Good morning, everyone, and a warm welcome to the Infosys Investor AI Day at our Bengaluru campus. A special hello to everyone who's joining us via the investor relations webcast. Today's proceedings are being recorded, and the audio transcript and the presentations will be made available soon on our website, so we request you not to take pictures or record the sessions while they're going on. Before we begin, I have some important housekeeping announcements. On your tables, you will find the agenda, an important information sheet, and the feedback forms. Please follow the timings in the agenda to help us keep the day running smoothly. The important information sheet also has the Wi-Fi details. We request you to fill in the feedback forms after every session. If you need any assistance, we have volunteers around wearing Infosys ID cards. Please reach out. Please take note of the nearest emergency exits on both sides of this room. They are clearly marked. 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With that, let's start today's program. For our first session on tech transitions, why is the AI transition different? Please welcome Nandan Nilekani, Chairman of the Board, Infosys. Thank you, and great to have you all here in these tumultuous times. Today I'll talk about, you know, tech transitions. I have had the fortune or misfortune of being in this industry for more than 40 years, and I've seen a lot of transitions, so I thought I'll talk about less about that and more about why this time it's different and what are the implications of this transition. Now, safe harbor clause. Now, we have seen technology shifts, you know, for centuries, whether printing press or telegraph, but over the last, you know, 60, 70 years, we have seen a much faster change and PCs, cloud, Gen AI, 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's a change, the way we address that change has been different. So we went from mainframes, to minicomputers, 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's 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, you know, 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 two things. The 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 we had, you know, when smartphones came, we could build applications where, instead of having a PC, you did it on the phone. You know, 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 on-prem and move it to the cloud. So you could do a lot of things to get going, but this time it's not that. This is a fundamental change in the way we do things. Obviously, there's a technology dimension, and it's all about having AI-native architecture, but there's 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's a huge challenge for talent. Talent will have to deal with a world where writing code will not be the goal, it'll be actually making AI, 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. You know, technology is 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, you know, every time you give a prompt, you'll 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's 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'll have mini computers from 1980, they'll have LAN from 2000, they'll have all kinds of things, and all coexisting in silos. That is over. We. 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's dealing with. There are reasons for that. One is the financial drain. Many large companies are spending 60%-80% of their IT spend on maintaining systems. There's 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 system. They want to flip the way they spend money, but they can't 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 there 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. Because the data is all in silos, you can't 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 in 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 have no longer have the option to defer this, and this is a huge, huge requirement, and obviously it's a huge opportunity for us. Now, the other thing which is, as AI becomes bigger part of the spend, the balance of advantage is moving towards build rather than buy. And that is actually what is. If you see some of the concerns about what will happen to SaaS companies and all that, it's 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, and with something to be built. And so we are. And that, again, actually benefits folks like us, because about building, who are we gonna build it for them? It's gonna be us only who will build it for them. So fundamentally, it's good for us. And the other thing which is there is that our view is that foundational systems will intrinsically become systems of record, but the interface, 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's, it's 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, you know, work which somebody has to do. So there's 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 technological change, I mean, you know, 2023 foundation frontier model had 100 billion parameters, today it has 1 trillion parameters. There are only 10-12 agent networks, there are 60 agent networks. So this is only going to go up. In the US alone, there are at least five frontier models. In China, there are big four or big five. So this is only going to go up. In India, we have seen so much action, and you'll see some big announcements this week on Indian-based sovereign models. I think this is something. Now, there are certain implications of this, because if I'm a businessman and I have to choose my technology, how do I make sure I don't make the wrong choice? Because something which I invest in today may be, 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's about organizational change, business change, retraining your people, thinking about non-deterministic approaches, changing your data so it's no longer in silos. So fundamentally, we have a situation where there's 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, some, nothing's going to happen because the problem is here, not there. You get it? It's about how fast companies can implement, so you have to look at that. And this is, we call this the deployment gap, but this is actually a concept by Professor Clayton Christensen at Harvard 25 years back. He called it Technology Overshoot, where technology gets ahead of the need. In fact, he argues that that's how newcomers come, because newcomers can then launch new products that are not as sophisticated but good enough for customers. 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's a massive competition, but enterprise deployment is not going to go up, and this Deployment Gap is what we can help to address. Again, it's a very important point. Now, I think talent transformation is huge. It's not that you will not need... You will need talent, but it'll go from, you know, QA testing or development. We have all kinds of new roles: AI engineers, forward-deployed engineers, AI leads, forensic analysts, data analyst. 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's really the challenge that all the firms will face. And this is something. 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 gonna happen, and I think we'll have sessions on that. But fundamentally, there will be a need for people, but they'll 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, you know, give it to a kid, and he'll generate a million lines of code, but that's 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 don't 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, you know, when there's a crisis to be sorted out, they're pulled in from Phoenix or Florida or wherever they are, and they have to solve problem, and nobody else knows how to solve. We have that kind of situation out there, undocumented dependencies. Taking brownfield systems and modernizing them is a hell of a lot more difficult than doing greenfield development. 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's a lot more complicated. Also, AI implementation requires laser focus. The very fact that you can generate stuff means you can generate slop. You know, in fact, five years from now, there'll be more AI legacy system than any other legacy system, and all kind of stuff will have been generated, and we'll have to clean that up also. So and even organization is not org. You know, you can have this fake productivity. Let's 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, 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. If you start by teaching them tools, then everything is a black box. Then, you know, it's like the guy who never knows how to calculate because he was born with a calculator. First-principles thinking is very important. All the more important as you think about strategic transformation of large enterprises. First-principles 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. It's the dealing with this context that is the hard part, which is where, again, you know, we believe we have a way of doing that. I'll give 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, yeah, by next year, we'll have self-driving car." It's 20 years later, all we have is a few cities in America with their self-driving cars because the context is different. Every city is different, every road is different, and by the time they come to Bangalore, it'll be 2047, because they're dealing with Bangalore traffic, it'll 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 don't get locked into a tool because that tool may no longer be obsolete 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 go make the house in order for this whole thing. And then massive change management. You're changing organization, business structure, people. I mean, this is like unbelievable change management. So unless you have leaders who can do effective change, nothing is gonna happen. It's also about strong collaboration across firms. So 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's not about using AI tools, it's about productivity out of those tools. Otherwise, you'll 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's a big thing. So my view is there is no opportunity gap. If anything, the opportunity is bigger than ever before. So don't 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, these are all the issues, and I'm 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's 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, to get to where they, where they have to get? Are they, 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. I'm hoping that today you will hear from our team, and they'll give you some reassurance that we are on the right track. Thank you very much. Thank you, Nandan. For our next session on the AI services opportunity, please welcome Salil Parekh, Chief Executive Officer and Managing Director, Infosys. Good morning and welcome. I think with that session from Nandan, I'm 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. First, one of the things we've learned again and again is what we see from our clients is that our clients trust Infosys in driving and delivering AI work. 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's across many things. It's 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's growth opportunity for AI services, and I'll 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 a CEO is engaging with us. There are several others we will share later, and you'll 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'm 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's 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'll actually share a couple of examples of this, of the work we're doing at scale, and bringing them into... 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's 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 sort of have the client share from their perspective, how Infosys has supported them in this AI journey. So what I'm showing you in the hexagon, in the AI value framework, is not theoretical. This is 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's 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. 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. All of this is the way we are now going to our clients. 