Cognizant Technology Solutions Q2 2026 Earnings Call

Ladies and gentlemen, welcome to the Cognizant Technology Solutions second quarter 2026 earnings conference call. All lines have been placed on mute to prevent 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 that 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 the handset before pressing the star keys. Thank you. I would now like to turn the conference over to Mr. Tyler Scott, Senior Vice President, Investor Relations. Please go ahead. Thank you, operator.

Good morning, everyone. Welcome to Cognizant's second quarter 2026 earnings call. I am joined today by Ravi Kumar, Chief Executive Officer, and Jatin Dalal, our Chief Financial Officer. By now, you should have received a copy of the earnings release and investor supplement. 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 risk and uncertainties as described in the company's earnings release and other filings with the SEC.

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. Good morning, everyone. Thank you for joining us. We delivered a solid second quarter with organic revenue growth at the high end of our expectations and year-over-year adjusted operating margin expansion. Nearly all our healthy sequential growth was driven by our organic business. We accelerated our evolution as an AI builder by building new capabilities, launching new platforms, and deploying frontier talent as we begin to unlock entirely new business categories and client value pools. Looking at the quarter's highlights, revenue grew 4.1% year-over-year in constant currency, led by strong performance in North America as large deals signed over the past year moved into full execution. Financial services grew nearly 12% year-over-year in constant currency, its second consecutive quarter of 10+% growth. Trailing 12 months bookings increased 5%. We signed seven large deals, each with TCV of more than $100 million, including three new logos.

As we expanded adjusted operating margins year-over-year for the sixth straight quarter, demonstrating continued profitable revenue growth. From an AI indicators perspective, our revenue and adjusted operating income per associate increased 4.6% and 7.1% respectively. Starting this quarter, we are excluding trainees who are not fully deployed for both the current and the comparable prior periods. Over 40% of our software development is now AI-assisted. We have over 8,000 AI engagements. We view strength in financial services, which includes some of the world's most technically sophisticated companies as a leading indicator for other industries. Our research reveals that financial services is well ahead with AI initiatives and advanced AI adoption. We are helping clients tackle significant technology debt by using AI to compress modernization timelines and are shifting towards outcome-based pricing. We also now see financial services clients leveraging AI for growth imperatives with new discretionary spend cycles.

We completed our previously announced acquisition of Astreya, a global IT managed services provider with deep expertise in data center infrastructure, enterprise networks, digital workplace services, and AI-first managed operations. Momentum is already building. Astreya will continue its work with Google to deliver services across its corporate engineering environment, including global IT ops, workplace collaboration infrastructure, and platform services. We delivered these results against a cautious demand environment while growing at the top of our peer group. While we expect that caution to persist in the near term, AI is driving fundamental change in our industry that we believe creates significant long-term growth opportunities. The key question is why growing AI capability has not yet translated into more enterprise value. Our research shows two-thirds of the Global 2000 have not yet realized measurable AI productivity gains.

One in four have paused AI deployments, and billions of dollars in potential value remain unrealized. The opportunity to address this gap is enormous. We estimate the $1 trillion system integration market can expand into $5 trillion-$6 trillion enterprise operations market, with $4.5 trillion of operational labor exposed to AI. Services firms are structurally positioned to capture this opportunity. As models proliferate and inference costs decline, models stop being the differentiator, and value shifts to the applied layer, which is context, governance, and business processes. This layer addresses how each enterprise applies AI, what it learns from it, how effectively it retains, reuses, and compounds that learning, and how well it protects the proprietary intelligence that constitutes its alpha. That is why we launched the Cognizant AI Delivery Operating System, a continuously learning delivery system that combines human expertise, organizational knowledge, client context, and AI intelligence.

It has three pillars: our engineering harness, which coaches engineers in real time, our business operations harness, which feeds best practices into a shared organizational system, and our Intelligence Spine, which connects intelligence across physical and edge environments. All of this is supported by our context engineering capabilities, which is the ability to assemble an enterprise's work graphs, guardrails, and tribal knowledge. Working with Cisco, we are using Context Fabric to build a digital signature of the account management role, forming the foundation for an account manager's digital twin and broader identification solutions. This AI solution, with a digital twin at its core, holds the potential to streamline daily operations and support on time in full, complete deliveries, significantly improving customer satisfaction. For a large North American bank, we piloted a solution for their fraud dispute management and KYC operational workflows.

Context was engineered through a custom solution that combines static knowledge from customer and operations interactions and documents with dynamic business context sourced through enterprise application APIs. The fraud dispute management multi-agent pilot solution has demonstrated the potential to reduce manual effort by more than 50%, while the KYC process can significantly improve decision efficacy, reducing the risk of fines and fees. Last quarter, I described how AI is reforging our industry's first principles and driving four significant shifts. First, becoming an AI builder rather than a traditional systems integrator, owning the full stack required to design bespoke AI systems. Second, rebuilding our talent model by shifting from a traditional pyramid towards interdisciplinary teams working at the intersection of domain operations and technology. Third, shifting our economics from labor to outcomes. Our mix of fixed price and transaction-based work has grown for three consecutive years, creating a more durable business.

