LTM Limited (LTM) Earnings Call Transcript & Summary

It is my absolute honor and privilege to introduce and invite our CEO and Managing Director, Mr. Venu Lambu, on stage as he delivers his strategic strengths as well as his keynote address. A huge round of applause for Mr. Venu Lambu, ladies and gentlemen.

I thought I'm tall already. There is something below me here, which makes me much taller. Archana, well done. Thanks for nice opening. Let me begin first welcoming you all for our Investor Day. As Archana said, this is our first Investor Day after we rebranded ourselves as LTM. And this is also my first Investor Day as the CEO of the company. Incidentally, though I joined in Jan 2025 as the CEO designate, but officially, the switchover happened at the time of AGM, which happened on 30th of May. So just 2 days back, I also completed an official 1 year in this role. So thank you. In the next 30 to 35 minutes, I'm going to talk about our strategy. You will see a mention about 5 years as the period in the strategy, but I want you to look at it as a strategy that is -- makes sense to the point of today because things change so fast, we keep improvising it as we go along. And -- but it's important that we have a goal, what we want to achieve over the next 5 years, both in terms of revenue, profitability, employees, capabilities and so on. So a lot of those things are directional in the nature, but hardcoded in what we want to do today. And then we keep improvising it as we go along over the next 5 years. So let me take you all through that strategy. But before that, as Archana covered this, I'll be joined by my leadership team for the rest of the evening here to talk about the respective sessions. And we also have the immersive showcase just outside of the break and in the separate banquet hall. We have our practice leadership, our delivery leadership actually were all here in this venue. So not only you can interact at the showcase and this thing, it's also an opportunity for you to actually meet extended team of LTM, right? So you have folks from who leads interactive practices, data and so on, right? So you -- and then also the vertical delivery heads are here as well. All right. So look, it's not been an easy year. I'm sure you all will vouch for me on this point. When I say not been an easy year, it's from a macroeconomic standpoint and from the geopolitical or from the time of changes that we dealt with. It sounded like in 12 months, I dealt with things that came up every quarter, a different topic, right? It started with tariffs, then led to H-1B, AI and then more AI, then you had war. So I'm not going to go to each one of the headlines, but it's been a year where I think a lot of us who have been in the industry, probably this is a new normal that in a year, you will have to -- if not the external factors, the innovation is going to happen at shorter cycle. So we should be ready to sort of manage both the positive sides as well as the negative side of any innovation that brings to our business model at a much faster pace. That's one big takeaway if I had to take out from the last year and if I want to share it with my team. While we navigated all these topics throughout the year, I know during the earnings call and during my various interactions, I would have come and spoken about H-1B impact, tariff impact, AI impact and so on. But we kept the engine chugging, right? And we believe we did a decent job, then 6% year-on-year growth. I mean this is -- and I remember at the end of Q1 or in the beginning of Q2, I did mention in a couple of calls that I would love to be near a double digit at the Q4, the exit double -- the Q4 run rate year-on-year for that matter. So we ended up at 8% in Q4. This was also the year where we had the record order booking, $6.6 billion of order booking. But I think what was fascinating about that order booking was we focused on large deals. And if you ask me, that's going to be one of my big moat of growth. And we have shown that in FY '26 that we can win deals, which are very large, and we can win either in the space that is occupied by an incumbent, maybe a player larger than us or winning against companies of different size and scale. And all these wins that we got led us to increase almost 100% year-on-year on our large deals compared to FY '25. So it was a massive traction over there. Probably we were one of the first ones to launch a brand of our own for AI. And it's not just about brand. It's about aligning to the concept that if you want to lead in the AI era, we need to develop an ecosystem. It's not about having tools or technology or a platform, it's about having an ecosystem where you have a platform, but you also have a talent in the ecosystem, you have the partners who play a role in the ecosystem and then you co-innovate and co-develop solutions along with customers in that ecosystem. And that's the approach we took at the beginning of the year when we had the BlueVerse launch, and that ecosystem has developed beautifully. I'm going to share with you some of the highlights of that in my presentation. But most importantly, you can actually see them in the breakout session outside. And that ecosystem, as in it keeps expanding, that is going to be the second biggest moat for us. It's not about having X capability or Y capability, it's about making it more all-inclusive capability. So that's another thing that we did. And probably, we are also one of the early ones to declare that we will have a business AI team, which is dedicated. Business AI is about how do you implement AI in reimagining the business process. It's a completely new addressable market. And we have a dedicated team, which we set it up in FY '26, and it's gaining a significant traction. And of course, I've shared with you in my various earnings calls about the success of Fit for Future and Sales Transformation. That was reflected in our margin improvement both at the EBIT and PAT level. And of course, all this came without impacting how our customers feel, and we had a good rating on the CSAT as well. So it's been a very satisfying year, I would say, and also a very defining year for us because it was not just about performing, it was also about transforming. How do we transform ourselves while we performing? That is what it is. And I'm going to share with you the elements that we have transformed as a company and the things that we will transform still as we go along in the journey. So let's take a step back and you all have your perspectives about what the market is and what the market shift is. Some of these things may not be new. But the pace of innovation is unprecedented. I'll give my own example. I started my career with a company called Digital Equipment Corporation, which doesn't exist now. It was a company, which used to make big computing machines called VAX/VMS and Alpha VMS systems. And the founder of that company said, "I won't believe in PCs." PCs are meant for home. Ironically, the company that acquired Digital and which was at that time was, I think, about a $20 billion company, a Boston-based $20 billion company. And ironically, that company was acquired by a PC company called Compaq. So I've seen personally the changes pretty early in my career. I should say I was fortunate enough to deal with all that. And then you moved away from client server computing to an internet computing era, which started at the back of 2000. And then the internet computing led to cloud computing and the mobile computing and analytics era in the mid-2000s, more so after the financial crisis. And the cloud computing journey when it started, it had so many questions from both industry analysts as well as the people who are in the industry. But now if you look back how each of this evolution transitioned, that led to a big opportunity of what we call as a digital transformation. Digital transformation is big. I mean there were times we were debating what is digital revenue, what is not a digital revenue. So personally for me, this looks like sort of a similar change, but the only big difference is the speed. The speed of innovation in all these changes happened over a period of 2 to 3 years. So you had the time to adjust the thinking, mindset, get the stakeholders along and pivot yourself into that. The adoption was a gradual and everything and the innovation was a gradual innovation that happened until it reached the stage of maturity in each of the stages. But in AI, it's rapid. It's the shortest innovation cycle. And I do believe that there is a huge opportunity for us in the tech services. I mean if I were to talk more so from LTM standpoint, I think AI adoption at scale is going to revolve around 3 big Cs: Context, Cost and Change. I'll talk a little bit more on what the context is, but just to give you a quick reference and then I'll carry it forward in my subsequent presentation. Context is the -- look, the intelligence is democratized now. Everyone has an access to intelligence. Some of the best intelligent models of the world. It doesn't distinguish between experienced employees. It doesn't distinguish between where you work from, but access is democratized. But applying of that needs a context. So that is where there's a huge success criteria about the context. And second thing is about change. As much as AI is about tools and technology, it's also about change management. How do you think -- I mean, I have known customers who have told me that when I invested $30 million, $40 million in technology, but I'm unable to see the productivity because people are using it as a shadow tool. They do their work, check it on the AI tool and say, okay, it's working fine, okay, let's move on. So the actual impact of that is a change management. It's a mindset change that is needed to make sure that AI adoption happens that directly gives the productivity benefit. And the last one, we have been talking about this cost aspect, at least over the last 2 months, if--at least I and Vipul in various conversations, we've been talking about it, but I see a lot of buzz over the last 2 weeks about the impact of cost. Nothing comes free. Enterprises are going to look at total cost of ownership. What is going to be my ROI on anything that I do on the adoption of AI? And that conversations are getting serious and they're getting real. So all these 3 Cs is one aspect of it, which is a huge opportunity for us. How do I help customers in context, change and cost is a huge opportunity. The second is 1 year back, we were debating agents replacing human, agents doing something, but I could tell you now that with experiences of what we have worked on with various customers, human-agent model is the way to go about it. Yes, there are certain aspects where the agents will pretty much replace the human effort. They could be an autonomous agent. They can do things on their own. But by and large, when I look at the whole of our company, agents are going to be the best buddies for our human employees. And of course, the local talent is becoming a big more -- a lot more of importance now because of sovereignty and the context aspect of it, I'll talk a bit more on that. Outcome mindset is improving with clients, and I see that as a big opportunity, decoupling the effort. And once that changes, the opportunity just skyrockets. Because then you're getting into a space of spend, which is not traditionally addressed by us. And of course, I spoke about AI is democratized. It doesn't belong to 1 or 2 frontier model players. It belongs to a lot more archetypes of the players who have their own intelligence play in the AI era. And that also throws up a new paradigm for value creation. How do I create value for my customers, with my partners, of course, with the investors and all the stakeholders that goes. So that's where I see the opportunity sort of emerging, and we'll dwell a lot more further on these points, having established that context. Let's take these numbers, $1.3 trillion is a spend on running the business, build and modernizing systems. I mean if I had to put it in a very high-level terminology, what is the business we are in, either we build systems, modernize systems or run systems. Within that, we do a lot, many things. We do a lot many things on data, SaaS, infrastructure, IoT and so many things we do. And that's why I simplified the narrative here on the spend, but it has got a lot many things inside that when you double click. But let's assume that's about $1.3 trillion spend. We acknowledge that there is a shrinkage of at least 25%, if I may say, 20% to 25%, definitely a shrinkage of these services of the traditional model. And we saw that in our business. The 6% growth that I spoke about in FY '26 came after all the productivity discussions that we have done throughout the year. A lot of you have asked me those questions in productivity during the call, and we have had a conversation about it. And this was a net growth after all that. So that means that there is a shrinkage happened and then we grew. So we have seen it. We believe there is a shrinkage happening, and we believe it will be to the extent of 20%, 25% shrinkage. But at the same time, as the modernization, look, 80% of Fortune 2000 customers still have a technology debt. They have a huge modernization debt that needs to be addressed to make an AI adoption hugely successful. But that modernization is not going to happen with a pure human effort. It's going to happen through a platformized approach. And that's why I talk about the platformized transformation here. And that aspect of it, apart from running systems on platform, that part of the spend will increase because that is where there is a sense of urgency to clients to actually modernize, whether it's data, infrastructure, applications or any of their integration elements or even modernize their products because they have to reimagine their own business model, so they need to modernize their product and reimagine how the business needs to go. So that business is going to go up. And the fact that every technology opens up a new opportunity. I mean, I can give these examples in many times on cloud and digital transformation. I mean cloud gave us a huge opportunity on migration, modernization, cloud operation, cloud orchestration. Digital transformation gave us a huge opportunity on the entire agile scrum pods kind of construct. So same thing is going to happen on AI, we believe, where AI will open up a new addressable market for us where we can agentify the business processes and help our customers to achieve the productivity that they want to achieve. This is where all the headlines are. This is where the headlines about job alignments, role alignments, role reductions or a job reduction, if you can't call it, it's pretty much there in the core operations area or in the general operations area because that is where there's a huge opportunity and there is a huge push to bring AI at the core of the operations, and we call that as a business AI services. So that's where we set up a dedicated team to address that, and that's a huge market that's going to come up. And of course, the build side of it is going to be lead -- AI-led engineering. All the application development or product development will be AI-led engineering construct. So that's going to be a huge opportunity. If we don't do AI-led engineering, there are no tokens to be consumed. And if there are no tokens to be consumed, you know who is going to be impacted a lot. So there is going to be a huge push to adopt AI-led engineering in all of our work, and we are already doing it, and I'll share with you some examples how we are doing it. In fact, we have developed some products that you will be able to see. We have our digital engineering head here. He can showcase the products that we have developed. As a part of the strategy, we also said, we went to the customer and said, what do you want from us. And we did it across our top 50 customers. Obviously, I don't want to share all 50 here for the paucity of time, but we picked up 3 or 4 big customers. While I'll leave you to read the text, but the headline is a client said, "I want you to help me deploy at scale." Second, they said, is that we want the talent, which is AI ready. And third is LTM, you guys have been working with us for 10 years, 20 years. Most of our contracts are led by either output or effort-led. Can we start discussing outcome-based. So these are the 3 broad category of feedback that our customers said. And that's been in play whole of FY '26, and it's going to be in play big, big time in FY '27 and beyond. Now what does it mean? Having heard the addressable market and the market that is shrinking in, we spoke about what our customers are expecting to do it. Let's look at the value chain in the new era. This diagram, I would not have drawn up in 2 years back. But now when you put the value chain of the services era, you have a platform and models that has emerged very strongly over the last 12 to 24 months. And then you have on the left side, yes, I mean, it's on my right, your left, is business operations and domain. And I spoke about how business AI is an addressable market where AI is going to be implemented big time. And look at where we were traditionally strong at. That was in the center. We built this business over a few decades in focusing on digital engineering services, data, focus on technology operations, on modernization. That's our core. That's where we built on. And I also spoke about which part of that is already shrinking and which part is expanding and where the opportunity lies. So that means our core position, we can't move towards the platforms and models because that's not our strategy, and that requires a different -- I think we miss -- I don't think we want to talk about moving into the direction. That direction is where we want to partner. We have announced a lot of partnerships throughout the year with our BlueVerse partnership, and you will soon hear us, we'll be announcing a partnership with one of these 2 frontier models as well. So very soon, we will share with you some updates on that. But that's where we will take the approach of partnership. Deep partnership, not just with the frontier models, but there are a lot of SLM players, a lot of agentic foundry companies, a lot of data companies. So that's where the partnership will play on the platforms and models. And we want to double down our investments, our focus and our capability building in strengthening not just the core, but also strengthening the domain where we have a strong presence. I mean, Harsh will talk about fantastic capability we have in the financial services. Vijay will talk about tech services, and I will share some things on the other capabilities and my fellow speakers also will cover a lot more vertical depth. That's our core strength, and we want to strengthen that more on the domain side because we always address customers from the industry standpoint. We have a complete alignment right from the sales to delivery vertical-wise. So we want to double down on the domain side. The more we align on the domain, our contextual capability increases. The more we increase our contextual capabilities, our ability to handle the business operations will increase significantly, in reimagining business process and delivering business AI. So that is why we came out with a position of saying that, look, if you want to do this, then we need to, as a company, we need to move from just solving problems to deliver outcomes. Solving problems is what customers have engaged with us over the years. Every time they called us saying that, can you help me to solve my cost problem? Can you help me to solve my modernization problem? Can you help me to solve my tech problem and so on? Now the conversation is about, can you help me to enhance my client experience? Can you help me to increase my member retention? Those kind of conversations will only accelerate. It's at the early stages, but it'll accelerate a lot. So we want to move -- that's one pivot. The second pivot is that, while the tech is the core to what we do as a business, but a tech without a context has no meaning, without a domain has no meaning. So we want to strengthen our tech domain convergence story. And I will share with you a lot of examples what it means in the tech domain convergence. And then from technology services to business creativity because that's where I believe if you want to get into the big addressable spend area, our positioning has to move towards business creativity. I didn't have an opportunity to share the brand and the narrative earlier apart from a few updates on the earnings call. So I thought I will spend some time in terms of what the rationale went about in repositioning the brand and the company as LTM as a business creativity partner. So I've got a very short video for you to watch that, and then I'll come back again. [Presentation]

