Cognizant Technology Solutions Corporation (CTSH) Earnings Call Transcript & Summary

I'm Arvind Ramnani, AI digital platforms and IT services analyst at Truist Securities. Thanks for everyone sitting in along with me, I have CEO of Cognizant, Ravi Kumar. Ravi, thanks for taking the time.

Sure. Thank you.

So just a quick introduction and then we'll jump into questions. Ravi became the CEO of Cognizant at a really interesting juncture of the company and a crossroads of what was happening with IT services. Since taking over, he has made AI central to how Cognizant works with its clients supported by over $1 billion investments in GenAI. And I think it's probably worthwhile mentioning. Ravi has been named to times AI 100 list for the past 2 years. I think the reason this is particularly important is because that list had a 75% turnover. So you just have like 25 folks that have made it 2 years in a row and think like it's something to be proud of for yourself as well as Cognizant as well.

Thank you. Thank you.

So look, we have had a number of conversations over the years. I have lots of questions here, not enough time, so we'll kind of just jump right into the biggest debate IT services. So for decades, right, this has been a fairly, I would say, well-run industry. The industry had a very predictable part to growth. You add people, more billable hours, but now with AI, kind of the opportunity basically moves very differently. What is already changing at Cognizant's delivery model because of AI?

So Arvind, thank you for hosting me. Look, the first principles of the tech services industry have to be reforged because this is a very different tech disruption. And let me tell you what that reforging means and what we have done at Cognizant. The first and foremost, this is a business which is no longer going to be about human capital. It's going to be about human and digital labor coming together to deliver a tech-led transformation. Tech-led transformation for companies like ours was about building systems. In the '90s, we build custom bespoke systems, then we build -- then we became a system integrator for classical software, then we actually became services player for technology and software, which was on the cloud and the plumbing was on the tap and the building was done by us. Fast forward now, I think this is a technology which is action-oriented mimics human labor. A lot of what we are seeing today is productivity led. It's still -- we have not got to a point where we do new things in new ways. We're doing old things in new ways. So the first and foremost, the first reforging of the first principles is we make the execution of this technology, not just into systems, but also business operations of companies -- this market was $1 trillion before building systems at a time when enterprises were globalizing. This market now is $6 trillion because you actually have a total addressable spend around business operations of companies, and you do it embedded with digital labor. So that's the first reforging of the first principles of this industry. The second reforgingof the first principles of this industry is you go more to an outcome-based business. I mean, enterprises across the world no longer want to buy software and no longer want to buy services separately. they want to bundle this and own -- they don't want to own the agency of outcomes. They want to transition this to a provider who can do that. The third is it's going to be a platform for services business, which I believe is something we have been doing for years now. In 2017, we bought this company called TriZetto and now we manage 200 million lives of insured health care in the United States. So platforms, human plus digital labor, outcomes driven are the reforgingof the first principles. If we reforge this, we see a bigger market than ever before. It's no longer a market. It's a $5 trillion market. If you don't reforge it, you just end up being -- doing software engineering, which then means you're doing it in the old way and you want to deflate that model. But if you expand yourself into the business operations of companies, it's a 6x opportunity?

Yes. That's a great picture, right? Like if you have to just kind of get this down to specific customers, -- you obviously have a range of customers, somehow more, I would say, kind of advanced in how they're using AI and how you're helping them. Can you talk about 1 of those customers and if you can a specific name or maybe the industry, I think that have.