Fourth, moving from delivering projects to underwriting results. Let me share our progress across these four shifts, starting with the first one, becoming an AI builder by strengthening our proprietary IP and ecosystem. This year, we launched a dedicated AI market unit, an elite team of business designers, industry strategists, and frontier engineers focusing on converting AI investments into realized value. We are already seeing early traction. For example, a large payer client chose us to help build an agentic development practice for its biggest division through pods of frontier engineers and AI agents. We cut manual effort by 60% for a mid-sized payer while improving the claims throughput. We compressed AI development cycles from months to days for a leading European online fashion retailer, advancing agentic workflows across supply chain inventory, returns, and customer experience.

On the partnership front, we established a dedicated Gemini Enterprise practice as a Google Cloud diamond partner. We joined OpenAI's Daybreak Consortium. We also expanded our partnership with Anthropic, becoming one of the small number of global premier partners in the Claude Partner Network. An example of this partnership at work is Travelport, where we partnered with Anthropic on a strategic AI transformation aimed at modernizing Travelport's software development and embedding AI across Travelport's travel, retailing, and distribution platforms. With A+E Networks, we partnered with Snowflake to deploy custom intelligent agents that transform complex advertising operations and legal workflows. By automating document validation and enabling natural language queries, we helped accelerate decision-making and significantly improved time-intensive processes. For our second shift, we are re-architecting our talent model into an AI-augmented early career talent led by more senior player coaches.

We introduced two new certified roles, frontier-certified engineers who audit workflows and build intelligent agents, and frontier business operators who manage blended human digital teams to deliver outcomes. We plan to scale this Cognizant Forward team to 5,000 frontier-certified engineers and 10,000 frontier business operators. We currently have 10,000 Cloud certified architects, the most of any organization globally, and we power one of the largest pools of Codex and Gemini Enterprise-trained badges. Last quarter, we introduced Cognizant SkillSpring, an AI-native platform that embeds agent-driven tutoring directly into daily workflows and gives associates real-time visibility into their AI proficiency and token usage. It is gaining significant momentum with our associates as learning time has doubled and AI usage has tripled. We have also opened this platform to early prospective clients.

Our third and fourth shifts move Cognizant from a labor-based model to an agentic and platform-enabled model, from delivering outcomes to underwriting results. This is why we established a new AI products and platform group earlier this year to unify Cognizant's proprietary offerings and scale innovation across the portfolio. Our platform strategy has two dimensions. First, our engineering platforms, which provide the foundation for everything we build, including accelerators, agent frameworks, and AI engineering tools that power our AI-native software development life cycle and agent development life cycle. They are increasingly powered by their own agentic workforce. Together, they compress the software cycle, improve productivity, and accelerate business outcomes for clients. Second, building on that foundation, our business platforms combine technology, data, AI, and deep industry expertise to create differentiated client value. Purpose-built for the industries we serve, they embed industry-trained agents directly into critical workflows.

TriZetto is the strongest proof point of our platform strategy. Our healthcare platform business generates more than $1.1 billion in annual revenue, through the first half of 2026, grew faster than the overall company while delivering substantially higher margins. What began as a software product has evolved into a broad healthcare platform ecosystem. TriZetto demonstrates how platforms can drive deep client relationships, create recurring revenue streams, deliver growth and profitability that exceeds traditional services. It is a blueprint for how we intend to scale platform-led growth across other industries. In healthcare claims, we built a pioneering auto adjudication solution that uses large language models to digitize adjudication knowledge and rules, enabling agentic AI to analyze claims, apply complex business rules, reach decisions with human validation wherever it is needed. It positions us to take a share in this large, high-volume category.

Other platform-led modernization wins include a global claims and risk administration leader leveraging Neuro AI, Flowsource, and our 3Cloud-enhanced Microsoft expertise. We signed a five-year agreement to accelerate processing times and upgrade core infrastructure. Cotality, a global data and analytics company, has deployed Cognizant Neuro Business Processes workflow across multiple business operations processes. It resulted in over 40% improvement in research task turnaround times, faster and higher quality resolution of their customers. We are also moving beyond delivery to underwriting results. We signed a major engagement with a leading insurance brokerage, committing to more than 50% productivity improvement over five years through AI and operating model redesign. We won on the strength of our domain expertise, reimagining the core workflows, accelerated delivery using AI tools from our partner ecosystems.

As we execute these four shifts, our AI builder model expands where we create value across 3 categories. First, our traditional work done dramatically more productively. Second, old things in new ways. Third, entirely new things that did not exist before AI. First, our traditional work done more productively. This includes autonomous software engineering or Vector 1 work, which over the past two years has driven both consolidation and productivity-led engagements. A great example of our success in this area is Novartis, which selected Cognizant earlier this year for a five-year engagement to transform its global ID operations. Building on a relationship that spans more than 20 years, we expect to leverage our Neuro AI platform to create a unified AI-powered operating model that combines automation, full stack observability, and agentic capabilities.

