Infosys Ltd. - Call Transcript 2026

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TO ALL STOCK EXCHANGES

BSE LIMITED NATIONAL STOCK EXCHANGE OF INDIA LIMITED NEW YORK STOCK EXCHANGE

February 24, 2026

Dear Sir/ Madam,

Sub: Transcript of the Infosys Limited Investor AI Day 2026

In continuation to our letter dated January 23, 2026 and February 17, 2026 regarding Investor AI Day 2026, please find enclosed transcript of the event.

This information will also be hosted on the Company’s website, at https://www.infosys.com/investors/news events/analyst-meet/2026/india/main.html.

The audio/video recordings are also made available on the Company’s website, at https://www.infosys.com/investors/news-events/analyst-meet/2026/india/main.html.

This is for your information and records.

Thanking you,

Yours sincerely,

For Infosys Limited

Anur Gurugopala Digitally signed by Anur Gurugopala Raju Raju Suryanarayana Suryanarayana Manikantha Manikantha Date: 2026.02.24 18:16:53 +05'30'

A.G.S. Manikantha Company Secretary Membership no: A21918

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Infosys Limited Investor AI Day 2026

February 17, 2026

CORPORATE PARTICIPANTS:

Nandan Nilekani

Chairman

Salil Parekh

Chief Executive Officer and Managing Director

Satish H.C.

Chief Delivery Officer

Dinesh Rao

Chief Delivery Officer

Balakrishna D.R

Head of Global Services

Mohammed Rafee Tarafdar

Chief Technology Officer

Anand Swaminathan

Segment Head – Communication, Media and Technology

Jasmeet Singh

Segment Head, Manufacturing

Dennis Gada

Segment Head – Banking & Financial Services

Ashiss Kumar Dash

Segment Head-Energy, Utilities, Resources & Services

Ambeshwar Nath

Industry Head-CPG, Logistics & Retail

Shaji Mathew

Chief Human Resources Officer

Sumit Virmani

Chief Marketing Officer

Nandan Nilekani

Thank you, and great to have you all here in these tumultuous times. Today, I will talk about tech transitions. I have had the fortune or misfortune of being in this industry for more than 40 years and have seen a lot of transitions. So I thought I will talk less about that and more about why this time it is different and what are the implications of this transition.

Now safe harbor clause.

Now we have seen technology shifts for centuries, whether printing press or telegraph, but over the last 60, 70 years, we have seen a much faster change in PCs, cloud, GenAI, Agentic AI and so on. So change of technology and the speed of change has been a constant for many decades now. And each time there is a change, the way we address that change has been different.

So we went from mainframes to mini computers to PCs, client server, LAN, web computing, mobile, enterprise apps, big data. And each time, we had to think of it in different ways, how do you think of it in terms of making it globally available through Internet or how do you do enterprise IT. So, each time there was a tech transition, it had certain implications for us. And firms like Infosys had to deal with what was new. So we are used to the fact that each time there is something different.

This time, the AI transition has been much faster than earlier transitions. If you look at the number of years it took to reach 1 billion users, Internet took more than 10 years, smartphones took 5 years, AI is taking a couple of years. Now, you have to realize that the AI speed is because of the first 2 things. Internet was already ubiquitous. Smartphones were already ubiquitous. It therefore allowed people to distribute a ChatGPT or Gemini or Claude very easily. So in some sense, the speed of AI is also because of the infrastructure of the previous era.

Now what has happened this time, is that this is a much more fundamental change to the way businesses will operate. So this is not a layer of technology. When smartphones came, we could build applications where instead of having a PC, you did it on the phone, like putting a front-end to an existing application. When cloud came, we could do a lift and shift. You could take the app from your in-prem and move it to the cloud. So you could do a lot of things to get going. But this time, it is not that. This is a fundamental change in the way we do things. Obviously, there is a technology dimension and it is all about having AI native architecture. But there is a whole business dimension to this. We cannot run business the old way, and businesses have to change, the customer journeys have to change, all those things have to change.

It is a huge challenge for talent. Talent will have to deal with a world where writing code will not be the goal. It will be actually making AI work, orchestration and those kind of things. So the jobs will change. And operating model, how do we make this at scale? How do you get a firm with hundreds of employees to change all the things and make it work? And of course, our mental models have to

change, because technology was always deterministic. You said A plus B equal to C. So no matter how many times you said A plus B, the answer was C. In this AI world, every time you give a prompt, you will probably get a different answer. And therefore, how do you deal with this non-deterministic world. But how do you make sure what you build has the robustness, reliability and resilience of the deterministic world. That is what the challenge is for everybody.

So this is a fundamental root and branch surgery of the way business is done, which is why this technology transition is so dramatically different from anything else that we have seen.

Now one clear learning we have is modernization of legacy systems cannot be deferred anymore. What happened over the last 60, 70 years is people would not replace the legacy system, they just added to it.

So if you go and look under the hood of a large enterprise, they will have mainframes from 1960, they will have mini computers from 1980, they will have LAN from 2000, they will have all kinds of things, and all coexisting in silos. That is over. If you really want a firm to take advantage of AI, you have to fundamentally clean this up. So this is a massive, massive cleanup job, which everybody is dealing with.

There are reasons for that. One is the financial drain. Many large companies are spending 60% to 80% of their IT spend on maintaining systems. There is no business value out of that. They want to go from 60% or 70% maintenance and 30% new systems to 30% or 40% maintenance and 60%, 70% new systems. They want to flip the way they spend money, but they cannot do that with that fundamental cleanup they need.

Moreover, many of these systems were designed in an era before you could have online attacks and so on. So security breaches, which you see every day, are just going up everywhere, and they are more state and non-state actors who are getting better at it using AI. So security is a huge problem for everyone. We have seen so many cases in the last few months.

And because the data is all in silos, you cant even innovate fast. So there are fundamental structural issues today we have. So demand side is absolutely demanding modernization. But the good news is, for the first time, because of AI, we have the tools now to do modernization fast and very quickly and at a much more economic way. So we have a huge demand, and we have the ability now to do it, and perhaps our team will talk about that.

So fundamentally, accumulated tech debt over decades must be paid. You no longer have the option to defer this. And this is a huge, huge requirement. And obviously, it is a huge opportunity for us.

Now the other thing which is there is, as AI becomes a bigger part of the spend, the balance of advantage is moving towards ‘build’ rather than ‘buy’. If you see some of the concerns about what

will happen to SaaS companies and all that, it is because of this, that building applications has become so simple that very often you may just build or you may replace something that you have, which you bought, with something to be built. And that again actually benefits folks like us, because they are about building. And who is going to build it for them? It is going to be us only who will build it for them. So fundamentally, it is good for us.

And the other thing which is there is that our view is that foundational systems will increasingly become systems of record. But the interface will be agentic, because agentic interface makes a lot of sense. Agentic interface allows people to produce something which is designed pro consumer or pro user. And agentic interface enables you to take out the complexity and hide it behind the agent, so the agent is simple to use. It is a very simple idea.

Now enterprises will, therefore, want to put agentic layers on top of all their applications even if they leave the system of record the same. And that is something which will be a combination of bought out agents as well as building their own agents, because finally, the agents have to be composable in a customer journey which is seamless, which is a mix of agents which are your own or from somebody else. Again, that requires orchestration and work which somebody has to do. So there is a huge amount of work required once they go towards ‘build’ rather than ‘buy’.

Now the other thing is, the pace of change is something which obviously we have not seen. We all know about the trillions of dollars being spent and all that. But even the technology can change. I mean, 2023, foundation/frontier model had 100 billion parameters. Today, it has 1 trillion parameter. There were only 10 to 12 agent networks. There are 60 agent networks. So this is only going to go up. In the U.S. alone, there are at least 5 frontier models. In China, there are big 4 or big 5. So this is only going to go up. In India, we have seen so much action, and you will see some big announcements this week on Indian-based sovereign models.

So I think this is something, there are certain implications of this, because if I am a businessman and I have to choose my technology, how do I make sure I do not make the wrong choice, because something which I invest in today may have fallen behind tomorrow. Already people are facing this reality. And therefore, how do you architect your technology, so that you can deal with this rapid change, is a very fundamental and structural need for enterprises. And again, they need help on that from somebody who has done this in 2,000 locations and understands the pros and cons of every approach.

But the main thing is that the technology is far ahead of its deployment. Because of this race and spending billions and some AGI and all that, the technology is moving faster than the ability of enterprises to deploy it. If you look at this chart, you can see that the model performance is going up, but the progress in implementing is not really, because implementing this is hard stuff.

Fundamentally, it is about organizational change, business change, retraining your people, thinking about nondeterministic approaches, changing your data so it is no longer in silos. So fundamentally, we have a situation where there is a deployment gap between the power of the technology and the capacity of businesses to use this. So if you guys think that some better product has come, nothing is going to happen, because the problem is here, not there. You get it. It is about how fast companies can implement. So you have to look at that.

And we call this the deployment gap, but this is actually a concept by Professor Clayton Christensen Harvard 25 years back. He called it technology overshoot, where technology gets ahead of the need. And in fact, he argues that that is how newcomers come, because newcomers can then launch new products that are not as sophisticated, but good enough for customers. And Satya in his recent blog talked about model overhang, which is the same idea. Fundamentally, the tech will keep getting better and better because billions are going to be poured into it, there is a massive competition, but enterprise deployment is not going to go up. And this deployment gap is what we can help to address. So again, it is a very important point.

Now, I think talent transformation is huge. It is not that you will not need, you will need talent, but it will go from QA testing or development. We have all kinds of new roles, AI engineers, forward deployment engineers, AI leads, forensic analysts, data analysts. So fundamentally, the challenge will be how do you take your workforce and make sure that they are reskilled and ready for the new business. And that is really the challenge that all the firms will face. So there will be roles. Now the way you hire will change, the way you train will change, the way you deploy will change. All that is going to happen. And I think we will have sessions on that. But fundamentally, there will be a need for people, but they will be doing different things.

Also, a lot of the talk of productivity is greenfield. Writing greenfield is not a big deal. I can take a tool and give it to a kid and he will generate a million lines of code. But that is not the real world. The real world is the fact that companies have trillions of dollars invested in their systems. They have technical debt. They have data silos. They do not have documents.

Somebody was telling me the other day that there are some old systems and, on contract, they have guys as old as me, 70, 75-year-old guys, because nobody else knows what the hell is going on. And then when there is a crisis to be sorted out, they are pulled in from Phoenix or Florida or wherever they are, and they have to solve the problem, and nobody else knows how to solve. So we have that kind of situation out there, undocumented dependencies.

So taking brownfield systems and modernizing them is a hell of a lot more difficult than doing greenfield development. And a lot of us get biased, because all the guys who talk about productivity are talking about greenfield development. And therefore, getting these large enterprise organizations productivity going is very, very different from individual tasks. It is a lot more complicated.

Also, AI implementation requires laser focus. The very fact that you can generate stuff means you can generate slop. In fact, five years from now, there will be more AI legacy system than any other legacy system and all kind of stuff will have been generated and we will have to clean that up also. And you can have this fake productivity.

Let us say there are two guys and they are having a fight. One guy will draft an email, which will be one paragraph. He will give it to AI to make it into a 10-paragraph email, because he wants to impress the other guy. The other guy will take the 10-paragraph email and summarize it to one paragraph. So both have used AI, but what have we achieved? Nothing. So how do we make sure AI is used? And therefore, you need usage guidelines, you need quality gates, you need explainability. So how do you make sure that AI investments lead to real performance and productivity and not just some make-believe stuff. This is something which is very important.

So what still matters? First, principle thinking. One of the things when we train people is they have to learn to do this without tools, because all of us learned to do this without tools. So when we got the tools, we knew how to use those tools better. But if you start by teaching them tools and everything is a black box, then it is like the guy who never knows how to calculate because he was born with a calculator. So first principle thinking is very important, all the more important as you think about strategic transformation of large enterprises. First principle thinking is very important.

Second, understanding enterprise context. Every company is different. Every company has a different legacy. Every company has different systems. Some of them have come from acquisition. Some have come because they have five business units, all buying their own version of technology. All kinds of reasons. Everybody has a complex estate of systems. And context is essential to being successful at AI deployment. And each context is different. And it is the dealing with these contexts that is the hard part, which is where, again, we believe we have a way of doing that.

I will give you an example. The self-driving cars, the first DARPA challenge was 2004. The first time they rolled out was 2007, 20 years back. Then everybody said, oh yes, by next year, we will have self-driving cars. It is 20 years later, all we have is a few cities in America where there are self-driving cars, because the context is different, every city is different, every road is different. And by the time they come to Bengaluru, it will be 2047, because dealing with Bengaluru traffic will be a different level of context. So enterprise context is so important, and that is something which cannot be done by a tool. It has to be done by capturing implicit knowledge and making it explicit.

Agnostic design. Again, what I mean here is, do not get locked into a tool, because that tool may no longer be -- it may be ,bsolete in two years. So how do you design for agnostic that you can choose any system. Getting the house in order, we talked about removing technical debt. We have to make the house in order for this whole thing. And then massive change management. You are changing

organization, business sector, people. I mean, this is like unbelievable change management. So unless you have leaders who can do effective change, nothing is going to happen.

It is also about strong collaboration across firms. The firms have to, because all the knowledge is implicit in the heads of different people, how do you make that explicit in one customer journey. Focus on productivity. It is not about using AI tools. It is about productivity out of those tools. Otherwise, you will get false productivity, which leads to more complications. And then this is an engineering game. AI engineering is a whole way of doing things. And that is part of your change management and transition that you have to do. And that is a big thing.

So my view is there is no opportunity gap. If anything, the opportunity is bigger than ever before. So do not get distracted by that. You should still ask the question, what is the firm doing to take advantage of this? What is the firm doing to transform its talent for this new world? What is the firm doing to design the services and products for this new world? What are they doing to tell the customer in a way that it resonates? What are they doing to make sure that the front-end conversations with clients are done properly? These are all the issues.

And I am sure everybody will not execute the same way. So there is an execution risk in doing that. So it is not an opportunity risk, it is an execution risk. You get it? And therefore, the balance of assessment is how do we know that each firm has the execution plan ready to get to where they have to get? Are they able to do it well? Are they able to do it with speed? Are they able to do it with scale? Are they able to do it with new mindsets? That is really the question of the day that all of you need to ask.

So I am hoping that today, you will hear from our team and it will give you some reassurance that we are on the right track. Thank you very much.

Salil Parekh

So good morning, and welcome. I think with that session from Nandan, I am sure you have a lot of clarity and a vision of where the industry is going, where AI is going and what are the real issues that one needs to look at. I want to share, in the next few minutes, where we see the opportunity today in AI services and how we are planning to go after it. In fact, already how we are going after it, and give you examples of that.

