new ways? 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. Suddenly with AI, the mainframe modernization has become a proposition which is very real, which can get done in 24-36 months. It's no longer a four to five-year journey.
Certain upgrades of ERP is far faster to get deployed than what it would have been before. 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. That's really old work, which is mainframe modernization, but now being done in a new way. Let me speak about the new work that we do and the new opportunity that we are getting. The easiest is to think about opportunity like Mythos. It didn't exist four months back. It is now here. If you start with Mythos and then you discover, let's say, potential 200 critical vulnerabilities in your environment, in application, database, in networks, you will have to go out there and fix it. 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. 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. For 30 years, we have lived in a world which had only single direction, which was the 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, and then there are few LLM providers, there is a few agents provider, and then there are branded agents, there are non-branded agents, then there's a whole training of agents, and then eventual deployment of agent, and then agent monitoring for potential drift that can happen.
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 probabilistic systems that will be built around LLMs, the whole runway for IT services company is around building and deploying those systems for large companies. It's a new work to be delivered in new way. Therefore, we remain very optimistic and bullish for what we can do with AI as new opportunity.
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. 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. I would think it's a great opportunity for the industry and for Cognizant.
From a messaging perspective, what I'm hearing is, yes, there's deflationary impact in the traditional business. 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 is 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. 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. Then as part of this, 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. At a strategic level, what does that evolution actually look like for the firm? How are you thinking about the forward business model and what is this role that proprietary IP, these reusable assets that you talk about, that you guys are building? Is it more a software-like revenue stream that you're moving towards?
Yeah. 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 three constituents. One is the skill, the second is inference or agents, and the third is platforms. We believe that you need all three to deliver a most optimized outcome to a customer, and any one is not going to be sufficient. Therefore, beginning of this year, we carved out platforms as a separate feeder of delivery to our customer. Within that, TriZetto is our flagship IP on healthcare side, where two-thirds of the claims in U.S. are processed through TriZetto.
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. Where does it help us is that, defined point A to point B journeys help us deliver far faster outcomes to our customers. I'm using example of TriZetto, but that is why we carved out platforms is similar to that. We have IP in multiple other places, which can be leveraged to deliver an end-to-end outcome. One additional factor why we see TriZetto as a great IP is it is suddenly with AI, the surface of monetization has multiplied significantly.
Traditionally, I sold the IP on what I call as PMPM model, as per member per month model, there are only so many members I can onboard on the IP every years in terms of growth. We announced that we have now made TriZetto headless, meaning that any agent or any virtual agent can come access that 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 agentic workflows, which needs to leverage that IP, which means I have additional monetization every time some agent comes and touches our IP. That's an additional monetization reason. Therefore, platform is a big priority for Cognizant.
The impact on margins, I think at the AI forum last week, the idea was as you pursue this strategy, margins should be biased higher. How do we think about the impact on the business model?
Platforms by design, have a higher investment and therefore higher risk, and therefore you anticipate a superior return in terms of gross margins. Therefore, yes, definitely as the platform become larger part of the portfolio, it should have a favorable impact on the gross margins and operating margins.
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 a wealth management agent. 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? How do we actually think about where you guys evolve to in terms of this IT strategy versus a small supplement versus pushing really hard on that frontier?
This is 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 Vector 2 is making organization ready for AI, and Vector 3 is actually deploying agentic workflows for our customers. Either having a wealth manager agent or an agentic nurse is a classic agentic deployments, with agent development lifecycle and monitoring of that deployment over a period of time, and that's absolutely new opportunity that we never had before. 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 a 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 agentic workforce, is through opportunities like this.
That's helpful. 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. 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 you work with clients on that? 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 do you just go down the whole path of 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?
Yeah. I think we are at a point where our customers have just begun consuming tokens materially. Of course, there are cases where people over-consume tokens. I'm sure many of you know about this. Therefore, there is a question now as to the throughput or the value created by tokens or inference, right? We work with our customer on all three models that you mentioned. Meaning I create the Tier 1, which is agentic development lifecycle, and I actually create an application or agentic deployment for customer, but I only charge for human effort that was there to deploy it. That's a smaller component. What we are increasingly seeing is customer is asking us to blend the inference as part of offering, but still it's very transparent with customer how much inference 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 customer says, "I don't care how much of platform you're using, how much of inference you're using, how much of skills you're using. You tell me if you can deliver this outcome at this price to me, then I will buy it." 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 customer today. It's not very tight fixed price. The evolution has already started.
Got it. I've also heard you mention in some very recent conversations that the fact that clients are questioning their token usage, their budgets, that it's perhaps a leading indicator of change or demand. Can you talk a little bit about that, and how you might help clients cross that bridge?
