Cognizant Technology Solutions Corp ($CTSH) Q3 2025 Earnings Call

Ladies and gentlemen, welcome to the Cognizant Technology Solutions third quarter 2025 earnings conference call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session. If you would like to ask a question at that time, please press star one on your telephone keypad. A confirmation tone will indicate your line is in the question queue. You may press star two if you would like to remove your question from the queue. For participants using speaker equipment, it may be necessary to pick up your handset before pressing the star keys. Thank you. I would now like to turn the conference over to Mr. Tyler Scott, Vice President, Investor Relations. Please go ahead, sir.

Thank you, operator, and good morning everyone. Welcome to Cognizant's third quarter 2025 earnings call. I'm joined today by Ravi Kumar, Chief Executive Officer, and Jatin Dalal, Chief Financial Officer. By now, you should have received a copy of the earnings release and investor supplement for our third quarter results. If you have not, copies are available on our website, cognizant.com. Before we begin, I would like to remind you that some of the comments made on today's call and some of the responses to your questions may contain forward-looking statements. These statements are subject to the risks and uncertainties as described in the company's earnings release and other filings with the SEC. Additionally, during our call today, we will reference certain non-GAAP financial measures that we believe provide useful information to our investors. Reconciliations of non-GAAP financial measures, where appropriate, to the corresponding GAAP measures can be found in the company's earnings release and other filings with the SEC. With that, over to you, Ravi.