So first, one of the things we have learned again and again is what we see from our clients is that our clients trust Infosys in driving and delivering AI work. And throughout the day, we want to share with you several client examples. Here are a few quotes, the first one from the CEO of a large telco, where we are doing extensive amount of AI work and how he sees the value that Infosys brings, another one from a COO, another one from a CIO. And these are the sorts of examples that drive how we have built our approach, how we are executing on the AI journey.

So today, we are doing AI work for 90% of our large 200 clients. So this is not something which is just here and there in pilots. In terms of the scale, it is across many things. It is small and large parts of large programs with large clients. What we have now done is introduced a way to look at what we are doing through an AI-first value framework. And this framework we are putting together in this Hexagon, which I will share in a few minutes in some detail what we are driving through with it.

We essentially see six large areas where there is growth opportunity for AI services. And I will go through this in a little bit. We will also have each of our different leaders, from their different perspectives, give you specific examples in each area.

First, AI strategy and engineering. This is really the area where we do a lot of strategic work. You will, in fact, see one of the client examples I shared where the CEO is engaging with us. There are several others we will share later. And you will see some videos of this as well. So we are doing a lot more AI strategy work. Infosys is a lot more engaged at that level, because AI is central in that sense to all of those conversations. But the building of agents, the orchestrating of what the platforms are, what the agents are, which are agents which are built by Infosys, which are agents built by clients, which are agents built by third party, and how to make all of that come together.

Second is data for AI. Data is absolutely critical. As I am sure most of you already have seen, each large enterprise is protecting its data. No one in the enterprise world, unlike in the consumer world, is sharing data broadly with the AI foundation models. Everyone is building their own data, and there is a lot of work to be done to make enterprise data ready for AI in this new era.

The third is process where a lot of business process that exists today, whether it was technology enabled or whether it was not, is being driven by agents into new worlds. One of the biggest areas

we see here, of course, is customer service and the customer service business case is driving huge change in how process is going to change with the AI world.

The next, again, you heard from Nandan, the legacy modernization piece. This is a massive, large opportunity that we are seeing. In addition to customer service, one of the largest opportunities, where we are essentially taking large legacy organizations. We will actually share a couple of examples of this, of the work we are doing at scale and bringing them away from the legacy landscape into the modern landscape.

Physical AI is something quite new where AI software is embedded into devices, and that is becoming a growth area because everything will have AI built into it.

And then AI trust, which is both about the trust and cyber, but it is also about responsible AI, which is something where Infosys is leading in making sure that things are built with a view to keep responsibility in mind in the scaling of IT systems.

So these are the six new areas that we are seeing. This is a large opportunity set, and this is where we see a lot of the growth coming.

Now here are some examples of clients we are already working with. Throughout the day, in a little bit more depth, each of the leaders from Infosys will share one or two client examples. We will also have some videos to have the client share, from their perspective, how Infosys has supported them in this AI journey.

So what I am showing you in the Hexagon in the AI value framework is not theoretical. These are things that are actually happening on the ground with Infosys. This is what we are executing. And this for us today represents 5.5% of our revenue in Q3, and it is growing at a robust pace. And this is something which is extremely dynamic. It is something that is working extremely well with our clients.

Now, this at the high level of six big blocks is something that we have as a visual of Hexagon. But within the company, we then break it down to 30 offerings, and then those get broken down to 100 sub-offerings. And each of those are things that are being enabled with our engineers and with agents built on Topaz, which we will talk a little bit more about later on. And with partnerships, you saw one of the announcements this morning but we are working with each of the large AI players in very close coordination to make our clients more successful.

And all of this is the way we are now going to our clients. So each client discussion today is focused on the Hexagon plus the 30 plus the 100. And what is it that we can now work with you as a client executive to make it real for you the AI benefit, whether it is for revenue growth, whether it is for cost optimization, whether it is for innovation. So our entire go-to-market team, all of our segment leaders,

all of our practice sales leadership are working with clients with this format to make sure that the AI becomes more and more embedded into the work that we are doing with clients.

And this is pulling back a little bit. We have always talked about Navigate Your Next. This is how we are looking at the journey. The Navigate Your Next has changed, because technology changes, and this Next is about AI. It was different in the previous Navigate Your Next, but the Navigate Your Next concept, the client relevance remains the same. Where we are coming from today, the issues that Nandan shared, where we are going in the future, what the opportunities are, and what are our strengths.

Our strength is absolutely clear - the understanding of what the client landscape is. There are some clients that we work with where we have probably as good understanding of their landscape as some of the client teams have. And this makes a huge difference in how we navigate through that, through our AI toolkit. Our domain knowledge, our engineering talent, which, with our training, I have always believed is one of the best that there is, and the platform and IP that we have built, which again, we will highlight a little bit later today.

Now there is a dynamics in AI services, which all of you know and we understand. One, there is a huge opportunity. The one that I mentioned, the 6 areas with an external analysis, we understand that the opportunity is between $300 bn and $400 bn in the year getting to 2030, so over that time frame at that time in 2030.

At the same time, we have several entities, have made estimates. We have our own views. The AI productivity leads to compression in IT services. However, today, we have a clear view that the opportunity is massive and large and that will become the driving force of what we will grow and drive through in the next coming years.

Now in putting all this together, we have created our own playbook, and this is essentially what we want to share with you in depth today in each segment of the day. Of course, my vision and objective is that we unlock all of the AI value for our clients, and we are absolutely on our path to drive that. There is one set of discussions on the new services, what we are calling AI first. So with our delivery leadership, with our segment leadership, we will share with you what it means, where we are doing it, what are the examples, what are the benefits.

Then we have AI augmented services, where we are taking all of our services and making sure that AI is infused into it to become even more relevant for clients. And again, our delivery leadership will share with you some examples of where that is working and how we are doing that.

And then we have foundational components, our platform, our IP, what we built in Topaz Fabric, which we want to share with you in a little bit more detail. We have a set of agents that we have

already built that are ready to deploy. We have things that we can drive with our clients to build their own platforms, so showcase that.

Our partnership ecosystem - this is extremely critical in this new era. And the partners are not only

the partners that existed in the past. There are some new partners, both large and small, that are extremely relevant, and we already have a very strong ecosystem and a way to go to market with those partners.

Our talent and culture, which we think is critical and how we are reshaping it. We are going through a huge reskilling process. As you have seen from the announcements over the last several quarters, our approach has always been on reskilling and making sure that our team that we have builds up to the new, and we are recruiting.

We continue with our recruitment. We have recruited 20,000 college graduates this year and through March, that will be the number. And next financial year, we also have a plan to recruit 20,000 college graduates, we want to talk about the talent and culture approach we have.

And then on the brand, we have a leading brand in the market. Our brand is one of the fastest growing. And what are we doing to keep that or do even better in the brand in the AI world. That is how we are actually getting a lot more connects with the CEO client base, which is what you need to succeed in the AI world.

So that is our playbook, and we will showcase that to you throughout the day.

With that, I will close, and I will pass it on to our delivery leadership team to go ahead with the rest of the presentations. We will, of course, come back at the end of the day, Jayesh and I, to discuss your questions and give you some more views on where I see things are going. Thank you for that.

Satish H.C.

Let me set the context before we deep dive into our Infosys AI Playbook. Every tech shift, whether it is PCs, internet, cloud, digital, each one of them led to rewiring of enterprise work and workflows. And AI is the next rewrite. Here is a typical enterprise landscape - sounds complex, this is one of the simpler ones. A typical enterprise will be far more complex because of the scale, because of the fragmentation, internal and external, the heterogeneity and the divergence of the operating model and the regulation with which it operates in, which is by design by the way, and of course, the technology debt.

So, AI in enterprise is not just about low-hanging fruits like localized efficiency or user productivity. Integrating AI in an enterprise is not just a software upgrade or a plug-in. If it was that simple in such a huge complexity, no surprise that AI projects fail. Then what is it about? It is about harnessing the full potential. It is about re-imagination of systems, processes and deeply embedded ways of working and rewiring legacy power structures. So, Infosys has done this enterprise rewrite with each of the past tech shifts, and we are now doing the next enterprise rewrite with AI.

So let us look at how is the enterprise tech stack changing. Typically, it consists of the systems of record, which deliver deterministic, programmatic capabilities, which are codified, structured processes and enforce policies and this delivers governance, accountability and compliance. We have systems of intelligence which facilitate engagement, collaboration, transactions, but usually, it is the humans who engage with data and the workflows.

Above the enterprise stack, lies a vast non-deterministic or non-programmed flows, these are unique, unstructured, they need novel problem solving. It needs experience, gut feel and is usually handled by humans. This is the layer, which is under served today, and this is ripe for AI-led transformation. So, it is a myth that enterprises need just 2 layers, which is AI and data, it is enterprise and algorithm.

How should we harness AI then? AI is not the end game of tech transformations. It is just another one, but it is a giant leap in raw capability, and it is not system complete. So, we need a multilayer transformational approach and purposeful orchestration to harness the potential of AI. This is why AI diffusion in an enterprise lacks the rate of AI adoption, and it needs time.

So how is AI transforming the enterprise stack? What does this enterprise rewrite about? The core intelligence of humans plus machines will be seamlessly shared across the layers, so that every layer gets reinforced within the enterprise stack. The systems of record need acceleration, so that the business and operational processes can become more efficient.

The systems of intelligence needs a seamless integration of structured and unstructured data, so that intelligence can be wired into user journeys, business processes, transactions and decision-

making. It is estimated that 60% of effort in an AI project goes into doing this. The models will come in later and this requires deep industry knowledge and context of the enterprise.

Encoding intelligence and AI in the flows leads to more automation and autonomy. And this is leading to the development of a new layer within an enterprise stack, which is what we call as the systems of cognitive work within an enterprise.

As an outcome, humans will shift from acting on data directly to a governance and oversight role on flows and decisions. All the new AI tools that keep coming at us at fast pace will get plugged into the systems of new cognitive work, and it will accelerate the re-imagination of an enterprise or its flows and decisions. Infosys will unlock tech debt and complexity and harness the power of AI to be enterprise grade and bridge the evolution adoption gap and expand our addressable market.

So how do we monetize this? Our playbook reflects the structural changes necessary in our industry, so that we drive value at the intersection of intelligence, engineering and domain. With AI, capability is a commodity, because it is available to all.

Sustainable mode for an enterprise can be created by deep integration in specialized workflows and unlocking unique organization knowledge and context. So every enterprise has got unique data processes, risks and complexity, and AI will not unlock uniformly across the enterprises because of this variance. So our client intimacy and deep understanding of our client context will help us, mitigate this and unlock value and also drive culture and change.

So we have built an engineering approach in our delivery where we can codify enterprise context, which will help accelerating scaling of AI. And this also enables enterprises to retain and protect this unique enterprise context within their four walls of the enterprise, so that they can keep their competitive differentiation, and this is not diffused into the AI models. Our depth in engineering and frameworks on IP and patents will accelerate AI readiness and adoption with our Topaz Fabric and our specialized talent in the form of full stack and FDEs.

For a financial services client, they were looking for an AI partner. When we started talking to them, we realized that they have a very strong enterprise AI platform that they have built. But then what we realized was they had an adoption gap, and we pulled out our agent control framework, as we call it, which would address the quality of code that is being generated, which would address the AI slop, which led to a poor adoption within their organization. So now we are working with them on taking our framework and fortifying their enterprise AI platform, so that we can accelerate that journey.

We have embarked on a talent transformation journey to build an ambidextrous workforce, which is deep in engineering and creative in reimagining work and workflows from first principles. We see new opportunities with domain stack. We have over 25 years of industry-focused experience and

when core intelligence connects with agentic economy, the play of AI elevates from how work is done to new outcomes that are possible. So we are invested in building the domain stack powered by our depth in domain and knowledge of how to deeply integrate AI tools and plug-ins into the flow.

We have created strong differentiation in our services stack. AI is now integral to how we deliver every service. Our services stack is powered by Topaz Fabric. We now have an approach to productize and reimagine work and workflows that will lead to a human plus agent model. We are also seeing momentum with new deal archetypes, legacy modernization with reduced risk, higher predictability on cost and accelerated timeline, large deals with integrated ops tech and transformation wired in, organization transformation encompassing enterprise stack and people.

This includes the AI-first DCC approach, which we have pioneered in the market. We have also elevated our play to take end-to-end accountability from strategy to actionable roadmap to execution and eventually outcomes. We have expertise in delivering both above the line, which is business value and below the line, which is efficiency. Infosys is best equipped to deliver enterprise AI ambition with the power of our client intimacy and our AI playbook.

A quick example. Here is a client, a CPG, who had an ambition of clocking about growing their revenue to $7 bn. They came to us to bridge their ambition and deliver an AI operating model, so that we will have an actionable roadmap and execute to it with executive AI value office, along with managing risk, governance and assurance. We used our Infosys IP and built their unified data foundation. We build their enterprise Agentic AI platform with the requisite guardrails. This enables them to rapidly innovate and diffuse AI across their functions. Today, they have 10 Agentic AI products in the business across different functions from R&D, sales, marketing.

Above the line, with the agents that we developed in R&D for product formulation, they now have line of sight to $50 mn revenue, which they did not have before. Below the line, we have been able to unlock $25 mn cost savings through just optimizing operational work. And then beyond this, we were also able to deliver 40% of business productivity improvement in functions like procurement and marketing. Thank you.

Dinesh Rao

Thank you, Simran, and good morning, ladies and gentlemen. Thank you very much for sparing your time here today. As we navigate the landscape changes with respect to AI, I think our priority in the last 6 months to 1 year has been how do we accelerate our customers from experimentation to really looking at an AI scalable at industry level.

Now AI as a technology is as good as what it can really understand from orchestration across systems of record, which Satish alluded to, ability to really understand the complex business processes and navigate. And most importantly, how does it even get to understand the deep-seated legacy systems that are there. And frankly, looking at all the complexities that we had with respect to some of the estates that we have been working as well as with the customers, we decided to codify our entire services across 6 strategic pillars. And these 6 strategic pillars are all integrated. In a way, a customer who wants to start the journey of AI adoption has to look at each one of them. Our intent is to take this strategic pillar to walk them through this entire journey of scaling the AI.

Now let me start with the pillar number 1, the AI strategy and engineering. Nandan alluded to it, so as Salil. Organizations are extremely complex. One needs to really look at the top down, looking at how do I really create an AI blueprint, a need of really setting up an AI transformation office. What would that mean? I need to first understand which business units, which business processes that I would be able to unlock the AI value. You cannot really be spraying across the AI in multiple different business units. Now that is a very key thing because that is where the value realization framework of Infosys comes to bear to look at how do I map the process to the value that it actually arrives with.