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's for revenue growth, whether it's for cost optimization, whether it's 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're doing with clients. And this is sort of pulling back a little bit. We've always talked about Navigate Your Next. This is how we are looking at the journey. The Navigate Your Next is change 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 an 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've 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 dynamic in AI services which all of you know and we understand. One, there's a huge opportunity. The one that I mentioned, the six areas with an external analysis, we understand that the opportunity is between $300 billion-$400 billion in the year getting to 2030. So over that timeframe, 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 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's 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's 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've 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, we think is critical in how we are reshaping it. We are going through a huge reskilling process. As you've 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. Next financial year, we also have a plan to recruit 20,000 college graduates, but we want to talk about the talent and culture approach we have. 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's 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's 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'll, 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. Thank you, Salil. For our next session on the AI services playbook, which is by three leaders, I would like to welcome Satish HC, Chief Delivery Officer, for the first part. Let me set the context, before we deep dive into our Infosys, AI playbook. Every tech shift, whether it is PCs, internet, cloud, or digital, each one of them led to, rewiring of enterprise work and workflows, and AI is the next rewrite. Here's 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, 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 plugin. If it was that simple in such a huge complexity, no surprise that AI projects fail. Then what is it about? It's about harnessing the full potential. It's about reimagination 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's 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 enforced policies. 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 underserved today, and this is ripe for AI-led transformation. So it's a myth that enterprises need just two layers, which is AI and data. Is enterprise an algorithm? How should we harness AI then? AI is not the end game of tech transformations, it's just another one, but it's a giant leap in raw capability, and it's 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 is this enterprise rewrite about? The co-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 and 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. 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, you know, 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 reimagination 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's available to all. Sustainable moat 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, of our client will help us—our 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, you know, 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, and IP, and patents will accelerate AI readiness and adoption, with our Topaz Fabric and our specialized talent, in the form of full stack and FDs. 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, you know, 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. You know, we have over 25 years of industry-focused experience, and when co-intelligence connects with agentic economy, the play of AI elevates from, you know, 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 plugins 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 and tech and transformation wired in. Organization transformation encompassing enterprise stack and people. You know, this includes the AI-first GCC 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 talking about growing their revenue to $7 billion. They came to us to bridge their ambition and deliver an AI operating model so that, you know, we'll 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 built their enterprise agentic AI platform with the requisite guardrails. This enabled 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 agent that we developed in research and R&D for product formulation, they now have line of sight to $50 million revenue, which they didn't have before. Below the line, we've been able to unlock $25 million 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. Thank you, Satish. For the next part of this session, I would like to invite Dinesh Rao, Chief Delivery Officer. Thank you, Simran. I'm audible? 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-... six months to an 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 a 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've been working as well as with the customers, we decided to codify our entire services across six strategic pillars, and these six 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 and see how does it. And then 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 one, the AI strategy and engineering. Nandan alluded to it, so as Salil. Organizations are extremely complex. One needs to really look at it 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 can't really be spraying across the AI in multiple different business unit. Now, that is a very key thing because that's where the value, 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 technology-centric 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 looking 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 AIO ps? Very recently, we've been working with the CEO as well as at 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 whatnot. So in a way, the journey is just not that I would start doing software development, but it needs to have a top-down 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 whatnot, to an extent of about 60%-80%. Today, the most of the time is spent on data in all of our projects. Data is one which accelerates your AI journey or potentially could also decelerate your entire velocity of how you're going to do it. So our frameworks 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 doesn't 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's 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's 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, you know, and in some sense, there is a framework if you really look at. So in one of the large industrial manufacturers, where it was 10 petabytes of data that we had to really bring in all together, harness it, as well as create the semantic ontology. Today helped them actually drive the supply chain optimization by over 20%-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 solutions. One needs 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 a human intuition works along with the AI and agent. And it is extremely critical that, you know, 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 know, you just can't 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 be really be contextualized to the industry and the business that you are really looking at. And most importantly, since it's 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've been now with Toyota Motor 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 double-click 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 agentic bring in agentic 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's the Achilles heel of our large organizations. Today, there is so much of tech debt sitting there. The code is obsolete. There is no written documentation, there are no availability of SMEs today. It's 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. What AI models, as well as powered by our Topaz Fabric today, enables us 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. 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. 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 data, 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 boom 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, on 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'm sure that all of you would raise your hand, but how many of you really trust the output that came by? I'm 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 et cetera coming in. So in a way, the trust has to permeate through all the other five 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 that trust to our customers, and we want to monetize the trust for our customers. Now, having looked at all these six important strategic pillars, right? I just want to dwell on one of the cases, Nova Chemicals. They're a large petrochemical manufacturer based out of Canada and U.S., and it's 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 et cetera. So we were invited to a program on 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 et cetera, 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 the 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 et cetera. 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'm sure that all of you know it's 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 hear... Play the video. Let's hear from the customer on what their experience has been and what we have done with respect to this journey. So where are we right now? We are in a journey to modernize legacy platforms. Let me tell you a story. About six months back, my CIO at that time and we traveled about four cities in five days in three different locations. Each and every, we've actually met four partners, and each and every partner was given the same problem: Take these 10 pieces of COBOL program and tell me how you will convert it. These COBOL programs were running on a NonStop, right? And every, I think you might have heard about HP NonStop, or it's also called a Tandem. These programs were running on a NonStop. Three partners, three vendors gave the typical, "Oh, we will do analysis, we will do design, we'll do unit testing, we'll do..." I'm like: No, that's not of interest. The day we walked into Infosys office, in the first one hour, they showed me a working prototype, working model of the converted code, right? No presentations. I was not interested in PPTs. I wanted to see a working model, right? And that is what actually impressed us to partner with Infosys. So if we think about what we did, they have a platform called iLEAD. iLEAD helped us do the documentation. These programs have been existing for the last 20-25 years. As you can imagine, a NonStop has been in operation for the last 25 years. Documentation doesn't exist. Nobody knows why a program was written. Nobody knows how to test a migrated COBOL program, right? Nobody knows what scale to test it at. So the documentation actually helped me big time to say program A talks to program B talks to program C. It also showed me the database dependencies, the table dependencies, right? So in one sheet of paper, I could now see that a given program, what it was dependent on, as well as the tables. So obviously, we are at a part of a journey where we are migrating. We are about 6 months into our journey. Without the iLEAD platform, I think we would have taken about four years to convert. With the iLEAD platform, I think we are targeting about 18 months. 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. 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're talking about, and thank you so much. Thank you so much, Dinesh. For the last part of this session, I would like to invite Balakrishna DR, Head of Global Services. Thank you, Simran. 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, operations, business operations. So 20+ 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 models, leading models from Anthropic, from OpenAI, from Gemini, from Amazon Nova. And in fact, we have also working with open source models, right? 