Thank you, Tyler, and good morning, everyone. Thank you for joining us. We are pleased to report another industry-leading performance in the third quarter of 2025 as revenue growth and adjusted operating margin again outpaced our expectations. Our results reflect the momentum we have built over the past two and a half years helping clients embrace AI. Our investments in platforms, intellectual property, partnerships, and in upskilling our people are evolving Cognizant into an AI builder capable of scaling agentic AI across the enterprise. As AI infrastructure expands, our clients increasingly need support from partners who can help them move from experimentation to enterprise-wide adoption with speed, precision, and trust. Turning to third quarter highlights, revenue grew 6.5% year-over-year in constant currency to $5.4 billion. All four of our operating segments grew revenue organically year-over-year. This breadth of performance across industries and geographies reflects the strength and resilience of our portfolio, capabilities, and delivery model. This is the fifth consecutive quarter of year-over-year organic revenue growth, our strongest sequential organic growth since 2022, and another podium finish to our peer group's winner circle. We signed six large deals, each with TCV of $100 million or more, bringing our year-to-date total to 16. Trailing 12-month bookings is up 5% year-over-year. And year-to-date, the TCV of our large deals is up 40% from the prior year period. We are focused on converting value from AI across our 3,500 early AI engagements and embracing AI in the delivery of our services and to drive internal transformation. As we do this, we are also increasing a fixed-bid, transaction, and outcome-based services mix. And we're beginning to see trends of nonlinearity emerge. For example, on a trailing 12-month basis, revenue per employee rose 8% year-over-year, while adjusted operating margin income per employee grew 10%. As we continue to scale our IP and platforms, we expect more examples of nonlinear AI-led growth to emerge. Importantly, we are expanding margins while continuing to fund our organic and inorganic growth initiatives and increasing returns to shareholders. Q3 adjusted operating margin improved 70 basis points year-over-year, driven by our disciplined expense management along with our increasingly AI-enabled delivery model. Our year-to-date performance has put us on track to outperform the revenue guidance we established at the beginning of the year. And we expect to meet the high end of the adjusted operating margin range we set then. For much of the last 30 years, IT services grew through a linear model. More people and more projects drove incremental growth. AI is reshaping that equation by compressing time, cost, and complexity and redefining how value is created. The opportunity to partner with our clients and drive outcomes is now more expansive, immersive, and elastic. The progress we are sharing today reflects two and a half years of focused execution, amplifying talent, scaling innovation, and accelerating growth to return Cognizant to a leadership position in the AI era. Becoming an AI builder means building the platforms and engineering capabilities that enable agentic AI to scale across the enterprise. Our progress begins with our workforce as we enable AI fluency across the 350,000 associates. We continue to fuel strength and future readiness for our associates through our learning engine and access to AI tools, which is why we are hiring more new graduates across the world this year and investing in their AI upskilling. In July, our vibe coding initiative earned Cognizant a Guinness World Records title for the world's largest online generative AI hackathon. More than 53,000 associates across 40 countries built over 30,000 working prototypes, improving their AI code assist skills and productivity, and we are continuing to expand into the AI ecosystem. Recently, we entered a new collaboration with Anthropic. Under our agreement, we plan to deploy Anthropic's cloud models and agentic tooling with our platforms to help clients scale AI, while also deploying them internally to advance our own operations. Our AI builder strategy is anchored in three distinct vectors: AI-led productivity, industrializing AI, and agentifying the enterprise. While each vector is advancing at a different velocity, together they're forming a flywheel of new value creation. Let me provide an update on vector one. AI-led productivity is the funding engine for enterprise transformation as we help clients accelerate software development, lower deployment costs, and reduce technical debt that we estimate is costing enterprises hundreds of billions of dollars in annual servicing. In the third quarter, approximately 30% of our internal code was AI-generated, significantly improving productivity of our developers. We believe it could reach 50% in the years ahead. A great example to illustrate our client impact with code assist platform partners is our recent award as the AI GitHub Services and Channel Partner of the Year in recognition of our achievements in helping clients with our AI transformation initiatives. Many clients have asked us for support in bringing vibe coding and code assist practices to their organizations. We recently launched a Cognizant Enterprise Vibe Coding Blueprint, bringing our playbooks and insights to clients seeking to build AI fluency across their own teams. This transformation extends beyond the developer community. Internally, we have embedded AI across more than 150 use cases from finance and operations to sales enablement and contract pricing. These applications are streamlining decision-making, improving accuracy, and accelerating cycle times. A primary tool for executing vector one is our Flowsource platform, which integrates generative and agentic AI across the full software development lifecycle. Flowsource is now being used at over 70 clients with an additional 120 in the pipeline. One of those clients is Pearson, where we are using AI and digital technologies to modernize the learning platforms, products, and applications by leveraging Flowsource. A proactive shift to AI-native and platform-driven engineering accelerates the software development cycle by enabling engineers to deliver enterprise-grade, AI-infused digital applications with greater speed and scale. This is showing up in our results with our approximately $2 billion annual run-rate digital engineering business growing about 8% organically year-to-date. Vector one is also fueling our large deal momentum as clients consolidate their software estates and shift to outcome-based models. They're capturing savings and unlocking higher value, often reinvesting those gains into vector two and vector three