Now the second key thing is how do I change the ways of working. Because you have so many models, so many platforms that have come by, one needs to really put together a very technologycentric AI platform with the models that it needs to go with, the governance that it has to happen and how do we make sure that we diffuse this particular platform across multiple layers of the organization, so that we have one standard way of really looking at how do I scale the AI.

The third layer, obviously, is to really look at the entire governance model that you need to put together. In a way, this AI has gone very strongly. The last one, obviously, is to really look at a purposeful selection of an AI infrastructure where you also need to continuously keep an eye on what would happen to my AI Ops.

Very recently, we have been working with the CEO as well as the Board of Danske Bank, where we really started working with them to looking at how do I enable this bank towards the journey of being AI first and digital first. So we set up an AI office along with the Board, working along with the CEO to really put together a strategy all the way to engineering, augmented that with innovation labs as well as identification of core processes like KYC, the fraud detection, credit and what not. So in a

way, the journey is just not that I would start doing software development, but it needs to have a topdown view of really setting up the strategy for an organization.

Now the second pillar, my friends, data is everything that AI needs. Today, organizations have multiple pools of data sitting. It is just not structured data. It is, in fact, even unstructured data, multiple modes in terms of videos, in terms of speeches and what not to an extent of about 60% to 80%. And today, the most of the time is spent on data in all our projects. Data is one which accelerates your AI journey or potentially could also decelerate your entire velocity of how you are going to do it.

So our framework here today is to really look at help our customers try and harness the data, transform the data, bring them all under one uniform data fabric. And it just does not sit there. At the end of the day, data also have to drive intelligence. The intelligence can be driven only by connecting the semantics and the ontology on top of it. And that is a very elaborate process one needs to go through, because the processes are different by region, by business units, and it has been all codified in systems of records as well as in systems of experience. And that is a humongous job.

The third layer there is to really look at how do I govern the data, because the data fingerprinting is extremely critical as you really look at who would get the access of data, so that in some sense, there is a framework if you really look at. So in one of the large industrial manufacturers, there was 10 petabyte of data that we had to really bring in all together, harness it as well as create the semantic ontology today, help them actually drive the supply chain optimization by over 20% to 30%.

Now the third pillar, Nandan alluded to this, this is all of reimagining the entire business processes. Most of the large enterprises today have point-to-point solution. One need to really look at in the context of domain and reimagine the entire business processes. And these processes have to be reimagined with respect to how the human intuition works along with the AI and agents. And it is extremely critical that every workflow by persona has to be reimagined and has to be codified the way AI would actually come by.

Now this also has to be contextual to the business and the regions where you really work by. You just cannot take a sourcing and a procurement process and say that it is a domain that it could be applied to every industry. It has to really be contextualized to the industry and the business that you are really looking at. And most importantly, since it is going to change and touch every one of us, it has to also be looked at how do I operationalize the entire workflow, the technology and the change management, all put together to really help realize the end-to-end value.

We have been now with Toyota Motors Europe working through a supply chain transformation process, where we took our industry asset of automobile on top of it, the entire agentic playbook, really looked at by persona of a buyer, a planner or a customer service agent and really doubleclicked to look at what does this transformation really mean. And just to look at one critical process

of drop ship, which is so critical for an automobile -- is to really bring in agent and orchestration there to really take away all the manual work and bring in much better inventory visibility.

Now the fourth pillar, obviously, is modernization and everybody has talked about it. It is the an Achilles' heel of our large organizations. Today, there is so much of tech debt sitting there. The code is obsolete. There is no written documentations. There are no availability of SMEs today. And it is not that the customers did not really try to do the legacy modernization, but the ROI and the time it took never stacked up in the past technologies. So what AI models as well as powered by our Topaz Fabric today enables is to make sure that we transition some of these large legacy, both on the data as well as on process side, into the most modern cloud-based microservices architecture. And today, it does stack up, and you would hear one of the examples very soon.

Now physical AI is something we believe is at the cusp of really accelerating the AI journey. Whatever intelligence that we all thought and built as a part of the digital workflow is finally now moving towards the actual physical objects. And this, friends, helps the acceleration of AI in a lot of the products that we really look at. Some of the key cases that we really look at is -- and in terms of data first, the new product introduction, wherein the entire process would be reimagined as well as infused with AI, the products have to be defined or designed with AI in the front. And with more and more products with the software bomb being larger than actually the physical and the mechanical, we have a huge play in terms of embedding the AI as we look ahead.

The second case is the intelligence today; the real-time intelligence is moving from cloud to the edge. Now this will help accelerate the decision-making at the edge, which means vehicles, the industry operations, running infrastructure, all of this would actually increase the advancement and usage of AI. And lastly, the autonomous systems today, the prevalence in a lot of industries as well as areas is continuously keeping on increasing. Towards that, we see that we accelerated the journey of actually infusing the AI in the physical.

And here, I also want to draw your attention to the two acquisitions that we did, one InSemi, which meant the silicon design as well as validation. The other one in-tech, on the automobile, directly fits exactly into this particular pillar where it would help us bring more context as well as acceleration in terms of enabling the physical AI.

The last, ladies and gentlemen, is not the least and the most important is the trust and the governance.

If I today ask everybody here, who has used the AI? I am sure that all of you would raise your hand, but how many of you really trust the output that came by. I am not sure whether everybody would raise their hand, because there are hallucinations, there are model breaches, and there are also governance issues with respect to the new AI Act and etc, coming in. So in a way, the trust has to

permeate through all the other 5 layers for us to really make sure that we have the output that comes in an enterprise, which is trustworthy. To me, the trustworthy AI in every enterprise would be a huge differentiator. So we want to build the trust to our customers, and we want to monetize the trust for our customers.

Now having looked at all these 6 important strategic pillars, right, I just want to dwell on one of the cases, Nova Chemicals. They are a large petrochemical manufacturer based out of Canada and U.S., and it is very asset intensive. As you know, the industrial operations is extremely complex. If an asset goes down, they would have an impact on the top line and the bottom line. In the current context, most of what they were doing with respect to maintenance was all manual, logs and etcetera. So we were invited to a program in smart maintenance, where we actually brought in the data across their machinery, their OEM manuals, their maintenance manual, the historical data, the log data and etcetera, to help a planner to really, with a simple NLP on a chatbot would be able to guide them on what part of the industrial operations have to really go through a maintenance.

And most importantly, we were also able to actually bring in orchestration with agent AI, where the OT systems and the IT systems come together. So seamlessly, we were able to really move towards actually creating the entire work order process, the planning process, which actually moves over from OT systems to the SAP or the ERP that we have. And we see the impact of bringing huge planning efficiency, asset utilization and etcetera. Of course, here, we partner with Microsoft, and we used all the stack of the entire Azure to really make it come to life.

The last case I want to really dwell is about Hertz. I am sure all of you know it is a very large mobility organization where we are today, as we speak, embarking on really modernizing their entire reservation, their fleet management, their pricing and the whole thing, which today is approximately 3 million lines of code actually sitting on a tandem computer. And I would like to here play the video, let us hear from the customer on what their experience has been and what we have done with respect to this journey.

So the important point to note is, models are there, I think workflows are there, but our context of Hertz in terms of what their processes are, how the code has been written, how the existing architecture is and what is the new modular architecture that we need to really help them migrate to is the context that this iLEAD brings to bear. And that is very critical as we really look at this entire legacy modernization.

So that, ladies and gentlemen, summarizes the six value pools that we are talking about, and thank you so much.

Balakrishna D.R

So my colleagues, Dinesh and Satish, talked about AI-first services, which is new value pool that is being created out of AI. What I am going to be talking about is AI augmented services. What we mean by that is taking our traditional services and infusing AI in that, and we want to be a leader in this space as well, and I will talk about how we are doing that.

So we have taken each of our services that we actually traditionally provide, whether it is application development, testing, modernization, migration, engineering services, business operations. So 20 plus services that we actually provide, and we have created detailed playbooks of how we can use AI in transforming the way we deliver these services.

When we are doing this, we are partnering with the best of the technology that is out there. We are working with leading models from Anthropic, from OpenAI, from Gemini, from Amazon Nova. And in fact, we are also working with open-source models, like DeepSeek, Llama, and we have even created our own coding model in this space, perhaps the only GSI that has actually created a coding model. So we use a combination of these models that in our engagements.

So in the Hertz example that you actually saw, we, used two models. We used a Claude model that actually generates the code. And one of the things about LLMs are they are better in critiquing output than generating output. So we used an OpenAI model to critique that output, and that is how we actually improved the accuracy.

So one is the models. The second is the tools. And in the tools, we are working again with the leading tools. GitHub Copilot was the first, and it had almost 100% of the market share. 2 years back, we set up a GitHub COE that was inaugurated by the CEO of GitHub. And then we are still ranked the number 1 GSI in terms of GitHub adoption. Just a few months back, we got this award as the leading GSI for working on GitHub.

But just not GitHub, we are working with Anthropic, we are working with Gemini, we are working on, on the models from Anthropic CLI. We are working on the new age models, Devin from Cognition, also we are working with Cursor.ai that you would have recently seen. So we are working with the leading models. We are working with the leading tools.

But then, again, as my colleagues also talked about, a lot of these models do not understand the enterprise context. They do not understand the standards that are there in the enterprise. They do not know other libraries that are there, other programs that are there. So we have to do a lot to actually bring that context into the tools that we are actually using. So, we do that by creating MCP registries. We create a knowledge graph of the enterprise context and we combine that. In the recent release that we just did with Anthropic, you hear Dario talking about that. They need Infosys to bring the enterprise context onto the models, and that is what we actually do.

In addition to that, we have actually created agents specifically for each kind of services. So in application development, we have taken the life cycle and said we need agents for requirements, design, architecture, etc. And we have created 100 such agents that we are using in each of our services. In addition to that, we have to create other tools. You saw in the Hertz example, the LLM models cannot digest huge pieces of code at one time. Some of these enterprises that we work with have millions of lines of code. If you give that to the LLM, they start to hallucinate. So, you have to shrink the code, you have to create graphs, call graphs on how these are actually associated, and that is when you get better output from LLM. So we have created all of these assets that are part of our Topaz Fabric, which my colleague, Rafee will actually talk about in more detail in the next section.

In spite of doing all this, we need talent. And sometimes people ask me, if LLMs are generating code, why do you need people? Why do you need developers anymore? And I think they asked the same question to Boris Charney from Anthropic, because Anthropic is continuing to hire developers. So somebody on X asked him, if Claude code can actually generate code, why are you still hiring software developers? And his response was that engineering is changing, but great engineers are a requirement still and in fact, the most important requirement going ahead. And that is something that we also actually believe.

The way that we actually deliver code or the way we support applications may change, but still you need great talent. And so what we are doing is to take each of our developers and training them on AI. So we have 90% of our developers that have been trained on AI. It will never become 100% since we are always hiring new people into the stream. But our intent is to actually have everybody be able to use AI in their daily work.

In addition, we need specialized roles, like forward deployment engineers that will create the platforms that the teams will actually use. And then we have created COEs for each of the partnerships that we have. So it is a combination of all of these that actually helps us deliver our AIinduced services.

It is the blueprint, it is the technology, our own technology plus the leading technology that is out there and the people that we actually create. The way we are going to market is also, as you saw in the Hertz example, is not about PPTs anymore, it is about actual demos, and that is what we see creates the impact, and that is how we actually go to market in all of our large deals.

I will talk about a couple of examples of how we are using it in actual programs that we are executing. What better example than Microsoft, who is in the leading edge of this kind of technology adoption, creating the technologies and also adopting this technology. So in Microsoft, we have a 360-degrees partnership. What we mean by that is that we go to market with Microsoft, we are one of the big customers of Microsoft, Microsoft is our customer. We provided services to Microsoft and we do multiple engagements with them, but I will give you a couple of examples of what we are doing.

Microsoft themselves are going through a big transformation. From enterprise agreements, they are going to what they call MCAs, Master Customer Agreements. What this means is that through the master customer agreement, they want to eliminate all the paperwork that they had to deal with in the enterprise agreement. They also are talking about evergreening the licenses. So enterprise agreements had only 2 years’ timeframe. This is perpetual agreement that you can use for multiple years. Enterprise agreements had a minimum seat count of 250. This actually has no minimum seat count. In addition, in MCAs, you are able to monitor your usage, adapt your usage, you are billed based on your usage, you get a very flexible billing. So multiple advantages for the customers of Microsoft by using this agreement.

And also for Microsoft itself, because you are not having papers and documents anymore, it eliminates and accelerates the way they go to market and also the operations that they have, On this engagement, they had to build the IT system to manage all of this. So Infosys is actually working with them to build that. We used all the technologies that I actually talked about, and we are getting 2x developer velocity and 35% improvement in the time to market.

The other engagement that we are working with Microsoft is on their Intelligent Cloud. So, as you know, this includes Azure and Microsoft Office. They carry a lot of mission-critical systems of Microsoft customers on these clouds. And it is important for Microsoft that for these mission-critical applications that there is no downtime and then it is actually trustworthy. So, Infosys is again providing support for Microsoft on this. And the way that we have used AI, is that AI agents today monitor the logs and predict issues before they actually happen. And they give all of this intelligence, which we call triaging, and then we route it to a specific support engineer with the information, so that they can actually work on it before the issue actually happens. So this is for both reactive and proactive issues. And so you can see that we get 40% improvement in the incident response and 10x improvement in the RCA turnaround.

So there are several such examples. The other example that I have here is Danske. Danske has a Forward '28 Strategy where they are looking at modernizing their entire landscape. They want to bring process efficiency and they want to completely create it as a digital bank. So you must have seen the press release where they have chosen Infosys for this transformation, and we are helping them on the AI strategy and transformation as well. And we are doing everything from AI strategy to implementation. We have set up an innovation lab for them, and we are creating multiple AI solution work streams.

So we are using Agentic AI in the code development, more than 2 million lines of code that we have generated. But then again, these lines of code that we generate has to be validated, and that is what our engineers have been trained to actually do. And 97% of the engineers are using that. In addition, we have created multiple AI solutions. They wanted to use ChatGPT, but they wanted the guardrails.

So we created enterprise ChatGPT, which has over 16,000 users. They created other solutions on risk and HR, which have been quite successful.

Mohammed Rafee Tarafdar

Over the next 10 minutes, I will cover how we are going to power AI-first services and AI augmented services using our IP and platforms. Now earlier during the day, we heard about the complexity with enterprise landscapes. And when I think about enterprise landscapes, I think about city maps. Every city is different. The map for each of these cities are very different.