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, in fact, used two models. We used a cloud model that actually generates the code, and one of the things about LLMs are they're better in critiquing output than generating output. So we used a 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. 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. We, two years back, we set up a GitHub CoE that was inaugurated by the CEO of GitHub, and then we are still ranked the number one 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 the models from Anthropic Claude. We are working on the new age models, right? Whether Devin from Cognition or also, right, 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 don't understand the enterprise context. They don't understand the standards that are there in the enterprise. They don't 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, right? 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, et cetera, and we have created 100 such agents, that we are using in each of our service. In addition to that, we have to create other tools, right? You saw in the Hertz example, the LLM models can't digest huge pieces of code at one time, right? Some of these enterprises that work with have millions of lines of code. If you give that to the LLM, they start to hallucinate. So you have to chunk the code, you have to create graphs, call graphs on how these are actually associated, and that's 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, right? And sometimes people ask me, "If LLMs are generating code, why do you need people? Why do you need developers anymore," right? And I think they ask the same question to Boris Cherny from Anthropic, because Anthropic is continuing to hire developers. So somebody on X asked him, "Why does Anthropic if, right, 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, right? The way that we actually deliver code or the way we support our 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 deployed engineers, that will create the platforms that the teams will actually use. And then we have created COEs for each of the partnership that we have. So it is a combination of all of these that actually helps us deliver our AI-enabled services. It is the blueprints, it is the technology, our own technology, plus the leading technologies 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, it is not about PPTs anymore, it is about actual demos, and that is what we see creates the impact, and that's 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, right? 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-degree partnership. What we mean by that is that we go to market with Microsoft, we are one of the big customers of Microsoft. We also are—Microsoft is our customer. We provided services to Microsoft, and we do multiple engagements with them, but I'll give you a couple of examples of what we are doing. Microsoft themselves are going through a big transformation. They are going from enterprise agreements 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, time frame. 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 also bill based on your usage, you get a very flexible billing. So multiple advantages for the customers of Microsoft by using this agreement. And also from Microsoft itself, it actually eliminates, 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, right? 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, right? So as you know, this includes Azure and Microsoft Office. They carry a lot of mission-critical systems of Microsoft customers on these clouds, right? And it is important for Microsoft that for this mission-critical applications, that there is no downtime, and then it is actually trustworthy, right? 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 in 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, right? The other example that I have here is Danske. Danske has a Forward28 strategy, where they're looking at modernizing their entire landscape. They want to bring process efficiency, and they want to completely create it as a digital bank. So we, you must have seen the press release where they've 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, 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's what our engineers have been trained to actually do, and 97% of the engineers are using that. In addition, we have created multiple AI solution. They wanted to use, ChatGPT, but they wanted the guardrail, so we created enterprise ChatGPT, which has over 16,000 users. They created other solutions on risk and right HR, which have been quite successful. We'll hear from the customer itself. If we can play the video. GenAI is already live today in Danske Bank. We have 20 use cases, from the most basic assistance to the more advanced agentic solutions. We've invested a lot in adoption by cultivating the right behaviors to deliver new ways of working so that people use GenAI both effectively and responsibly. At an enterprise level, tools such as our internal chatbot, Danske GPT, is used widely across the bank. Working with partners like Infosys, we're not just adopting this technology, we're actually helping to define what good, responsible GenAI looks like at scale in banking. With this, I'll hand it back to Simran. Thank you. Thank you so much, Bali. For our next session on Infosys Topaz Fabric and AI Next, AI Platform Suite, please welcome Rafee Taraftdar, our Chief Technology Officer. Over the next 10 minutes, I'll 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 I, when I think about enterprise landscapes, I think about city maps. You know, 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's 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 very, very rapid manner. 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. This is where we are bringing the 25+ 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're 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 two 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 these 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 multi-speed IT governance projects so that they can onboard these AI tools at speed. They need to put these guardrails, and that's where 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've 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 a 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. Now, Topaz Fabric enables five 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 their enterprise systems, and Fabric can integrate with any model, any framework, anything that they have. So that's 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'll 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 prepackaged as part of our Topaz Fabric, and that's 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, a 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 to market 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 three 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 Cloud 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, and on trust with Cambridge, with Columbia, and Cornell. 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. This was creating an issue for them, and they said, "We want to be able to process these customer case 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 didn't work. We said, "We'll 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. When we started, the extent of automation was about 0%-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, you know, 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's 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. Thank you, Rafee. Now, for our next session on unlocking AI value in communication, media, and technology, please welcome Anand Swaminathan, Segment Head, Communication, Media, and Technology. 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'll 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 six 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's 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. 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's 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. Again, here, AI is a huge factor. On the other side, if you look at the tech and the media side, you know, we are all seeing the huge spending surge that's going on with AI in terms of expanding the AI farm. But the issue has been that most of the AI is getting... 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 wants to work with Infosys, because of our native understanding of many of these industries. 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, you know, 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's 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 roadmap. 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, say, talking about customer experience, you know, reimagination of processes. It starts with that, whether it's 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's a big opportunity. But along with that, it's not as simple as that because you have a lot of legacy tech in many of these companies. Many of these tech, there is end of life or end of support. So there is a big question about, are we gonna buy new platforms or are we gonna just build the platforms? And the obvious answer now seems to be going towards building platforms in an agentic AI network 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's about really applying AI, 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 in a huge enterprise 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, you know, 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, you know, the big question was, how are we, how are we gonna 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, you know, many of the other important critical elements, like improving their own employee experience by bringing agentic AI. Now, let me actually let the CEO of this company speak to you directly about his own experience in working with Infosys. Can we roll the video, please? At Liberty Global, AI is no longer an experiment. It is becoming foundational to how we run our business and how we serve our customers across Europe. And Infosys has been a critical partner in helping us turn that ambition into reality. Together, we operate and continuously evolve the connectivity and entertainment platforms that support tens of millions of customers across our footprint. We innovate faster, and we now have the engineering capacity to move ideas into production quickly and reliably. Over the past year alone, Infosys supported more than 1,000 platform deliveries. Now, these included major launches like Super Search, which serves around 8 million TV customers, using advanced large language models to make content discovery conversational and intuitive across linear, on-demand, and streaming. Now, we're also using AI to fix and transform how we operate. Through AI programs like Agent Assist and Customer Assist, which are now deployed in multiple markets, we're enabling more self-service journeys, improving satisfaction, and reducing pressure on our care teams. As a result, we've seen things like outages reduced by 50% year-on-year, and 60% fewer customers impacted. Our relationship spans more than two decades, including over a decade as a formal strategic partner, and it underpins major programs across our company. If I had to narrow it down to one thing that sets Salil and Infosys apart, it's trust. We have great mutual respect for one another, and I always know I can call them on any issue and get a straight and honest answer, and that matters to me more than anything. Thank you very much. Thank you, Anand. For our next session on unlocking AI value in manufacturing, please welcome Jasmeet Singh, Segment Head, Manufacturing. Well, hello, everyone. I'm delighted to share with you today what we see as happening in manufacturing on AI, and how are we capitalizing on this opportunity. You know, 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 driving investments into smarter products, smarter operations, and as-a-service 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's 