All right. So look, for us, this is not just about the name change. This was an opportunity to pivot our positioning to the area where customers want us to pivot. And this was also the reason we came out with this Outcreate. So it's also a mindset change for our employees, whom we call as outcreators. And you will hear from Chetana on some of the initiatives that we are doing in giving them an opportunity to Outcreate in our environment. Sorry, let me just go back. How do I go back? Okay. So this is our Outcreate framework. So the positioning is business creativity partner. The markets we will address is, of course, it's a balanced portfolio that has been one of the observations and questions that has come from your -- all of you at many places. And we are conscious about it, and I want to make sure that we work on a journey, which is balanced portfolio. And I'll talk a lot -- a bit more about that as we go along. And then the thing about how do we pivot, the strategic AI pivot about whether it's about how do we strengthen our domain tech convergence so that it strengthens our business creativity position? You'll hear it from Guru on that. Reimagine capabilities, we had probably 13 or 14 practices a year back. We have now realigned them into 3 simple line of businesses that is positioned to deliver the outcomes. And you'll hear it from Krishnan, how we reimagine the capabilities and of course, how we reimagine the ecosystem as well. And to enable all that, the talent has to be future-ready. Chetana will talk about that. And of course, we need to bring more local expertise that complements with our global collaboration as well. And of course, the cost. We can't continue to have the same cost parameter. We need to bend the cost curve a lot, and we have done that successfully in FY '26, and the journey is going to continue. And Vipul will share a bit more about what are the initiatives we are doing to bend the cost curve further. It's not just from a margin standpoint, it's to be competitive and also to reimagine services differently. So on the domain tech convergence, I'm going to pick a few aspects of the framework, but you will hear it from others in a bit more detail. On the domain tech convergence, I would strongly encourage you to see that in the booth. But there are a few examples here, which essentially shows how we intend to deliver domain tech convergence. If I just take an example of financial services, we have 20-plus agent solutions and lending operations, and Harsh will talk a lot more on that. So domain tech convergence is all about bringing an agentic solutions, which is very verticalized and industrialized. And that is what is going to be part of the BlueVerse ecosystem because intelligence is democratized, context and domain are premium. We are not going to compete with the frontier models. I know there is a different narrative about what part of it will be competition, what part of it will be partner. But if I leave that aside for a moment, we are going to make those partners, those frontier models successful by applying the context. And that's where our clients need help on. And that can be done only through this domain and tech convergence model. And that's where the real opportunity lies for us to help our customers to implement AI at scale. Let me give you a few examples of how we bring the tech domain convergence in creating our own SLMs. We have launched a team which is working on creating an SLM for a very industry-specific because you can have a tech domain convergence by having domain consultants and creating some frameworks and solutions. But in the AI world, it is important you have an SLM, which you can take it to the customer, use the customer data, fine-tune the SLMs and implement it and help them to adopt the AI at scale. And these SLMs will be developed, co-developed along with customers. We invested in first SLM model called Voicing, as some of you would be aware, and that's already doing great business for us. So we already have a contact center SLM, but some of the SLMs that you see here are the SLMs, which is work in progress, either done by us or done along with our partners. So that's another example that we will start monetizing these SLMs as we go along in our journey. Because this is the third moat, if I may say, in terms of how SLM plays a role. The LLM has a role in enterprise. Similarly, the SLMs have a role. And all this will be done as part of our BlueVerse ecosystem as well. And it's going to be outcome priced and nonlinear model. Reimagined capabilities. We essentially had the run, how you run systems, infrastructure, application, data, and so on. So things were spread across the company because the practices were built as the technology evolved and each of the practices had capabilities to run systems. So now we have brought all the capabilities that is required to keep the lights on for our customers and to move their run on a platform-based approach under one single line of business called iRun. And same thing, the capabilities that is needed to modernize technology, systems and reimagine experiences, we brought it under a line of business called iTransform. And the third one, which I mentioned earlier, is about reimagining business process and agentifying business process, creating SLMs at the point of tech and domain convergence is what we call as a Business AI. And all this will be underpinned by domain-driven engineering, the software engineering aspect, which will run our LTM BlueVerse ecosystem. Krishnan will double-click these capabilities with a few examples in his presentation. Partner ecosystem. We have been talking about, what do we mean by partner ecosystem. I think the conversation that we are having with partners are so different than what it used to be 3, 5 years back. The SLMs that I spoke about, we're going to develop with a partner called Uniphore, which we announced a partnership, which is a leading player in enabling and creating the SLMs. And we'll also work with our other partners like Voicing and so on to create SLMs as an example. So when we talk about partners, it's about value creation now rather than the focus on transactions. So the needle has moved a lot about creating joint offerings. And we'll continue to announce more and more such strategic partnership in FY '27 and as and when the opportunity comes up beyond that. The value proposition is not just about SIs. SI, whether I do something on Copilot, if I do something on Gemini or any of the other models companies, it's not to go as an SI. It's to go as saying, how can we help you to align the services to the more business aligned. And then, of course, the GTM approach is going to be leveraging the domain tech convergence with a joint investment. Uniphore is one classical example where we made the joint investment in the GTM as well apart from the S&M. Same thing we'll work with hyperscalers. All the hyperscalers that we work with, we have a dedicated team now. We have a dedicated team which works with each of the hyperscalers in accelerating the AI with everyone whom we work with, whether it's Microsoft, Google, ServiceNow, Salesforce, Databricks, Adobe and so on. The talent strategy for reimagining partner ecosystem, and they're going to play a significant role, and I'm grateful to that in reimagining our talent skill set. A lot of certifications that we're driving within the company is done in collaboration with our partners, whether it's a hyperscaler, technology platform partners or the model partners. In terms of geographies, very clear. We focus on 4 segments: Financial Services, Tech and Services, Consumer, which includes TTH, the travel transport vertical, retail and consumer vertical, media entertainment vertical as part of the consumer. Production is about energy and utilities and manufacturing comes into production. So these are the 4 major segments or 4 segments, market segments that we operate. It's very clear we will lead in these segments. In fact, we have scale in some of these segments. I mean financial services and tech services are not very far off from being $1 billion business units. And these are the segments where we will lead fundamentally. And so is the consumer. You saw some of the big deals we announced last year were in the consumer segment. Two of the largest deals that we announced were in the consumer segment. And production, we have a very unique and differentiated capabilities, both in our manufacturing area as well as the energy utilities area. Some of the big names there are our customers. So we will lead in these segments. When I say lead, we want to really dominate and lead in those segments. And we also want to scale the geographies. We want to change the mix of the geographies. So we want to scale the geographies. The Europe is definitely a focus area. You saw an announcement of a deal. I'll talk about that, and Vipul will spend more time on that. And of course, being very focused in the rest of the world rather than spreading too thin. So we'll focus, double down our Europe focus, and we'll be very focused in select few countries within the rest of the world. So that's our geographic narrative as part of the strategy. If I have to bring all that into a single frame. I spoke about BlueVerse ecosystem. I spoke about 3 capabilities being reimagined, spoke about the market segments. Now if we want to bring all that into one single frame, this is how it looks. Let me take a minute just to quickly cover that. This is the contextual models that we are working with our customers. Let's take this as an example, investor onboarding Unitrax. This is a model that is already under development in our financial services vertical. So we have a platform of our own where we support some of the major fund institutions in the North America region. So we are working on the SLMs for that. Same thing. We have an iNEXT, which is our industrial AI capability team. Using that, we are developing an SLMs for the smart plant operations. And marketing services, you will hear a lot about our strengthened marketing services at the back of CraftStudio. You will see some of them in the breakthrough rooms as well, in the breakout rooms as well. So that's another example of how we will build contextual solutions, contextual models at the foundation of it. And the assets that support us are our BlueVerse platform, the studios. We're going to launch studios at most of the major locations in the world. We now have in Bangalore. We have one in London and one in Dallas, but we'll do it at most of the other locations. And we're going to have an AI labs, which are very focused in innovating with our partners. Whether it is Copilot Lab working with Microsoft or Gemini working with Google or working on the Agentforce with Salesforce or Now Assist with ServiceNow and so on and also the 1 of the 2 frontier models, I'll give you an update very soon on that. So that's the AI labs and of course, digital employees. You will see some digital employees in the breakout rooms. The digital agents that we have developed, we have given them a persona. We have given them a name, we have given them a face so that they're part of the employee ecosystem and you have a mentor who takes care of that. So that's our assets. And where do we make money from in this. We make money from here. AI-led engineering, data for AI, integration, governance and assurance. It's going to be big. Assurance, especially post-Mythos, it's going to be huge. And we're already seeing an awesome conversation on that. And FDs, we have identified 1,000 of our best talent to be trained on FDs apart from doing lateral hires on the forward deployment engineers. So we're going to make money on all this. And while we focus on delivering reimagined capabilities on our core, which is especially iRun and iTransform and on these segments. So this is how I will summarize all the elements that I spoke about. Now all this is a strategy, but it requires an execution mindset. In FY '26, we have demonstrated that whether it's in Fit for Future, Sales Transformation, our execution muscle only got strengthened in the year. So we have a framework for execution. I call it as a New Horizon program and the new horizon is essentially whatever is good today is not good enough. So we have to challenge ourselves to create a new horizon for ourselves. And it's going to be focused only on 3 things. First is growth where we need to outperform and there is a separate team which is focused on creating growth initiatives within the company as part of the strategy. The second is pivot, the pivot that I spoke about, there's going to be a lot more pivot that will come in our journey. So you need to have a strong execution mindset to deliver that pivot. That's our Horizon two. And excellence is all about bending the cost curve, being efficient in the way we run business and look for opportunities for the margin expansion on a continuous basis. So this is how we look at the execution framework on Horizon 1, Horizon 2 and Horizon 3. Let me take a very quick few minutes in giving you some view on the 360-degree partnership with Randstad. And then after that, I will hand it over to the next speaker. And I know Vipul also is going to cover a bit more detail on this. So between both of us, I hope whatever best we could answer at this point of time, we would have answered you. I covered this the day we announced. This is a 360-degree deal partnership. The conversation which started in one of the 2 levers evolved into a wholesome 360-degree relationship. We spent good amount of money on our subcons. There is a great opportunity to realize savings and build the localized talent and building a localized talent is a different capability. It needs a partner who can support you to build the local talent in the geographies that where we want to scale. So that's one part of the partnership. The second part of the partnership is enabling the GCC for Randstad, which I spoke about. And we already started the initial ramp-up planning for that. But the deal won, we had to wait for the regulatory approvals and the consultation process to go through before we announce the closing of it. But the tenets of that is very clear. The focused geographies, and these are the geographies where we are not present. So the one thing that I'm taking away -- taking the risk out of this is no integration risk. I have absolutely nothing to integrate. Let's take Australia as an example. We have 7 salespeople. We are getting more than $100 million revenue over there. So integration risk is away. We didn't want to spend time on integration. Hence, this asset became so attractive for us that it was completely into the white space area, and it fits in very well for us. And also in the verticals, which will only add up to our production segment and the -- we can scale a lot more in the regional banks. The regional banks in Germany, in France, in Australia, some of the big banks are the customers here. And we have a fantastic capability globally, and we can bring that and scale that faster. And of course, very differentiated capabilities on the service line as well. I think the first slide I had shown it when we announced it. So let me give you a little bit more color on a few aspects. I'm not going to cover each one of them. I think in the call, I mentioned 65% of revenue comes from top 25 accounts in Europe and 80% of the revenue in Australia comes from top 10 accounts. The other important aspect of that is the average relationship of top 20 accounts is 10-plus years. And in fact, the top 5 is almost 15 years plus relationship. Great customer CSAT. And the other interesting thing that really attracted us was the -- I'm sorry, is the security clearance to work in aerospace, defense, and sovereign cloud solutions. You need to have the security assurance roles and certifications that are available. So across -- more so in the context of Europe, and we get that access over there. The third important thing, which I would just like to call out is look at the white space of the top 25 clients average IT spend. It's huge. Just looking at the addressable spend, not their overall IT budget, just looking at the addressable spend as the cross-sell and upsell opportunity that we can bring in by bringing our SAP, Oracle, cloud data and so on. And the average tenure, it's not here on the slide, but the average tenure of the consultants in top 25 accounts who work with our accounts are about 10 years or so. So there is a stickiness of the accounts, and there is a history of relationship with those accounts, and which is what we want to capitalize on and cross-sell and upsell a lot more. All right. So before I do, there's also one more new topic. The new topic is how are we going to innovate commercially. And some of the things will take time to adopt. This one will take slightly longer time to adopt because commercial contracts don't change overnight. There's a bit of a lag between the innovation and what you can do versus the readiness. But we want to be ready. We want to be ready and we want to apply where it is applicable, and we already started proposing that. So the BlueVerse currency is essentially our new pricing innovation, new commercial model. If you want to deliver outcomes, you need to be able to combine any of these 1 or 2 elements, have the ability to combine people, accelerator, which could be things like SLMs as an example, it could be agents as an example, platforms because clients may expect us to integrate the platforms. And then the clients would say, you know what, LTM, please manage the tokenomics for me. You deliver the outcome, you manage the entire token for me. So we should be able to give a unified currency to our customers, which integrates 1 or 2 of these components. And that's what BlueVerse currency is all about. And we have done an extensive research about it, taken external help as well in redefining this. We tested with a few customers. As we speak, we are putting bids into a few client organizations. I'll take you through a few examples of that, 2 of them in the traditional area and the third one is in the newer area. Let's look at application development and maintenance. We are actually working on how do we move from a classic decoupled effort. Decoupled effort and while there could be some fixed price, but can we make a percentage of it variable, which is linked to the resolution of the tickets or the resolution of the service that we deliver as an example, in the run side of the engagement. In the agent engineering factory, if you want to deploy agents at scale, enter AI at scale, we can't do SOW to SOW. We need to set up an agentic factory. And we are talking to the customers in setting up an agentic factories and deliver the outcome based on the size of the agents that we develop for them, whether it's small, medium and complex agents. The third example is about business AI, which is completely business outcome based. I mean for the travel and hospitality customer, the conversation is around how do we charge you on a per member retention, based on the business operations that we reimagine and agentify. So that's why this is our new currency model, which is aligned to outcome, measurable business impact and most importantly, it becomes reusable engine, how we structure our pricing, especially in the large deals. And as I said, we started the journey. The readiness always lags from terms of audio readiness, but I would expect over the next year or 2, you will see more examples that I can share with you in my -- the next available opportunity. All this will lead to where. All this will lead to a bold ambition. I know you will all calculate and find out what is the CAGR. We have done that too. But I want to tell you that it's a bold ambition. And I do believe that as a team, we are all very excited, and we have the right mindset to accept a bold ambition of ours and which is in 5 years, can we grow our revenue by 2x? And can we increase our margin by 200 basis points? So that we could come around 17%, 18% EBIT range. So that's our ambition that we have. I know there will be some questions and all that, and Vipul will elaborate a lot more on his session as well. So with this, let me stop here and invite Harsh for the next session, and thank you for listening. Thank you.