Now if I bring any AI model,any tool any platform, the only way to accelerate is by creating runways within an enterprise that can help us accelerate AI adoption from pilots to hundreds of projects, and that is where Infosys Topaz comes in. And we do it in 5 different ways.

First, we have created a rapid experimentation and innovation infrastructure where our teams working with our clients evaluate the latest developments that are happening in the AI space. They look at all the noise that is happening and identify tech that is relevant. They then build proof of values that are very relevant for their business and then they demonstrate the art of possible in a veryrapid manner. And today, there are about 39 such innovation labs that we are running with our clients across the globe.

Second, we take a very value-centric approach in how we look at the end-to-end process, because over the last few years, we have realized that use cases cannot deliver significant value. And this is where we are bringing the 25-plus industry blueprints we have in order, to come with already reimagined business and IT workflows that we can use with our clients to accelerate. So today, when our consulting teams, what they do is they sit with our clients, they understand the problems.

Then they use the product discovery and vibing tool from our Topaz fabric to very quickly identify good solutions, build a prototype, and then using exponential engineering, they actually create a production scale application by end of the day. And then using this, they are able to demonstrate how we can reimagine the complete workflow.

The third, Nandan talked about creating an architecture that is very evolvable. Now what we have done is, the way we have designed our IP and platforms is to make sure that we give optionality to our clients. They can pick any model they want. They can pick any agent framework they want. They can run on any AI platform. They can run on any AI cloud, and we can integrate with any AI-native tool that they have partnered with. And today, in most of our production deployments, we have a number of varieties that today we are already supporting.

The fourth runway is to build that enterprise context. Now here, we are doing 2 things. One, based on the work that we are already doing with most of our clients, we have built an enterprise context. Think of it like a map. Whenever I want to navigate in a city, you need a map which tells you how to go faster. So we have built this enterprise context or map that tells me how the systems work, what

the infrastructure looks like, where the apps are running, where the data resides, and how they all connect.

And on top of it, we are building an industry context. The industry context tells us what happens within a retail, within a bank, within a CPG context. And these are the models that we are bringing out of the box through a graph technology, and we are building these enterprise twins. And this is what will enable us to accelerate. Eventually, in most enterprises, to drive projects at scale, they need multispeed IT governance projects, so that they can onboard these AI tools at speed. They need to put these guardrails, and that is why we are building a lot of tooling that enables them to deliver these AI solutions in a very trusted manner. And all of this comes through Infosys Topaz.

Now in the IP and platforms that we have been building at Infosys, we have always kept our customer needs in mind. So if a customer comes and says, look I want an end-to-end vertically integrated AI and agentic platform, then we use AI Next as a platform to accelerate value. Or if the customer says, look, I have already made some investments, I want a composable modular agentic and AI platform which can help accelerate my own AI journey at speed, then we essentially bring the Infosys Topaz Fabric. So with the combination of these, we are able to meet most of the demands of what our enterprise customers have.

Now let me talk about Topaz Fabric itself because over the last few months, we are starting to integrate all the different IP that we have at Infosys, into one common way through which we can deliver our services. Topaz Fabric enables 5 key capabilities. The first is this builds on our customers' existing investments. So this is not about replacing what they have. So this works above their model layer, above their platforms, and above theirenterprise systems. And Fabric can integrate with any model, any framework, anything that they have. So that is the abstraction that we have already built within this.

Second, it provides close to 600 agents, which have been purpose-built for different AI-first services, AI augmented services and also for industry-specific flows. So this is something that we bring out of the box to accelerate the journey for our customers. Third, what we have also done is, we said we will create out-of-box integration. So we have out-of-box integration with most of the coding tools, . We have out-of-box integration with different models. We have out-of-box integration, with business platforms like SAP, Oracle, Salesforce. We have out-of-box integration, with data platforms like Snowflake and Databricks and with enterprise platforms with ServiceNow.

Now what this enables is it enables us to deliver value to our customers quickly. Now in all of these, it is also about bringing the enterprise context and hybrid intelligence. So the way we are doing is we are starting to build a number of different ontologies and models that comes pre-packaged as part of our Topaz Fabric, and that is something that we bring out of the box. And as we deploy it, we

learn from the data, we learn from the processes. And this is how we create a closed feedback loop where the context keeps improving as it gets used over a period of time.

Now lot of our clients also want to use a lot more predictability in the way AI is deployed. This is where we bring a lot of deterministic rules, couple it with AI models, and we also bring our own small language models in order to create a right value proposition from cost as well as from a time-tomarket standpoint. Now all of these is backed by a lot of deep research and patents that we have filed over the years.

Now while we are doing a lot of innovation internally inside within Infosys, we also acknowledge that there is a lot of innovation happening outside. So we are working with AI native partners in 3 different ways. One, if they have a platform that is really good at doing something, then we are leveraging it for the tasks that are relevant to enterprise. For example, Nandan talked about brownfield. So our Cognition partnership is largely to use Devin for a lot of brownfield engineering, because we find that, that is really good at it.

Second, we are building embedded agents that can work within our partner tooling. So today, we have built agents in Fabric that can run within Claude Code, that can run within GitHub Copilot, that can run within ServiceNow. So whichever platform the client has, these agents work within that environment.

And third, we have integrated with their tooling, so that we can cover the end-to-end value chain that is required to accelerate the journey. The next is also we are focused on where the industry is heading on AI, and this is where we are working with universities to do joint research. We do today on agentic technology, on also scaling, and on trust with Cambridge, with Columbia and Cornell. And we are also doing this a lot more with the research centers that we have set within Infosys. This is to make sure that we continue to build on what will come next.

Now let me bring all of this to life with an example where for one of our logistics client, they were finding that the customers were able to process the orders and bookings in a lot more accelerated manner, and this was creating an issue for them. And they said, we want to be able to process these customer care services in a much more accelerated manner. This is where we took AI Next as a platform, because they had already tried with multiple platforms and they did not work. And we said, we will use AI Next first to uncover the existing knowledge, because they had rules that are very specific to each customer. So we pulled out close to about 8,000 different rules that exist there.

The second innovation that we brought here is that we automated the entire workflow. So when we started, the extent of automation was about 0% to 10%. We took them to about 70% automation in their entire workflow. What this meant is the turnaround time reduced from 24 hours to about 30 minutes. The next is, they also were very concerned with the sovereignty of the stack. So what we

did in this case is we used the Mistral and Pixtral models to make sure that it addresses the sovereignty needs that they had. And this today supports orders or bookings from 116 different countries, and it supports 15 different languages. That is the power of what this could do.

And eventually, as we started scaling, cost became an important driver. So we had to bring in a lot of optimizations to reduce the cost significantly for that customer. And this is something that we did over the last 1 year, and today, this is live. Now you can see a lot of these IP platforms as you come to our living labs, and I encourage all of you to please spend some time and experience these technologies that we are talking about. Thank you very much.

Anand Swaminathan

So let me share a few things in the next 10 minutes, particularly around what is driving AI demand in the communications, media and the tech business. And what is the value that we are seeing when we work with our clients on AI. And I will also give you one concrete example where we have delivered AI at scale. AI is no more an experimentation with many of the telecom, media and the tech companies. They are looking at making AI a core operating model on which they want to drive customer experience, engineering and network resilience, as well as operational efficiency.

So the communications, media and the tech business spans across the semiconductor companies, the OEM platforms, to the hyperscalers and the media and entertainment companies. There are 6 defining themes that are driving the demand for AI. First is if you look at the telecom companies, they are facing a huge growth challenge. There are only so many consumers to buy mobiles and each consumer can only buy so many mobiles. Then if you look at the B2B business, which has traditionally been a challenge for the telecom companies, it is not growing at a good rate for them. So with the B2B and B2C growth issues, AI is giving them a breather.

Now companies are reimagining their customer journeys using AI. And on the B2B, they are rethinking how should they go to market. In one particular case, we have done a joint venture with an Australian company called Telstra, where we own the majority stake and we will jointly be responsible for taking the B2B non-connectivity solutions to the Australian market. And we are engaged with a variety of our telco clients doing a lot of B2B and B2C work. Sovereign and sovereign cloud is a big opportunity for the telecom companies.

Now outside of U.S., every country is really looking at telecom companies to provide for the AI infrastructure, to provide for data residency, to provide for cyber resilience and to be able to operate, within the country, the required AI apparatus to keep the businesses going. So that is a big opportunity.

Third one is around the productivity expectations. They have to bring down their unit costs. And the huge challenge for them is the traditional productivity factors are not enough. So they are looking at step change in productivity improvements. And again, here, AI is a huge factor. On the other side, if you look at the tech and the media side, we are all seeing the huge spending surge that is going on with AI in terms of expanding the AI farm. But the issue has been that most of the AI is actually sold within the tech companies in terms of model building, model training and is not really getting diffused to other verticals or other industries.

So, the opportunity for Infosys is actually to bring our domain knowledge across the different industries and really work with the tech companies and make sure that we are able to take the

products and services and actually do the implementations. So this is one reason why many of the tech companies want to work with Infosys because of our native understanding of many of these industries.

And finally, there is a huge race across the tech companies towards gaining AI market share, and that is opening up a lot of opportunities for us in terms of working with them on the engineering spend as well as in enabling them in terms of creating new channels, either on the sales or on the partner side.

To participate in the AI opportunity, two things are very important. One is trust with the clients and second is the scale. So if you look at the telecom, media and the tech business today, it is a very concentrated segment where there are very few big companies and then a long tail of small companies. So we have deliberately developed a long-standing relationship with some of the leaders in this industry. And that is evidenced by the fact that almost 60% plus of our revenues come from the top 15 clients.

Now what it means is these are the clients with whom we have the level of trust and advocacy to work with them on their AI road map. And with each of these clients, we actually are engaged in one or more of these opportunities today. So where we see, again, AI getting deployed and value getting created, three broad areas. First is customer experience. Now Nandan touched upon it, talking about customer reimagination of processes. It starts with that, whether it is a B2B or a B2C. And how do we rethink the process in an agentic world and how do we improve the customer satisfaction and customer retention. So that is a big opportunity.

But along with that, it is not as simple as that because you have a lot of legacy tech in many of these companies. Many of these tech gear there is end of life or end of support. So there is a big question about, are we going to buy new platforms? Or are we going to just build the platforms? And the obvious answer now seems to be going towards building platforms in an agentic AI framework. And that gives companies like us a huge opportunity. And we are seeing an additional improvement or an incremental of about 30% on the Net Promoter Score in many cases.

So when it comes to network resilience or engineering reliability, so we have a solution framework called Infosys Smart Network Assurance, which is today part of the Topaz suite of products. Using this, working across many telcos globally, we have been able to improve the network resilience and bring down outages significantly. Now as far as the operations are concerned, it is about really applying Agentic AI in a way where we are working on a human plus agent model to drive the unit economics and be more efficient for our customers.

So let me talk about one particular example where we have really scaled AI across a large enterprise. So Liberty Global is a leading broadband and mobile , fixed/mobile broadband communications

company based in Europe. They operate across many European countries and with about 10 million subscribers subscribing to their entertainment and connectivity platform.

Infosys today owns and operates the entire stack of hardware, software and services for Liberty Global. And this engagement is built on a per subscriber basis. The fee is based on per subscriber. So as the subscriber count changes, Infosys fee also changes. So what we have been able to do in a situation like this is using the Agentic AI framework, unlock value in the software stack, which is something that traditionally has not been done by many of the service companies.

So when we took over this undertaking of completely owning a 10 million subscriber platform, which is a highly critical platform, the big question was how are we going to deal with hardware and software, but that is what is giving us the biggest opportunity to unlock savings today. Now also applying a very unified Agentic AI thinking for the entire platform, we have been able to improve customer experience.

In one case, for example, where if you imagine a customer at home who has an option to use an Apple remote or some other device to interact with the TV, we are giving better features or richer features using the entertainment platform at Liberty through our Agentic AI framework, where through a natural language, the subscriber can speak to the television and get the shows he wants instead of doing a search with a clunky device. Now this improves engagement of the subscriber to Liberty as a brand as against going via some other brand.

Similarly, we have improved network resilience in this company, and you can see the metrics out there and as well as many of the other important critical elements like improving their own employee experience by bringing Agentic AI.

Thank you very much

Jasmeet Singh

Well, hello, everyone. I am delighted to share with you today what we see is happening in Manufacturing on AI and how are we capitalizing on this opportunity. Leading manufacturers are leveraging AI, embedding it into their product, embedding it into their workflows. They are driving agentic execution to unlock value. In fact, in our recently published Manufacturing tech index, 75% of the manufacturers embed AI into their enterprise strategy.

Now we see three big areas of opportunity for us. Number one, everything is getting connected. And what that means is, it is driving investments into smarter products, smarter operations and as-aservice models. Now it has been talked about before, but the industry has got a rigid tech stack. That is leading to and driving AI-led modernization. That is opportunity number two. Now AI lives on data. And manufacturers have a treasure trove of data. They have it in the smarter products across operations. And so that is opportunity number three.

Now as we look at the manufacturing value chain from design to service, we are obviously seeing a huge amount of applicability of AI. But let me talk about the make part of AI. Now it makes sense that I talk about this because this has the potential to drive operational performance improvements and agility.

As an example, for a leading industrial manufacturer, we are leveraging computer vision and AI to assess product quality in their manufacturing operations. This is driving a 10% increase in throughput. Now it is not only across the value chain. We see now that we are able to solve much more complex problems leveraging AI, and we are unlocking a lot more value. And this is cutting across also the horizontal areas like finance, HR and legal.

Now let me make it real with a couple of examples. This is a mission-critical process at Rolls-Royce. You know Rolls-Royce manufactures and sells aircraft engines on a power-by-the-hour basis. Engines require maintenance. They need to come into the shop for overhaul, for maintenance, and they come to the Rolls-Royce MRO facilities for that. Every time an engine comes, it means that aircraft or the airlines could be facing an aircraft on-ground situation and Rolls-Royce could be losing revenues. So you can understand that the imperative is to try and get the engine back on wing and not idle as quickly as possible. The process in question is the reviewing and authoring of the repair procedures of every engine that comes in. Now each engine is unique because it has its own unique operating parameters. The multi-agentic solution that we have developed for Rolls-Royce is delivering significant benefits, a 40% reduction in engineering effort. First-time right rates are increasing from under 40% to 75%. And because we are able to accelerate the entire process, it is providing a multimillion-pound revenue uplift for Rolls-Royce.

In the words of Declan, as you can see, Infosys has successfully operationalized the Agentic AI solution. It is an approved EASA, which is the regulator. Remember, this process is manual. It is highly regulated and safety first. It is an EASA approved European Union Aviation Safety Agency approved solution. And that means we can now scale it across Rolls-Royce.