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, you know, I'll 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's 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 in to 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 aircrafts 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 multi-million GBP 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's highly regulated, and safety first. It is an EASA-approved, European Union Aviation Safety Agency's 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 billion in revenues company. It's a leader in power, wind, and electrification. They're, 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+ agents, multi-agents, use cases for GE Vernova in production. Now, let's hear directly from the CEO, Scott Strazik, and Justin John, who is the AI leader. Can we please play the video? Hi, everyone. Scott Strazik, CEO of GE Vernova. At the start, I would just say that we have a very strategic relationship with Infosys, both technically, working through engineering areas, also a lot of functional support. As we started down on our AI journey, it made a ton of sense to go to Infosys as a partner of choice. We're in the early stages with AI today. We're really looking at it as a way to amplify the potential of the company, the potential of our teams, by using these tools, and as an incredible growth vehicle. We're really happy to have Infosys along on the journey with us. Thanks, Scott. You know, we kicked off our GenAI acceleration program in August of 2024, and Infosys was with us from the very beginning. They helped us form our strategy, build out our governance process, and design our AI platform. Now, most importantly, they're helping us execute on our many use cases. They've also been able to scale at the pace we're trying to move at and do it with quality talent, which is really hard in today's market. Now, for some context, we're executing over 100 use cases across GE Vernova, and Infosys is helping on many of them. The impact from these use cases has been transformative. Now, as we look forward, we've only really begun the AI transformation at the company. You know this, we talk about that a lot. And so as we scale our efforts internally, I'm really looking forward to scaling that partnership with Infosys. No question, Justin. I, I appreciate your leadership every day. For the whole Infosys team, thanks for everything you do. Now, what a phenomenal message! 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 1, the AI opportunity for manufacturing is massive, and we are already delivering, you know, value at scale. Number 2, we have the depth and breadth 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 from the video... in the video, we have the capabilities to drive this from AI strategy to scaled execution. Thank you. Thank you, Jasmeet. Before we get to our next session, which will be followed by lunch, I would like to inform everyone that after lunch, we will be heading to the Infosys Living Labs for a walk-through on our enterprise AI in action. For our next session on unlocking AI value in financial services, please welcome Dennis Gada, Segment Head, Banking and Financial Services. Hello, everyone. You know, 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, right, 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's 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, you know, 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, you know, 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, you know, driving large-scale 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. You know, 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, right? 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, you know, huge opportunity to continue to expand. In fact, one of the CEOs and CIOs of a large banking organization we spoke to talked a similar concept to the, you know, 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 in order to benefit from and leverage what's already there. We also see a diffusion of AI use cases across all the sub verticals, right? We are working, for example, significantly on fraud prevention in the payment space, in the consumer banking space. And this, 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's a lot of work on customer experience to contact centers, to UI/UX, but also beyond that, right? For many relationship managers in commercial banking or advisors 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 advisors can spend more time with their end clients. We also think that agentic commerce and payments will take off significantly. It's 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'll talk about one of our flagship client examples in financial services, Citizens Bank. It's a top 15 bank in the U.S. 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. This has been an ongoing journey. We've helped Citizens Bank move 100% to the cloud, one of the few financial services institutions in the world that has achieved that. We've already built some industry-leading platforms on the cloud. And then, with this foundation now, we are helping them accelerate the AI journey, you know, 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. More broadly, the bank has talked about a $450 million 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. Would like to play a short video to talk about the journey at Citizens Bank. As we reimagine the bank, we're looking at transforming a lot of Citizens' business processes. We've set an ambitious goal to deliver over $450 million in revenue and operating cost reductions over the course of the next two to three years. I'm really excited about that and the power of AI to enable that. We've kicked off 47 different initiatives across the bank. About half of those initiatives are going to leverage AI to really deliver significant benefits for the company. We really believe that AI is going to be a game changer for the banking industry, and we want to be at the forefront of that here at Citizens. And that's why we've opened up the hub here in Bangalore. I'm so excited. We're building it from the ground up. It's, it's really a first in the industry. This is not only a technology initiative, this is also a business initiative. So we have 75 use cases across the bank that we're using AI. Having the center of gravity here in Bangalore, we're going to be able to take advantage of the talent that there is in both the AI space, agentic space, and data spaces. So that's an example of a first-of-its-kind AI innovation hub right here in Bangalore for Citizens Bank, dedicated to support their Reimagine the Bank program. Now, beyond Citizens, right? If you look at the financial services industry, as you know, it's the largest segment for us at Infosys, and we work with organizations across the spectrum, right from the large global banks to the regional banks, to card providers, asset and wealth management firms, and so on. 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, you know, for our top three card provider, we've been working with them on the modernization journey for their core cards platform, right? 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, right? 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 advisors to get better data insights, higher productivity, so they can focus on, on their end clients. In summary, you know, financial services industries, as all of you know very well, is very complex. There's a lot of legacy, there's a lot of regulatory oversight, but it 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, and, you know, drive the organizations forward. At Infosys, we have, you know, deep expertise in the industry vertical. It's our largest segment. We have strong capabilities that we talked about earlier today. and also, most importantly, we have the depth of the relationships, right? Many of these organizations we've 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 AI-powered and, you know, pivot to a completely new operating model for the future. There's a lot of work to be done. We are excited. We are, you know, just getting started, and we think we will be super successful. Thank you. Thank you, Dennis. Before we proceed for lunch, everyone, a few important announcements. For departures in the evening, like I said, we'll have coaches to the airport at 4:45 P.M. and 5:00 P.M. If anyone needs an airport transfer earlier than that, please inform the help desk outside in the lobby. As you finish lunch, in the next 30-35 minutes, we will walk down to the Infosys Living Labs or take buggies, which is right next to this building, and to see enterprise AI in action. On your registration badge, you will find a number that indicates your group for the Living Labs tour. Group volunteers will be arriving here by 1:25 P.M. Request you to join the volunteers and proceed towards the Living Labs in Building 45, where you will collect your headsets and will be briefed by your group leaders for an immersive experience. Kindly access elevators to the right side of the banquet hall when you exit for the Living Labs tour. I now request everyone to proceed for lunch on my left and straight back outside this room. For people who have specifically asked, requested for Jain food, you will find it at the counters on my right side of the hall. Thank you. See you after lunch. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in that journey for more than 5 years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I wanna say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than 5 years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our frontier firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence, adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the frontier firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing.... We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partners at Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than five years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence, adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision. We moved away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I wanna say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, Security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than 5 years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision. We moved away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, Security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern, pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than 5 years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, Agentic AI, Now Assist, AI LLMs, Security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than 5 years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence, adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say, on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I wanna say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize, and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction and experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than five years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier F irm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence, adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, Security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than 5 years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision. We moved away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than five years now. Knowing our business and it helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than five years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence, adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I wanna say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, Security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem: not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than 5 years now. Knowing our business and it helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier F irm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence, adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, Security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than