This is where our AI Builder Strategy is aimed, helping clients move from labor-intensive operations to intelligent self-service and increasingly autonomous technology environments. Second, old things in new ways. Here we see a significant pipeline across secure AI services, mainframe modernization, SAP S/4HANA migration, and SaaS reimagination. Long-standing enterprise challenges that AI can now solve far faster for a fraction of the cost. Cybersecurity is a clear example. AI is turning security from a cost center into a remediation opportunity as machines expose vulnerabilities at unprecedented speed. We are positioned for this moment by combining frontier models with our 5,000-person security practice and all the leading frontier program partners, including CrowdStrike, Palo Alto Networks, Zscaler, Anthropic, OpenAI, Microsoft and Red Hat. We see a strong pipeline forming across these partnerships.

Third, entirely new things made possible by AI, including context engineering, reinvention of business flows, agentification of business operations, and Physical AI. With Physical AI, as intelligence begins to govern physical environments, we believe a new domain of autonomous operations will open. We launched our sovereign Physical AI platform as a service to position Cognizant ahead of this iPhone moment for robotics and infrastructure. This builds directly on a capability we have built over the past decade. More than 10,000 of our associates have trained AI and machine learning models for the world's largest technology companies. We are now repurposing that expertise for the enterprise through our AI model training and data services.

For example, for a global automotive manufacturer, we train models on the company's products, technical data and visual content to achieve accuracy generic models cannot match, automate complex processes, and unlock value from knowledge the company already owned. Public sector is emerging as a meaningful business as our organic and inorganic investments gain traction. In Q2, Cognizant Government Solutions, built on our Belcan acquisition, secured a landmark engagement with the State of Iowa to modernize its ID infrastructure. We have a growing pipeline across defense, federal and state government, including AI infrastructure and citizen experience, while TriZetto expands the opportunity to health agencies, including our work supporting the Department of Veterans Affairs in partnership with Signature Performance. This builds on our long-lasting U.K. public sector practice. For example, with the Home Office, we developed and support the foundational data platform behind the U.K.'s migration and border systems.

In Q2, we won expanded Home Office work across software engineering, testing, delivery, and managed services for critical case working systems, improving caseworker productivity and reducing manual intervention. For His Majesty's Revenue and Customs, we recently won an additional work to help configure low-code services in support of build and DevOps functions that is valued at more than $250 million over the life of the deal, including option years. Our AI Builder Strategy is also gaining traction outside the U.S. For example, a global pharma company in Europe selected Cognizant as a sole partner to build and scale its enterprise data, AI and agentic AI capability through a three-year agreement covering 68 projects initially. As the client's official AI Builder, Cognizant will translate their agentic AI vision into a production-grade governed enterprise platforms spanning all business domains globally.

Cognizant helped a large European bank to identify its mortgage processes by building a mortgage operations agent, which brings multiple specialized agents together to support complex decision-making, analyze business rule outcomes, proposing remediation paths, and generating clear and actionable insights. To conclude, we are in the midst of a profound transformation with a clear vision for the industry's future and confidence in the expansive AI-led opportunity ahead. Our actions, deploying interdisciplinary talent, shifting to outcome-based platforms, and opening new value pools are designed to drive sustainable growth. As we redefine Cognizant, we remain focused on our growth, on our goals of delivering top-tier growth, consistent margin expansion, and EPS growth ahead of revenue. Thank you to our associates, clients and shareholders for your continued dedication, partnership, and trust. With that, I will turn the call over to Jatin.

Thank you, Ravi, and thank you all for joining us. We are pleased with our second quarter performance, highlighted by industry-leading growth and steady adjusted operating margin expansion. We achieved these results while continuing to invest, including in the completed acquisition of Astreya, new frontiers skilling initiatives, expanded partnership, and our AI labs and platform-led offerings. We also deployed more than $1.1 billion through share repurchases, reflecting our conviction in the long-term opportunity AI creates for Cognizant and our critical role as an AI builder. While market conditions remain complex, we have continued to deliver on our commitments while investing in and evolving our business for the future. Moving on to the details of the quarter. In Q2, revenue grew 4.1% year-over-year in constant currency to $5.5 billion. Our sequential organic growth was at the high end of our expectations.

Year-over-year performance was driven by volume growth, increase in third-party product revenue associated with our integrated offering strategy, and inorganic revenue from our investment in 3Cloud. From geographic perspective, growth was once again driven by North America. From a services perspective, our BPO practice once again led growth, while demand remained strong for data and cybersecurity driven by AI adoption. We are also seeing strong growth from industry-specific AI-led transformation in financial services and life sciences. By segment, financial services again led with healthy growth across banking, capital markets, and insurance clients. Growth is also being driven by strong performance in the U.K. public sector. We are seeing legacy modernization programs accelerate as clients advance their AI journeys to address significant technology debt. This is also reflected in sustained bookings momentum. Health sciences was stable. Demand remains cautious and cost-driven, with clients prioritizing vendor consolidation, legacy modernization, and compliance, while discretionary spend faces tight scrutiny.