The second example is on GE Vernova. It has been referenced before. Now GE Vernova is a $40 bn in revenues company. It is a leader in power, wind and electrification. They are at the forefront of the energy transition, and their aim is to electrify the world while simultaneously decarbonizing it. They selected us as the AI strategy partner for them. This is from strategy all the way to execution.

The reason why they selected us was because of what they saw we were doing internally to become an AI-first company. We are able to bring together not only the AI expertise, but the knowledge that we have in product engineering, in business process as well as in IT, seamlessly to deliver this significant transformation at scale. We have already delivered over 25-plus agents, multi-agent use cases for GE Vernova in production. We are delighted to be a partner to GE Vernova on this exciting journey.

I want to leave you with three key takeaways from manufacturing. Number one, the AI opportunity for manufacturing is massive, and we are already delivering value at scale. Number two, we have the depth and breadth in not only in AI expertise, but the knowledge in product engineering, in business process as well as IT to again drive this transformation. And lastly, as you heard in the video, we have the capabilities to drive this from AI strategy to scaled execution. Thank you.

Dennis Gada

Hello, everyone. First of all, I have to say, I really feel at home talking about the impact of AI in Financial services to an audience which is largely full of people who come from the financial services industry and understand some of the nuances and challenges. What we see in the industry today is that financial services is really at the forefront in terms of adopting and scaling with AI. And this is different from some of the previous tech shifts, for example, cloud or even digitization, where there was a little bit of lag effect or catch-up for the financial services industry. But this is different.

Financial services firms, whether it is banks, asset and wealth managers, custodians, card providers are really leaning in and leading with AI. I think the reason for that is that this is one technology and business shift that firms see, which can simultaneously bend the cost curve as well as the growth curve and also help in managing risk and compliance, which is, of course, very important in this industry.

So, this has a lot of conviction with the CXOs. As you can see, many of the quotes from the CEOs around using AI for augmented intelligence, using AI not just for efficiency, but really driving largescale transformation within the bank and looking beyond productivity to growth. So the good news for us with all of this is what we see is a significant increase in spend towards AI initiatives. And we are well positioned to benefit from that, both in terms of the AI-first services as well as the AI augmented services that we talked about.

But it also does come with some of the constraints and challenges. It is not a technology or a use case challenge, but more around regulations, data privacy and most importantly, change management and adoption, which is where we see a huge opportunity to continue to expand. In fact, one of the CIOs of a large banking organization we spoke to, talked a similar concept to the deployment gap that Nandan mentioned. Even if AI technology were to stop evolving today, there is still so much to be done for financial services firms to benefit from and leverage what is already there.

We also see a diffusion of AI use cases across all the sub-verticals. We are working, for example, significantly on fraud prevention in the payment space, in the consumer banking space. And already, there was a lot of work done on machine learning models in the past, but AI provides a lot more capabilities to take it to the next level. Similarly, there is a lot of work on customer experience through contact centers, through UI/UX, but also beyond that. For many relationship managers in commercial banking or advisers in asset and wealth management or financial analysts like many of you in the room today, AI provides much more of data and insights and helps with the productivity, so that these relationship managers and advisers can spend more time with their end clients.

We also think that agentic commerce and payments will take off significantly. It is still at the starting point, and that will result in a lot of unlock of new business opportunities for financial services clients.

All of this is, of course, on the foundation of AI-based software engineering, AI-based process orchestration and data transformation that we spoke about earlier today.

So, I will talk about one of our flagship client examples in financial services, Citizens Bank. It is a top 15 bank in the US and has grown significantly over the last several years, both organically and through acquisitions. And they have just embarked upon a program called ‘Reimagine the Bank’, where the main objective is to use the power of AI to significantly grow and expand the services that the bank provides as well as drive efficiency. Infosys has been selected as the strategic partner to help the bank in this ‘Reimagine the Bank’ initiative. In fact, just a couple of weeks back, we opened an AI innovation hub dedicated to support Citizens Bank in this initiative right here in Bangalore.

And this has been an ongoing journey. We have helped Citizens Bank move 100% to the cloud, one of the few financial services institutions in the world that has achieved that. We have already built some industry-leading platforms on the cloud. And then with this foundation now, we are helping them accelerate the AI journey. Using our Topaz Fabric suite of agents, we are helping them build their own Agentic AI and GenAI platform, which will help across the bank to deploy several use cases.

Some of them are already in production. For example, we see a 44% reduction in calls to the contact center generated from the mobile app. And more broadly, the bank has talked about a $450 mn cost run rate reduction target as part of this ‘Reimagine the Bank’ program. This is not just about cost efficiency, but this shows the power of AI to drive structural transformation in a leading bank like Citizens. So that is an example of a first-of-its-kind innovation hub right here Bangalore for Citizens Bank dedicated to support their ‘Reimagine the Bank’ program.

Now beyond Citizens, if you look at the financial services industry, as you know, it is the largest segment for us at Infosys. And we work with organizations across the spectrum, from the large global banks to the regional banks, to card providers, asset and wealth management firms and so on. And we are seeing a huge amount of increase in work that we do with them on AI. In fact, 15 of the top 25 financial services clients have selected us as their strategic partner. We work with all of them, but for 15 of them, we are specifically been selected as a strategic partner for AI services.

If I take a couple of other quick examples, for our top 3 card providers, we have been working with them on the modernization journey for their core cards platform. This is 40 million lines of code written over the last several decades, and we are using AI to do this modernization. With that, we are seeing a 50% reduction in the time taken to do the modernization and significant efficiency benefits. The beauty of this is, with the success of this program, this particular client now wants us to do 2, 3 more of these modernizations, which were almost impossible to do in the past. So it just shows how much velocity this creates based on success of delivery on some of these programs.

Similarly, for one of the large global wealth management firms, we are helping them build the Agentic AI platform, support a lot of initiatives for the financial advisers to get better data insights, higher productivity, so they can focus on their end clients.

In summary, financial services industry, as all of you know very well, is very complex. There is a lot of legacy, there is a lot of regulatory oversight, but has also always faced a bit of a growth and a cost challenge. And I think, AI is a catalyst that can really help accelerate some of those mitigations and drive the organizations forward. At Infosys, we have deep expertise in the industry vertical. It is our largest segment. We have strong capabilities that we talked about earlier today.

And also, most importantly, we have the depth of the relationships. Many of these organizations, we have been working for more than a decade, and that gives us a lot of institutional knowledge and context, which we can use to help them on the AI journey. We really see this as a huge opportunity to bring the hybrid intelligence, human plus AI, and help these organizations become truly AIpowered and pivot to a completely new operating model for the future. There is a lot of work to be done. We are excited. We are just getting started, and we think we will be super successful. Thank you.

Ashiss Kumar Dash

Hello. Hello and good afternoon. I hope you had a good session in the Living Labs. And you could see some of the things in action.

Now over the next 10 minutes, I am going to talk about an interesting segment called energy, utilities, resources and services, and I call this interesting because AI has created a circular economy in energy, utilities and resources sector. While these sectors are heavy users and consumers of AI, they are also critical enablers of AI. If you look at utilities today, particularly electric utilities, they power and decide where the next data center should be and how fast the AI data centers can grow. In fact, there are views that electricity is the only limiting factor in growth of AI. So they have a massive, role to play. A data point here, the projection for data centers to consume about 10% to 12% global electricity by 2030 is almost four times the current level. So that is the amount of growth that we will see in the electricity sector. Oil and gas have always underwritten the global energy and stability of supply of energy. Now with data centers, natural gas and CNG are becoming the transient fuel so that we bring more reliability and load balancing to the grid, we can dispatch the load when the wind is not blowing and the sun is not shining. Resources, interestingly, are the providers for raw materials that AI runs on. So the new metals; copper, lithium, nickel, cobalt, rare earth materials and aluminum are very core to scaling AI anywhere in the world.

Now with that kind of an interdependency, we are seeing circularity in action here. Energy decides the physical scalability of AI; utilities decide the reliability and sustainability of AI; Resources decide the material availability of AI because of the materials they supply. And services continue to be the big consumers of AI when it comes to inferences because of primarily the B2C nature of their business.

What we are seeing in the sectors across these different sectors in the segment is that demand has gone up, digital intensity is at an all-time high. There is not only growth, there is also margin expansion for these players because of what AI is creating for them. AI is also becoming the operating system for many of the industrial implementations, whether it is subsurface computing, whether it is digital mining or remote mining, whether it is grid reliability and prediction of the load on the grid, AI is sitting right at the heart of it.

My colleagues, Dinesh and Satish spoke about this earlier. This is an asset heavy and ERP heavy industry. So ERP is all over the place. There are a lot of business rules, data, compliance, regulation that has been built in the ERP systems over decades. That, in a way, has created this opportunity where we can put AI to unlock value from the data and also create an orchestration layer for a better human experience. That creates a unique opportunity for SIs like ourselves to go in and look at the ERP landscape and see how we can get the best value out of it. Obviously, we are helping clients

move their opex savings because of AI to do a lot more discretionary projects, a lot more transformational projects that AI has unlocked for them.

What differentiates us is a triangulation of our deep understanding of the client's context, our extremely rich domain consulting skills and engineering AI at an enterprise scale. When we triangulate these three, it has put Infosys in a pole position when it comes to AI. And the proof of the pudding is we are the AI partners for 15 of our top 25 clients in this segment. We do work across the AI framework.

We have created digital twins for a very large oil and gas major to take asset telemetry and make the assets more intelligent, more automated, reduce their downtime. We have worked in AI-grade data engineering for a very large electricity provider to predict the load on the grid and ensure they invest on the grid where there is congestion to provide electricity to the data centers. These are mathematical problems that could not have been solved at a granular level that we are doing today.

We are working with helping 20 plus of our clients reimagine their business processes-F&A, customer service that Salil talked about. This is unlocking new opportunities for us to bring AI at an enterprise level and then commit to the outcomes that the client desires for-growth in revenue, reduction of costs which is expansion of margins and customer satisfaction and innovation?

Another example is the agentic AI platform, where we are building enterprise-level agentic AI platforms to drive significant change in the way client imagines their workflows and then make the agents, more of a co-worker with our clients' employees. We have done 15 plus such implementations in this segment.

Of course, also the full stack modernization of clients' legacy systems, which is a huge opportunity. You saw the example of Hertz. We are doing 15 plus different ways and different flavors of bringing in AI to modernize assets that have been sitting there for the last 15, 20, 30 years, where clients can immediately unlock value. A great example is a very large airline, where we deployed agentic AI to modernize their systems to give better customer experience and predict the delivery of baggage to the customers on time.

Let me bring this to life with two examples. This is a mega deal that we signed with BP, a super major. The challenge was to enhance enterprise-wide adoption of AI. Here, we are talking about massive scale that cuts across all of the value chain elements, starting from production and optimization to dynamic pricing in their retail & convenience stores, contract automation for the thousands of contracts that they sign, IT operations and corporate functions. We picked 50 plus AI and agentic AI initiatives and then brought in our partnerships. The technology stack that we used here was on Azure Foundry, OpenAI stack. For the developer productivity, we used GitHub Copilots at a very, very large enterprise scale. The outcomes were measurable - 18%-year one improvement

in IT operations efficiency, 50% faster contract validation, which is something that we are very proud of and 95% payment accuracy. The proof of the pudding is in a statement that the CEO made in their Investor Day, which said Infosys and Palantir are their top two partners who are making AI real for the entire company, and he calls it super cool. And the other quote is from the ex-CIO.

Let me give you another example. And this example is about scale. This is an example about complexity. This is an example about also not having fragmented but unified implementation of AI. This is for the largest oil and gas upstream operator in Australia. Their challenge was to bring in and build an enterprise AI platform that cuts across different parts of their organization, starting from their production and operations, contracting, procurement, finance, HR and IT Ops. We identified 16 plus high-value AI use cases for implementation, and we used Amazon Bedrock as the agentic AI for upstream functions and then Azure OpenAI foundry for corporate functions.

The way we approached this is on four value vectors. The bottom of this, the foundation layer is the enterprise-grade platform that we build for them. On top of it, we built agents for intelligence and insights to their operations, agents for employee experience improvement, agents for asset operations. The results are visible - 20% to 35% efficiency gains in upstream value chain and 15% to 20% productivity gains in just improving the employee experience on a day-to-day basis because when they deployed this and they picked the corporate functions for agentification, this was one of the goals that we picked with them. We are bringing an outcome focused Responsible AI framework that is working at scale, at speed. I am very very excited for doing this for so many clients across the sector

Thank you

Ambeshwar Nath

Good afternoon. Let me start off by walking you through the market outlook and the role that AI has for our clients.

If you look at the quotes from market leaders in our industry, there is a common trend that is emerging - AI is no longer just about pilots. Today AI is embedded deep into our clients' operations that is helping them improve their revenue, efficiency and driving better customer value.

If you look at the sub-verticals that we operate in, within consumer goods, we are seeing a significant impact of AI in multiple business use cases. We are seeing precision revenue growth management being one of the key levers, where millions of data points regarding demand, price, promotion, channel is ingested near real-time to determine whether value is being created or lost. This is creating a potential of an improvement of 3% to 5% in the overall gross profit. There is a significant focus of applying AI for hyper-personalized marketing, driving deep individualized consumer campaigns as well as driving AI planogramming compliance.

Within the retail sector, we are seeing evolution of large language models for loyalty programs. As of 2026, 70% of all loyalty programs we will engage in will have an AI component in them. We have seen the evolution of camera vision in stores to help store operations and we are seeing a strong evolution of agentic commerce.

Within logistics, we are seeing AI evolve in terms of doing demand sensing, route optimization as well as leveraging it for waste reduction, that is resulting in benefits of 15% to 30% in terms of overall cost reduction.

So all in all, we are seeing an extensive use of AI for business outcomes and business use cases for business relevant problems for our clients. As Infosys, we are uniquely positioned because we bring in deep domain expertise, a strong understanding of data, a strong understanding of AI technology capabilities and governance to bring it all together to deliver meaningful outcomes.

Let me talk about two specific examples of how we are bringing some of these business relevant capabilities to bear.

The first one, and I would believe that most of you have seen this in the Living Labs a short time back. Ralph Lauren is one of the leading high-end fashion and apparel companies across the world. They are also, as of today, one of the fastest growing companies. But one of the uniqueness about why they are growing this fast is because they are true innovators. They were one of the first companies to evolve into going online for digital sales when e-commerce was just evolving. Today they are looking at how they can bring fashion and technology at an intersection to drive more meaningful conversations and path to purchase with their consumers. Clearly, one of the key

elements they are looking at is how can they replicate the whole concept of stylists which happen in stores and how can they bring that same culture online. In store, there are always challenges. You do a lot of manual merchandising, there is a lot of interaction with individual consumers. But the beauty is, if you do it online, even if there are millions of consumers engaging, each consumer is a separate segment in itself. You can do hyper-personalization at scale as if you have got a merchandiser working with you to style your exact outfit the way you want it.