To market focus. Now we'll have 2 sessions. First will be by Mr. Harsh Naidu, who is the CBO for Financial Services. A huge round of applause for Harsh as he takes the stage.

Good afternoon, folks. Thank you so much for spending this afternoon with us. My name is Harsh Naidu. I've been with the group for about 28 years. A large part of it has been living in New York and working in the financial services space with the group. And I've had this incredible privilege of building a very large part of this unit. So I take immense amount of pride in what we have done. But more importantly, I feel as excited as day 1 today because I think the environment is absolutely fascinating. We've never had these kind of conversations. I mean, I talk to a lot of CIOs, and we are having a whole lot of conversations. And I feel that I'm going to make an honest attempt to distill all of that and give my perspective today. And I sincerely hope that helps us begin to answer this whole big question that's in the room, what's AI going to do to our industry or to coding jobs? And if we were to go by social media folks, 5 people, 20 prompts, 100,000 a year, it's a frictionless world, and we can run an enterprise IT unit. But nowhere is this debate on Sci-Fi versus friction more pronounced than in the BFSI space. And I think we understand what friction is because our landscape is like a bowl of spaghetti. I'm sorry for the food reference, and you can probably figure out why. It's more like the Eid ki sewaiyan. It's got more complexity to it. It's got more texture to it. It's got more nuances to it. But one thing is clear. The pace is accelerating, like I have never ever seen before. And if you see a whole lot of enterprises, I mean all leading enterprises have announced a CIO -- or sorry, Chief AI Officer or an AI Wizard. What amazes me or I think what I find interesting here is that, most of them are reporting into either business or operations. I don't think I've come across an AI wizard who works in technology as such. And then I feel there is this -- probably this conversation that has shifted because of this or the conversation has shifted because of which this is happening. The conversation is shifting from productivity to what I call value creation. Now layer that with a narrative of software is cheap or pretty much free at this stage. We are seeing an explosion of software. That is what I mean by strategic layering of AI. Almost all our top clients have a traditional budget and a budget on top of that, which is marked for AI. Now what this is doing, it is creating multiple conversations across the spectrum. And I will try and drop some kind of a nuances to -- I probably feel this is why we are seeing a lot more supplier consolidation because the kind of work we are doing is getting a lot more intricate. It is a lot more embedded. It is in smaller pockets. I mean instead of doing one large project, we are doing 35 smaller projects in different shape or form, which have higher impact. So I feel that it is important for a partner to know the ecosystem well, know the application landscape well, know the domain well. So there is a level of homogeneity in the conversation in the room. But more importantly, there is an issue around governance. The partners will be so deeply embedded, they're finding it difficult to govern. So I feel that there is this change happening, and we've done about 10, 11 supplier consolidations. Happy to know that -- happy to tell you that we are on the right side of most of it. But that's not the point. The point is that, that layered spend that you're seeing is going in a very interesting bucket. And actually, I would call it 3 buckets. The bucket 1 is where you're reimagining your business. I mean you're reimagining a product or you're reimagining your entire operations landscape. The build versus buy debate is raging. We've had some successes, bigger failures so far. Operations transformation. I mean for every dollar we spend in this industry on technology, we spend $2 to $3 to $4 on operations. I think that's one area ripe for disruption. So we looked at all of this, and we said, look, we need to approach the market slightly differently. Earlier this year, we've organized ourselves into micro verticals. These are our micro verticals. Each of these micro verticals is headed by someone very senior, somebody knows the landscape well, can have leadership level conversations easily. The leader has below them, a set of architects, enterprise and data, below them is domain, and we've also verticalized top 3 layers of our delivery. So there is consistency in conversation that we have. Another reason is the distance between imagination to prototype has to shrink because you have to fail fast. That's the promise of ROI on AI spend. I mean you can lower risks, faster or quicker ROIs on what you do. And I think this model -- I mean, in our view, this model lends itself much better to this kind of an orientation. But we also decided that we need to take a punt on where and how this whole AI adoption is going to happen in our industry. We've now kind of zeroed down on a model where we're calling a left brain and a right brain model. Our thinking is that -- and we are actually seeing a lot of that in practice now, a whole lot of SLMs, product-specific or subproduct-specific SLMs will be built in. There is an orchestration layer that essentially governs how these SLMs talk to industry LLMs on compliance, on regulation, on even cost from a cost perspective. So we are starting to build a whole lot of this for our clients. Our thinking today is that we are about -- we have 1 account which is $100-plus million account, 2 are at the cusp from there to about $600 million to $800 million accounts by the time we are done with our Lakshya plans. Because we feel that we are an engineering-only firm. That's our core DNA. We understand architecture really well steeped into it. So this is our moment to really partner and add more value. So as I said, there are bucket spend happening in 3 broad buckets. I just want to talk about a few of the things that we are seeing in this space. I mean I think -- I feel that they are kind of representative. For a very large bank, we worked on a treasury product. Historically, this would be a product that you buy off the shelf or you build something really complex. I mean we've taken an AI-first approach on this and build a treasury product, which is integrated to SAP. Now they can white label this product for either other banks or they can sell it directly through SAP as a module on top. And what is the good part? We are resellers on this. So -- I mean, if we were not to approach this as AI first, this would be a very complex project. It will have licensing issues and a whole bunch of other challenges that you would not want to deal with. This is another one that we're doing for insurance company. Again, we are reimagining their businesses. I mean their thought process is, can we use AI to really upsell, cross-sell to our existing customer base, but again, and also underwrite a product if we define -- do a custom product and if we can define that product for them. We looked at about -- we are building about 35, what do we call data lakes for them for different product size products, putting SLM on top, which essentially collects data from here and a bunch of unstructured data creates intel, develops what I would call a far more dynamic customer 360, and is able to propose projects that another SLM clears and sends it out via an AI agent for execution. I think this is the model we are beginning to see. So the thought process probably is that -- I mean, as we see, the decisioning is a lot more closer to business. Business is really trying to harness the power of this technology. I feel that no conversation in this room is complete without talking about the Mythos impact. I don't know where to put it because I think in my view, it belongs in a category of its own. I've seen firsthand, Venu and I were supposed to travel for a meeting, Mythos gets dropped, our meetings get canceled with the top tech players. All of them are in DC. This is a serious issue that the industry is currently grappling with. Of course, there are these 50 companies that are working on it. There is a network thing. There is other things that they're working on and the industry will find some solutions. But I feel this would also point out tons of vulnerability in the application and in the data space, and we are preparing for it. We've created a 10-tower program, partnering with top companies, AI companies for each of those towers and watch this space, you should hear a lot from us. We've also become, I think, probably the only service provider who is a part of FS-ISAC partnering with large banks in the U.S. and regulators to build a comprehensive solution. And as I said, the debate on build versus buy is raging. I mean it's shown us some early promises, has had even bigger failures. But I think the number of conversation in this space has just exploded. I'll talk about 2 very different case studies. One is a completely new build. So a large bank, which is trying to build its wealth business all over again has decided to build 2 big rails on its own. I mean, it's bespoke rails. So onboarding the whole customer experience rail where from mass affluent to private wealth, that whole experience layer is one, and it is homegrown and built on an AI platform. Even what we call product -- I mean, just like a swivel chair, like all the products are integrated into this dashboard and ability to plug in and plug out products with that. This is like a utopian dream for a wealth manager. I mean a few of you guys probably know how complex the tech landscape behind our wealth management businesses. Earlier, we'd have done this on a Salesforce, Pega, ServiceNow, a lot of custom code, private licensed products. But I think this is beginning to shape. I think one good big reason is that if things were to go wrong, we would fail fast and shut it down and take another path. And I think that threshold of reduction of risk is what probably is driving a lot of this conversation. Sorry. Similarly, for a large insurance company, we are doing a global rollout of a DocTree platform. Because of lack of multi-tenancy or the way the product is designed, there would be the cost of implementation would be huge. So we reduced the core functionality and build this massive layer on top of multi-tenancy. All parameters are kind of -- all the variables are parameterized, so you can plug and play a country fairly easily. What it has done is cut my implementation time by 1/15. And cost is -- there's a phenomenal reduction in cost because some of this can be very expensive for smaller countries. So we are seeing these kind of conversations pick up significantly in our business. And I mean, I'll just throw out a random statistic. Earlier, we would have worked on like 10, 15 proposals, 20 proposals in a week. We are talking about it in hundreds right now because everybody is trying to ideate something. And a lot of conversations are in these 3 buckets. Operations transformation, nowhere the promise of AI driving savings is more prominent than this. Again, as I said. for every dollar we spend on technology, our clients spend about $3 to $4 on operations. Apart from cost, it also does 2 things. I mean, it limits the experiences that people deliver to their clients. And I think that's probably a bigger value proposition because I sometimes -- we as a partner sit on bids that custodians make to asset managers. And their biggest question is, oh, we want a better experience for our clients. So I think that is going to become prominent. I just want to talk one case study because I think it's like really massive. A very large bank is looking to spend about $200 million to $300 million to take out $1 billion of annual spend on operations and just radically change the entire experience. So we're looking at about 1,100-odd processes in 30, 30-odd, 35-odd products in about 50 to 60 countries. And it has been -- the details on this -- we've really gone up to the detail of, of course, cost, but experience at every stage, who touches it, how do we work on regulations. And this is very different because, the assumption is we can build an infinite amount of software to work through some of these challenges. And this is what is I find fascinating. I mean I think we're starting to see some early returns on this. And I think that is giving us a lot more confidence as a partner. Because we are an engineering company. We understand the application landscape really well. 85 -- large percentage of what we do is largely application development engineering. So we feel excited and we feel that we have the right kind of people to do this kind of work. So I feel that we, as a company, are in a very interesting zone. We have the right kind of capabilities, which is data domain, architecture, even delivery depth, the right set of clients where we have the right to play. I mean we work with almost 7 of the top 15 banks. We have a very good set of logos. We have a right to do -- we are strategic partners in most of them. We have the right set of relationships and the right set of credentials to do what it needs to be done in these changing times. So excited to be a part of this journey and happy to have conversations with you guys as we go along. So thank you very much. So let me have -- on one hand, banking is an industry which is a late adopter of technology. I would like to invite my dear friend, Vijay Ram, to talk about technology. They are the ones who are creating this new world, a very different world from where we live. So Vijay, over to you.