It must demonstrate a clear ROI. As Ravi mentioned, TriZetto had a strong quarter. Products and resources was steady. Clients in retail, consumer goods, and travel and hospitality continue to navigate pressure from geopolitical uncertainty, supply chain disruptions and elevated oil prices, we are seeing momentum in manufacturing, logistics, energy and utilities, where physical AI and smart manufacturing are creating compelling opportunities for us. Within communications, media and technology, demand among comms and media customers is muted, consistent with last quarter. With technology customers, demand remains strong, driven by AI-native engineering, digital operations, data and cloud services. As Ravi noted, we are already seeing momentum with Astreya, and we are confident our joint capabilities can generate attractive growth synergies in the years ahead, driven by AI infrastructure build-out.

Turning to bookings. We delivered another strong quarter of large deal bookings, signing seven deals, each with TCV of more than $100 million, including three new logos. On a trailing 12-month basis, bookings grew 5% and represented a book-to-bill of 1.3. Annual contract value decreased modestly, reflecting the impact of lengthening contract duration due to a greater mix of large deals. We are pleased with the growth we have delivered in new and expansion bookings, which grew in the mid-teens in the first half of the year. Moving to margins. During the quarter, we incurred approximately $84 million in costs related to the Project Leap program we announced last quarter.

As a result of India Labour Codes and subsequent regulations notified by the Indian government in Q2, we recorded $81 million one-time benefit for a partial reversal of the India defined contribution obligation liability we had originally recorded in 2019. Excluding these impacts, second quarter adjusted operating margin was 16%, up 40 basis points year-over-year. Operational efficiency and favorable currency movements more than offset higher third-party cost and compensation costs, as well as the impact of our recently completed acquisitions. To details of EPS, cash flow, and capital allocation. Second quarter adjusted EPS was $1.37, up 5% year-over-year, driven by revenue growth, margin expansion, and lower share count. EPS was negatively impacted by a higher interest expense associated with $1 billion we borrowed under our revolving credit facility to fund the Astreya acquisition and share purchase activity in the quarter.

DSO was 88 days, up five days year-over-year, primarily driven by a change in the business mix. This factor also led to a corresponding increase in payables, and therefore, the impact was neutral to cash flow. Second quarter free cash flow was $459 million, bringing year-to-date free cash flow to $652 million. During the second quarter, we deployed $1.1 billion on share repurchases and bought back over 22 million shares at an average price of approximately $51 per share. This includes $500 million accelerated share repurchase program announced in May. Year-to-date, we have returned $1.9 billion to shareholders through share repurchases and dividends and remain on pace to return about $2.6 billion. This represents more than 10% of our current market cap. We have also deployed $1.3 billion on acquisitions aligned with our AI Builder Strategy.

We ended the quarter with cash and short-term investments of $1.1 billion. Turning to guidance. For the third quarter, we expect revenue to grow 3.8%-5.3% year-over-year in constant currency. This includes approximately 200 basis points from our recently completed acquisitions. As we discussed on our last earnings call, our prior guidance range contemplated an improved discretionary spending environment at the midpoint. Instead, macro uncertainty has remained elevated. We have therefore revised our full-year revenue guidance range to 4%-5.5% growth in constant currency. This includes 150 basis points of inorganic growth, unchanged from our prior expectations, but similar to last quarter, our M&A pipeline remains active. We are focused on executing with discipline on opportunities aligned with our AI Builder Strategy.

While discretionary spending has remained pressured, we have maintained good traction on large deals, which we will expect to continue to ramp in the back half of the year. Our revised guidance range assumes the discretionary spending environment remains stable at the midpoint, while the high end contemplates an improvement in short cycle revenue in the fourth quarter. There are no changes to our Project Leap cost estimates or expected savings, and we continue to expect the program will run through the remainder of the year. Our adjusted operating margin guidance is unchanged at 16%-16.2%, representing 20 to 40 basis points of year-over-year expansion. Our free cash flow conversion guidance for the year remains 90%-100% of net income. Full-year tax rate is now expected to be towards the low end of our prior guidance range of 25%-26%.

Based on our current expectations, we expect our third quarter rate to be above the high end of the full-year range. We now expect full-year weighted average dilutive share count of approximately 460 million, down from our prior estimates due to the pace of repurchases in Q2. Interest expense has also increased modestly, reflecting a lower cash balance and the drawdown of our revolver this quarter. As a reminder, the previously disclosed enactment of the Indian Labour Code Reforms in 2025 has resulted in a higher run rate of other expenses. We expect this below-the-line cost related to our India defined benefit plan will be around $10 million per quarter for the foreseeable future, consistent with the first half 2026 run rate. This is in line with our estimates in our initial guidance in February, but we are highlighting it to support your modeling.