Absolutely. So in 2024 and 2025, I would say it was the first chapter of AI much more broad-based. It wasn't as duvanced. We dug a hole and a basement with the same machine most of it -- the world is in a flattish growth trajectory. A lot of it was productivity led and we kind of swapped the wallet share to transfer the productivity to clients. And if you could do that efficiently, you could actually win more wallet share with clients. It doesn't mean new spend cycles existing spend cycles done more efficiently. That's what happened in the first wave. I would say, later part of last year, we saw a second swim lane triggering off -- that second swim lane was doing old things in new ways. Say, for example, for a large telecom client in Asia Pacific, we are migrating their mainframe into public cloud. And the idea of doing that is you could take the MIPS down, a doolar of -- of a line of code in Cobalt used to cost $10 to refactor. Now it costs $1.50 to refactor. And you transition that to public cloud and the money actually gets refactored from a different spend pool to services as well as to cloud providers -- it's actually old things being done in new ways. Recently, we set up a campaign for a frontier finance organization. In this swim lane, it's not new discretionary spend cycles. It's old spend cycles reorganized to give us momentum and runway. Frontier Finance is the ability to run your finance function using a frontier first model. So every Fortune 500 CFO spend 70% of their effort and time on controls and 20% to 30% on growth. If you can flip it, you could actually orient finance towards growth, you could orient finance towards more real time and you could power a finance function, which is driven on growth imperatives rather than controls. Again, you take an old thing. It's done in a different way, and you do it in a new way. One of my favorite campaigns in recent times is pharmacovigilance or drug safety. I have 4 deals now where we have drug safety, which is called pharmacovigilance, we compete with clinical research organizations to win that business. We used to do a small sliver of work, which is mostly contact center driven. And now we could own the end-to-end cycle and deliver better insights, faster life cycle, faster shrinking the speed at which you could do drug safety at a lower cost. This is a spend cycle our clients were already having. They were not just having it with us. They were doing it with clinical research organizations. So how do I make a function frontier land and create a new spend cycle for us versus the clients are still spending in the same way. So this is a swim lane, which has got activated, and it is creating significant traction. So you layer it with the consolidation, then you layer it on top of it with old things in new ways and then doing new things in new ways, which is primarily which kind of cater to growth imperatives of organizations. For example, for a wealth management firm in the U.S., we are building agentic work around their independent wealth advisers and actually deliver more capability to those wealth advisers who are actually on a subscription model with this company. So it is a new discretionary spend cycle. Now if I go industry by industry, Arvind, when financial services is activated on all 3 swim lens. And that's why for 3 quarters in a row, we have sequential -- we have year-on-year growth, which is at double digit. Now if all the industries activates this you would actually see the industry back at double-digit growth. I mean if you -- if somebody used to ask me a question, you seem to be very optimistic why is into the industry growing at double digit. The only reason why it's not growing at double digit is -- the first file is deflationary. You actually consolidate, when you consolidate, you are passing on productivity to clients, which means it is going to be deflation to revenues. The second swim lane is new spend cycle coming to tech services. The third swim lane is discretionary led, which is clients actually spending more because they're going to -- they're planning to do new things and they're planning to do things which are related to growth using AI. If all 3 are activated, you can layer this to double-digit growth as an industry.

Yes. No, that makes sense. I mean my own research at least suggest something a little bit different, which is like some in the industry are not going to make it to the other side, right? So when do you almost say, like someone like a Cognizant that's kind of lean in like fairly hard, you look at your commercial licenses with the frontier models, Anthropic, like all your 350,000 employees have it. That's not the case across the industry. So could you make the case that when we -- when you see the final flip like everyone doesn't win, right? Everyone is--