initiatives. It is creating a self-reinforcing cycle of transformation. A great example of this in action is our cloud and infrastructure modernization business, which grew 10% year-over-year in the quarter. Our AI tooling and services in the space has helped over 25 clients so far to build, respond, and resolve to reliant and resilient IT infrastructure. Now more on vector two, or industrializing AI as the scalability layer. It's about moving AI beyond experimentation into enterprise-grade systems, building AI-ready infrastructure, integrating contextual data, and operationalizing AI responsibly. It also involves developing new business operating models leading to an interplay of software and agentic layers, human and agentic capital, and structured and unstructured data to reimagine an enterprise. We are leading this effort with our consulting-based framework and methodology to help clients reimagine business processes as they develop and deploy agents. And we are deepening our expertise with the next-level capability set including Agent Foundry, a framework and library of industry and workflow-specific agents helping power agentic AI at scale. Together with our clients, we have developed more than 1,500 agents across the company. Second, AI data training services, where we have over 10,000 specialists fine-tuning models with domain-specific context. We have supported leading tech companies with training their machine learning systems long before GenAI entered the mainstream, and we are now bringing this same expertise to Global 2000 clients. Third, small language models development. Fourth, context engineering, which we believe is one of the most critical emerging disciplines in enterprise AI to capture enterprise workflows, domain and tribal knowledge, personas, rules, and execution patterns. It is the connectivity tissue between models and outcomes. In partnership with WorkFusion AI, we are deploying context engineers who are helping clients build tailored foundations for AI adoption. And finally, IP on the edge, which I began describing last quarter, is a horizontal foundation layer where we are bundling platforms like NeuroAI with services and IP to deliver outcomes. With 400 platform deployments already in motion, we are helping clients modernize core systems to reduce risk, accelerate time to market, and improve experiences. As we build layers of contextual value on foundation models through a combination of context engineering, SLMs, and multi-agent systems, we are delivering numerous production-grade AI use cases. To bring this to life, we helped a national grocery chain optimize its in-store picker process for online orders, reducing fulfillment time by 20 to 45% through smarter inventory selection, product substitutions, and routing. This is driving a measurable increase in online orders. Lastly, vector three, or agentifying the enterprise, is about unlocking exponential agentic capital. Historically, we built software for humans. With agentic AI, we now reimagine processes end-to-end by deploying agents with humans in the loop to deliver outcomes. This expands the enterprise's surface area, enabling a blended human-plus-agent workforce across new domains. The agentic development lifecycle, or ADLC, differs fundamentally from the traditional software development lifecycle, or SDLC. SDLC is structured and deterministic: input in, output out. ADLC is adaptive and outcome-driven. You design for behavior, supervise performance, and evolve capabilities over time. We believe ADLC significantly expands our addressable market, demanding deep ownership to manage human-digital collaboration. As an AI builder, we are creating an agentic ecosystem where agents reason, adapt, and collaborate, unlocking service capabilities that weren't possible before. Cognizant is an early launch partner for Google Gemini Enterprise, an AI-powered platform designed for enterprises to drive unified, secure AI capabilities. It seamlessly connects enterprise data, tools, and workflows and leverages Gemini models to enable agentic journeys. Some client examples include reducing order response times from 5 days to 90 seconds with digital sales agents for a leading food distributor; helping a leading provider of cell-free DNA diagnostics reinvent patient education, access, and onboarding processes; modernizing order management for a crop sciences company using Agentforce; delivering intelligent lead generation for a top labeling and packaging provider. With TietoEVRY's core education platform supporting health plans, we have deployed multi-agent workflows that connect TietoEVRY agents to front-end experience platforms such as Salesforce, Genesys, and ServiceNow to address common interactions such as requesting ID cards from a member or giving a provider the status of a claim. We believe much of vector three will flow into our Intelligent Operations and Automation practice, which is our BPO business, including BPaaS services. Our BPO revenue grew 10% in the last two quarters and is on track to reach $3 billion in annualized revenue over the next several quarters. We believe agentification will unlock new labor pools, including roles that don't yet exist. As digital labor diffuses into enterprise operations, the nature of human endeavor will evolve. Together, our work across vector one, two, and three reflects our evolution into an AI builder company, one that blends deep domain expertise with platform innovation and interdisciplinary talent. 30 years ago, IT services companies were builders, crafting the foundational systems that powered industries. Over time, the role shifted towards integration, development, and maintenance, and growth became more linear. Today, Cognizant has a unique opportunity to reclaim the builder mindset and capture a greater share of the fragmented AI market. The scale of this opportunity is extraordinary. While the global software market is in hundreds of billions of dollars, the surrounding labor spend represents many trillions more. Classical software has barely penetrated that space. We believe AI's winners will be those who diffuse into this labor spend and reshape how work gets done. Software and agentic development cycles will coexist, and Cognizant is poised to generate layers of value in this expansive new role for enterprise reimagination. In closing, we are proud of our Q3 results and the momentum we are building financially, commercially, and strategically. We are evolving from a software implementer to an AI builder, powered by an engineering heritage, an AI-ready workforce, and proprietary innovation. We know long-term success will be determined by the outcomes we deliver for our clients, our people, and our shareholders. Thank you again for joining us. I'll now turn the call over to Jatin.