Our EPS guidance has increased to $5.70-$5.82, representing 8%-10% growth versus 7%-9% growth previously.

Finally, we continue to make progress and advance on our evaluation of a potential primary offering and secondary listing in India. We are working in close collaboration with external stakeholders and regulators. We will make a decision on this once we have visibility of the revised regulatory framework. We are pleased with the progress made to date and remain committed to acting in the best interest of our shareholders. We will provide updates as appropriate. With that, we'll open the call for your questions.

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 that 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 do ask that you limit yourself to one question and one follow-up. That is star one to register a question at this time. Our first question today is coming from Maggie Nolan of William Blair. Please go ahead. Hi. Thank you.

I'm hoping you can give us a little bit more commentary on the business momentum in the context of bookings growth compared to last quarter, as well as that second half ramp-up that you had previously expected from large deals. Maybe update us on how those signings and ramps are progressing and how it now shapes your second half expectations.

We've continued to have good bookings momentum. Last quarter, we did 22% bookings growth. TTM this quarter has been 5%. If you take the first half, it is 6%. It's a tough compare also because we had two mega deals last year. Last year, we grew by almost 18% in quarter two last year. Keeping all this in context, I think we have done pretty well on bookings, and I actually feel very confident about bookings for the rest of the year as well. Now, one of the nuances which we are excited about in our bookings momentum is financial services is really running hot. You've seen in Q4, we had 9% growth, 9+%. In Q1, we had 10+%, and now 12%. Financial services has literally, has overwhelming increase in bookings in the first half, and I expect that to remain very strong in the second half.

We did seven large deals, three new logos. We are starting to see activation of $50 million-$100 million deals, which have significantly improved. If you take those two large deals out and compare from last year, $50 million-$100 million deals have gone through a massive bump. $25 million-$50 million deals have gone through a massive bump. Percentage of new business has bumped up by 10% in the first half. In comparison, 10+% in comparison to the mix, which is also good because it kind of translates to new revenue, incrementally new revenue for the second half. We've had a pretty good step-up change in our bookings momentum. When we entered the year in 2025, we were at $27 billion TTM, then we got to $28 billion TTM, and in the last two quarters, we are at $29 billion TTM.

We are starting to move up and bookings are going to be a little bumpy between quarters, but if you look at the aggregate numbers and you look at TTM and you look at the tail velocity of the last two quarters, we feel super excited about the second half as well.

Thank you. Then on the BPO business, you've seen good traction there. Can you talk a little bit more about where you're seeing that traction from an end market perspective? Is it really your vertical expertise that's helping there? Or is it more the partnerships that you outlined with some of the model providers and others that are important in this space? What's driving the success there and how can you perpetuate it?

Great question. In fact, BPO has always been a blockbuster service line for Cognizant over the last three years. We continue to lead on industry vertical BPO over the last few years. In fact, even when I came on board in 2023, the BPO organization was called Intuitive Operations. It had embedded itself with data and automation and machine learning then and now AI-led BPO. Maggie, I've actually mentioned this in my remarks as well as in all my commentary in the last one year. The expansive opportunity of system integration services goes from a $1 trillion market where we build software systems for companies to embedding technology, which is AI technology, agentic, into business operations of firms. That is going to move our market from a $1 trillion to $5 trillion-$6 trillion.

It is actually much, much more expansive than ever before, and it kind of brings data technology and process all together. We're very excited about the BPO business with the strength of our model company partnerships where we can not just apply it for software engineering, but apply it for business operations, vertical and horizontal, and also platformize that business. Our TriZetto business is running at a higher velocity than the rest of the company, and the BPaaS business underneath it, which is healthcare operations, is equally running with the same velocity. We want to replicate the platforms, AI-led agentic business operations for companies. Just to give you an example, we have a blueprint for F&A, frontier-led F&A. We have a blueprint for frontier-led customer operations.

We've started to put that in the mix, which effectively means we can embed digital labor and human labor and deliver outcomes through frontier operators, as we coined it. It's a new archetype of a role, and deliver those services to our clients. I'm very upbeat about the future of our business process operations and agentic-led business process operations. We also have a training capability now, which is the AI data training services. Historically, we did it for the magnificent seven companies. Now we are transitioning that capability to AI-led into the Global 2000. If intelligence is not going to be drawn centrally and if enterprises are going to build distributed intelligence, they're going to have their own specialized models, we think we have a unique service to attach to it. We have 10,000 plus associates who work on data training services.

That's a part of the BPO organization.

Very helpful. Thank you. Thank you.

The next question is coming from James Schneider of Goldman Sachs. Please go ahead. Good morning.

Thanks for taking my question. Ravi, I think relative to your comments about corporates, one out of four pausing their AI progress because of cost or return issues, can you maybe talk about more tactically, you've talked about how Cognizant can address that opportunity, but can you maybe talk about more tactically what customers are doing then? If they pause, what is their immediate action? Are they going back to more traditional implementation work or outsourcing work, or are they just pausing until they can get a better handle on the scenario? How long would you think it would be on an average engagement before you can really see, for Cognizant, a big uptick at customers like that?