five years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence, adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, Agentic AI, Now Assist, AI LLMs, security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than five years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, Security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than five years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, Agentic AI, Now Assist, AI LLMs, security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem: not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than 5 years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence, adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, Security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than 5 years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision. It moved away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I wanna say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, Agentic AI, Now Assist, AI LLMs, security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction and experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than five years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier F irm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision, and move away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, Security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than five years now. Knowing our business and helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. At Microsoft, we see AI and agentic AI as the foundation of how enterprises will operate going forward, not as isolated pilots, but as production-scale, business-critical capabilities. This is core to our Frontier Firm vision, where organizations embed AI deeply into how work gets done across the enterprise. Infosys is a strategic partner, helping bring that vision to life for our joint enterprise customers. What differentiates Infosys is their ability to operationalize Microsoft's AI solutions into secure, governed, and scalable enterprise platforms. This goes well beyond deploying tools. It's about enabling trust, integrated AI intelligence, adopted at scale. Together, we are advancing agentic AI, where intelligent agents actively orchestrate workflows across business functions, driving productivity, faster decision-making, and measurable outcomes. Infosys exemplifies the kind of partner we want to scale with, one that co-innovates, co-engineers, and delivers responsibility at enterprise scale. Together, Microsoft and Infosys are helping enterprises realize the Frontier Firm vision, embedding AI and agentic intelligence into the core of how work gets done, and enabling organizations to operate in a more intelligent, adaptive, and AI-native way. Hello, Salil and, Anand, and the rest of the Infosys team. Can I first say on behalf of Telstra, thank you so much for the partnership. We are really building a stronger and stronger partnership. It's a very long partnership, more than 25 years, but the level we have brought this partnership to the last few years have been amazing. We are not an AI company, but to be a leader in connectivity and be one of the leading telcos in the world, we need to be a leader in AI. And to do that, we need partners that push us, that challenge us, but also work together with us on that vision. We moved away from traditional transactional relationship with partners, including our partnership with Infosys, to a more outcome-based strategic relationship. You are one of our most important partners in the whole software and IT ecosystem, and to see how our teams start moving away from just playing with AI to make AI a daily part of the business. So for us, this partnership is so critical, not only for today, but also for the future, where we have that ambition to be a connectivity leader and also to ensure that we deliver on our connected future strategy. So Salil, Anand, and the Infosys team, keep challenging us. We want to be a leader, and we want the best partners, and you're one of them. Thank you so much. Infosys is one of those partners where the results speak louder than anything I could say here, but I want to say it anyway. What I appreciate most about this partnership is that Infosys doesn't just deploy ServiceNow, you industrialize it. You take our AI capabilities, agentic AI, Now Assist, AI LLMs, security, and you build secure, governed, enterprise-scale platforms that customers actually adopt and use, and that's the part that matters. Because AI doesn't just create value sitting on the shelf, it creates value when it's put to work, and that's exactly what we're seeing together. Look at what's happening with IKEA. Infosys and ServiceNow are automating sales and service workflows, accelerating response times, and moving critical processes from manual execution to AI-led operations. That's not a pilot, that's production. That's real outcomes. We're seeing the same pattern with Tyson, Lumen, PepsiCo, and others across retail, telecom, energy, and financial services. This is what bold looks like in a partner ecosystem, not just talking about AI, but putting it to work at scale, in production, with measurable results. You are demonstrating right now what it takes to move customers from AI experimentation to real, repeatable business value. That's the kind of impact customers recognize and that earns the right to grow together. Thank you for the partnership, and I'm super excited about what lies ahead. Hello, everyone. My team is responsible for delivering operational excellence for our enhanced customer satisfaction experience for mission-critical customer solutions and workloads. AI gives a lot of opportunities in that space, and one key scenario, among many others, where we have been using AI together with Infosys, is to predict and prevent issues, and as well to speed up the support we deliver. Infosys has been a great partner in our AI journey for more than 5 years now. Knowing our business, it helps us really to bring together the best of both worlds from Microsoft and Infosys, driving our AI solutions to enhance customer support experience. Microsoft AI and Infosys AI expertise leading to significant synergies and really increasing productivity by, in some areas, up to 50%, and as well, really support our customer satisfaction improvement program. The impact we are seeing today is really tremendous, and we are very energized to continue that journey together with Infosys. Hello, everyone. I request you all to please be seated so that we can get the show back on track. Thank you. Welcome back, everyone. We hope that you had an insightful visit to the Living Labs and saw all of our value pillars in action. Now, for our next session on unlocking AI value in energy, utilities, resources, and services, please welcome Ashish Kumar Dash, Segment Head, Energy, Utilities, Resources, and Services. Hello. Hello, and good afternoon. 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'm going to talk about an interesting segment called Energy, Utilities, Resources, and Services. 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're 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, massive role to play. A data point here: the projection for data centers to consume about 10%-12% global electricity by 2030 is almost four times the current level. That's 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, right? And resources, interestingly, are the providers for raw materials that runs, 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 industry sectors, across these different sectors in the segment, is the demand has gone up, digital intensity is at an all-time high, and 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. And 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, and 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. And 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, 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. And what differentiate 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've 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've worked in AI-grade data engineering for a very large electricity provider to predict the load on the grid, forecast 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 problems, mathematical problems, that could not have been solved at a granular level that we are doing today. We are working with... helping our 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 cost, that is expansion of margins, and customer satisfaction and innovation, right? So another example is the agentic AI platform, where we are building enterprise-level agentic AI platforms to drive significant change in the way client, client imagines their workflows, and then make digital, the agents, more of a coworker with our clients' employees. We have done 15-plus such implementations in this segment, and of course, not to mention the full stack modernization of, 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 client can immediately unlock value. A great example is a very large airline, where we really 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. Now, this is a mega deal that we signed with BP, a super major. Now, the challenge was to enhance enterprise-wide adoption of AI, and here we're talking about massive scale that cuts across all of the value chain elements. So starting from production and optimization to dynamic pricing in their retail and convenience stores, contract automation for the, you know, 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 an Azure Foundry, OpenAI stack, and of course, for the developer productivity, we used GitHub Copilot 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 they 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. The other quote is from the ex-CIO. Let me give you another example, and this example is about scale, this example about complexity, this example is about also not having fragmented, but unified implementation of AI. This is for a large scale for the largest oil and gas operator, 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+ 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 approach this is on 4 value vectors. The bottom of this, the foundation layer, is the enterprise-grade platform that we built 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%-35% efficiency gains in upstream value chain, and 15%-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. Let's hear from the CIO on... Let's hear from the sponsor of this program on how this whole initiative went through. Can I have the video from Woodside, please? Can you talk a little bit about what are some of the trends you're seeing in the oil and gas sector when it comes to AI? Woodside's a global energy company, so we supply energy around the world, and I think being in the oil and gas industry is a really exciting place to work. Fundamentally, last year was a year of foundations for us. And so our AI strategy really fits into our digital obligation of providing intelligent systems for our staff to interact with, and that supports Woodside's ambition and goal to provide the energy that the world needs reliably, at low cost, and with lower carbon. But we had some 300+ use cases. We scaled our AI pods from about 1 last year. We've got 11, 12 running now. And scaling up one of our AI pods to 11 or 12, that was done in conjunction and in partnership with Infosys. And, you know, if I look at the number of people, which is 100+ in those pods that we onboarded and hired over a year, versus the number of people we were able to hire in Australia, was about 10. And so, you know, the ability to leverage our partnership with Infosys, your brand, and your expertise, and to be able to scale, was really helpful to us. To summarize, I think we are bringing an outcome-focused, responsible AI framework that is working at scale and at speed, and very, very excited to be doing this for so many clients in this sector. Thank you. Thank you, Dash. For our next session on unlocking AI value, retail, CPG, and logistics, please welcome Ambeshwar Nath, Industry Head, CPG, Logistics and Retail. Good afternoon. Let me start off by walking you through the market outlook and the role that AI has for our clients. If we look at the quotes from market leaders in our industry, there's a common trend that's 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 demand 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%-5% in the overall gross profit. There's a significant focus of applying AI for hyper-personalized marketing, driving deep, individualized consumer campaigns, as well as, as well as driving AI planogramming compliance. Within the retail sector, we are seeing evolution of large language models for loyalty programs. Today, as of 2026, 70% of all loyalty programs we'll engage in will have an, have an AI component in them. We are seeing 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, in terms of route optimization, as well as leveraging it for waste reduction. That's resulting in benefits of 15%-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 or 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 as well, a short time back, but Ralph Lauren is one of the leading high-end fashion and apparel companies across the world. They're also, as of today, one of the fastest-growing companies. But one of the uniqueness about why they're growing this fast is because they're 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 paths to purchase with their consumers. Clearly, one of the key elements they're 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? Now, in-store, there are always challenges. You do a lot of manual merchandising, there's 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've got a merchandiser working with you to style your exact outfit the way you want it. And 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's 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 look books. 