Yeah, 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 your token cost gets consumed. Where Cognizant comes in play is 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 user's queries or agent's queries. For the 30% of the queries you need to filter out will go to any of these models. That is fine. You don't need to send every possible query to an LLM.
In a very industry-specific domain, you don't have to work with a large language model. You can work with a narrow language model. 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're generating from it.
Got it. 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 one or two 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?
Yeah, I think there is certain amount of prominence that a few models, Large Language Models, have got because of their universal applicability. 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 ROI question very favorable for customer versus the question mark that sometimes comes when, for simple queries, we 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. 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. That's where we add value, where we marry the two. You are not solely relying on one type of delivery to get to an optimal answer. I think the question of token cost and token usage is relevant, and I think that's where companies like Cognizant can add value.
Got it. Maybe just putting together the last few questions in one big wrapper here. I think the idea I'm trying to get to is what does Cognizant maybe look like three or five years from now, right? If enterprises start to move meaningfully from 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. Where are you in that vision three or five years?
Our vision comes from what customer requires. We believe a large Fortune 500 customer would be using maybe 70% deterministic systems that we use today, and maybe 30% would be probabilistic system built around LLM. We would have a meaningful role to play on both sides. We have sales capability for probabilistic system, we have sales capability for traditional deterministic system. We will have delivery, as I mentioned, with a combination of human skills, inference 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 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. I think the final thing here, just maybe I want to talk about the pace of change, right? I think the idea here is how quickly it is. If we go back to just Anthropic's blog post last week, about how things like the software engineering capabilities of 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? Let's say you were working on something six months ago, and all of a sudden there's new capabilities introduced. How do you work through that and plan around all of that?
I'll answer it in two 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, work for us. Our chief AI officer is probably one of the most respected names in the valley. That's how we keep up with the pace of what's going on. 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. In 10 days it will be 110. I think there is a new model which has been put up by Anthropic this morning. If you see the value that enterprises have delivered, that is nowhere close to 100. You all may have a different number.
Somebody will say 10, somebody will say 15, somebody will say 20, but it's nowhere close to 100. 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. I think there is that gap that is visible today. Organizations and companies like Cognizant who are primary advocates for our customers to stitch the ecosystem well and deliver that value. If technology continues to progress, that's great. I think today the bigger and burning question is, how do you bridge that gap between technology potential and value to the enterprise?
In fact, I think that'll lead us to the next section here, which just the broader demand environment. When you talk to clients today, right, I think there's this concern about what I would call mounting 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, at the same time, there's a lot of concern from a client perspective. Can you help us understand what's going on?
Yeah, I think there is. 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 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. There is that pause or delay that we have definitely observed. While currently the traditional work is being done far more efficiently with use of AI, the new work related with AI will take its course as it continues to build momentum around itself.
I guess at this point, what would it take to get clients to engage maybe a bit more aggressively or invest a bit more aggressively? 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 get clients over that cliff or are we just stuck here for a little bit?
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. When large enterprises are consuming that, they are all smart buyers. At some point, they would start investing effort on how do I get ROI on that. That will come. I certainly don't think that you need to do something different to land that. I think we are taking a slower step but firmer step towards AI-adopted world, and result of that and discretionary business for IT services industry is two quarters away, three quarters away, four quarters away, difficult to call, but I definitely see ourselves walking towards that.
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. 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? Are we seeing enough signs that we're moving between the vectors at this point or?
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. We're beginning to see a meaningful pipeline for vector two, which is making enterprises ready, especially on data side, which is Vect or 2 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. That's also beginning to pick, but still that is relatively small and some way to go. Vector 2, I certainly am beginning to see a good traction on Vector 2.
Does it all have to move in sequence, meaning you've got to do Vector 1 first, I don't want to say dissatisfied with the level of productivity that you're getting, so you're like, "I got to rebuild my infrastructure." You go and rebuild it, get your data cleaned up, and then you finally move to Vector 3 or who's on Vector 3 today?
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. It is not sequential in some form. 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 two years back on a great data structure. They don't have to go to Vector 2 .
The companies which are using agentic workloads effectively are simpler organizations, what I'll call single geography, a large line of one or two businesses. They are effectively deploying agentic workforce as of today. It is not a conversation of future. They have replaced human efforts with 40%-50% of virtual effort, and they continue to do so. If your business is 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. For the, what I would call the native company or the ones that have tried to make the Vector 3 type of transformations, how are they measuring the benefits? Are they seeing what they are supposed to be seeing? Because there is this mixed narrative of it is 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? That ultimately becomes one of those situations where when we look about past cycles, it is the success at 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." You kind of see this exploration.