Jim, great question again. Thank you. Look, the first chapter of AI adoption was broad-based, open-ended, experimental, and the technology was magical, so everybody tried to use it in a way that they could find some magic coming out of outcomes. As you productionize this, which is the second chapter, you're going to go very nuanced. You're going to start to focus on not token consumption, but token economics. You're going to start to optimize where you use advanced reasoning and where you don't use advanced reasoning. The step back from clients is to say, "Wait a minute, I'm spending a lot of money on tokens. I'm spending a lot of money on that entire AI stack. Am I getting the value? If I'm not, let me revisit how to optimize it and get value out of it." The capability is out there, I've said this.

The production value is way below, and the bridges to that production value is assembling context, assembling tribal knowledge, setting the guardrails, grounding the technology into the heterogeneity of an enterprise. We have started to believe now that we have a role to play, a big role to play in that process. Starting from building the harnesses, where you can capture the context so that when you do the transactions on a regular basis, you can create repeatability. Model routing, which means, depending on the kind of task, you could use an open weight model, you could use a costly, expensive, closed frontier model, or you could use a cheap closed frontier model, or you may not use a model.

Then creating learning loops between human effort and machine effort so that you could integrate human and machine effort together, which means we have a methodology called BASIS in our consulting organization, which allows us to reinvent and reimagine those processes. If you put all of this together, there's a lot of heavy lift needed before you can actually productionize it and get value. We are building platforms, and we are building services underneath it. Our clients are coming back to us for a variety of things, starting from productivity, which is related to software engineering, which was historically for the last two years, very mainstream. Now, going back to my previous response, they're coming back to us on business operations. Business operations is where the future is because you want to embed this technology into business operations.

If $6 trillion of AI ramp on the infrastructure is going to happen in the next three years, $15 trillion-$20 trillion has to come out of value from enterprise businesses. That's not going to come from system building through AI first software engineering, but it'll actually come from embedding it into business operations. Clients are using us for getting the frontier capacity, the frontier engineering and frontier operator capacity, platforms, harnesses, context engineering, reimagining the workflows. Some of our clients are starting to ask us to deliver an AI-infused rate card, which means you embed the pre-training costs and you embed the inference costs into that process. Software engineering is very mature. Business operations is actually evolving now, and we have a third harness for physical AI, which we are preparing, which we think will be the future as we go forward.

That's the broad story. The ability to build that bridge is what will drive companies like ours to add value in the process.

Thank you. Maybe as a follow-up, financial questions sort of maybe for Jatin. Can you maybe talk broadly to sort of your overall hiring headcount plan in relation to gross margins? I saw headcount tick down a little bit sequentially. I'm assuming a lot of that was just Project Leap and some efficiencies there. Maybe talk about your hiring plans over the next two or three quarters, and to what extent you expect to be able to hold gross margins at or above the current level. Thank you. Sure, Dan. As you rightly observed, we have flattish headcount between quarter one and quarter two.

We continue to add the recent college graduates to the company, as you indicated in the past, and we have made good progress by the end of first half, and we remain on track to get to approximately 20,000 by the end of the year. The Project Leap is also underway, as a result, you will see a certain amount of headcount reduction. On the balance, we expect that the headcount should remain range-bound versus an increase. That's how we are budgeting or we are planning for rest of the year. Long as gross margin is concerned, you have, I'm sure, noticed the improvement that we were able to execute between quarter one and quarter two, which is roughly 60 basis points.

We are still trending a little lower than last year. We will continue to work on it during the course of the year. I do hope that we continue to show an improvement in that number as quarters progress.

Thank you. Thank you. Our next question is coming from Jamie Friedman of Susquehanna International.

Please go ahead. Hi. Good morning and good results here.

I wanted to ask about the linearity of the remainder of the year and, Jatin, the sequential assumption on the Q4. It looks like if you're at or just above the midpoint on the Q3, you could be flat to slightly down in the Q4 sequentially, but there is some M&A in there. If you could help us think about how you're thinking about the sequential Q4, in particular on an organic basis, that would be helpful.

Sure. We have modeled it based on the trends that we see every year. What we have superimposed this year are a couple of variables. One is the larger new and expansion percentage of bookings that we have seen from the beginning of this year. We also have seen the ramp-up of the deals which are in transition phase now and which will move to billable volumes in quarter three, quarter four. Finally, we do have a view on furloughs. As you know, the furloughs typically are represented largely by banking and financial services, and that is continuing to be very robust this year. We have assumed a slightly lower proportion of furloughs coming in quarter four.

The assumption is a slightly superior sequential growth in quarter four compared to what we have seen traditionally in quarter four, which is typically negative because of the bill days impact and furloughs.

Perfect. Then, Ravi, I just want to ask about products and resources. It's been a couple of years now since Belcan closed. You had a ton of inorganic in the period of comparison in the Q3. At a higher level, though, how is products and resources performing relative to what you had expected when you closed the deal? Thank you. Yeah. That's a great question.