Thank you. We will now be conducting a question and answer session. If you would like to ask a question, please press star one on your telephone keypad. A confirmation tone will indicate your line is in the question queue. You may press star two if you would like to remove your question from the queue. For participants using speaker equipment, it may be necessary to pick up your handset before pressing the star keys. In the interest of time, we ask that you limit yourself to one question and one follow-up. One moment while we poll for questions. And our first question comes from Jim Schneider with Goldman Sachs.

Good morning. Thanks for taking my question. Ravi, I wonder if you could speak to the new business pipeline you're seeing for smaller deals at this stage and whether you're seeing any kind of significant uptick there or not. And then relative to larger deals, are you seeing any pull-in or extension in terms of the commencement date for those large deal new bookings?

Thank you, Jim, for that question. Look, large deals have nicely balanced in between the two swim lanes I've been talking about. Early on in 2024 and early 2025, a lot of it was consolidation, productivity-led. Now we are seeing a new swim lane evolve, which is AI innovation, which is primarily agentic cycles deploying AI into enterprise landscapes. And therefore, if you've noticed, our digital engineering business has grown at 8% in the last few quarters, our infrastructure AI has grown by 10% in the last few quarters, and our BPO...

Business is rocking, actually it's growing at 10% again. We're starting to see a combination of productivity and innovation-led growth. I've always been saying this is a double engine transformation. While you can apply it on software cycles to get productivity and transfer the lower cost of deployment for higher spend of software, on the other end you can apply agentic capital on enterprises. There's so much infrastructure spend which has happened, it has to create a build opportunity, and that's why I keep saying we are an AI builder. We are seeing discretionary small projects starting to come back in financial services and healthcare, and that's all related to AI spend. So as you save on one side from software cycles, you transfer that money to innovation. Very healthy pipeline. I'm excited about the large deals, I'm excited about the discretionary coming back on small deals which is AI-led. So much infrastructure has been spent that it has to trickle down to services. There's always been a lag between when hardware was spent, then the software, then the services. That cycle has shrunk now and we'll be breaking that cycle. The services spend is going to catch up because of the extraordinary spend on compute and AI infrastructure.

Our next question comes from Tien Sinhang with JP Morgan.

Thank you. Nice results, well done. I want to ask on the revenue per employee, which looked like it was up 8%, operating income also better than that, up 10%. So just understanding the lift there and if it's sustainable and or even structural given some of the AI returns that you talked about.

Thank you for that question. In fact, just to follow up on the previous question, we're also seeing mega deals. Last quarter we did two, the quarter before we did one. So mega deals are also starting to line up because that savings can be underridden for innovation. Now coming to your question, this is an interesting lead indicator: revenue per person and margin per person. Revenue per person went up by 8%, margin per person went up by 10%. It's indicative of how we are becoming an AI builder company with platforms, intellectual property, software, and services all bundled together. So we're excited about that. It's a combination of things. Our fixed price managed services business is going up. It has gone up from 43% in 2024 to right now almost close to 47%. That gives us a chance to deliver work for outcomes and therefore create more revenue per person and margin per person. It is actually going to transition, and Jatin has been talking about it, that we are a fixed price, time and material, and transaction-based business. We are going to go from more fixed price, more outcome-based, more transaction-based, less time and material in the future. So productivity has gone up 30%, which means there is more throughput. So you can actually create more throughput, share the savings, lower cost of deployment with our clients. So effectively, putting all this together, this is a very good proxy for AI services, and that's why we thought we've been tracking this but we thought we should let analysts and investors know about it.

Yeah, that's a good proxy, good data points for us to have. Just on my follow-up, then I'll ask on gross margins probably like I usually do. Just thinking about near-term gross margin performance potentially given the expected deal ramps and the mega deals and what have you. Any specific callouts on gross margin in the next couple quarters?

And our next question comes from Maggie Nolan with William Blair.

Hi, thank you. Can you shed some insight on how you're tracking the success of upskilling your employees with those AI-related skill sets?