The third element, which is extremely important is to reduce the path to purchase, how do you ensure that you are able to connect to real-time inventory so that the actual order can get placed. That is also a significantly complex puzzle as part of these engagements.

As part of what we did, and this was an engagement we did jointly with Ralph Lauren and Microsoft, first thing we did was we ingested 50 years of Ralph Lauren archives and lookbooks. So all that information was fed in, and then we created a whole natural language processing capability which allowed consumers to engage in a manner which is similar to the way you would engage with a human being. So you are engaging with an AI capability, but it felt very similar. So if you wanted to choose an outfit of the nature you want or you wanted for a specific occasion, it is happening as if you are talking in real life. It drove significant hyper-personalization. The path to purchase on an online setup was, if you can imagine, you search for an item, then you browse the item, then you click it in the shopping cart. It is a long-established process, but this was a seamless process because

you were connecting to inventory, you could cut the path to purchase to something which is very immediate.

This has resulted into significant benefits, and this is just the start of the journey because this is evolving. But today, this has led to a 50% increase in interactions of styling queries that are coming in. The overall results of Ralph Lauren showed a 12.2% increase in revenue. A lot of that, a significant amount was contributed through their online and digital capabilities. So this is one of our best examples in the industry where we are driving hyper-personalization at scale, leveraging the power of AI.

Moving on, I would like to now talk about a second example. We talked about a global high-end fashion apparel retailer in my first example. I want to move gears and talk about a regional player because that is very important as a lot of our regional players are also leveraging the power of AI.

Posti is Finland's logistics leader. It is a legacy organization, 400 years old. It has a legacy of over 40 years. Its primary business many years back was just the postal business, but they have evolved over a period of time to get into parcels, into supply chain, into warehousing. So now they are an end-to-end logistics player.

As the postal volumes are declining which is true for the entire industry, they have been focused on how can they pivot to the new, how can they move from being a player that has legacy debt to a player that can drive new-age capabilities, who can focus on the run-to-growth element and pivot themselves into building differentiated capabilities.

Infosys is an end-to-end partner for them. We are the single partner that is doing entire IT services for them. As part of that entire exercise, we have evolved an AI-first operating model, which is really across run and transform. So how do we bring in AI in every part of the Infosys set of engagements that we work on. Today, we are running with over 20 plus initiatives on AI across their postal business, across their parcel business and their freight business. So we are cutting the entire value chain and applying AI at all points of the ecosystem. The whole focus is around run-to-growth. So how do we reduce the run cost and then push them into growth initiatives that can allow them to be a leader within the Nordics market.

Lastly, they have not gone with one solution. We are the single partner who are driving AI orchestration for them. They are working with a variety of AI toolsets, and we are ensuring that we are orchestrating it to deliver outcomes. Now what this has resulted in. It has resulted in a 50% software generation by AI tools, it has led to a 35% improvement in productivity and the mean time to recover considering that they had a large amount of legacy systems, has been improved by 70%. So this has created a massive impact.

I would like to just summarize by highlighting two points. One is the fact that we are seeing AI evolve within our client landscape. It is critical, it is a true game changer for our industry. Second, together with our clients, we are working on a number of business-enabling capabilities all centered around having AI at the center and this is helping us create differentiated value and long-term association with our clients

Thank you.

Anand Swaminathan

At this time, I think you should have gotten a fair idea about, one, the opportunity in front of us, and it is more the execution risk that can hold any IT service company back. It is not the opportunity itself. Then if you looked at the AI value framework that Salil unveiled, one of the main components on that is the partnership ecosystem. It makes it even more important now that we have a very strong set of partners to be able to go to market.

As the AI evolution was taking place, one of the things we did was to really rethink our partner strategy and construct a partner ecosystem that really got us thinking about the future and will help us to actually cross the chasm because the AI stack has to be reflected in how we are going to get the partnerships done

Today morning, we announced a big partnership with Anthropic is an example of how we are moving this forward. So we need model companies, we need infrastructure companies that actually are investing in terms of AI transformation. Similarly, a set of companies around the chips to application layer to infrastructure to cybersecurity, to model are all part of the AI stack.

Normally, people are equating AI to one particular company or one particular category of company. It is not. It is a very complex set of components you need to make AI come to life. Enterprises want the stack to be governed which is very complex. It is not easy to manage the contracts or manage the performance obligations of these companies or to get the business outcomes they want unless they have somebody who is going to take ultimate accountability for it. So where we come in is we are at the center of this.

Yes, AI is reshaping the IT services value chain. But we are in the middle of it. We are orchestrating outcomes for our clients and managing risks for them, not just emanating from us, but also from the ecosystem of partners we have. So all the partnerships that we have been announcing and we have announced are a very carefully curated set of partners who will actually make the needle move for our clients.

These are partners that will help us with specific industry workloads rather than generally come with one specific set of capabilities. That is what differentiates Infosys as against other companies in terms of how we look at these partnerships.

So if you see the AI value framework, across the framework, already, we have started executing some really solid programs in AI that is giving us referenceable templates to actually take it to more clients.

So in the case of the first component, which is the AI strategy and engineering, with a leading investment management firm, we help them to actually work with an NVIDIA stack, where their

onboarding of investors accelerated by about 30%. This is an investment firm that has more than 5,000 plus rules, regulations. I do not need to explain that to you this particular group of people as you might understand how it works in your world. We were able to bring in NVIDIA as a center point on the stack and be able to drive that change.

In another case with a leading telco, we were able to create a digital sales assistant where 95% of their fiber sales was done by the digital set of agents instead of human-powered selling. Now that is the power of the partnerships that we have because in order to bring this to life, you need preestablished relationships, training between companies and contracts so that the friction to market is less. We also have that way a mutual understanding of how will we manage together the total risk that needs to be spread between us and the partners.

Similarly, on the data for AI, a lot of examples have already been taken by my colleagues. They all talked about many of the examples, including the Polo Ralph Lauren example, which is an amazing one. But in addition to that, we have actually delivered a variety of work in terms of actually helping the clients to get the data ready for the AI world. That is about moving it into a place where it is easily retrievable and is curated and it actually can deliver insights.

On the process AI, one of the areas where we see most of impact with the AI and the opportunity is really bringing AI into our BPM plus IT stack. When Rafee spoke, he announced one of the AI products from Infosys is AI Next. So if somebody wants a full platform, they can actually get AI Next from us. AI Next, in this particular case, with one of the leading restaurant chain has actually enabled them to faster processing of their vendor invoices. Similarly, there was an example you must have heard about one of our clients in which AI Next played a very crucial role in accelerating the supply chain.

On the legacy modernization, one other area of this value framework, which is another area where we see a huge amount of opportunity. Bali spoke about a few opportunities there. Our partners together with us, have created a set of accelerators that are enabling our clients to move from legacy architecture to the AI architecture faster. One example is a mainframe modernization program that we have done for Hertz. You saw the Hertz customer come and speak. Similarly, we have many other examples of where we have really taken this to our clients and getting them the benefits.

On physical AI, Dinesh spoke about some of the examples on physical AI, which is around how do we really bring the AI in terms of the robotics and other physical products and make sure that they are actually getting ready for the AI world. Those examples are also building.

What I would like to sort of sum up is to say that the opportunity is really in front of us. There is no question about that. We have the conviction on that. But at the same time, we also acknowledge that this requires complex execution across multiple components. One of the key components is the

partnership ecosystem. As an organization, we have invested heavily and really brought in a set of partners that will help us to transcend this to a position where we can be a leading AI player.

I would like to conclude this with an announcement we made with Anthropic today. And one of the Anthropic execs says the following about us. So can we please roll the video?

So in summary, we think trust, scale and partnerships are going to be the three pillars in which this transformation needs to happen. And we have the trust with the clients and the partners. We have the scale, and we have also a fantastic partner ecosystem that will help us to actually carry our clients on this transformation journey

Thank you.

Shaji Mathew

Good afternoon, everyone. Through the day today, we heard how AI is transforming the industry, how it is reshaping the work all of us do. That necessitates a complete transformation in our workforce as well. I believe in the saying that it is not the organizations, which has got the best technology that will win, but those are the organizations which prepare their workforce to embrace that technology that is going to win.

Our AI transformation strategy is built on three pillars. We have seen today in the map that there are AI-first services, there are AI augmented services. So we need to augment everybody in the organization with AI. At the same time, we also have to create the deep engineering and domain skills, which are required to execute the AI-first services. And this duality is what we call the ambidextrous organization.

So the first pillar is, therefore, a new talent operating model. Once we have this dual architecture, it is important to have a career architecture, which can hold this talent. Therefore, the need to create a new career model as well. And of course, the most important part, in my mind, is how do we develop the talent to have an AI-first mindset. So what we will do now is to double-click on each one of this very quickly.

In an ambidextrous organization, we need to enable all the people in the organization. We also need to create the deep engineering and the domain expertise. So here, what we will talk about is how we are going to create this deep expertise. That is through channels. One is by external hiring. Recently, you would have seen an advertisement where Infosys is now recruiting fresh engineers up to INR21 lakh salary per annum.

And that is really towards developing this deep engineering talent. We go to some of the best engineering colleges in the country, the IITs and NITs. We have a differentiated assessment to get these people in. We are also doubling down on our full stack engineers. We are doubling down on getting domain experts from the market.

But all of us know that this technology is changing at such a rapid pace, and we do not have enough AI experts in the market. Therefore, the success of the organization will lie on who are able to develop this talent internally. So we have created the bridge programs, and these are real hands-on training programs, working on sandbox environments. And once someone has gone to this bridge program, we would do the assessment.

The assessment center of excellence that we have does the assessment on a 5-point scale all the way up to AI-led simulations. And we also know that, in a new AI world, the skill is the new currency, not necessarily the jobs and the roles. So the CQ, which is our Capability Quotient, which really assess the skill of the employee has got four dimensions. It assesses people on technology, it assess

people on domain, looks at people on the foundational skills as well as on societal skills. It is a comprehensive framework that we have to assess people on their skill.

And the business incubator series is a new way of identifying the best of the best ideas, AI-focused ideas from the company. Last time when we ran this, we had 1,000 ideas and the selected ideas will get funding from the organization. They will also get mentorship. And this is a way to develop entrepreneurial skill within the organization as well as to develop deep AI engineering talents within the organization, which will culminate in certain platforms and systems.

Now coming to the next part of the pillar, the strategy, the second pillar, which is the career track. Once we have an ambidextrous organization, it is important to have a career model, which can encapsulate this new organization. Traditionally, we have a unidimensional career architecture. We will have people joining us as software engineers at the bottom of the spectrum. They will go through the career trajectory and become all the way till the Executive Vice President.

In the new career model that we are working on now, people actually branch out in this Y architecture. The one on the left side are all the people in the company who are enabled on the AI. But on the right side are the people who once take the bridge program and when they get assessed and then they get moved to the specialist frame. And these are the deep engineering or the domain experts that we have, we are creating within the organization. Also on the far right, what you see is the expertled organization. This is a flat organization structure that we have. They bring in really deep engineering expertise to the organization. They act as the catalyst of the accelerators around which the larger specialist ecosystem work around them. And it has got roles like distinguished technology, engineers and so on and so forth.

This career architecture also looks at another angle that is in a human plus AI paradigm, humans will do some part of the work, AI agents will do some part of the work. Therefore, there is a need for us to look at our job roles to redesign, so that it works in the new human plus AI paradigm. Also, the career architecture enablement looks at introducing some of these new roles, which are not existing earlier, like the ones that we heard earlier, AI strategists, responsible AI engineers and so on and so forth.

Now coming to the third pillar, which, like I said, is probably the most critical and the most difficult one to do. We look at our talent from multiple spectrum. At the bottom that we see, these are the consumers of AI. Everybody in the organization are consumers of AI, whether they are from the software engineering discipline or they are from HR, from legal, everyone are consumers of AI. And therefore, we are enabling everybody in the organization on AI. Today, about 90% of the organization is enabled on AI.

Then we have a set of people who work on AI models, who are developing the AI agents and so on and so forth, these are the AI builders. Obviously, they are trained and developed in a different way. They have more deeper expertise. Then at the top of the spectrum are the AI masters. These are the people who work with the large language models, who create our own small language models and so on and so forth, who set the vision, who sets the direction. And they obviously have quite a much-differentiated enablement program.

Now the forward deployed engineers are the ones who are embedded in the client's organization. They are at the intersection of consulting, technology as well as the client context. Now there is a much more deeper program to enable them as well as to assess them. The 5-point assessment mechanism that I spoke about, which culminates with AI-led simulation and case study-based assessment, that complete spectrum is used to assess the forward deployed engineers.

So this is for the larger cross-section of the company, in fact, pretty much for everybody. But the picture is complete when we look at the entry-level folks as well as the senior most leaders in the company. Now you must be familiar with the Global Education Center that we have set up in Mysore, where all the fresh engineers from the campuses come and get their training done. The complete foundation program has been revamped now to enable AI, to make AI-based training program, so that every engineer who comes to the doors of Infosys from day one, they all get enabled on AI.

On the other side, the leaders, it is critical that leaders lead from the front with this entire AI vision that we are talking about. As we speak, this week, we have a program that is going on in our Mysore campus in collaboration with Harvard Business University, where most of our executive vice presidents are going through an AI immersion program.

We also had a similar program last month for all our senior vice presidents in Mysore campus again, and that was in collaboration with MIT. So that kind of completes the entire breadth and length of the talent that we have in the organization. And like I said, we have a differentiated program for everyone in the organization, and that is how we are preparing for the future.

So in summary, from an AI transformation perspective, we are doing three things. First, we are building deep engineering and domain expertise. Second, we are redesigning our career architecture to future-proof the company for the years to come. And third, we are developing a future-ready workforce, leveraging the best-in-class training infrastructure that we have, both from a physical perspective as well as from a digital perspective.

So I will summarize by saying that this AI transformation is bringing a lot of challenges in front of all of us. But I think we are very confident with the transformation that we are doing within the organization, we are ready to embrace this challenge as well as to leverage all the opportunities that is lying ahead of us. Thank you.

Sumit Virmani

Good afternoon, everyone. I know it is been an intense day of learning. And first of all, thank you for really taking the time and spending this entire day with us. Now I am quite pleased to actually talk to you about the role of the brand in the larger AI journey that we are on. As most of you know, mindshare leads market share. Now that is something we have heard as an adage.

For many, brand is a nebulous concept. But for this audience in the room, the financial analysts, I am sure you realize that a well-managed brand can indeed drive much stronger impact on the business, be it on the revenue dimension, the market share dimension or the shareholder value dimension.