So this is moving so fast. If anybody is telling you they figured this out, I mean, they're not telling you the they're not telling -- they're not giving you the full picture. Here is my end state, which I can think of. The baseline is you need deployment capacity on the other end, to embed frontier intelligence into everything you do in a company. Cognizant has 18,000 cloud-certified architects, but that's not by not Star. We are the largest on the planet. 18,000 cloud-certified architects, which is the highest level of certification in Anthropic. I'm just giving 1 example in OpenAI, we have like 6,000 codex badges. That's the largest again in the world. In Gemini, we have 5,000. All of that is not by not start. My not start is bend the cost curve at scale and flip this model on its head and get the 300,000-plus employees in the company, frontier let. That's my nods. So that's the basic -- that's the baseline on which I'm running. That capacity is going to help me deliver frontier intelligence into enterprises. Now we have -- we are absolutely convinced that capabilities out here, the production value is here. The gap between the capability and production value is the bridge companies like ours do this. I mean it's a tale of 2 cities. On 1 side, we want to pace the frontier, which is a talk in the last 1 week because the front it is very dangerous. So on the other side, we're dealing with the frontier can't do basic things because it's not grounded in the context of a company. So I think you need that capability. And as long as you bend the cost curve at scale, companies like ours will stay relevant for the future. I mean go back to any disruption, including the digital wave. You had boutique digital capacity, delivered at high cost. I used to work for such companies. They don't exist today. I mean I used to work for a company called Cambridge Technology Partners, which was absolute pioneer in digital technologies. It doesn't exist because it couldn't bend the cost curve at scale, which is what clients need, which is what enterprises need. Now the second important thing -- as this technology evolves and as we learn through this process, between the intelligence and the enterprise, there are layers of value we can capture. Every time there's a disruption, that opportunity presents to us. We don't capture the layers. We always relegate ourselves to be a system integrator. Now we could capture those layers along with the deployment capacity at the end. At the end, the deployment capacity is like a services company. But the layers of value are because this is a bespoke opportunity, we could build the trust layers, we could build the context layers and we could also build the hardness around it. And I was telling Arvind off-line, the hardness is built today, which are available today in the market like cursor and cognition at all. They are for software engineering. The hardest is you could really bid could be for business operations because that's the universe. And what can you do that? You could capture the context so that the next time you come in, the context is distilled enough, and therefore, you're efficient in using your frontier. You could do the model routing so that you could straddle between the most expensive frontier to the cheapest open weight model. You could coach the human with Frontier intelligence live as they're doing the tasks so that they are not applying their mind on what actually transfers to the frontier and what remains with them. And you could create a compounding factor at the end by leveraging the network of clients who deliver work specifically for things, your clients don't mind sharing their alpha because it's not core to their business. Say, if you're doing accounts payable or you're doing procure to pay. It's not core to people's companies businesses. It's not core to companies alpha. So they would be willing to pass it on to get the network effect. If you're doing health care operations or you're doing underwriting, that is core to your business. You may not pass the alpha. So our endeavor from the intelligence to the enterprise, we have deployment capacity here and we have layers of value. Some we can capture, some we can try, some, we will not be successful. There will be boutique AI ecosystem players will come into the mix. There'll be hyperscalers who will also absorb these layers and some of the frontier models, some of the fronter model companies will also build commercial value proposition because they want to capture the value because they don't have an ROI for the kind of investments they're making. So our endeavor is not just hold that capacity at scale, bending the cost curve, but also capture some of those layers of value. And today, we're building a trust platform, where we have an agetechardness for business operations and for software engineering, I'm more optimistic about business operations. We are also trying to build 1 for physical AI, which is going to be the next wave. And we have built extraordinary muscle on context engineering which is probably the new craft of writing code in businesses. If writing core was a craft, the new craft is embedding the context into intelligence so that intelligence is applied better to businesses. So that's the journey we are going through. And therefore, I believe this business is going to be a platform plus services business. Now how far have clients gone on the outcome pricing? I mean that's an evolving science. Since 2023, since I have come on board, our time and material business, which used to be roughly 52% and our fixed price plus outcome-based business used to be the balance. We have flipped that number completely. And right now, our time and material business is only 42%. And the rest of it, 58% is fixed and outcome-based. And outcome-based has a spectrum all the way from transaction-based pricing to outcomes. So it's an interesting time because the first principles are getting reforged. It's also an interesting time because it's not just labor is not the only input to your business. Labor is 1 of the major inputs to your business. and intelligence -- Frontier intelligence is embedded into it, how you can build the economics around it, how you build a craft around it, I think, is very important because our clients are no longer buying -- or rather, they have it intend to not buy intelligence directly. They want to buy it through us, which means we have to be efficient enough in embedding that intelligence and also routing it to the right economic models embedded into our work and take the risk and manage the return through that process.

Yes. This is a fairly comprehensive answer. Just a couple of threads on that, right? One is you said kind of your fixed price is kind of moved to 52%. 58% outcome basis. Yes, yes. And within that Servosaid that contains many different segments. But the segment that's basically kind of tied to AI, whether it's the 18,000 cloud kind of 18,000 cloud code engineers or the 6,000 codex engineers, the folks are basically trained and who are extensively using AI tools. Is that bill rate like a step function different or just like a slight premium.