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're engaging with, with an AI capability, but it felt very similar. And so if you wanted to choose an outfit of the nature you want, or you want it for a specific occasion, it's happening as if you're talking in real life. It drove significant hyper-personalization and the path to purchase on an online setup, if you can imagine, it's always you search for an item, then you browse the item, then you click it in the shopping cart. It's a long-established process, but this was a seamless process. Because we 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, more than 50% of the interactions, 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 to 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'd like to now talk about a second example. We talked about a global high-end fashion apparel retailer in our first example. I want to move gears and talk about a regional player, because I think that's very important as well. A lot of our regional players are also leveraging the power of AI. Posti is Finland's logistics leader. It's original, it's a, it's 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've evolved over a period of time to get into parcels, into supply chain, into warehousing. So now they're an end-to-end logistics player. And as their postal volumes are declining, as 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, the largest partner, and 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. 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+ initiatives on AI across their postal business, across their parcel business, and their freight business. We are cutting the entire value chain and applying AI at all points of the ecosystem. The whole focus is around run to growth. 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 industry, Nordics market? Lastly, they have not gone with one solution. We are the single partner who are driving AI orchestration for them. So they're 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 a 50% software generation by AI tools. It's led to a 35% improvement in productivity, and the mean time to recover, considering they had large amount of legacy system, has been improved by 70%. So this has created a massive impact. What I will do now is play a short video which talks a bit about how Posti has evolved its AI journey, and how our sponsor looks at Infosys's involvement. Posti is Finland's leading postal and logistics provider. Today, parcels, freight, and warehousing, supported by a growing portfolio of digital services, form the backbone of their operations. To power the next phase of their growth, Infosys came on board to reimagine their IT services with agentic and generative AI to accelerate software delivery. Our focus isn't in technology for its own sake. It's delivering better business results, better customer experience, and greater efficiency. That's why we've been investing in AI for years, with strong results in different parts of the organization. Today, about half of code is machine-generated, with effort reductions of up to 35%. Expanding agentic AI across their IT organization will considerably reduce downtime, which Posti has already reduced by 95% with the help of Infosys. But this is just the beginning. Infosys has played a key role in building and taking our AI-driven capabilities into the daily life of our IT operations. The contribution has been instrumental in powering the next phase of our evolution and helping us become a leader in logistics and e-commerce sector. But more than that, true digital forerunner in the Nordics. So I'd like to just summarize by highlighting two points. One is the fact that we are seeing AI evolve within our client landscape. It's critical, and it's 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. Thank you, Ambi. For our next session on partnership ecosystem for AI value delivery, I would like to again welcome Anand Swaminathan, Segment Head, Communication, Media, and Technology. Right. At this time, I think you should have a, you should have gotten a fair idea about, one, the opportunity in front of us, and it's more the execution risk that can hold any IT service company back. It's not the opportunity itself. And 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. And as the AI evolution was taking place, one of the thing we did was to really rethink our partner strategy and construct a partner ecosystem that really, you know, 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. Like, today morning, we announced a big partnership with Anthropic. Is an example of how we are actually moving this forward. So we need model companies. We need infrastructure companies that actually are investing in terms of, you know, 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. Now, normally, people are equating AI to one particular company or one particular category of company. It is not. It's a very complex set of components you need to make AI come to life. And enterprises want the stack to be governed. It's very complex. It's 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 gonna 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, and we are now 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. And again, these are partners that will help us with specific industry workloads, rather than generally come with one specific set of capabilities. That's what differentiates Infosys as against other companies in terms of how we look at these partnerships. So if you see the AI value framework, now, 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 helped them to actually work with an NVIDIA stack, where their onboarding of investors accelerated by about 30%. And this is an investment firm that has more than 5,000+ rules, regulations, and I don't need to explain that to this particular group of people, as you might understand how it works in your world. Now, we were able to bring in an NVIDIA AI stack, or NVIDIA as a center point in 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 agent or set of agents instead of human-powered selling. Now, that is the power of, you know, the partnerships that we have, because in order to bring this to life, you need pre-established relationships, training between companies, and contracts so that the friction to market is less. And 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. You know, 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, and that is about moving it into a place where it's 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. And already, you know, when Rafee spoke, he announced one of the AI products from Infosys is AI Next. So if somebody wants a full platform, you know, they can actually get AI Next from us. So, 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, CMA CGM, which is 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. So here, our partners, together with us, we 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, that you saw the Hertz customer come and speak. Similarly, we have, you know, many other examples of where we have really taken this to our clients and, you know, getting them the benefits. So 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. And, you know, those examples are also building. So essentially, what I would like to actually, you know, sort of sum up is to say that the opportunity—the opportunity is really in front of us, and there is no question about that. You know, we have the conviction on that. But at the same time, we also acknowledge that this requires complex execution across multiple components, and one of the key components is the partnership ecosystem. And I think as an organization, we have invested heavily and really brought in a set of partners that will help us to transcend, you know, this, you know, from the current position 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 exec says the following about us. So can we please roll the video? Hello, everyone. Thank you for having me at your Investor Day. So look, the companies winning with AI right now, they're the ones who have the right ambition, but they're also marrying that with the right approach to deployment, governance, and partnerships. Infosys has spent four decades building that foundation for enterprises around the world and establishing a deep position of trust, and that is not something that you can replicate overnight. And that's why we're incredibly proud to have Infosys as a strategic partner. At Anthropic, our goal is to build AI responsibly because we believe that that is what it takes for enterprises to actually trust, adopt, and put AI to work at scale. And Infosys has always approached technology in the same way, because you've managed multiple technology cycles over four decades at an incredible scale across all industries and all geographies. That history gives you all the credibility that most companies simply do not have. You are embedding this frontier intelligence into Topaz, the AI platform that reaches across your entire client base, 2,000 organizations, 59 countries. So when Infosys scales AI, you don't just scale it to one company, you scale it simultaneously across so many industries and so many countries. And at Infosys, you've done that better than almost anyone, and you've done it across four decades, across multiple technology cycles. I'm genuinely excited about what we're going to achieve together. So in summary, you know, we think trust, scale, and partnerships are going to be the three pillars in which this transformation needs to happen. 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. Thank you, Anand. For our next session on the human AI workforce reimagination, please welcome Shaji Mathew, Chief Human Resources Officer. 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 of 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 organization which prepare their workforce to embrace that technology that's gonna 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 part of the pillar, the first pillar, is therefore a new talent operating model. Once we have this dual architecture, it's 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 these very quickly. So 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're going to create this deep expertise. That is through two channels. One is by external hiring. Recently, you would have seen an advertisement where Infosys is now recruiting fresh engineers up to INR 21 lakh salary per annum, and that is really towards this developing this deep engineering talent. We go to some of the best engineering colleges in the country, the IITs and the 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 don't 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 hard, you know, hands-on training programs, working on sandbox environments. Once someone has gone through this bridge program, we would, we would do the assessment. The Assessment Center of Excellence that we have does the assessment on a five-point scale all the way up to AI-led simulations. 