Good afternoon. I think I will be very quick with my presentation because I do not want you to reduce your time when visiting our booths. So I do not want to preach to the choir because you all know very well what's happening in the tech industry. So with that, basically, what I wanted to cover today is that, okay, what are the client priorities, what we are seeing because of our association with them over the last few years and also because a few of the relationships are there over the decades. So like our strategies evolve from our revealing with the clients. And at the same time, I think we also understand the client priorities. And from the priorities and look at what are the opportunities for us, some may be new and some are existing ones which we need to do as well. And how are we ready to capture those opportunities and also the proof points. And that is my outline of my presentation today. So if you look at it, you would have heard about this generative AI adoption and okay, with that, everyone would like to have their industry-tuned models and the systems because -- which is the change which has happened over the last few quarters and maybe slightly more than a year. Then if you look at all these investments which they are doing, at least many of my clients and all the generative AI build-outs, the infra build-outs, which takes a huge and phenomenal amount of investments in the hardware chips and everything related with the technology and the energy, of course, to house all the stuff in the real estate. Then of course, you heard about sovereignty, and it is -- that is the one which is driving, okay, everyone is prepared for that sovereignty part of it because now they need to -- it seems to be the one which is on top of every of our clients' priority. Then there is the context engineering and surfacing the context. And especially when the SaaS providers are also leveraging their tool knowledge and okay, they wanted to drive the outcomes and which effectively means that everyone is trying to do everything and leveraging the workflow knowledge. And I think thus surfacing the context intelligence and the context engineering is becoming predominant, I think is a major part of our clients. Well, all these investments which we talked about it, and of course, there are going to be a lot of optimization on the OpEx. So with these investments and with these optimization needs, and I think if you look at it, and I think that is going to be our addressable market. And with this sort of an addressable market, what we have got and okay, probably if you just look at what are the opportunities for us. Some of the opportunities and which have evolved over the last 1 -- slightly more than 1 year, they were not the opportunities which we used to address it. And I think those are the opportunities which are coming in our way. And especially the hyperscalers and the chip manufacturers and both of them wanted to try to manage both the upstream and the downstreams. And that is like the client spend and our revenue, which was not there before and which is the one which is coming up. And of course, the strategic partner on the client growth initiatives. And I think I'll take this opportunity to say that one at least in our revenues and north of 70%, we do with the revenue-generating units of our clients and around 30% is on the cost centers. Thus, I think anything and everything what we do with them and we always look at it, and I think that's what all the tech companies look at it, how we can drive their revenues. First thing is the buying and next thing is the adoption and second thing is the consumption. So how do we drive those things. And of course, you know about that the future, the tech disruptors. And I think Venu has covered it nicely about those partnerships with the 360 degree partnerships, what we are having with them. And what is that making it relevant for us with our customers. Then of course, the sectoral growth and demand, I think the tokenomics consumptions are increasing. And previously, what used to be the size and the scale of our engagements, and I think they are increasing exponentially and creating more demand, which we need to address that. And of course, I don't want to touch more on the land and expand. But of course, one thing I wanted to call out is our investments on the BlueVerse. The BlueVerse land and expand with our clients, and I think is differentiating us and because they are not seeing us as like a me-too sort of a player, what all the services what we used to provide before. So with that, these are the opportunities. But how prepared are we and how capable are we to expand that. So we came with the Silicon to Services offerings. And the bottom most of the pyramid, which you see is that, that was not our revenue source a few years ago. Because now if you look at it, every hyperscaler has got a semicon. And thus, we are the ones who are doing the silicon validation. While we are doing the silicon validation, not like a stand-alone silicon validation, that silicon, how it gets into the compute equipment of our hyperscalers and how do we certify that. And thus, what sort of a labs what we need to create. And the one what you see Lab as a Service, and I think that was an offering which has started around roughly around 6 quarters ago. And now today, we scaled it up to such an extent that I think it's more than $80 million per year sort of an offering. And now that is the one which differentiates us with our clients. While the silicon and silicon in the context of the AI infra build-out. The AI infra build-out because the CapEx what the companies are investing and how quickly that they can go live and thus generate the revenue. Recently, you would have heard about one of the hyperscalers and gone live 6 weeks ahead of the schedule and which really, really increases their revenues a lot for the sort of the CapEx. And in these AI infrastructure build-out, conventionally, we know the CPU data center build-outs, and okay, we drew the things from there and I think that how we need to do that on the AI infrastructure, especially on the integrated offerings. And the second thing is on the deploy and run because the deploy and run is the one which typically takes a lot of time where the compute or the GPU is not going to be available for the clients so fast. So the automation, if you talk about one-click deployments and such a huge AI data center, I think this is one which we have started and which, sort of, of course, we learned from CPU data centers and now we have mastered that one there, I can say that. Then comes the AI-ready product development. Previously, if you look at any of the features on the platforms, if you look at it, it used to be around at least, if not more, 3 months. So now the faster time to market of the feature development and the problem management and also the life cycles of our customers to be managed. And another most important thing is these are the stack of the customers, who used to do all the POCs and stuff like that almost around 1.5 years ago. So that AI-assisted product development to keeping AI at the center or AI native, whatever you give it and thus, how do they scale up and there's a brownfield environments on that. Thus, the product development life cycle or feature development life cycles have got a phenomenal inclusion of the AI, which was not possible a few years ago. Thus, we have also included the -- because you know very well about all the stuff, the security part of it. And that is the one which we almost rehashed our product engineering stack and the platform development. With that, now I'm sure that most of you would have been seeing that one, okay, what sort of the supply chain disruptions, because of induced ones, and okay, the aggressive timelines and geopolitical tensions and tariffs and whatnot. So we are the ones who are working with the cross-section of our clients on how do we do the supply chain optimization and how do we manage that one with the AI infusion there into that. This is the one which is critical. And I think one on this, I think even it's improving even our clients' revenue streams as well. Now the last part of the services. And I'm sure that most of you know about the support always used to be seen as reactive one. Support is always used to be like the cost part of it. And I think with that AI infused the professional services or the support what we are doing. The standard KPIs, we are not measuring it. And of course, the average handling time, resolutions and stuff like what is the thing, what it is generating the revenue for our clients by the cross-sell and upsell, while doing the support. Thus, I think this is an offering stack, which we came with. Yes, offering stack is there. And okay, I also wanted to show you some proof points, whether they are just of the lab sort of the stack or are we delivering any sort of an impact. Out of many, many of this one, I think covering each of the layer, I just picked a few of the proof points there in that. And if you look at it, the first one, which I was telling about the AI data center sort of stuff. And here is the one we do actually do the qualification testing of the silicon, what it gets into the equipment and how many of the qualifications what we do before we induced AI and our throughput used to be around -- we used to be around 250-plus qualifications in a year. And these qualifications are like a memory chip to the CPU to GPU to anything. So now with AI, now if we increase the throughput by 4x. Now we do 250 qualifications per quarter. What does it mean to our customers? They will go live and they can get the latest, greatest of the silicon into their systems. And thus, it could be a Lab as a Service. It could be seen as a cost. But of course, this is what we take the KPIs and these are the outcomes what we deliver to our clients. And if you look at it, it is like quite a significant one. Again, I do not want to delve a lot into the details about it. But this is a new service, which we've incubated and I think which is applicable for all our clients, Lab as a Service and Qualification as a Service, which was not our revenue stream a few years ago. Coming to another one, which I was talking about the AI on the engineering part of it. Yes, you heard about many times, how much of the code, which the AI writes and stuff like that, but that is not all. And how do we make the platform and the production ready and whatever code comes from, whatever the GitHub's to anyone and how do we really, really make it relevant for the business. Of course, we do take the reduction in the key process time and also the productivity gain. I very quickly wanted to go to the planet-scale threat detection and malware analysis. You know very well about this geopolitical stuff and okay with that, the threats in all the devices is like so much. I just want to tell that when the 2 billion devices, we really, really safeguard them with the threat signatures, what we generate and what we put into them. And any of those assets, if it gets into their stores, then we do certify that, I think, it is safe. These are all the things I look at which we started it and look at, that is the sort of the numbers what we talk about it. Of course, I'll leave that contact center operations part of it. Previously, what we used to measure was the average handling time. Now we reduced the human intervention, almost around 85% of the conversational AI accuracy, which we get it. What does it mean? And I think we are not even allowing those 85% of the stuff to be raised as a ticket for someone to work on that. And of course, just look at the scale and the campaign operations and also the content operations. We do almost around 3 billion e-mails per year campaigns, which we run. And also, we run those content operations by doing close to around 250-plus markets we serve it, and we do those many inspections. The last one I kept it because which is a well-kept secret, if I can say, because we also have got the Bluetooth IP, and that is the one which drives us a lot of our -- good amount of our nonlinear revenue. But I think that millions and millions, and I think I do not have the exact number, but of course, last time I have been told it's close to around 1 billion of the Bluetooth devices, which we use has got the LTM IP in that. And by the way, any Bluetooth manufacturer wanted to get it certified and get that logo of Bluetooth, and that will be tested and validated on the platform, which LTM has built. And that's all I have got. And with that, this is for my business talk to end. And I definitely -- if you've got any questions you wanted to know more, I rushed through the presentation, I think during those group visits or otherwise, and I think you can just talk to us, okay. So Archana, over to you.