Thank you for that question. We are pioneering this effort early on. We were the first company and probably the only company in our peer group which speaks about percentage of code and software development cycles assisted by machines. That's at 30% and we're constantly tracking to stay ahead of the curve. We are the number one company on GitHub Copilot. In fact, we are the GitHub Copilot AI partner of the year. We have been the launch partner for Google Gemini Enterprise. We just signed a deal with Anthropic on cloud. We have created a hustle inside the company that the only way you should write and the only way you should be assisted in software development is through machines, and that has become the way of doing work at Cognizant. In fact, we are on the Guinness Book of Records for the highest number of people on a hackathon concurrently. We ran that to create culture and create a permanency in the way we write software. We are the only company which is actually saying we are going to hire more school graduates than ever before. We are doubling those numbers from last year because we think we can actually create significant productivity leap with our extraordinary training infrastructure. So all of this put together, we seem to be on a pioneering opportunity. This is on one swim lane which is software development. Of course there is a ton of work on the agentic development which is much more surface area, much more expansive. So our double engine, both on productivity and on innovation, is deeply embedded with skilling, reskilling, hiring from schools, building productivity alongside machines, and that is the only way we want to create throughput. If we do this well, software has elasticity to be spent more. If we do this well, that money is going to be transitioned to agentic capital and working alongside agents for human workforce. I think the amplifying potential of humans is going to be significant. We've pretty much trained almost all our employees, more than 250,000 plus employees, on AI-led skills.

Thank you, that's helpful. And then did we expect large deal and mega deal signings to impact the quarterly cadence of revenue and margins in 2026? Can you help us think about the ramp over the course of the year from a modeling perspective?

That's a great question. In fact, if you notice, in 2023 our trailing 12 months range of bookings was in the range of 24 billion and it's now actually at 27 plus billion. So we've had tailwind from 24 into 25. Our annual contract value is very nicely stacking up to the total contract value. In fact, our total contract value from large and mega deals has gone up by 40% while the number of deals is 16 so far and we have another quarter to go. Just the TCV value has just significantly gone up, it's gone up by 40%. So we think we have tail velocity going into quarter four as well as going into the next year on large and mega deals. I don't see any shift on that. On the contrary, I actually see that on two swim lanes: in 23 and 24 the swim lane was productivity, and in 25 we are starting to see innovation-led, agentic capital-led, much more expansive. I've always been saying one swim lane is software, another swim lane is agentic. The agentic is more expansive, more elastic, and more immersive. We're seeing large deals on it. In 23 and 24 a lot of it was America-based. Now we are seeing Europe and Asia Pacific starting to be a part of it, and we are excited about the momentum we have created in Europe on large deals.

We'll go next to Surinder Thind with Jefferies.

Thank you, Ravi. Can you maybe talk about the partnership strategy here? And how important it is to maybe partner with each of the major providers versus maybe being a bit more selective and becoming more of a partner of choice with maybe some of the individual providers, whether it's GCS versus Anthropic or OpenAI or however you're thinking about that strategy.

Surinder, thank you for that question. Partnerships traditionally were SaaS companies and classical software companies, of course cloud-based hyperscalers as well. Now I would add more things to the mix. SaaS companies and classical software companies will transition the business logic to the agentic layer which they build on it. I mentioned this in my remarks that the machine was always with the software companies and we were a system integrator. Now we are an AI builder, which means we have intellectual property, platforms built. It's a very heterogeneous and fragmented AI market. Our clients are not saying come in with your capability, they're saying come in with your machine, which means you have to actually have the platforms. It could be partner-led, it could be our own. And we are actually therefore investing into platforms and intellectual property. In addition to that, we have this new thing because now the machine actually belongs to the frontier model companies, which is OpenAI, Anthropic kind of firms. In fact, that's one of the reasons why we partnered with Anthropic. So we are activating multiple swim lanes: our own custom platforms built on enterprise software companies where we have long-term partnerships, SaaS companies, but we also are partnering with frontier model companies. We could create custom AI agentic capital directly, and the Anthropic partnership is an indication of that particular swivel. So it's a much broader partnership lens including startups. I work with Writer, I work with WorkFusion, AI. These are layers of value on top of the LLM, and some of those layers are owned by us, built by us, some of them are partnered. And of course the frontier model companies allow us to create a swim lane with the engine actually belonging to them, but we build the layers of service around it. So we think it's more expansive and more broad-based.

That's helpful. And then as a follow-up, can you maybe talk about the IP that you're building? And more specifically, you mentioned having upwards of 1,500 agents in production. How does that impact the revenue model at this point? Are you able to charge for some of that? Do you keep some of that IP or is it more of a core base and then you kind of custom build agents that then simply...