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 assesses people on domain, it looks at people on the foundational skills, as well as on societal skills. It's a comprehensive framework that we have to assess people on their skill. 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 uni-dimensional career architecture. We will have people joining us, 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 stream. And these are the deep engineering or the domain experts that we are creating within the organization. Also, on the far right, what you see is the expert-led 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 or the accelerators around which the larger specialist ecosystem work around them. It has got, you know, roles like, distinguished technology engineers, and so on and so forth. This career architecture also look 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 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 set the direction, and they obviously have a 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 five-point assessment mechanism that I spoke about, which culminates with AI-led simulation and case study-based assessment. Their 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 programs, so that every engineer who comes through the doors of Infosys from day one, they all get enabled on AI. On the other side, the leaders. It's 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's going on in our Mysore campus in collaboration with Harvard Business School, 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 complete 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. 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 all very confident that 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. Thank you, Shaji. For our next session on brand as a growth catalyst, please welcome Sumit Virmani, Chief Marketing Officer. Good afternoon, everyone. I know it's 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'm 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'm 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're seeing on your slide out there is how brands, not just across categories, well-managed 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're seeing out there on the screen is an example of how we think about our strategic partnerships. Some of the brands that you're seeing on the screen are possibly one of some of the largest brands in the world, but there's a consistency in all of them. They actually play at the intersection of passion and executive participation. For decades, brands have leveraged these kinds of partnerships to showcase their reach to the world. 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's the magic in the partnership, because it becomes a showcase to a billion people in the world what Infosys AI can do. I want you to take a look at this video, because this will give you an example of how one of our biggest partnerships is being shaped through this approach. Let's have the video, please. When you join hands with a sport, what do you play for? For 10 years, we've played for more. We've played for 1 billion fans to bring them experiences and closer to the game. We've played for every athlete taking serve, with artificial intelligence that changes their game. We've played for the sports storytellers, with insight that uncovers the finest moments. We've kept playing beyond the court. To inspire, with a legend that stands tall and an icon shaping it, turning athletes into brand ambassadors. We've played across the globe, under the Melbourne sky, on the red clay, by the historic glass. We've played where history is made. We've shared moments and memories, victories and breakthroughs. And we'll keep playing, because this isn't just a sport, because when it's love all, the best is yet to come. Well, that in itself sounds wonderful. I guess the natural question would be: how has it tangibly impacted the brand? So let's take a look at that. What you're seeing on your right is a ranking by Brand Finance, a leading brand consultancy firms based out of London, and they've seen Infosys as the fastest growing brand in its category around the world, 6 years—for the last 6 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're seeing at the bottom of the screen are some of the initiatives we've 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's 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's the enterprise AI radar or whether it's 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? That's 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'd like to know how is brand Infosys faring on the enterprise AI association. 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's the word of mouth that you're seeing consistently rising. What's to the bottom right is how the focused effort on thought leadership is driving media share of volumes around the world. And as, again, as you can see here on here, we are almost growing that by over 100%. 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's across the digital ratings or whether it's across AI ratings, Infosys has consistently been on top when it comes to leadership rankings... I'll 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's also demonstrated tangible impact on the market share gains. So the question then in front of us is: what's 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 we--how we will do it, is through unlocking AI value, and that's going to be the endeavor of the brand over the foreseeable future. Thank you very much, and I think it's time for the conversation and the questions. Thank you. 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 the 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. Okay, good afternoon. I think the good news is, most of you look like you're 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 a lot of questions on your mind. So first, my sense is we have a really comprehensive set of AI offerings. You've seen the hexagon. You've seen that beyond that, we have 30 offerings, 100 sub-offerings. And you've seen examples across each of the six areas, which give you a view of what's going on with the new services, and you've seen all of our augmented services and the impact there 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's quite clear that large enterprise clients trust Infosys. You saw some of the videos, and you've 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's not theoretical anymore. It's really practical. It's happening in the field, and we have examples of that. We have examples of large delivery teams working on that. Then we spend 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, a 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. Then the partnerships, of course, we announced a very large one today, but we have several of these, and it's very clear from how the partners are looking at Infosys that it's 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've 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's growing very fast, and that helps. 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 a summary. And with that, let me request Jayesh to join us here, join me here, and then we can go through with the Q&A. There's a question there. Thank you. So the first question is, you know, you highlighted in the morning that the net new opportunity from AI will be $300 billion-$400 billion. Could you also elaborate what do you think is—Sorry, the incremental opportunity is $300 billion-$400 billion 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, you know, from months to weeks. What does that mean from a compression perspective, and what are we looking at on a net basis? Some of it broke up. So what you're saying is, we've talked about the growth, expansion and what is the net number. So there, what we have shared in the morning is really, on the 6 areas, what we see as the expansion from what we've seen externally, the $300 billion-$400 billion quantification, from a couple of sources. On the compression, we've talked about, again, what we understand to be where the compression is coming from, whether it's on application development, whether it's 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. Hi. Hi. Thank you for hosting us and taking us through in detail through your verticals and service-based 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? So there, the focus, in addition to the points you mentioned, is one, on the platform and Topaz Fabric capability that we built, 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's where the real difference. The execution is where the real difference will come in. 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. Sorry, I didn't follow that thing. Yeah. So, I’ll take that, Salil. So if you look at the large deals that we have signed on last few years, last few quarters, we haven’t 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 majority, we have not seen the deal timeline shrinking significantly. Hi, Salil. Yeah, 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's 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. 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 a ERP migration, but it's 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. Yeah. Hi. Yeah. Right. Yeah. Hi, Salil. Just in response to the first question that you answered, that we're not kind of quantifying the compression factor. If I were to take a look at it, let's say, direction wise, let's consider this 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's 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 dev, adoption cycle of this technology, where would you place us at this point of time? So there, if you step back a little bit, there are usually sort of 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's 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's 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's 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's not something which is large, and it's not something that is insignificant, but we don't 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're already seeing signs of visibility. It's a function of AI leverage, meaning more AI being used. It's a function of macro, and it's a function of the compression. So the, these are a little bit more intertwined than would otherwise be. I would request to give your name and the name of your organization, since we are webcasting this session. Hey, this is Jonathan Lee from Guggenheim. Thanks for hosting 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 at hand? And how should we think about the impact to margins there ex potential, you know, currency benefit? So there, I'll start off, Jayesh may have some things to add. One of the things we've been very clear about is, we, 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, 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've 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, 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've done in the past. With that sort of a mindset, we will continue using the balance sheet as well. Yeah. Just, just to add to what Salil was saying, if you look at this year, nine months into the year, we've 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-to-day, whether it was partnerships, whether it was tech investment, 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 and stability. As Salil said, our endeavor is to ensure that, you know, all the investments come off, come out of our margin guidance. Yeah. 