Now we have our CDO and CGO, Mr. Gururaj Deshpande and Krishnan Iyer who will walk us through our capabilities and delivery model, how we operationalize the strategy at scale. Please welcome [indiscernible].

Alright. Good afternoon. Welcome to all again. My name is Gururaj Deshpande. I'm usually based in Bangalore. I'm the Chief Delivery Officer at LTM.

And my name is Krishnan. I'm the Chief Growth Officer based in Hyderabad. Over to you, Guru.

All right. So we thought we will give you a quick view of what it means to outcreate on our delivery and on our capabilities, right? And you all heard Venu speak about the 3 horizons. Horizon 1 was growth. That's clearly our North Star. Horizon 2 is pivoting, the need to pivot like never before in the context of AI and all of the disruptions around us. And last but not the least was Horizon 3, which was really all about excellence and the importance of doing outdo as we called it, right? So this one really is about giving you a bit of a snapshot around what we are trying to do, the how part of how we are bringing the strategy to life. Work has clearly begun in terms of bringing this to life. Talking about enterprise outcomes, right? So clearly, we have been in this business for a while, right? So we have been delivering significant outcomes to well over 700-plus enterprise clients at scale. So we understand scale. We understand complexity, right? And if I were to talk about a few data points to just drive home this aspect, right? So 14.5 billion transactions that we processed per year for a single client, right? So you can imagine the complexity behind all of that. 125 million-plus compute cores that we support as part of our infrastructure business to keep a very resilient infrastructure humming and running so that our clients can sleep at night. Talking -- again, there are many more to talk about, right? Then talking about the millions of support tickets that our teams look at and solve, right? Many of them using AI in the course of what they do. We support millions of SKUs as part of managing the e-commerce estate for our clients and likewise, thousands of online properties that we take care of. And last but not the least, with AI now being center stage, the millions of lines of code we generate using AI tools on a day-to-day basis, right? And of course, the enablement that we are trying to do for our people, again, running into millions, millions of hours. So the point here is we have a great foundation for us to build on as LTM. I talked about the scale and complexity and Venu spoke about the context, right? And what I'm going to talk about leads me into context, right, context how it's becoming super important. It's becoming really a super asset for us as we kind of pivot into our next part of the journey. So that's the outcreate diagram that you saw. And I'm going to zoom into the domain and the tech convergence part for a few minutes. So domain and tech convergence, again, Venu already gave a bit of a context around what it means, and I'm going to really instantiate this in the context of the verticals that we operate in, right? So as I said, domain has been our mainstay. We work with some of the biggest names in the domains, the verticals that we operate in. Technology, again, is becoming hygiene. It's a given. There are tool sets coming in on a daily basis, right? This space is changing by the day, by the hour, right? So tech is almost a given, right? It's really at the convergence of domain and tech that we wish to bring this home that we wish to really make this a mainstay, the moat, right, the differentiation in terms of our competition and how can we sort of break away the pivot point that Venu spoke about. In that convergence, I think, lies the art of the possible, and I'll talk about a few examples there. That's where the art of the possible is in terms of process reimagination. Process reimagination has been, you could say, the holy grail for many, many enterprises for several years. But I think this technology is really helping in sort of ride the wave of process reimagination and cut down the cycle time. I think that's the super important point. So you already heard from Harsh about financial services. I'm not going to cover that in detail. There was enough example in there about how we are really reimagining this business for our clients. And likewise, Vijay spoke about the whole tech services piece, so I'm not going to talk about that as well. On the consumer side, again, we have 4 broad subsegments that make up consumer for us. We have retail CPG, we have travel transport, hospitality. We have healthcare, life sciences, and we have M&E, media and entertainment. And again, we have production, as Venu spoke about it, manufacturing and energy & utilities make up the manufacture -- the production segment for us. Now what our teams have been doing in the last, you could say, almost 18 to 24 months ever since the advent of GenAI started is we have been looking at this entire book of business from the context of a domain and technology and looking at every segment, subsegment, sector, subsector, every large account that we play in and looking at the entire -- the value chain, talking to clients about it and saying, how can we reimagine this business? So that's the work that our delivery teams have been up to. And if I zoom in, let me also add that in the process of that 18 to 24 months, our teams harvested well over 1,000 domain-centric use cases, right, using AI as a lever. How can we reimagine this part of the business? How might we, right? That's the leading statement that our teams used to start to reimagine some of these things. I'll give you a few examples, right, as part of that. Let me pick up consumer as an example. If you take retail and CPG as a segment, there used to be a time -- we have all been in the industry for a while now. There used to be a time where the journey from data to insights would take months, if not years, right? And this journey is today collapsed into almost being real time. A transaction happens, a sale happens and then it has to turn into insights, almost on a real-time basis, right? That's what AI is doing today. So the journey has moved from months into days into almost being real time. So that's one big trend. The second big trend in retail is consumerism is no more about humans, right. Look at this interesting thing unfolding in front of us, right? The consumption is all now digital. You're most likely selling to a bot, you're most likely pleasing an algorithm than pleasing a human, right? So that's how things are changing, right? So clearly, the retail segment is changing. I spoke about data, selling to a bot. And then, of course, with all of the geopolitics and all of the forces that we spoke about, the need to manage your inventory, your fulfillment, your cycle times around all of that. Again, the last year itself, we have had many incidents like wars and what have you, right? The so-called black swan events unfurling at very short notice. So what does it mean for retailers, right? So we are doing lots and lots of reimagination. If I just talk about a few things in one instance, in the sense part of the diagram, let me just point you there. Yes, the sense part of the whole -- the value chain, we are trying to look at this process and saying for particular customers, "How do you convert inspiration into a purchase, right?" There are times when somebody is scrolling an app or it could be an Insta page or TikTok and somebody is looking at a recipe, "How do you convert that moment of inspiration into an actual sale, right?" So that's the piece around intent prediction and all of the AI. At the bottom right there, you can see all of the benefits of doing all of these use cases. Then the whole concept of audience segmentation I spoke about the various categories that retailers will have to sell to. Then there's the whole AI commerce engagement agent, almost engaging people in a conversational style all the way from product discovery until the point of making a sale. So many more such examples. There is the whole supply chain disaster that I spoke about, how do you manage inventory, how do you get the signals converting it into replenishment cycles. And again, the next best offer as well. So that's just the retail part of it. Likewise, if I zoom into travel, transport & hospitality, we work with some of the biggest airlines. We work with the biggest hospitality companies of the world, right? And we are trying to break all of that down into multiple areas that we have started to look at. Again, I don't have the time to go through all of that in detail, but you see the abstract segments of the value chain in here all the way from -- this one is sort of zoomed into the hospitality part of it. I have not covered the whole -- the TTH space. But again, if you look at what's happening out here, so clearly, loyalty is such a big part of the whole hospitality business, right? So if you look at loyalty, how do you really manage loyalty? We build some of the best loyalty programs for our customers. How do you then convert that into loyalty-driven revenue shares for our clients? Or if you talk about the fraud piece, right, in the airline space, frauds, chargebacks are a very big part and customers lose billions amount of dollars as part of the whole process. So how do you really bring in AI, bring in agents, bring in decisioning into the whole value chain, right, to solve for these billions of dollars? So that's what we're doing on the TTH space. And if I look at media and entertainment, again, we have been at M&E for a very long time, and we work with the biggest of the best names in the industry. We have grown with the leaders of the industry through their mergers and acquisitions, and you all read about all of the happenings in this space as well, right? But if you look at the value chain from, as they say, script to screen, right, that's a very [indiscernible] term that we use for the M&E industry, right? If you look at it from script to screen, there's a whole lot you could do to bring in AI into making this more efficient, more accurate and so on and so forth. So if I talk about a couple of examples, again, a lot of what I'm saying, you can go to the booth and fully appreciate the power of what I'm saying, right? If I spoke about hospitality on the earlier slide, so you could go there and look at reimagined travel, for example, right? Or you could look at intelligent commerce. That's another showcase we have as part of the booth. And as part of media and entertainment, I will talk about the use cases and the solutions, but we have encapsulated all of this into a platform that we call as MediaCube, right? So again, MediaCube is one of the booths, you can go out there in the break, very interesting stuff that we are doing with all of our clients in the space. I'll pick a couple of examples. If you look at content creation, right, think about the thousands and thousands of hours of footage that's already available, of content that's already available. How can you take all of that and use AI and start to use it for things like, say, multilingual dubbing, right? How can you establish dubbing? How can you get dubbing in regional languages in a matter of short time, right? So things like that, resulting in a much lesser cost per finished hour of footage, for example. Or you could look at content management, right? How do you look at content tagging, again, look at all the footage that exists, and we have done this for several clients. We have shown proof of concepts for many others. How do you take the footage and break it down, tag it and establish some sort of a semantic search capability on content, right? Or you could even look at voice-based discovery of content, right? You're talking and interacting in natural language using voice and saying, "Hey, I'm not feeling great today. Can you give me a couple of samples of things I should be watching?" So AI enables all of this at scale, right? It's no more proof points, but certainly enables this at scale. And then things like rights clearance and all of that, that you could start to talk about. So that's what we're doing on M&E. I'll also switch quickly to a couple of our other sectors as part of production. If you talk about manufacturing as a segment, right? So clearly, there is now abundant information coming through from your factories, but there is still a lot of inefficiency on the shop floor. The data is still not coming together. There is still a quest for AI-led autonomous operation at scale. There is still a quest for a connected factory, a smart factory. And again, you will see this concept in the booth outside in terms of a connected factory. But we have started to put it all together and say, what does this mean for our clients, right? So again, a couple of examples. I will talk about -- if you look at demand generation here and broadly look at inventory management, right, sales inventory, operations, the whole process, we have started to look at it with AI and what does it mean. And we have started to solve for things like parts intelligence, how do you reduce mean time between failures. For example, if it is an automotive major, you can start to think about who looks at inspection, quality inspection, how do you inspect a welding joint in an automobile company, right? Can we completely reinvent that through AI, right? What models can I use? Do I need an SLM for this? Do I need some other model for that? Or can I use my own BlueVerse platform as part of solving for this problem statement? And likewise, if you look at things like service, service is a very big part of the manufacturing industry. We don't realize it, but service can be a very large revenue stream for manufacturing clients. It's never really just about the shop floor. So if you look at service and that's aftermarket revenue, right, that we speak about, how do I bring in asset intelligence and maintenance intelligence? For example, if you're inspecting bearings, can I use thermal coordinates using AI and solve for bearing inefficiency? Can I predict breakdown of a bearing well before it actually breaks down. So things like that, right? And then again, many other functions that could be around DSO improvement and things like that. The good news is coming from a heritage of our parent company, this is a space we understand well. It could be manufacturing, it could be energy and utilities as well, right? So we understand the domain, and that sort of gives us an edge as well with our heritage where we come from. And then finishing up with energy and utilities as well. Again, it's a space which is very asset heavy. It's a space that is slow to change. It is a space that's also very regulated. So in the context of all of that, we are trying to see how do we really solve for some big heavy problems in the energy and utility space, right? So it could be around in upstream things like estimating a well rate if a well does not have all of the instrumentation around it, right? How do I use the basic data to establish AI-driven well rate estimation and bring down essentially the cost of exploration, that's one. Or it could be looking at field operations, how can I improvise in terms of scheduling, prioritization of tasks that we hand over to field staff? Or it could be around, again, inspection, I spoke about in the context of something else or even horizontal use cases like legal claims validation, right? This is something we have solved for with a particular client of ours using our own BlueVerse platform, how do I bring down claim processing time? How do I make it touch-free, right? How do I really get many legal people out of the loop, right, make it sort of touch-free in a lot of ways? And the other part we are doing as part of each of our domains is bring in domain-oriented people. For example, in energy and utilities, we are getting people and experts from the University of Petroleum and Energy Sciences, right? These are things we would not do in the past. So we are bringing in talent that sort of understands the domain, hits the ground running on day 1 and can be productive. Yes, I was saying before I hand it over to Krishnan, who will talk about the outcreating capabilities part, and I'll come back eventually to talk about what's the enablement that's happening, right? How are the roles changing in the context of delivery, in the context of AI coming in, right; so I'll finish off with a bit of an insight around evolution of our own roles around skills and enablement as well. But let me hand over to Krishnan for now.