Yeah. Where your agentic deployment, the answer is very comprehensive and very clear. It is a night and day difference. One of the customers who spoke in our AI forum spoke about saying he had a few hundred agents who were performing a service. He has moved to agentic model. The number has come down sharply. He is left with maybe tens or twenties 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. What he talks very proudly is that of his few hundred agents, the virtual agents he has trained are the he picked top five 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 base agents. So his quality of outcome has increased significantly. Number of days of turnaround has become few minutes. So it is a comprehensively superior outcome.
Got it. Maybe shifting a little bit to, I do not want to get too near term focused, but there is a lot going on in the current environment when we think about it from a geopolitical perspective of where we started the year. There is a lot of excitement. Maybe can you talk about just the evolution of client behavior in maybe the last 90 days, just where we sit in this macro geopolitical uncertainty and how, at least from your perspective, what are the changes that you are seeing at 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. We spoke about in our quarter one 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. Things have not changed since then, meaning, we still don't see any acceleration on demand side from that situation. 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 revenue from our recent acquisition, Astreya, but that is yet to close, and once it closes, we'll make an announcement around.
Got it. When we think about just are you seeing any geographic differences? Because there's also concern about how the different economies are evolving in, 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, when Ravi took over as the CEO of the company, U.S. was among the first to start showing the traction and the growth. From Europe took a little while, but we have had some mark events in Europe in last few months, and we remain quite optimistic for 2026 for Europe too.
I guess the final 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 leading the space in terms of the investment here versus maybe what's going on in retail or healthcare at this point?
BFSI is always a leader in appreciating potential of new technology and its deployment. We certainly see BFSI leading the whole investment on AI, 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. Finally, is communication and media space, which also is not in its prime in terms of growth. There are company specific priorities that our customers are tackling.
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 discretionary spend, yourselves, industry-wide as well. Can you talk a little bit about that dynamic? In one of the narratives out there's concern that as these tools advance, the AI models advance, clients are trying to do more themselves, that's keeping discretionary. How do you think about what might be going on there?
Yeah. 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. We have had very large successes with setting up GCCs for our customers. Even customer GCCs, we are helping them run in India or elsewhere. I think it's just in outside BFSI, it's more micro and the fast pace of AI tech evolution that is probably pausing decision-making a little bit.
Got it. 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 get over this hump, right? I'm not talking about predicting next quarter or week, but just how we think about it broadly.
I think 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. I have a feeling that when it changes, it will be very clearly visible. We speak also about our ACV growth numbers every quarter. When you see ACV growth maybe couple of quarters inching up, I think that would be a great indicator that finally we are seeing the discretionary back.
Got it. There's an interesting comment that Ravi had made. Right now you have peer-leading growth, but then he talked about getting to breakaway growth. Can we talk a little bit about that, the aspiration there and the genesis of that? When I think about it strategically, financially. Why lay that goal out there right now? You're already peer leading, and now you've set another aspirational goal about getting to breakaway growth.
I think the comment for breakaway growth is in light of the opportunities that AI Builder or Vector 3 provides. I think if we get to that zone, I think a higher growth is definitely possible because we are going after a much larger TAM than what was visible before. That's a statement of aspiration that as we move from a traditional trillion-dollar TAM to a much larger TAM, you would accelerate your growth rate significantly.
Got it. Then just wrapping up here. Maybe something on just 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? Then I've got a couple follow-ons about the strategic use of it.
Sure. 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. Last year we did not have a large M&A. We were consolidating Belcan that we had acquired a year before. We had a large $1.4 billion of the 2.5%, roughly 60% was on a buyback. This year, we started with slightly higher allocation for buyback because the share price was lower. In month of May, when we realized the share price has dropped further, we went with another $1 billion. Collectively $2 billion of buyback is what we have planned for 2026. That, as you can see from, is nearly 80% of the $2.5 billion of free cash flow that we generate.
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. 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.
In the current environment, I can understand the share repurchase component of it leading into it. What about this idea of, 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?
Yeah. 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. Clearly, we are picking up businesses which help us gravitate faster and more substantively towards the AI opportunity. We'll continue to look at that. Good thing about the current market is that good companies or good opportunities are available at reasonable prices. We'll continue to invest there.
You also balance where you are in your share price, therefore repurchase made eminent sense at that price, we went all out. 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.
Final question. I noticed we're down to the last minute or two 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? 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. 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 edge to Cognizant. Idea is that we invest in that IP, and we bring that IP to our Fortune 200 customers. That's an additional investment. The dollars are not that big, but it just provides a very different ledge of innovation to go to customers with. Yeah, I think these are the few ideas to double down on.
Okay. Well, fantastic. We've covered a lot of topics here, I really appreciate the time.
Thank you very much, [Robert]. Thank you.