I think it's a combination, Surinder. It's a combination. Look, on Flowsource, which is a platform which sits on top of code assist platforms, it gives us the opportunity to get better productivity, and that productivity passes on to our revenue per person and margin per person metrics. Our other IP and platforms we're building are the ability to take the raw power of AI and make it enterprise-grade, which means it could be the accuracy of the models. Yesterday we got a patent on a new way of pre-training a model, not based on reinforcement learning but based on evolution strategies, which our labs got in. So we're building a platform around it. We have a platform around multi-agent systems, which means you could have agents talking to each other. One example is in Trizetto, our Trizetto agents talk to Salesforce agents and Genesis and ServiceNow agents, and they actually deliver outcomes like ID cards and status of a claim automatically and auto-adjudication of claims and stuff like that. So the platforms are all about taking the raw power of AI and making it enterprise-grade. It could be on accuracy, on responsible AI, it could be on new ways of pre-training the model, a variety of things which are needed to make it enterprise-grade. There is so much infrastructure spend which has happened that value has to trickle down, and the use cases which are now coming out production-grade, we are able to generate more of it because of the intellectual property we've built. We are also closely monitoring partnerships. The context engineering piece is a unique pioneering opportunity for us. This is a contextual computing era, which means you need to feed the context, could be the tribal knowledge, the workflows, the data flows, the hustle of a company, and you have to feed it into the LLM and create a contextual agent who is much more productive than a generic agent. It's a science which is evolving. So we're building intellectual property along with a partner of ours. I think this is going to be a platforms plus capability kind of a model, and therefore I call myself an AI builder company. We are pioneering that effort of transitioning from just a capability firm to a platforms plus capability, and historically we had that culture with our healthcare business where a lot of it is platform plus services with Trizetto.

And our next question comes from Darren Peller with Wolf Research.

Thanks guys. Just a financial question first. Just when I look at the guide of two and a half to three and a half percent constant currency for fourth quarter, just what are the puts and takes there? Any early insights into how budgets are shaping up also into '26 would be helpful.

Just one quick addition there. I would say look, the activation of AI innovation use cases, we've gone from 2,500 to 3,500 this quarter, so literally a 40% jump. So the money you save on the software cycles on productivity is going to be underwritten to innovation cycles. That is triggering off very well. And I don't think CIOs are saying they're going to cut their budgets. Nobody has told me that. They're all saying how can you give me more value, and that's why we are benefiting out of this.

Okay, that's helpful. And then maybe just one quick follow-up would be if you could just discuss, you're obviously doing well with larger deals. Maybe just discuss the competitive dynamics for some of the large deals you're seeing and what's allowing you guys to continue winning them. And then how important is price in the discussion, and maybe build into that what AI can do for you on pricing if you could pass through some of your savings into.

Yes, look, price was always a linear thing in the past because it was labor-related and productivity of tooling was not in the mix. I would say it was a minority. Right now pricing is productivity, and it depends on how much you can use your platforms and how much you can use your AI tooling and the culture we have established now. So pricing is kind of linked to how fast we can keep that runway on productivity. Large deals on consolidation and productivity will always be price-sensitive because they're done for savings and creating more velocity. The innovation side of it, that's going to be much less sensitive to price because you are actually delivering new products and new services using AI. I don't see much of a change in the pricing. I would actually say if the other swim lane gets activated, which is innovation-led, you will get strength behind the pricing.

And our next question comes from Yogesh Agarwal with HSBC Bank.

Hi, good morning. Just have a question actually totally disconnected to the quarter and demand etc. Just in the past few quarters Cognizant performance has consistently improved and now you're growing almost at the top end of the peer range. But I'm sure you would have noticed as well the stock still is at a significant discount to the peer group. So I was just curious, any thoughts on secondary listing in India? Is it something on the table and any puts and takes for the same? Just curious to know your thoughts please.

No, that's great to hear. Thanks, Jatin. Thanks.

Our next caller comes from Rod Bourgeois with Deep Dive Equity Research.

Yes guys. So I want to talk for a second about financial services vertical. You mentioned improved spending there. We are seeing some of that across the broader sector. Can you speak to what form that improved spending is taking in the financial services vertical? And in particular, are you now seeing those clients moving beyond AI for cost savings and into more AI-based reinvention at those clients? Thanks.