Good evening to all of you. You would have seen the exciting amount of opportunities that Guru has outlined, especially in the domain tech, it kind of brings a completely new set of opportunities, which wouldn't have existed if it were not for AI. So thank you, Guru. I'm going to talk about -- more about the reimagined capabilities. Venu highlighted it in his speech. It revolves around 3 things. One is iRun, second is iTransform and third is the Business AI. What I'm going to give you is a few peekaboo into the -- how we are reimagining some of these capabilities, what are the markets that are opening up because of that, right? And while I do understand the compression because of AI, I'm also going to talk about the myriad of opportunities that have got unfolded in each one of these areas. So let's talk about build, right? Now we do almost $15 million -- 15 million lines of code, which is generated by AI per month. Obviously, the efficiency of AI is not still there, but almost 30% of it is accepted the way it is. For some of the workflows where you use Claude and Cursor, it could go up to 50%. So that's one part. Second is there are almost 100 human AI interactions per day per developer. What it means is that you can -- the efficiency of the developers have gone up almost by a factor of 30%. So this is what some of the high-end developers work at from a level of interaction with AI to get it right. So that's what you can -- that's what is happening, right? Clearly, AI delivers, therefore, at almost 30% to 40% faster speed, enhancing the quality and accuracy. Interestingly, what's also happening is there is a lot of legacy modernization, which is, again, a huge market. And I'll talk about that a bit. If you look at it, almost 70% of all the systems in the companies are legacy, and they need to be modernized. In fact, almost close to 60% to 70% of the IT budget of many companies going to managing legacy. So if you really look at it, how can you derisk the legacy code modernization, how can you accelerate the speed much faster. What we have developed at LTM is we have something called AppIQ. What AppIQ does, it reads the old code, converts it into the modern code, right, and obviously understands all the interactions that go between the various systems and it develops -- and develops some code. Now what this has done, what AppIQ has done actually is significantly reduce the time it takes to modernize legacy applications. And as I said, 70% of customers' systems are legacy. Now when you're talking about a 50% time reduction and also a 50% cost reduction to do it. So imagine what is the more faster you are able to modernize, the more business you get, where the clients will be able to do the same business, no, have multiply more effectively. So for example, they had a $10 million budget. You could -- and if you modernize 1 system, now suddenly, you can modernize 2 systems and also in the same amount of time. So that's what things like AppIQ has brought to the table. The second thing that's happening is if you look at agents run the life cycle end-to-end. Now this is something which is enabled by our AgentIQ. So everything right from planning to detecting to building, everything, the whole thing is -- can be done by Agentic AI with obviously some human in the loop. And then there is FusionIQ, which is another tool that we have developed, which significantly shortens the time it takes to test, automates the test generation, the whole testing process and so on and so forth. So we will see a significant opportunity ahead of us, ahead of companies like LTM. And we've actually deployed this whole thing in many of our customers, and this is a huge market that we are super excited to go behind. Then there is the whole iRun capabilities. Venu talked about it. If you really look at the whole apps ops landscape, what we have done is combine the whole operations management of apps, infra and the platform apps altogether. And then we have said all the L1 and L1.5 and even L2, how can we do it in a consolidated fashion? In fact, 60%, there's a huge vendor consolidation going on. In fact, we have won a lot of deals in this particular space, almost 60% of the deals that we won and we are really going aggressive in this space because it meets the customers' hyper productivity requirements, and this is real. And we've got the assets to go behind this. Now even today, as we talk, almost 60% of the tickets are now handled by AI augmented operations command center. When I say AI augmented, it's both human plus AI. But soon, there is a very high degree of autonomous agent that is possible here. Similarly, there's a very significant amount of triaging that goes on between text and voice and enable auto solve. What's interesting while we're doing this, the [ structured ] statured intelligence -- sorry, I'll just go back -- I think there's somebody speaking behind, which is disturbing me. So if you look at what we are doing is building the knowledge fabric and also the agentic orchestration. Knowledge fabric is nothing but understand and building the knowledge mesh that is required to solve the problems for any operations business, right? And this eventually, you can extend it to even business operations. But right now, it is all about apps, infra and the platform app. So this is the knowledge fabric that we are building, which is key, which is powered by our LTM BlueVerse. And then also the whole agentic orchestration, so many of these tickets can get solved autonomously. And obviously, it is shifting more from SLIs and SLOs and SLAs, which is the typical old ways of managing to outcome ownership, and we are guaranteeing outcomes for our customers. Okay. Now this is -- there's somebody talking in the back. If you can tell them to stop, that will be helpful because it's significantly interrupting my -- there's somebody talking in the back. Interactive experience, now this is something which is a phenomenally big market. Now we are already addressing. We are already in the MarTech addressable spend. Keep in mind, any company which has a revenue they spend around 3% to 5% on MarTech. They allocate on marketing. Basically, 3% to 5% is marketing spend. And out of that, 25% to 40% goes on MarTech spend. And the MarTech spend actually is growing by around 17% per annum. So this is a huge market. So the market that we are going behind is almost a $700 billion-plus market, the MarTech spend, and this is something which kind of goes across marketing, commerce and services. There is also a degree of hyper-personalization, which is context-aware and hyper-personalization we are able to drive. And this is something new we are launching, which is under the CraftStudio banner, which is BlueVerse CraftStudio, which is where technology meets creativity. And that's a big, big thing where we are looking at generating content through AI, not only generating content, but also driving more personalized experiences. And this goes across all kinds of media. And this is what our CraftStudio is. And I'll show you a quick video of how we are looking at spreading CraftStudio across and capturing more and more of the marketing spend. [Presentation]

So as you can see, this goes across ads to enterprise animation -- enterprise content to animation and so on and so forth. This is a huge market we're going behind. And last not least, we are talking about the autonomous enterprise, industrial AI. So industrial AI, this is a big area of strength for us as Guru highlighted and even Venu highlighted in their segments, both manufacturing and energy utilities are really solid businesses for us. And if you really look at it today, even we are already kind of supporting a lot of industrial workers and digitizing a lot of physical assets. And this is the area of IoT. In fact, if you recollect the slide Venu showed, that's a new big opportunity that one can go behind. And this constitutes a lot because what is happening is there is a lot of software, which is getting into both the physical AI world and you have the enterprise AI. And with Edge is just becoming much more powerful. So a combination of both enterprise data and the industrial data is what will drive much more real-time intelligence for our clients. And therefore, if you really look at it, the whole physical AI, again, it gives you a lot of closed-loop autonomy with edge intelligence and self-healing industry operations, huge cost saver. Expert AI, again, we're talking about industry copilots. Physical AI, here, we are talking about digital twins, Although me and Guru are digital twins in the industry segment, right? And this is, again, hugely -- a huge hit with some of our manufacturing clients where you can actually test out what's happening from an AI perspective without having to make a lot of investments, et cetera. And then obviously, the whole vision AI. So a combination of all the Physical AI, Expert AI, Fabric AI and Vision AI is super powerful. And this is again something which we are super excited about from an opportunity set perspective, runs into -- the opportunity runs into billions and billions of dollars. And now we come to the last page, which is more about how the autonomous enterprise is unfolding. So if you really look at it, every company has SAP, Oracle, Microsoft Dynamics, you call them systems of record. You will have some systems of engagement, these all being ServiceNow, et cetera. And you have all the data platforms, right, data and platforms, which is all your hyperscalers and Snowflake and Databricks and so on and so forth. And on top, you will see the systems of action, which is AI agents built by each one of the different platforms, right, SAP Joule and Microsoft Copilot and you see the Agentforce and Now Assist. So a combination of all of these. Now what is happening is many of the enterprises already have, obviously, the systems of record and they already have the enterprise data layer. And how do you combine both. So you will see more and more offerings in the marketplace where it's a combination of both because how do you extract intelligence from the systems of record, you need the data platforms and most of the data platforms or models are coming to the data platforms and not otherwise. So you will see that you will see a scale of AI adoption in many companies go up significantly high. Just to give you a sense of how much the platform that we have mentioned here are growing, they're all growing at around 15%. And in some cases, the data platforms are growing at 30%. So there's a huge market that is available for us as soon -- I mean, as long as we think platform plus data together. So we have to think about how customer relationship management is transformed. We have to think about how field services is transformed and so on and so forth. So that's what we talk about all the integrated offering squares. We build offering squares to tackle most of the business problems that we have. We're building SLMs across the business -- enterprise business processes. So if you look at order to cash, record to report, procure to pay across those, we are building SLMs. And then obviously, we have functional consultants. And then we're targeting the buyer persona. Historically, we've gone behind the CIO persona, but this is where also there is a lot of spend decisions are being made. Harsh talked about how many of the Agentic AI -- the Chief AI Officers are reporting into the business, and that's an untapped. That's a huge market potential as long as you can combine everything together and go and solve for a business outcome. So that's what's happening at LTM and this has opened up just a huge market for us, the combination of how we look at data as well as platforms together. With that, but all -- to do all this and to execute all this, we require talent and talent has to be reimagined. So with that, over to you, Guru.