And what you are seeing on your slide out there is how brands not just across categories, wellmanaged brands drive a revenue and a market share uplift, but even if you look at the data to the right, well-managed brands across decades outperform not just the industry, but the market as well.

So with that as a philosophy with which how we nurture our brand, I want to actually give you an example. Just like any other strong brand, we follow a multi-channel, multi-offering, multi-segment approach to marketing. But we attempt to do it with a little bit of disruption and a little bit of a twist.

And what you are seeing out there on the screen is an example of how we think about our strategic partnerships. Some of the brands that you are seeing on the screen are possibly one or some of the largest brands in the world, but there is a consistency in all of them. They actually play at the intersection of passion and executive participation. And for decades, brands have leveraged these kinds of partnerships to showcase their reach to the world. But when we think about these platforms, we think of them not just as a platform to showcase brand visibility to billions around the world, but to also ensure that these platforms are becoming better, more disruptive to their end consumers by leveraging Infosys cutting-edge technology.

That is the magic in the partnership because it becomes a showcase to a billion people in the world what Infosys AI can do.

Well that in itself sounds wonderful. I guess the natural question would be, how has it tangibly impacted the brand? So let us take a look at that.

What you are seeing on your right is a ranking by Brand Finance, a leading brand consultancy firm based out of London. They have seen Infosys as the fastest-growing brand in its category around the world six years -- for the last six years. To your left is another highlight of Kantar BrandZ Top 100 brands results and Infosys for several years in a row is now being rated as a Top 100 brand, not just in the IT industry, but across categories.

How is that larger brand awareness translating into enterprise AI resonance? And what you are seeing at the bottom of the screen are some of the initiatives we have undertaken to really drive strong association with enterprise AI. To begin with, we were the first in the industry to launch an AI services brand called Infosys Topaz. We then went around with multiple channels, whether it is enterprise AI conversations or enterprise AI World Tour around the world where just in the last 12 months alone, 700 of our clients have interacted and understood our approach to enterprise AI.

We even invested in expanding the Infosys Knowledge Institute to really drive research on where AI is headed, whether it is the enterprise AI radar or whether it is AI indexes across different verticals. The idea simply is how can we be the engine of knowledge awareness and brand awareness in the enterprise AI category. And that is why what you see to your left is how Infosys is being seen from an enterprise association platform when it comes to the entire industry. This is the research that we undertake quarter-on-quarter because this is a space that is changing quite rapidly. And we would like to know how brand Infosys is faring on the Enterprise AI association. And clearly as you can see across the last year alone, we are at the top of the stack.

You saw multiple client stories played out over the last few hours. And that is a metric that we track very, very closely because we believe that the biggest proof of our success is our client voice. And that is the word of mouth that you are seeing consistently rising. What is to the bottom right is how the focused effort on thought leadership is driving media share of volumes around the world. And again as you can see year-on-year, we are almost growing that by over 100%.

And this is not just how Infosys see it inside out, this is also how one of the stakeholders that all of you track very, very closely, the industry analysts, they are acknowledging it as well. Whether it is across the digital ratings or whether it is across AI ratings, Infosys has consistently been on top when it comes to leadership rankings. And I will bring the conversation back to where I started, which is how has all that translated into business impact for Infosys. It has not just helped us stay ahead on the revenue curve vis-à-vis the market, it is also demonstrated tangible impact on the market share gains.

So the question then in front of us is what is next? And how are we thinking about our brand going forward. What we intend doing clearly is to really live the promise of ‘navigate your next’ as our clients' transition through another big turn of technology. But to really bring this promise to life is how we are going to do it is to unlocking AI value. And that is going to be the endeavour of the brand over the foreseeable future. Thank you very much.

Infosys Limited Investor AI Day 2026 February 17, 2026

CORPORATE PARTICIPANTS:

Salil Parekh

Chief Executive Officer and Managing Director

Jayesh Sanghrajka

Chief Financial Officer

INVESTORS/ANALYSTS

Ankur Rudra

JP. Morgan

Kumar Rakesh

BNP Paribas

Aditi Patil

ICICI Securities

Sudheer Guntupalli

Kotak Mahindra Asset Management

Vibhor Singhal

Nuvama

Jonathan Lee

Guggenheim

Pankaj Murarka

Renaissance

Aditya Chandrasekar

UBS

Gaurav Rateria

Morgan Stanley

Surendra Goyal Citigroup

Kunal Tayal

Bank of America

Sandeep Shah Equirus

Kawaljeet Saluja Kotak

Shaan Chugh Capital Group

Moderator

Thank you, Sumit. For our last session, please welcome Salil Parekh, Chief Executive Officer and Managing Director and Jayesh Sanghrajka, Chief Financial Officer, to summarize the day, followed by question and answers. For Q&A please raise your hand. A mic runner will reach you. Kindly state your name and organization before your question. To keep things efficient please keep to one concise question. And hand your mic back after asking the question. Salil, over to you.

Salil Parekh

Good afternoon. I think the good news is most of you look like you are awake. So hopefully, the day has been exciting, interesting and a lot of depth from our side. Let me maybe spend just about a minute or so with a summary, and then Jayesh and I can answer questions. I know at lunch we had a few discussions and there are a lot of questions on your mind.

So first, my sense is we have a really comprehensive set of AI offerings. You have seen the Hexagon. You have seen that beyond that, we have 30 offerings, 100 sub offerings. And you have seen examples across each of the six areas, which give you a view of what is going on with the new services, and you have seen all of our augmented services and the impact that is on them.

Second, the opportunity is huge. I think we started that discussion with what Nandan shared. I gave an indication of what we see external analysts quantifying these opportunities as. These are large value streams, and as we go through over the next few months and quarters, we will dig into many of these individually to share with you what the value looks like, how we are going after it to make it more real for all of you as you look at Infosys.

Third, I think it is quite clear that large enterprise clients trust Infosys. You saw some of the videos and you have seen videos of client executives, CEOs, CIOs, COOs, very senior executives really mentioning the word trust and looking at Infosys as a strong partner there. We are working with so many of our clients across the Hexagon in each of the big areas. So, it is not theoretical anymore. It is really practical. It is happening in the field, and we have examples of that. We have examples of large delivery teams working on that.

Then we spent a bit of time on the platform itself, whether you look at what we built with the Topaz Fabric capability, what we built with AI Next and the individual agents that we built within that and how we can integrate across with different platforms, client platforms, third-party platforms.

You heard a lot about our talent, deep engineering talent and actually even the culture, which is much more of innovation within the company, how can we do something new? We are not a company simply of break and fix. We are a company of innovation, and that helps in AI because there are new ways of doing things and our engineering culture and mindset is a big advantage for us.

And then the partnerships, of course, we announced a very large one today, but we have several of these. And it is very clear from how the partners are looking at Infosys that it is a joint work activity. You heard from one of the videos from one of our partners, the depth of knowledge that we have, the depth of talent that we have and the capability that the partner brings, that really makes a difference with our clients.

We have already put in place a very good go-to-market, which is really take everything we have on the Hexagon, the 30, the 100 and start to meet all of our large clients to see where AI can start working with them for them and make an impact.

And then we have an incredible brand that is working well. It is growing very fast, and you can see the elevation of the sorts of relationships that Infosys has in part because of the strong brand that we have built, and we will continue to build.

So that in a sort of brief way is the summary. And with that, let me request Jayesh to join me here, and then we can go through with the Q&A here.

Ankur Rudra

So, the first question is, you highlighted in the morning that the net new opportunity from AI will be $300 bn to $400 bn. Could you also elaborate what do you think is the incremental opportunity is $300 bn to $400 bn on new services. Could you elaborate what would be the potential for the net opportunity adjusting for the compression that you might see in many of these services? A lot of examples through the day highlighted that everything can be done faster, fewer resources or from months to weeks. What does that mean from a compression perspective? And what are we looking at on a net basis?

Salil Parekh

So, what you are saying is we have talked about the growth expansion and what is the net number. So there, what we have shared in the morning is really on the six areas, what we see as the expansion from what we have seen externally, the $300 bn to $400 bn quantification from a couple of sources. On the compression, we have talked about, again, what we understand to be where the compression is coming from, whether it is on application development, whether it is on infrastructure. We have not quantified that number for any external use at this stage. We have said that the expansion number from what we see today looks larger than the compression number.

Kumar Rakesh

Hi. Thank you for hosting us and taking us through in detail through your verticals and service wise capabilities. What I have understood is that you spoke about one of the key advantages Infosys has

is strong understanding of clients' data estate and the context of the client. Now that would be something which many of your peers would also claim to be having.

The second part is the partner ecosystem, which you are developing and building strong relationships. Again, that is something which here or there, put together your competitors also would potentially say that they have. So what exactly in terms of delta or incremental capability that you see that Infosys has that helps you to have a better right to win when going for enterprise implementation of AI tools?

Salil Parekh

So there, the focus in addition to the points you mentioned is one on the platform and Topaz Fabric capability that we build, where we built our own agents and we can integrate other agents. The other is the way we have identified clearly what the six areas of growth look like and how we are executing on that, working with our clients to make sure that we are positioned in that by reskilling our people, by building investments in those capabilities and making sure that each client, we are building that capability to grow into those areas. That is where the real difference, the execution is where the real difference will come.

Aditi Patel

Has our pipeline to TCV conversion timelines improved, because now leveraging AI, we are able to build prototypes or working models much faster and clients are able to view the ROIs much faster?

Jayesh Sanghrajka

So, if you look at the large deals that we have signed in the last few years and last few quarters, we have not really seen the timeline of those large deals shrinking at this point in time. We have seen the deal timelines remaining similar based on what we have signed till now. Of course, the future is yet to be seen as we sign. But at this point in time, we have majorly, we have not seen the deal timeline shrinking significantly.

Sudheer Guntupalli

Hi, Salil. Yes, thanks for the insightful presentation. So recently, one of your competitors had made a comment that many ERP migration programs are seeing a significant compression in time from years earlier to weeks. So do you see that practically happening? Or you think it is probably a corner use case in a specific context and that might not be extrapolatable to the entire ERP implementations or migrations as such? Thank you.

Salil Parekh

On ERP migration, we have not seen that, that specific point is valid. So what is valid is what I think some of our leaders talked about, which is when you look at modernization, which is not only an ERP migration, but it is an overall modernization approach from old legacy landscape to a more current landscape. There, we see that the speed or the timeline is much more compressed. And of course, the cost is much more reduced.

Vibhor Singhal

Yes, hi, Salil. Just in response to the first question that you answered that we are kind of quantifying the compression factor. If I were to take a look at it, let us say, direction-wise, let us and compare this to the earlier digital cycle. There also, we saw an initial compression in the IMS and some parts of our business. And then, of course, the new opportunity took place. So we generally see a compression, then that followed by the opportunity that comes in.

In terms of relative comparison, where are we in the cycle at this point of time? Have we seen the peak cannibalization of revenues? Do you think we can further go down? Or do you think from here, we are more closer to the inflection point where the net revenue from GenAI becomes positive? So in the entire adoption cycle of this technology, where would you place us at this point of time?

Salil Parekh

So there, if you step back a little bit, there are usually multiple dynamics that are at play. Typically, if there is nothing else changing, the macro is one important factor for Infosys for IT services revenue growth. And then there is a change of technology, which is going on, which is another factor. So what we see today is that in many ways, for us, the macro is improving. If you look at what we see in the large markets we are operating in, for example, in the U.S., we see overall with the changes in regulations with tax reduction and maybe an impact on interest rates, we see some move on the macro.

On the tech cycle, it is difficult to say where we are because each of the six areas are different in the way that will play out. So for example, some of the things on process can go very fast. For example, in customer service today, there is a huge move. And there, we will be going after areas that we are not today currently in our revenue base. So this is all incremental for us. But we have the technology, we have the partnership, we have the capability.

In some of the other areas like modernization, we have a lot of good technology. It is something that will again be incremental now. Some of the others, maybe as the timeline flows out, we will see how they play out. Data is happening very much more quickly at this stage. In terms of the compression, those are things that, as we mentioned throughout the day, we see that visible, but it is not something which is large and it is not something that is insignificant. But we do not see an acceleration of that either at this stage.

So my sense is if you look at our current business situation, next year, we definitely see in Financial Services a strong growth, we see in Energy and Utilities, a strong growth compared to this year. So we are already seeing signs of visibility. It is a function of AI leverage, meaning more AI being used. It is a function of macro and it is a function of the compression. So these are a little bit more intertwined than would otherwise be.

Moderator

I would request to give your name and the name of your organization since we are webcasting this session.

Jonathan Lee

Hi. This is Jonathan Lee from Guggenheim. Thanks for hosting us today. A lot to be excited about here around the AI opportunity. Can you help us understand the level of investment necessary there to reskill and hire laterally to meet the opportunity in hand? And how should we think about the impact to margins there ex potential currency benefit?

Salil Parekh

So there, I will start off, Jayesh may have some things to add. One of the things we have been very clear about is we want to make sure we invest in this AI capability build-out, AI Topaz Fabric and platform and other tools that we have built to invest in scaling that up, scaling up the go-to-market and scaling up what we need to do in terms of training.

We have also put in place some years ago, a very strong program to support our margin and make sure our cost is more and more efficient. So, our view for the period in the future is we will maintain our margin guidance, and we will take all of that, that we save, which is quite substantial from our margin program and invest that into scaling up AI even faster. So we are ready with that from the operating, from the income statement point of view.

From the balance sheet, we are also ready to make as required, appropriate acquisitions, which will fit our overall value framework, which is what we have done in the past. With that sort of a mindset, we will continue using the balance sheet as well.

Jayesh Sanghrajka

Yes. Just to add to what Salil was saying, if you look at this year, 9 months into the year, we have been able to maintain our margins stable. That was on the back of FY '25 where we expanded our margins by 50 basis points. All of this is after absorbing all the investments that we saw through the day today, whether it was partnerships, whether it was tech investments, whether it was training investments, the sales and marketing investments that you see in our P&L already which has

impacted, you know, 50 basis points. So we have absorbed all of that and delivered on our margins and maintained the margins stability. As Salil said, our endeavour is to ensure that, you know, all the investments come out of our margin guidance.

Pankaj Murarka

Hi. This is Pankaj Murarka from Renaissance. Salil, I have two questions. One, when you called out that we have 5.5% revenues coming from AI and in the context of, you know, that is still I think a small number, while we have seen a lot of use cases today, but in the context of, you know, the Fortune 2000 clients, I still think there are few and even the deal sizes seem to be small. So my assessment is that probably average deal size is about $4 mn to $5 mn or something like that. So how far are we where we start seeing $50 mn, $100 mn deals and where the adoption really becomes accelerated?.