In fact, one of my endeavors is to go beyond financial services and healthcare and create more diversity in our portfolio. We're very pleased with the performance in products and resources over the last few quarters. We've got some good traction, new logos. You've seen a section on my earnings around Belcan and its tailwind with other public sector opportunities we have won using the Belcan engine. That is a great add to our portfolio mix. We're starting to see significant traction with our clients on Physical AI, which I spoke about. We have built a harness around it. It's called the Intelligence Spine. We have just now hired a new leader for oil and gas. We are continuing to make good progress on diversifying our portfolio and product and resources is one of the important areas to do.

I actually believe the AI opportunity will actually be. You will see a leapfrog of digital enhancement on physical things in product and resources, and you will equally see low margin businesses, which is what I mean by it, is our clients, our enterprise clients, they're going to use AI to unlock more value than high margin businesses just because of the productivity opportunity there. Product and resources is going to be one of our high investment zones in the future, and we'll continue to invest to make it a very important portfolio for Cognizant.

Thank you so much. Thank you.

The next question is coming from Darrin Peller of Wolfe Research. Please go ahead. All right.

Hey, thanks, guys. Can you just touch on how you'd assess the competitive dynamics in the market right now, just especially for the larger deals. How important is pricing in these discussions right now? Just when you're having these discussions with customers, what do they look like when large deals come up for renewal? Just productivity savings they're demanding now versus prior.

Yeah. Look, we have done productivity-led large deals over the last 3 years. We have outperformed on our margin performance versus what we originally assumed. We've done pretty well in winning more than plan appears, and that has led to a large deal momentum over the last 2 years. Now we have progressively gone into newer things, which is my second and third swim lane, which is doing old things in new ways, which is primarily, say, I do a mainframe migration using frontier models, or I do SAP HANA migrations, or I do a vulnerability remediation coming out of security or SaaS reimagination. All those are starting to become large deals. New things in new ways, which is primarily using AI to do things which we didn't do before for growth imperatives or smaller deals because they come more modular.

The mix of deals has changed as we progressed on this process. Specifically on productivity, look, unlike in the past where you had circuit breakers and how much you could do on linear productivity because labor costs are not as nonlinear. Now you have a level of nonlinearity because you could pass on that productivity to clients and outbid what you have actually committed to clients and keep some for yourself. That's why our margin profile on all our large deals, both $50 million above and $100 million above, is actually trending much better than what we originally signed the contracts for. As long as we stay ahead on AI-led productivity for software engineering and business process operations, and we keep staying ahead of it, we can pass on the productivity, stay competitive in the market, and still be margin accretive for ourselves.

That flywheel is working very nicely for us, that's why we're continuing to win large deals which are productivity-led. We will start to see that move from software engineering to business process operations, where the span, where the expansive opportunity is going to be much, much more.

All right. That's helpful. Thanks, Ravi. Maybe just a quick follow would be around what the path looks forward for scaling the frontier certified workforce that you described earlier. Just what degree will this come from new hires versus existing? Then just how are you going to keep differentiating as other companies try to develop frontier workforces also?

We're doing it at scale. We are hiring at the bottom of the pyramid from outside. We are doing it at scale, building bridges from inside. Just look at where we are. Earlier this week, we announced we have the largest pool of Claude certified architects on the planet. We have 10,000+. We just finished a hackathon today with OpenAI in India. We had 10,000 associates getting badges. We're doing a similar exercise with Gemini. We're also doing a lot of work with open weight models. At scale, bending the cost curve and having the context of businesses to deploy that talent and get value out of it is what will drive companies like ours to be on the cutting edge. You need a combination of things. You need to know how to reinvent the flows.

You need to know how to audit the flows, integrate the agentic work into the business flows. You need to know the context. You need to do it at scale, at a lower cost, bending the cost curve. That's what we're doing. I'm pretty confident that we will have the largest pool of certified Frontier engineers and Frontier operators, which is a new archetype of a role we have established. Frontier engineers is about engineering agentic into biz flows. Frontier operators is about operating those flows, which has digital and human labor together. Bending the cost curve is what we have done for the last 30 years at scale. That's what we are continuing to do. It's a combination of building a pipe from outside, building a pipe at the bottom of the pyramid, early careers, and building bridges from inside.

Thanks, guys. Thank you. The next question is coming from Tien-tsin Huang of JPMorgan Chase.

Please go ahead. Hey, good morning.

Thanks a lot. I just want to ask around financial services. That was up double digits. It's growing at a premium over the other sectors. In the past, we've looked at that sector as maybe a leading indicator of another subsectors would follow. Do you see that potentially being the case here? Should we be encouraged that that could be the case, or is there something unique that maybe is a little bit different in terms of their willingness to adopt some of these AI-driven projects?