Thank you. All right. I will use the next 7, 8 minutes to quickly talk about the talent reimagination, and I'm going to cover a little bit of it. Chetana, our CHRO, will cover this in a whole lot of detail as part of our broader talent strategy as well, right? So yes, so talk about future-ready talent, right? So service reimagination, and I know this is a concept that you have started to hear across the industry as well, right? So you may not be seeing it for the very first time. But let me break this up a bit for those of you who are sort of seeing this for the very first time, right? So the hypothesis and what we are seeing already play out in some of our clients, in some of our leading verticals, if not everybody, right? Obviously, there will be fast adopters and then there will be people who wait and watch and there will be the laggards in the industry. So we do see that play out in our 700-plus clients. But I think where this is all heading to is the space is one of building with AI, right? The nudge across everybody, across all of our employees, the industry is talking about go out there and build, right? That's the thing. And there are all these tool sets to go out there and build, right? And then, of course, in the context of the domain that we spoke about. So we see a gradual shift. I'm not saying this is going to be an overnight shift. We will see a gradual shift into a bulk of AI builders in the context of domain, being aware of the domain, and that is our core, right? So that's what we call as the AI builders. Then there is always the big question out there that we keep hearing people on the media ask us and sometimes clients ask us as well is, "Hey, what happens to entry-level workforce, right?" So that's the question. So we clearly see there will be a space for entry-level people, right? There will be a space for learners. Maybe going to shrink a bit. That's why you see a smaller box at the bottom, but there will be a space for learners. And of course, there will be -- as you can see here, there will be digital employees augmenting every level, right? There is -- within LTM itself, we talk about 1,500-plus agents. Then there are all these toolsets available to produce lots and lots of code, but there will still be a need for learners. Clearly, that's not going to go away. And I'll talk a little bit about what does it mean to the people coming in as well. And at the top, there is, of course, going to be strategy and governance, many of times in the context of a domain, sometimes across the whole delivery estate, right, across what we all do. So that's -- and that takes us simply if we then zoom into the middle, which is the AI builder box, right? What does that mean? So we clearly see there are 3 tiers of people, right? There are 3 tiers of AI builders. One is what the industry has started to call as AI engineers, right? And again, this is an evolving space. You will see people talk about it slightly differently. Broadly, we talk about it as AI engineers. So this will have multiple flavors. AI LLM engineer, sometimes they will call it LLM engineer. There's a full stack developer, which already was the case. Full stack is taking on a bit of a new meaning. Sometimes some people still call AI engineers as full stack builders, right? And then there is a platform engineer, data engineer, obviously, data is the entry point to AI. So there is a need for data engineers. And then AI Ops engineers, right? Krishnan talked about iRun as a support offering and a lot of that will come through the AI Ops engineer persona. That's one. And then you -- again, you've all heard the terminology of a forward deployed engineer or a forward deployed architect. The difference is the way we see it, the bulk will be AI engineers. There will be a little smaller segment of forward deployed engineers. May not be comparable in volume, but again, we are still building this out. The FDE pool is about people who understand a client's context. So if the question is, hey, can we have a fresh engineer becoming FDE on day 1? Probably not. Probably they'll start as an AI engineer and eventually become FDE, right? So FDE is in the context of a domain. And again, if you are a senior FDE, you probably start to play the role of an FDE architect as well. And like I said, there will be AI program managers or pure-play domain SMEs, again, sort of orchestrating all the work that happens. So those are the 3 tiers. And then I wanted to spend a minute or 2 on where is the skill reimagination, right, in the context of these roles, these are not a complete set of roles. We have just taken 3 sampled roles, 3 big ones, you could say, and start to talk about where is the shift, right? How will our people need to make the shift? And we have broken it down into 3 dimensions of what we call a tool set, skill set and mindset, right? So if you take an exponential AI engineer, in the earlier world, there was like a software engineer, maybe somebody did front end, somebody did back end, somebody did just data or full stack, right? So slowly, we are seeing the shift into what's called the exponential AI engineer. Exponential world comes from an expectation of hyper productivity. Some people call it 10x. Again, industry has different terms for that. The expectation for them in terms of tool set is to be really aware of the models out there, what model can do best, what model can solve which problem, right, an awareness, an acute awareness of models and the APIs to deal with the models. And the skill set is changing into context design, right? How do I design for the context that I'm in? And the mindset obviously is shifting to systems thinking. How do I connect the dots for value? This is no more about just one piece of the solution. It's about a connected solution. It's about getting the solution in the context of connecting several dots. And today's intelligence is in how you connect the dots, right? That's the system thinking. Now if you look at the FDE, again, the skill set, like I said, is an LLM play ground. The skill set is hands-on. Everybody is a builder. This is a new thing, right? Everybody is a builder. And if you're an FDE more so, right, you are still building. You're not commanding AI engineers, but you are still building in the context of the client. And the mindset is slowly shifting to day 1 builds in client environments. If you were in the industry many, many years ago, you would spend many months on requirements and then there will be like a design phase and then you'll start to code, all of that. But today's mindset shift if you're a forward deployed engineer, is to build day 1 in the client environment. So you go with the tool sets, you go with your proprietary frameworks sometimes and you start to build. And you show evidence of the build working in the client environment, right? That's what FDEs do. And the last bit I spoke about AI Ops engineer, the tool set suddenly becomes -- in the good old days, an AI Ops engineer relied on tribal knowledge many a times, that they relied on knowledge sitting in people's heads. And today, it's slowly shifting into a tool set of a knowledge graph. Of course, we have our own knowledge graph through BlueVerse. The industry talks about a knowledge graph. So essentially, these engineers will start to rely on tool sets like a knowledge graph. That's your assimilated knowledge across the enterprise, right? That's what will help you self-heal many tickets, auto solve. Many tickets, doesn't even get to a person, maybe it gets solved by an agent or it gets deflected and then very rarely it gets into a human, right? That's the whole knowledge graph piece. So the skill set, of course, is AI workflow, orchestration. And the mindset is agent first, get agents to work. It's never about let me go solve it. So that's -- those are some of the key shifts we're starting to witness. And again, there are many other roles as well. But hopefully, this gives you an idea of the key shifts that we think are already beginning to happen, right? This is very much underway. And again, I go back to this diagram that I showed and close with what are we doing about it as LTM. So we have several ways of making this enablement happen. On one hand, we have our own L&D platforms. We run what is called Shoshin School, which is our learning and development institute, you could say, or a platform. Shoshin stands for a begininer's mindset, right? So we are always learning every day is day 1. So L&D platforms, we have several tie-ups, many more that we are exploring. We have a tie-up with MIT and upGrad to train some of our people with orienting them on how do you talk to CXOs, how do you open up their thinking with an AI-first approach? And at the same time, we have something going on with Emeritus and Northwestern Kellogg. Likewise, we tied up with IIT Kharagpur for some specific content that we could start to use in AI. Then we have a collaboration with the Indian Institute of Creative Technologies. You saw the video that Krishnan showed and the whole play that we have with CraftStudio. How will all of that come to life, right? How do we sort of have a handshake in the creative space. So that's the one with IICT. I think Venu spoke about the labs. These are our own internal focused communities. In terms of different spaces that are happening, it could be DevOps, it could be AI trust assurance. What used to be plain modular testing is today AI trust assurance backed with the best of the models, the best of the frameworks. Likewise, we have always had a focus on hyperscalers, but there's so much happening in that space. Quantum is a space that we continue to invest in, in terms of research, may not be right here right now, but we are already seeing client interest, seeing client interest through quantum. And there are many problems out there, which cannot be solved through classical models. They need quantum, but that's still an emerging space, but we have invested significantly into quantum already. And then Adobe, Azure, these are some of the other partners that we work with very closely in terms of content, certifications, trainings. So there's a handshake there. And then our own builders community, we invest a lot into community as a learning platform, right? The Hack2Future is something that we do. Sometimes we join hands with partners to do hackathons, builder events. Microsoft, obviously, is a big partner of us. We do that. And the last one that is very interesting, we actually sponsored an AI filmmaking hackathon with the International Film Festival, Delhi, right? So this is phenomenal, and we had a chance to present this as part of the NASSCOM Summit and everywhere else. So this is -- clearly we are leading the way in terms of the creative economy and kind of showing how it has done as well. Last but not the least, I go back to the point about the learners and the entry people. We are already working, seeing the need in terms of where do we want an entry-level engineer to be versus where they are today in terms of coming up from colleges. We think there is a need to shift left. What it really means is we work with the colleges themselves. We kind of -- we have a set of colleges that we hire from. In a given year, we hire about 6,000, 7,000 fresh graduates. And we do the shift left with respect to working with these colleges and really making what they need as part of their curriculum, right, making us a part of their curriculum in terms of the AI certifications we need, the hands-on skills we need, the projects that we need them done as part of the whole curriculum. So that's the shift left and what we call is Orchard in terms of our own program. So that's my last slide. Time is up, just in time. So very happy to say that we see a lot of recognition coming out in the market, not just with our clients in terms of repeat business, but also from our partners. In fact, we won the Golden Peacock Award for AI. That was the most recent one. We have won awards with NVIDIA as their Rising Star Consulting Partner of the Year. Many more. I'm not going to read all of that. AWS gave us the Consulting Partner of the Year in terms of Application Modernization, similarly with Microsoft and a whole lot of partners like Databricks, Snowflake and ServiceNow. I think that's as much time as we have to talk about reimagination, outcreating on capabilities, on delivery. Hopefully, we left you with a good sense of what's happening in our backyard. Thank you.

Moving ahead, we saw all the people and the next session is about everything, talent, people, culture, passion and purpose. May I call upon Ms. Chetana Patnaik, our CHRO, to take the stage, please. A round of applause for Chetana Patnaik, please.

Thank you. Once again, good evening to all of you, and thank you, Archana, for being a wonderful host throughout the afternoon and moving towards the evening. We all heard Venu talking in the afternoon today about the new Horizon program of LTM, which has 3 horizons: one, growth, pivot and excellence. For any organization to grow at scale and pivoting transformational initiatives and also bring excellence at every stage of it, talent sits at the heart of it. And our talent, our Lakshya strategy 2031, our talent is going to outcreate in that process. And I'm going to take you through today as to how we are outcreating the talent strategy at LTM. Just to take you through the LTM talent landscape, we have 87,950 outcreators across 42 countries with a diversity ratio of 30.9% and 92% AI literacy and some of that I'm not reading across. But I want to tell you that for every organization, the technology shift, which the sector is going through, for us, it's not a technology shift only, it's a shift of the organizational initiatives of how we will get the work done, how we will deploy the talent and how we will create the differentiation at the marketplace. Let me take you through the 4 leading indicators in this slide, which are about the pressure in tech what you had heard from Guru that even if we have a diamond-shaped pyramid, but we will continue to build our bottom of the pyramid along with the digital agents, the learners. The second uptick is about the AI literacy of last year 75% to 92% this year. The most significant one is the learning hours of our outcreators, which was 79 hours of last year to 104 hours of this year, which shows that how committed we are for the learning of our talent in the organization. And all this reflects with the stickiness of our talent in the attrition coming down from 14.5% last year to 13.25% this year. While we saw the talent landscape at LTM, I want to take you through that the big shift which the industry is going through. Most of the organizations are seeing 5 structural shifts across, one is the skill-based and capability-based organization, which is a big shift from the earlier grade-based, hierarchy-based and role-based organization. What it means is that as Venu spoke about the BlueVerse currency that skill is going to prevail over the title, skill is going to prevail over the experience. The second shift is no more is going to be a human-based workforce, but it is going to be a combination of human and digital agent. The third is talking about the no more is going to be a training-based classroom-based training programs or learning program. It's going to -- the training or learning program is going to be linked to business outcomes. The fourth is about not getting limited to a location, but how do you kind of build your organization at a similar level in across boundaries and across geographies with a local context with the global mobility. And the fifth one, which is very, very significant for us to note is that the organizations are not going to be the differentiator with a technology led, but it is going to be the differentiator with the leadership who will create that ecosystem of technology, culture and enablement for people to grow. With all these things, HR is going to play a very critical role of moving from a business partnering to a real business value creation. I will tell you that how we are structured at LTM to face these shifts. The 4-pillar strategy of HR strategy of ours, the talent strategy of ours is already in motion. It's no more in the slide, but it is in motion. The strategy pillar 1 is about the designing the talent and career for people in the organization. What it means is that we are reimagining the entire talent value chain. And what it means is that the traditional sourcing is moving to a hackathon-based GitHub pilot-based sourcing. What it means is that the sourcing will no more on the profile, but sourcing will be with experience and exposure at the workplace. The second part in the reimagining supply chain value chain is about how we are bringing in the learning experience to the workplace so that the learning becomes a part of the workflow and no more sits in the classroom. We are also looking at that how competency plays a critical role in a talent profile that the real-time client experiences also gets mapped into the profile of the talent. All this is giving us an edge in the marketplace of using AI as a tool of search and -- match and search of the profile at the internal marketplace of LTM. While we are reimagining the talent value chain in the organization, we are also looking at that how do we rewire our HR processes to the reimagining the entire value chain of the talent from attracting to onboarding to performance management, to the competency assessment, to the learning and development and until the growth of the individual. The second pillar is about culture of continuous learning. As I mentioned to you that learning will no more be confined to a classroom, but learning will be as a part of the workflow of the individual at the workplace. So therefore, there has to -- and it also shows that 104 learning hours, which was 75 hours last year, that shows that how committed we are in terms of bringing learning in a real-time experience for people. While doing this entire thing you would have heard Venu and Guru talking about that how change management becomes very, very critical for organizations who are going through a pivotal change with AI as a technology. So our leaders are role modeling, bringing in the change management for each one of the outcreators at the workplace that AI is not a threat to the organization, but AI is an enabler and a coworker for you, which is going to power you with more insights and more help in the work. The third pillar is about attracting -- the third pillar is about accelerating the talent readiness. It's all about how do we kind of build an organization, which will have a sustainable leader over a period of time. And our talent continuity initiative, which talks about doing a rigorous talent council across unit, across geography, across organization and also identifying critical talent and doing succession planning for each of the role holders, enabling with an individual development plan and mentoring and coaching -- real-time mentoring and coaching when they are at work with a stretched assignments, with the rotation of roles is actually helping us to build an organization for future-ready leaders. While we're doing it for that critical talent, but we are also looking at the high potential in the organization who need the upliftment, and we do that through the leadership lab, which we do it every quarter to identify the high potential talent with consistent performance who can be funneled through the leadership development program of the organization. The fourth one is all about the talent localization. You would have heard Venu talking about that how we are actually bringing the context with a local perspective that while we are going to have a global mobility, but we will bring in more context of localization, we will bring in more perspective of the local talent ecosystem, which will help us to create an edge in the marketplace. At the heart of this 4 pillar lies our HR as an enabler with AI transformation. I will take you through what all transformation we have been doing in HR as a part of the transformation journey in the next slide. But I also want to take you through the partners and the partner ecosystem and the academic who are partnering with us in terms of strengthening our leadership development program, our talent acceleration program in this journey. Some of them Guru have mentioned, but some of them are mentioned here, which are L&T EduTech, Coursera, Richardson, for our sales enablement program and SDA Bocconi for our next-generation project development -- project manager development program. When we are doing all this investment, what is that we are setting the goals for us for the next 4 to 5 years? Very clear. One, we want to have a 100% AI fluency. That means we want to move in from AI literacy to AI fluency. And as our leaders have said about it that the mindset and the skill set both has to develop when we move from the literacy to fluency so that they look at AI not only as an adoption, but use AI as a part of the overall ecosystem. The second outcome what we are chasing for is our entire HR or people processes need to be reimagined along with the new value chain. It starts from, as I mentioned, from the onboarding to exit. We also -- third one, which we have set as a target for ourselves is our leaders will be our role models. As we grow and spread across geographies, it is necessary that we have a distributed role model -- leaders role modeling the core culture of the organization, but at the same time, bring a local context to it. That means we will have a core EDP at the organization enterprise level, but then we will have a localized value proposition at every center beyond boundaries. At the same time, we are also chasing that how do we build the organization with leadership stability. Therefore, 200-plus leaders of tomorrow, which we want to develop over a period of time, who will be the future leaders of LTM. Last but not the least, as we grow, we know that the diverse leadership, not only by gender, but by all means, will bring the right perspective to the business and bring the right creativity in bringing value to the organization. So that's the fifth outcome what we are chasing for. As I mentioned to you, while doing this -- I spoke to you about the 4 pillar of HR strategy and the foundation at the heart is the HR transformation journey. I'm very proud to share with you that at LTM, 2 years back, we started the HR transformation -- tech transformation journey, wherein we spoke -- we initiated every unit of HR with the AI maturity model and baselined it that where are we and where do we want to travel. So far, you would have seen that there are 160 use cases which are being deployed with 2 hackathons in HR we have done so far. And then the AI initiatives are spreading across hiring to onboarding to learning and development to performance management system, to the employee engagement, to the employee resolution, query resolution as well as to the attrition prediction, onboarding predictions as well as the analytics and dashboarding. Before I take a look on that, Rishi is the super agent of HR, who's called RAIma and she's a digital agent. [Presentation]