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's still, I think, a small number, but we've 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 $4-5 million or something like that. So how far are we when we start seeing $50-100 million deal, and where the adoption really becomes accelerated? Meaning, that's one, first question. Probably, if you could answer that, and then I can ask the second. So there, I think one of the reasons we wanted to share 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's a long runway, because essentially over the next several years, there'll 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 three, four, five years where it became 65%, 70%. And so that's the sort of a play that you have. We don't know if this is gonna 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. Sure. One of the more 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 billion over the next five to six years. For a long-term stakeholder or investor in Infosys, how should one think—what are the 3 or 4 things that will change from a financial metrics perspective, probably 5 years out, as we navigate this journey from where we are today? From the financial metrics? Yeah, that's right. From where Infosys is today, as we navigate this journey, let's say, over a period of five years, you know, if you could put that in context. So, so if you look at, you know, every tech cycle, and this is no different, from a cycle perspective. Of course, the matrics can be different. But the way we have always articulated 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, right? We said that in the digital era, we, we are saying the same thing today. If you look at our RPP for last two years, we have delivered, you know, superior RPP. Our RPP has grown, 3%, both FY 2025 and FY 2026. If you look at margins, it is showing, you know, resilience. We, we grew margins by 50 basis points last year. We have stable margins this year, despite all the investment that we talked about. So I think that is, that is exactly what it's going to boil down to if you're looking at, you know, any tech cycle in my mind, from a services perspective. Hi. Hi, this is Aditya from UBS. Just a couple of questions. So, you guys have spoken a lot about Topaz, and it's good to see. I think it's moved beyond, you know, those pilot use cases to more enterprise-wide 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 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. So that's the first question. And secondly, on headcount, I think you've given a plan on how you'll be reskilling talent and hiring more specialized talent, et cetera. 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. Pricing. I'll 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, I don't think there's going to be one model that is going to, you know, apply to every client. It will depend on, you know, the client context, what does the client want, and how, how we- we are able to best, you know, justify the value that we are creating for the client. So it's always going to be the combination, like it's always been in the past. You know, we didn't have just one model. There are various model that worked, and depending on, you know, the client's context, you, you close on the pricing model. I think that's how it's going to remain on that. Yeah. On the- On the utilization and the specialized talent, I think we'll see more and more of that happening, but equally, we will also see. I think in somewhere in the day, we 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's appropriate to use tools, when it's 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 you 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. If I can just add to what Salil was saying, if you look at even in the digital cycle, when we started this cycle with 25% digital, we are now at, you know, pretty much 65+% 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, right? So we have a strong, you know, training culture, and that is what we have always dipped into, to retrain and repurpose our employees. 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 is now getting blurred? And what does it mean from addressable market perspective for service providers? Second is a question related to the evolution of pricing model that you talked about. If it creates room for nonlinearity, what's the headroom that you get from investment point of view to accelerate your journey in AI? Thank you. 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, you know, 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's effective for the client, then there'll be multiple... It's not gonna be one thing for every client, and so there's gonna be different builds for different clients, which, my sense is will be a larger, opportunity for Infosys. Yeah, on the pricing, you're right. If you're, if you're able to have a larger part of revenue enabled through, you know, platform or agents, it will create, to that extent, a nonlinearity. 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, it will create a little bit of a nonlinearity. Yeah, 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 doesn't matter? Because the 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 most of that goes back to the customer. So any way to kind of capture the value better? Thanks. What was that question? The question is, you saw so much of revenue, or savings for the client, is there a way to capture? Oh, capture it. I think there was some discussion in some of the client examples on outcome-based. Now, if we manage to do some of those with some sharing, we might see some of the benefits of it. Today, if you... You know, in one of the early charts, we sort of showed the model is moved 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's not more equitable. But in the future, when it becomes more aligned, the model and what's in the enterprise, then you could see that part of it could be there. If some of the outcome base works, you could, you could imagine that, you know, some, some more comes our way. But it's, it's not visible today, so it's not something that we, we are looking, for example, in the coming year. But it's something which is in our mind, as what Jayesh was saying, that it's still early days. We'll see how the pricing approach will develop. Again, if I can just add to what Salil was saying, there are also indirect ways to capture the benefit, right? Like, again, through the presentations, you would have seen, you know, multiple sectors, 15 out of the top 25 clients, we are the AI strategic partner. Now, that only comes in when you've created significant value for the client, right? You saw in Anand's presentation that, you know, 6 large part of the telcos, we are large players. That only comes, again, when we have created significant value for the client, and you become a strategic partners for the client. So those are the other benefits of creating that value for the client. It's not just that everything gets passed on to the client. You also get a lion's share of the client's, you know, landscape. Kunal from Bank of America. You know, 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's not gonna be a labor-intensive model, can the profitability of these projects now meet thresholds better than earlier? Couldn't hear. Small language model? Yeah. So there, first, the small language model, I think, for us, is a very good indication of the depth we have, and it's very useful on a limited data set in a large client already today. So we are seeing some benefit of that, whether it's, 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. There are growth markets that we have looked at. For example, we think, you know, markets in the Middle East are very strong, but it's not like an emerging market, but it's a growth market, where we will look for some of these models. But right now, the adoption. It also depends the adoption in that, in that environment, in that geography, before we can go into adoption in the mid-market as a client base also. Hi. This side. Yeah, this is Sandeep from Equirus. Just one question: when the enterprise client penetrates the adoption of AI, what I mean is, most of their legacy application modernize, they do the data layer properly, they migrate it to the cloud, and they enter the post-AI mode. In that period, what could be the growth rates of the industry and the Indian service provider? Because in that phase, the growth could be lower because everything could have been automated. Why I'm asking is, many investor has a doubt that in a post-AI adoption era, the terminal growth rates could be negligible. I didn't follow that. I didn't get the question. Can you- If you could repeat or maybe increase the volume. We couldn't hear very well. Hello? Yeah. Yeah. What I'm saying is, 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 most of the applications' data could have been modernized. 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's 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's 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's much like what we have today, where there are new things happening even on older platforms, which let's call it, are somewhat stable today in terms of they're implemented. But there's always new work that people are looking for. But that's 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'll do, but there's even more work because there'll be more and more things that'll be going on in that post-AI era. We have time for just the last two questions. Hey. Hi, it's 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're deploying in a client environment, are those homegrown or are those of frontier model companies? 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'll be a host of those agents from which some selections will be done, and of course, clients are also building agents. Right. And And Salil, for agents of third party, let's say, what is the services intensity? So, for example, let's say $1 is spent on a third-party model, you know, and you basically customize it and configure it for the client environment, then what are the services revenues that you get versus $1, which is captured by the, let's say, a frontier model company or a external third-party agent provider? So there, we don't have very deep, exact stats on that, that we can share. What we see is different because if you look at a software development life cycle, part of it, as you know, is the cost of building the software. So in this case, the agent. But there's a significant part beyond that which is integrating it into the environment, making sure that it's working in that environment and the performance attributes, and then the security attributes. So we've got some examples of what we've done, as we've shared today, in these situations, but we don't have a statistic that we can share that one equals to X because of the ratio. As we go through the next few quarters, we will definitely internally build up a larger dataset on that, and then we'll see if that becomes something that we can share publicly. There you go. Hey, Salil, and Jayesh. Thanks for organizing today. This is Sean 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've modeled 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're 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're gearing up for this year, or should we expect that to still take j time, given the AI-centric efficiencies that you're, you know, factoring in? So there, I think first in Q3, we had a headcount growth, I think it was 3,000 or 4,000- This year we added 13,000. For the year, we've added 13,000 in 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's a macro element and there's 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 these together, if in the last three quarters we've done 13,000 net headcount increase, my sense, we will continue with the headcount increase in the coming quarters as well. Thank you. That brings us to the end of the Q&A. Thank you, Salil and Jayesh. Thank you, everyone. Thank you, everyone. Thank you, everyone. Thanks. Thank you. Thank you. 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