Absolutely, Tien-tsin. Thank you. Look, financial services has always been a pioneering industry, high on technology spend. They create a symmetry using technology, and they're on the cutting edge on AI. In fact, financial services, we are probably the number 1 company on growth in our peer group. All of last year, we did higher single digits. At the end of Q4, we got to 9%. Q1, we got to 10%. Q2, we are now at 12%. Financial services is activated on all 3 swim lanes, starting from consolidation, productivity, sharing the productivity to modernization of landscape using AI and refactoring landscapes, which is my second swim lane, to the third swim lane, building new things using AI and generating growth imperatives. I am actually super optimistic that financial services will lead the path and other industries will follow.

Other industries will sometimes leapfrog as well. I now start to see that in industrial clients who are looking at the miss they had in the digital revolution to leapfrog directly into Physical AI. You are absolutely right. I think it's a leading indicator to what's going to come. If you just look at our Q4 exit rate, just as a company, it's also powered by financial services. We are super excited about the fact that we are exiting. If you take the midpoint of our guidance range, we are exiting on a high, and we will be on the winner's circle.

Good. Thank you for that, Ravi. Maybe for you, Ravi and Jatin, I have to ask a question on tokenomics. We don't mind just thinking about that. Any update with respect to cost, usage, what you're hearing from your client base? Any update there? I know you talked a lot about that at your AI event, but love to hear an update if there is any. Thanks. I think it's continuing to be the hot topic.

The conversation has moved from consumption of tokens to optimizing token usage, not using tokens where not needed, using open weight where needed, building specialized models using open weight as the base. Making a difference between using expensive closed frontier models to not expensive closed frontier models. Capturing the learning and creating a learning loop and building an alpha around it. All of these have become so much a hot topic of discussion, especially customers who are doing business operations. Software engineering is more mature now. Business operations is not as mature. This is going to be a hot topic of discussion for the next 12 months, and it will actually lead to more and more work and more and more services for companies like us.

It will also mean bundling pre-training and inference costs along with our services. We now have arrangements with all three frontier model companies to bundle those services and bundle the inference and bundle the pre-training costs, which means we will have to build that craft, and that craft is an important craft to build it because it will then mean that the input factor is not just going to be human effort. It's going to be human effort, platforms, software, frontier services, all bundled for an output which is not effort-based, but outcome-based.

Thank you for those thoughts.

Thank you. Our final question today is coming from Bryan Bergin of TD Cowen. Please go ahead. Hi. Good morning.

Thanks for taking the questions. I'm curious if you can share any rough mix of the managed services business that already incorporates GenAI-led efficiencies. I'm really just trying to understand the balance of the multi-year book that still needs to go through a cycle of renewals so that we can better project a potential crossover point when you see a potential acceleration from AI activity that can more than offset that existing base compression and other factors.

Yeah. As you know, our revenue has roughly two components, time and material and fixed price. Fixed prices, both are now 50/50. Our view is that time and material is continually every time we renew it, and that short cycle business, typically 6 to 9 months, sometimes 12 to 15 months, but never more than 2 years or 2 and a half years. That's a short cycle business. That's continually embedding in itself, even on managed services basis, the benefit of AI into itself. On the remaining 50%, which is fixed price book of business, typically the contract lengths are between 24 and 36 months on an average. Of course, there could be some which are 5 to 7 years and some could be shorter, but typically on an average between 24 to 36 months.

If we believe that we have started this journey of embedding AI into our solution more actively from beginning of last year, which is 2025, we are roughly halfway into it, and we have probably another half to go.

Okay. That's very helpful. Thank you. My follow-up is on Project Leap. Just any further details, how much of the plan have you actioned thus far? Any kind of in-year savings from the program that you realized here in 2Q? Just anything important for us to consider as far as the pacing of cost and savings yield as you go through 3Q and 4Q.

Sure, Bryan. We continue to execute the program. We have taken up approximately $84 million of cost in quarter two, of which 50, 55 is related with the employee severance and remaining is related with facilities and software and some of that. As you know, we have baked in the savings from the program as part of our guidance range, and we believe we are executing well towards that goal. I think you should continue to see the rest of the year evenly spread from Project Leap execution between quarter three and quarter four as we move forward. We get full year benefits next year. It also reshapes the cost of technology deployment in the market. To a large extent, this is about margins, but it's equally about growth. Can we get more growth using a baseline where productivity is shared with our clients?

That's clear. Thank you. Thank you.

At this time, I'd like to turn the floor back over to management for closing comments.

Thank you so much for joining in today. We are very excited about our quarter two earnings. Continue to be on the winner's circle. We have confidence of staying at the winner's circle for the rest of the year and create some nice tail velocity for the next year. We are excited about the activation of all three swim lanes, productivity, doing old things in new ways, using AI, and as I talk, we are seeing accelerated momentum on new things in using AI, which is primarily driving growth imperatives for enterprises. Thank you again for joining the call today.

Ladies and gentlemen, this concludes today's teleconference for Cognizant's second quarter 2026 earnings call. You may now disconnect or log off the webcast at this time and enjoy the rest of your day.