Thank you, Rod, for that question. Absolutely. I think this is probably my fourth or fifth quarter where we have done year-over-year growth as well as from the start of the year we've been sequentially growing in financial services. It's been one of our best performing industry groups. I think the spend has gradually transitioned from cost takeout, consolidation to more innovation. I would say if you take the 3,500 projects we're doing on AI-led innovation, a significant chunk are actually moving from experimentation to enterprise-grade AI. And all the platforms I'm speaking about in the call, a lot of them are getting implemented in financial services. In fact, insurance, which is a part of BFSI, has also started to spend. It's a sector which kind of was lagging a little bit but it has started to spend as well. So we are very, very excited about the future of financial services. Over the last few years, Cognizant per se, we've had quarters where we haven't performed in the previous years, but the turnaround has started from the middle of 2024 and here we are. It is actually our best performing industry group, the spend cycles are great, and clients are actually innovating much more. Every segment of financial services has accelerated in terms of spend, and the discretionary is coming back because the value you get out of discretionary now is much higher because the cost of capital is high but the deployment costs have gone down. So that is giving clients the confidence to experiment more and actually take it to production. So financial services will be one of our bellwether industries in 2026 as well.

Great. And then moving to healthcare, I mean there's been some policy uncertainty in that vertical. You've also got the Trizetto asset. Just can you speak a little bit about the outlook for the healthcare vertical in general and in particular with Trizetto and all of the AI work that you're doing? Are you seeing BPaaS as an increased opportunity there? Just any color on the healthcare outlook? Thanks.

Absolutely. In fact, if you look at healthcare, if you take the last 20 years, the number of surgeons and number of doctors has pretty much remained flat. But if you look at administrative costs, they've probably gone up like 600 to 700%, the number of administrators in that business. So transitioning that spend to predictive care, I think, is the future. We have 200 million plus members on our Trizetto platform. We own the BPaaS cycle. In fact, you answered my question. BPaaS is our hottest offering. It gives us the opportunity to not just share our platforms but equally the operational strength of running healthcare operations, I think, is an important consideration. One of the reasons why our BPO business is 10 plus percent growth this year is also because of BPaaS. So we are very, very excited about our BPaaS offering, AI instrumentation in our Trizetto business, and our lead in healthcare. We are probably the number one player in healthcare in the United States.

We'll go next to Jonathan Lee with Guggenheim Partners.

Great, thanks for taking my questions and good to see the outperformance here. You called out last quarter that you were expecting the 4Q exit rate to be just under 4% at the high end of the outlook range. Given the outperformance this quarter, can you help bridge the gap between the 3.5% at the high end of your 4Q outlook today and the 4% exit rate you pointed to last quarter?

Thanks for that context. Sean, can you help us also better understand your pyramid initiatives and how you're balancing the needs of clients while managing margins, particularly as you move into higher value AI-related work in vectors two and three that may require higher skilled talent beyond that of freshers?

So we have been very vocal about the fact that we see actually freshers and AI as a very complementary strategy, and we believe that expanding pyramid at the bottom in our industry really helps us accelerate the organization's journey on AI. So from that context, we more than doubled this year the number of freshers we took last year to this year, and that journey will continue. One from a cost management standpoint of pyramid, but even greater context is how we can accelerate the enterprise to become more AI ready and AI builder as Ravi spoke about. And also we are now hiring freshers in the markets, which is primarily a principal market being US. So we are doubling down on this with a broader pyramid and a shorter path to expertise.

Thank you. And that does conclude our question and answer session. I would like to turn the floor back over to Ravi Kumar for closing comments.

Thank you so much for joining us today. We're very excited about our strategy of being an AI builder company, which is a combination of AI-led capability, platforms, intellectual property, and partnerships which allow us to be on those two swim lanes: one on productivity, one on AI-led innovation, with a much more expansive, elastic, and immersive opportunity to serve our clients. So we're very, very excited about our future and thank you again for listening to us today.

Thank you. This concludes today's Cognizant Technology Solutions third quarter 2025 earnings conference call.