So I mean, it's interesting. On 1 side, we have outcome-based pricing. And I put this chart of cost observability on the y-axis and outcome of severability on the x-axis. And you always think the upper right is the best one. The upper right is not the best 1 here. clients actually look for the best suitable candidates for outcome-based pricing are high outcome observability and low-cost observability. Interestingly, if the observability on cost is very high, clients don't want to do outcomes because they can see the cost. So you could do an outcome-based pricing, but you're not going to get a non-linear return. If the cost is not observable, clients want to leverage that. So it's -- interestingly, outcomes have to be on this side, observability on cost has to be lower for clients to actually say, "I want to do outcome base pricing. If the absorbability is high on both sides, you will do a fixed-price deal. and fixed price deals have not outcome based. Sometimes fixed price deals can actually be more -- can be less margin accretive because the observability of cost is so high that clients will not pay you more than what you should and you still take the risk. So outcome-based pricing starts from a spectrum of managed services to transaction-based pricing to owning the outcomes means, say, for example, in our TriZetto business, we went from selling perpetual license to subscription-based license to transaction-based pricing. So now, our clients are saying, "Why should I pay you for a transaction if my transaction is autodicated with AI? So I should be paying you on number of lives you manage for us. So a number of lives managed to us is actually on the other side of the spectrum, which then means I'm actually managing a number of lives versus number of transactions underneath it. So it's a spectrum all the way and getting there is going to be a long haul, but we have spoken about outcome-based pricing for like 25 years. This is the first time we've got a chance to own it. And because we're doing operational work more than software engineering, we could own it. I mean, I could go to a company and say, I will know your customers' process or I could own your accounts payable process. and give me a cut of it. A good example, we work with 1 of the largest food distributors where we've managed -- we managed the Ragentek process of credits, I mean food distribution as just low margin, and the credit cycles, I mean, when you return your goods back because you don't like them or they have not come to your expectation, the money flows back to you the goods go back and the money flows back. It's a long cycle, we could shrink it and increase your working capital efficiency. And if we do so, we can take a cut out effect. Clients have not gotten that far. They're still engaged with us on a fixed price model over a period of time that would happen. Now coming to time and material, interestingly, that's becoming outcome-based as well. I have an AI-infused rate card, which I introduced last year. from A0 to A4, A0 is being all human effort, A1 being human offered validated by machines, A2 being machine effort validated by humans. A3 being all autonomous effort. And we presented to a few clients. Clients loved it. They gave us a premium on the rates as you go from A0 to A1 on the human human labor. Then our clients came back and they said, "Wait a minute. I thought I got a great deal on this, but the inference and the pretraining costs are on the tap, and that is not controlled. I'm paying directly, if I put all of it together, I don't think I'm getting a good deal. So now clients are saying, take the inference costs, take the pre-training costs embedded into your billing rates and tell me what your new billing rate is. which means I have to give a billing rate for human and digital labor. If I have to do that, then I have to have traceability of the tasks which these people are going to do so that I could measure the pre-training and influence costs. So it's a craft we have to build so that, that then becomes the opportunity to arbitrage with clients on expertise. It's not yet happened, but clients are starting to think about -- I mean, the A0 to A4 rate cards have happened in many of my clients, so you don't give the rate card based on experience. You give rate card based on whether it is fully autonomous or fully manual. But embedding digital labor is 1 more level of complexity, which means the tap on and topic and the tap on open AI is going to be through us. By the way, we have a prebuy arrangement with all the 3 frontier model companies. There are deals where we have told our clients, we will deliver this outcome. We went public on 1 particular 1 called Travelport, where the client is actually delivering operations and software engineering, embedded with the Frontier model. They are not paying the fronter model company. We are paying a frontier model company, so we have started to do that kind of work, but it is a craft we have to master on so that our clients give us that work along with the Frontier intelligence because we can do it better than them.