Salil Parekh

So there, I think one of the reasons, what we shared today in terms of AI is to give a real depth on what we are doing on AI. Now as you rightly pointed out, we shared the number of 5.5%. We still have a lot of other work that we do within Infosys, which is making up the other parts of the business. What we see in this AI activity is it is going across many things in the areas that we described and becoming part of almost every discussion.

And so our sense is it will now continue to grow. We will see how that growth is but starting at that sort of a level, there is a long runway because essentially over the next several years there will be a shift. If you sort of look back a few years, we started to call out our digital numbers when they were around 25% or 20%, something like that, and we had a shift over 3, 4, 5 years where it became 65% to 70%.

And so that is the sort of a play that you have. We do not know if this is going to go in that sort of a range in 18 months or will it take 7 years, but we are well on our way with what we have created to start to play it as our clients are absorbing it. And even if it goes faster, we are ready, even if it goes at that pace, we are ready.

Pankaj Murarka

Sure. One of the most important things that we learned during the discussion today is the context. So in the context of, you know, what you laid out, the new opportunities of $300-odd bn over the next 5-6 years, for a long-term stakeholder or an investor in Infosys, how should one think, what are the three or four things that will change from a financial matrices perspective probably 5 years out as we navigate this journey from where we are today?

Salil Parekh

From the financial metrics?

Pankaj Murarka

Yes. That is right. From where Infosys is today, as we navigate this journey, let us say over a period of five years, you know, if you could put that in context?

Jayesh Sanghrajka

So, if you look at, you know, every tech cycle, and this is no different from a cycle perspective, of course the metrics can be different. But the way we have always articulated it is, if you are riding the tech wave and ahead of the curve, it should reflect into better growth, better RPP and therefore better margins. We said that in the digital era, we are saying the same thing today. If you look at our RPP for last 2 years, we have delivered, you know, superior RPP. Our RPP has grown 3% both FY '25 and FY '26. If you look at margins, it is showing resilience. We grew margins by 50 basis points last year. We are stable margins this year despite all the investments that we talked about. So I think that is exactly what is going to boil down to, if you are looking at, you know, any tech cycle in my mind, from a services perspective.

Aditya Chandrasekar

Hi. This is Aditya from UBS. Just a couple of questions. So you guys have spoken a lot about Topaz and it is good to see, I think it is moved beyond, you know, those pilot use cases to more enterprisewide use cases. But if you could also touch a little bit on how we should think about the pricing models there? Because for example, in the walkthroughs we learned that some of the projects that needed huge team sizes have now, you know, been compressed to just use of the platform and maybe a very lean team.

How should we think about how the pricing is evolving in or pricing model rather is evolving in those kind of projects and any rough or, you know, a framework at least to think about margins as well in those kind of projects? That is the first question.

And secondly on headcount, I think you have given a plan on how you will be reskilling talent and hiring more specialized talent etc. But as of today you do have a wide kind of a fresher or bottom of the pyramid talent. How should we think about the utilization of that workforce now because incrementally we will be getting into more and more projects where we will have leaner team sizes and maybe just more specialized workforce rather than the kind of fresher coders, to put it simplistically? These are the two questions. Thank you.

Jayesh Sanghrajka

So, I will start with pricing. So if you look at the pricing models today, the models are evolving, right? You have various examples of outcome-based pricing, you have examples of pricing which is combination of outcome-based pricing plus an agent pricing or a platform pricing. But I do not think there is going to be one model that is going to apply to every client. It will depend on the client context, what does the client want and how well we are able to justify the value that we are creating for the client.

So it is always going to be the combination like it is always been in the past, you know, we did not have just one model. There were various models that worked and depending on the client's context you close on the pricing model and I think that is how it is going to remain on that.

Salil Parekh

On the utilization and the specialized talent, I think, we will see more and more of that happening, but equally we will also see, and I think, somewhere in the day we did mentioned that, there will be recruitment with college hires, with freshers and making sure that they learn without the tools and with the tools as they can develop their own experience, and know when it is appropriate to use tools, when it is appropriate and how to assess code that is generated by the model.

So those skills will still remain quite important and in that context, even if we have more specialized talent, utilization as a metric will still remain pretty important, that will be a driver in a different way there are, depending on the specialized talent and the scale and size, there are different levels of utilization but it will still remain an important metric.

Jayesh Sanghrajka

Yes, and if I can just add to what Salil was saying, if you look at even in the digital cycle, when we started the cycle with 25% digital, we are now at, you know, pretty much 65% plus of our revenue coming from digital, we retrained our employee base from what were digital at that point in time, which was representing only 25% of our revenue, to more than two-thirds of our revenue today. So we have a strong training culture, and that is what we have always dipped into, to retrain and repurpose our employees.

Gaurav Rateria

Hi. Gaurav from Morgan Stanley. Nandan made a comment in his presentation about build versus buy. Do you think the lines between software and services are now getting blurred, and what does it mean from addressable market perspective for service providers?

And second is a question related to the evolution of pricing model that you talked about. If it creates room for non-linearity, what is the headroom that you get from investment point of view to accelerate your journey in AI? Thank you.

Salil Parekh

On the first one, I think my understanding of that is essentially it expands massively the amount of work that we can do, if we can look at some of those. And I think there it will probably be some of the things on the edge, maybe not the core sort of systems of record and so on. But you could imagine some of the things on the edge which could be more easily built, as Nandan was saying, in the build as opposed to the buy. And then if that is readily doable, and it is effective for the client, then there will be multiple, it is not going to be one thing for every client. And so there is going to be different builds for different clients which my sense is will be a larger opportunity for Infosys.

Jayesh Sanghrajka

Yes. On the pricing you are right, if you are able to have a larger part of revenue enabled through platform or agents, it will create to that extent a non-linearity. At this point in time, as I said earlier, these are early days, right? So everybody is testing new models, but to that extent yes it will create a little bit of a non-linearity.

Surendra Goyal

Yes hi. Surendra from Citi. So through the day across the presentations we heard a lot about value generation, savings, productivity. So my question is, is there a way for Infosys to capture that value better? And does AI kind of result in any change to that, either for good or for worse or it really does not matter? Because one of the key issues has been that like over the years we have seen a lot of presentations across the industry, talking about millions and hundreds of millions of dollars of value, but again it seems like a most of that goes back to the customer. So any way to kind of capture the value better? Thanks.

Salil Parekh

I think there, there was some discussion in some of the client examples on outcome based. Now if we managed to do some of those with some sharing, we might see some of the benefits of it. Today, if you -- in one of the earlier charts, we sort of showed the model is moving faster than the reality at the enterprise. So what tends to happen is the enterprise people at a more discussion level are expecting the model type of benefit in the pricing or the cost and we are not able to make that happen.

So today it is not more equitable, but in the future when it becomes more aligned, the model and what is in the enterprise, then you could see that part of it could be there. And if some of the outcome-

based works you could imagine that, some more comes. However it is not visible today, so it is not something that we are looking for example in the coming year, but it is something which is in our mind as Jayesh was saying there is still early days, we will see how the pricing approach will develop.

Jayesh Sanghrajka

Again if I can just add to what Salil was saying, there are also indirect ways to capture the benefit. Like again through the presentation you would have seen, multiple sectors, 15 out of the top 25 clients, we are the AI strategic partner. Now that only comes in, when you have created significant value for the client. You saw in Anand’s presentation that, large part of the Telcos we are large players. That only comes again when you have created significant value for the client and you become the strategic partners for the client. So those are the other benefits of creating that value for the client, it is not just that everything gets passed on to the client, you also get a lion’s share of the clients landscape.

Kunal Tayal

Kunal from Bank of America. Given all your investments into small language models as well as proprietary solutions, I was wondering if there is a bigger opportunity that you foresee in either of mid-market customers or then emerging market kind of customer base? Mid-market because, I wonder if there can be larger turnkey programs that can come your way and emerging markets because if it is not going to be a labour-intensive model, can the profitability of these projects now meet thresholds better than earlier?

Salil Parekh

So there, first the small language model, I think for us, is a very good indication of the depth we have, and it is very useful on a limited data set in a large client already today. So we are seeing some benefit of that whether it is more for alignment or more even for code development. You heard I think, in one of the sessions the discussion on our own model for code development into that. We have not looked right now at the mid-market because the cost of sales is very different. So we have to figure out if that will work in that market.

And emerging markets, we have not looked beyond, I mean, I would not call them emerging markets, and there are growth markets that we have looked at. For example, we think, markets in the Middle East are very strong. But it is not like an emerging market but it is a growth market, where we will look for some of these models but right now the adoption, it also depends on the adoption in that environment, in that geography, before we can go and adoption in the mid-market as a client base also.

Sandeep Shah

Yes. Once the enterprise clients enter the post AI adoption where most of the application modernize, data layer has been created, cloud migration has happened. In that phase the investor worries the terminal growth rates could be much lower because of the applications, data could have been modernized.

Salil Parekh

So there, I think in terms of what happens in the post-AI world, of course that seems a little bit far away today but what we think is, I was with one of the people who was building the foundation model a couple of weeks ago, and they gave a good sort of example. They said that the amount of software demand that is there for writing software is becoming 100x in terms of the size. So even if you go into the post-AI world, there is productivity impact, we still see a huge amount even if you assume a 10x productivity benefit to a developer of what is available in terms of what is to be developed, what is to be built.

So the post-AI world in my mind is not a static world where everything is done, it is a world where there are large enterprises starting to use agents in many areas, having very strong platforms, using for example Topaz Fabric as a capability but also building new functions, new features, managing things. It is much like what we have today where there are new things happening even on platforms, older platforms, which let us call it are somewhat stable today in terms of they are implemented.

But there is always new work that people are looking for. But that is the nature of the economic growth where there is always new features, functions that tech is driving and more tech across the enterprise which can give you different areas to work on. So my sense is in the post-AI, of course the transition is very exciting with all the work we will do, but there is even more work, because there will be more and more things that will be going on in that post-AI era.

Moderator

We have time for just the last two questions.

Kawaljeet Saluja

Hey hi. It is Kawaljeet from Kotak. Thanks a lot for the presentation which was quite insightful, Salil and Jayesh. I have a few specific questions. The first question is that for AI agents that you are deploying in a client environment, are those homegrown or are those of frontier model companies?

Salil Parekh

So on agents there are multiple people who are building agents. So first we are building agents. Second, some of the foundation model people are building they are a little bit more broad-based. Then there are third-party companies that are also building agents. And then of course the public

cloud players are building agents of their own, and other third-party agents that they are providing. So there will be a host of those agents from which some selections will be done. And of course clients are also building agents.

Kawaljeet Saluja

Right. And Salil, for agents of third party, let us say what is the services intensity? So for example, let us say a dollar is spent on a third-party model, and you basically customize it and configure it for the client environment, then what is the services revenue that you get versus a dollar which is captured by let us say a frontier model company or an external third-party agent provider?

Salil Parekh

So there, we do not have very deep exact stats on that that we can share. What we see is different, because if you look at a software development lifecycle part of it as you know is the cost of building the software. So, in this case the agent, but there is a significant part beyond that which is integrating it into the environment, making sure that it is working in that environment and the performance attributes and then the security attributes.

So we have got some examples of what we have done as we shared today in these situations, but we do not have a statistic that we can share that one equals X because of the ratio. As we go through the next few quarters, we will definitely internally build up a larger data set on that, and then we will see, if that becomes something that we can share publicly here.

Shaan Chugh

Hey Salil and Jayesh. Thanks for organizing today. This is Shaan from Capital Group. I guess just a quick one, you know, for the last many quarters our sort of net headcount has been pretty flattish as we have muddled through sort of the macro challenges and so on. I mean with the AI services picking up, are you internally gearing up for sort of net headcount to start ticking up again?

I know we are hiring sort of a lot of freshers and we also have that counterbalance with some internal efficiencies and of course just natural attrition. So when you factor all of that in on a net basis, is that something you are gearing up for this year or should we expect that to still take some time given the AI-centric efficiencies that you are factoring in?

Salil Parekh

For the year, we have added ~13,000 net headcount for the first three quarters. My sense is, we will continue to add headcount as we go through. And it sort of comes back a little bit to an earlier discussion, we were having, which is there is a macro element and there is an AI element. And we will see, my sense is the macro will potentially be better, and of course we have a very good sense

of the AI opportunity set. So when you put both of those together, if in the last three quarters we have done ~13,000 net headcount increase, my sense is we will continue with the headcount increase in the coming quarters as well.

Moderator

Thank you. That brings us to the end of the Q&A. Thank you Salil and Jayesh. Thank you everyone.

Salil Parekh

Thanks. Thank you everyone.

Jayesh Sanghrajka

Thank you,

AI assistant

Infosys Ltd. — Call Transcript 2026

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Infosys Limited Investor AI Day 2026

CORPORATE PARTICIPANTS:

Nandan Nilekani

Salil Parekh

Satish H.C.

Dinesh Rao

Balakrishna D.R

Mohammed Rafee Tarafdar

Anand Swaminathan

Jasmeet Singh

Dennis Gada

Ashiss Kumar Dash

Ambeshwar Nath

Shaji Mathew

Sumit Virmani

Nandan Nilekani

Now safe harbor clause.

Salil Parekh

Satish H.C.

Dinesh Rao

Balakrishna D.R

Mohammed Rafee Tarafdar

Anand Swaminathan

Jasmeet Singh

Dennis Gada

Ashiss Kumar Dash

Ambeshwar Nath

Anand Swaminathan

Shaji Mathew

Sumit Virmani

Infosys Limited Investor AI Day 2026 February 17, 2026

CORPORATE PARTICIPANTS:

Salil Parekh

Jayesh Sanghrajka

INVESTORS/ANALYSTS

Ankur Rudra

Kumar Rakesh

Aditi Patil

Sudheer Guntupalli

Vibhor Singhal

Jonathan Lee

Pankaj Murarka

Aditya Chandrasekar

Gaurav Rateria

Kunal Tayal

Moderator

Salil Parekh

Ankur Rudra

Salil Parekh

Kumar Rakesh

Salil Parekh

Aditi Patel

Jayesh Sanghrajka

Sudheer Guntupalli

Salil Parekh

Vibhor Singhal

Salil Parekh

Moderator

Jonathan Lee

Salil Parekh

Jayesh Sanghrajka

Pankaj Murarka

Salil Parekh

Pankaj Murarka

Salil Parekh

Pankaj Murarka

Jayesh Sanghrajka

Aditya Chandrasekar

Jayesh Sanghrajka

Salil Parekh

Jayesh Sanghrajka

Gaurav Rateria

Salil Parekh

Infosys Ltd. Call Transcript 2026