Thank you. Thank you, RAIma. While we develop this, but I cannot miss to tell you that at every step of our transformation, we built the responsible AI into it. We make sure that it is -- while we're scaling the transformation, but we've also been scaling equally on the responsible AI part of it, governance part of it. So these are some of those data points wherein she is sitting on the contact center at the shared service desk and 60% of auto results resolution she has done of employee queries. Overall, the employee satisfaction has gone up to 3.5 out of 4. While we are doing so many things, I want to tell you that these are some of those industry recognitions and some of them have been told -- mentioned by Guru, but I want to pick up some of them which are not CII National HR Excellence Award. We achieved -- there is a significant achievement in HR excellence. Some of them from AI, some of them from Brandon Hall on talent management. Basically, these are a few of the recognitions from the industry bodies, which talks about mostly our talent management, our talent attraction strategy and the AI -- usage of AI in talent management as well as in people's life cycle journey. Before I move to the next film, I want to leave with you that we are committed to build a culture, which will create a community of outcreators in the organization, which will be led by our role-modeled leaders in bringing in the psychological safety, humanness in the organization who will outcreate our Lakshya '31 strategy. I leave it with all of you to see the people video of LTM. [Presentation]

Good evening, everyone, and thanks a lot for staying with us so far. I hope I don't bore you too much. Okay. So I think this next session, I'm going to focus on how our strategy, with Venu and all my other colleagues have already spoken about, is going to work towards out creating shareholder value and how it is that we are looking at it from a finance standpoint also. What is shareholder value? Shareholder value basically can be looked at as a combination of dividends and the price appreciation, which the shareholder is getting from a company or from owning a part of the company. On that statistic, if you look at it from the time that we got listed in 2016 till date, we have paid out dividends of almost INR 11,600 crores over this period of time. And the total shareholder return that we have generated is about 557% at a 21% CAGR. That's good for the past, but we have to also have the responsibility of making sure it happens in the future, which is what the strategy that we spoke about is all about. Okay. Just a quick recap on what were the strategic pillars that we were operating on last year and what did they help us achieve. I think last year, we spoke about -- around Q1 earnings call about 4 strategic pillars that we were focusing on. One was sales transformation, large deals focus, AI Pivot and Fit4Future program. And I'm happy to say that in all of these 4 levers, we did achieve a lot of success. So in terms of the sales transformation, our focus was on focusing on key accounts and diving deeper into those key accounts. And happy to say that we -- as a result of those efforts, we've managed to add 8 accounts in the $20 million-plus category, 12 accounts in the $10 million-plus category. And we also started measuring our sales -- performance on sales productivity metric, plus a lot of focus into deal structuring, which also, in a way, helped us in the second pillar in terms of large deals focus. On the large deal side, I think we have been announcing large deals quite regularly. And if you see the large deals that we did in the last year or we signed up in the last year, there was almost a 100% increase in the large deal wins year-on-year, including 2 mega deals that we announced, and we still have a strong large deal pipeline as we enter into FY '27. And I think this pillar has been definitely very successful for us, and we are going to continue to work on strengthening it further. On the AI Pivot side, I think the full strategy that we spoke about is building upon the AI Pivot that we are focusing on. Some of this we had invested and started last year. So the BlueVerse ecosystem got launched last year. The service line investments we started making last year already, some of which we have seen outside the results of those in the booths that we showcased today. The partnerships that we had already started working upon from last year, and I think in some of the partnerships, again, you would have seen a lot of mention coming in the way we have been working with those partners to deliver solutions to our customers. And I'll talk a bit more in detail about the customer zero program because what we also decided to do as a part of our strategy was to have AI-infused enabling functions, which, in other words, is basically another way of saying lean and all muscle enabling units to get our costs on the overhead side down. So that is -- and also as a way of getting the experience of how we use AI in some of the functions that we are having internally in the company, scale it up, how to manage the change and how to kind of get the entire organization to believe and adopt AI in their daily work. The Fit4Future program, I think we have spoken about it in all our earnings calls. It did deliver on what we set out to achieve, reducing the cost of delivery, re-baselining the indirect cost, and it led to an overall improvement of EBIT by 90 basis points in FY '26. So what's next? Venu spoke about our Lakshya strategy and the fact that the new Horizons program that we are now working on is in a way a governance mechanism for the strategy. We have already spoken quite a bit about the growth and the pivot pillars out of this. Let me focus a bit more on the excellence pillar of the New Horizons program. Within the excellence pillar, I think some of the initiatives that we are working on are de-linking of revenue and cost, I think Venu spoke about the BlueVerse currency as well as the outcome-based deals that we are going to be working on. This is one part of the focus on that. And the reason why I'm keeping it out here is because this also directly adds to the margins. You start getting into outcome-oriented deals, the margin profile changes from what it has been traditionally. Large deals, managed services, how we can use AI more effectively through iRun, iTransform and get more and more large deals, consolidate more and more wallet share of our customers, platform-led revenue, the BlueVerse business AI revenues. Again, that is an important part of the exercise. Enhancing productivity with AI, iRun and iTransform are again a part of that, as well as the AI-infused lean enabling units, improving the cost of delivery. So again, this is one of the traditional levers in terms of pyramid optimization. That is going to continue to be a focus for us because the traditional levers are not moving away in terms of their importance. They still continue to be important. Expanded span of control with a lot of AI infusion can we rethink and re-baseline our costs and look at how we can expand the span of control to do more work with the same number of people. ARC management, lean market units with the scaled portfolios and higher span. Overall, the focus on this pillar is continuing as it was in the last year. And this is definitely one of our main focus areas of the 3 pillars, each of the 3 pillars are important. That's the message which I wanted to leave with. Moving on. I spoke about customer zero initiatives. Now this slide captures some of the key areas in which we have introduced AI internally in the company. And I've tried to cover as many enabling units as possible, but of course, it doesn't cover the full arena. DELEX is our delivery excellence team where we have implemented QMS agent, DevOps Copilot for automated code review and testing, predictive delivery risks. In the finance system or finance side, we have implemented AI-based vendor invoice validation. And the line below is showing some of the results that have been obtained. In the AI-based vendor invoice validation, for example, it has helped us in 60% straight-through invoice processing without human intervention. We have kept some human intervention still as a checker. But otherwise, it's a straight-through processing, which we have been able to achieve with this. FP&A agent is the kind of a super agent for finance, which we are developing on a data lake where all the financial data and operational data from various systems comes together. And instead of having dashboards, we can now have an agent which answers whatever query you want to raise to it, either by way of creating a dashboard or by answering the question straight away in qualitative and quantitative ways. Revenue accounting agent is another big use area, which has saved a lot of time for our accounting team in terms of generating accounting documentation for the various client contracts that we sign. On the legal side, again, we have been able to implement some of these agents very successfully. SOW & MSA obligation extraction helps us have a warehouse of various contractual provisions and terms that we have signed up, and helps us monitor and use that for further decision-making. Client risk assessment responses, contract red-lining agent. I think talent side, Chetana has already covered, so I will not get into that. But again, in terms of some of the accuracy rates that we have been able to achieve on the SOW & MSA obligation extraction, we have been able to achieve almost 90% accuracy. Contract red-lining agents, almost 60% acceptance of the markups done by the agent. So overall, I think this slide basically showed you how we have been able to adopt AI internally. And this also kind of gives us the confidence that when we are talking about transforming business outcomes for our customers, this is the way we can go about doing it. Of course, we have to apply the customers' context and domain into that situation. But having the experience of doing it gives the confidence also. Now in terms of our overall Lakshya strategy, if I were to look at it from a finance lens, I'm looking at it as 5 big blocks. One is an accelerated growth road map, which is led by our AI Pivot. And I think Venu and Harsh and Vijay talked about it quite a bit. From a risk point of view, our focus in the strategy is also in terms of scaling and diversification across clients and geographies, which gives a balanced portfolio. Execution excellence, I spoke about it in the execution piece of the new horizons. Investments in line with our strategy. We have already been investing in our AI Pivot, and we'll continue to do so. In addition, I think our investments from an inorganic point of view also have been focused on basically a philosophy which focuses on either capability, access to customers or geography. And I'm going to talk about a bit more in the context of the deal that we recently announced and how that deal fit into our strategy. And lastly, but not the least, is the resilience in the balance sheet. That, we are committing to maintain as we go along. And I think we have been doing -- we have done a good job so far in terms of maintaining a strength in our balance sheet, which also works in our favor in winning customer deals and customer relationships. Coming to the deal that we recently announced, I think Venu spoke about it. It's a 360-degree partnership, which is starting. But let's look at each of the components and how it fit into our strategy, synergy and the financial impact side of it. From a strategy point of view, this deal is helping us get scale in Europe and Australia, which is important for us to be able to get a seat on the table for large deals in that region. That -- this deal is helping us achieve that. Domain expertise in regulated high-growth verticals. Venu spoke about 800 security-cleared personnel. Some of the clients that we are going to be inheriting as a part of this transaction are operating in areas which are -- which require security clearances and that also ties in with our sovereign cloud focus and the sovereign AI solutions focus. Accessing new marquee accounts, Venu did talk about the fact that there are -- the top 25 accounts in Europe in this proposed acquisition have an annual IT spend on average of more than EUR 500 million. So big scope for further expansion in that. Geographic and portfolio diversification in terms of fitment into our strategy of scaling Europe faster and also Australia. Also the other two components of the deal, which was the MSP on the talent ecosystem that introduces efficiency in our talent ecosystem and generates some savings for us. And we are starting a new client relationship with Randstad Group as a large deal to begin with. What more can you ask for from a strategic fitment point of view? From a synergy point of view, if you look at it, domain-led digital engineering, cybersecurity and adding depth to iNXT is some of the capabilities which come to us in this transaction. We also established a global delivery model for the entire consolidated business once it gets consolidated. There is a decent potential or more than decent potential for cross-selling of LTM's capabilities across cloud, data, enterprise, CX and AI. Sovereign compliant AI solutions enabled by local security cleared talent. So some of these are the synergy levers that obviously are there in this whole transaction. Coming to the financials. I think I've received a lot of queries about the deal valuation and the financials of the company and what impact it can have on LTM. Now let me tell you that in terms of the financials of the target entities that we are acquiring, they have been going through some amount of tail account rationalization, which we spoke about on the day we announced the deal. And as a part of that, they have also had some one-off costs, which are not going to be recurring. So if I take that into account and look at the valuation, it is an attractive valuation, but it is not so far off that it can be classified as something different. The on-site gross margin of this business is in the region of 19% to 20%. And with the global delivery muscle that we bring to the table for this business, the scope for margin improvement on fresh business that we contract is very high. So potential for margin expansion with synergistic growth is very much there. And on top of it, we have the cost savings from MSP and the GCC IT deal that we have signed up with Randstad, which will also contribute to the overall deal as we look at it. So overall, we expect that there will not be a material margin impact in FY '27 from this transaction. From year 2 onwards, revenue growth, synergies and our new Horizon program that we are continuing to operate on will support margin improvement overall, not just for LTM the way it exists today, but the way LTM will be after the transaction completes. So in summary, I will just conclude with this slide to say our target, which Venu spoke about between FY '26 to FY '31 or our ambition is to double our revenue and to improve our EBIT by 200 basis points. These are some of the levers which we believe will help us do that. From the left-hand side are the growth levers, which are powered by our outcreate strategy, domain tech convergence leading to new addressable revenue, which Venu spoke about. And I think all the speakers after that, Harsh, Vijay, Guru, Krishnan spoke about that. Reimagined capabilities, which will help us capture existing wallet share by getting more volumes from our existing clients, by winning vendor consolidation deals, by winning large deals. Scaling segments and geographies, this is already underway, as you can see. And if we continue to scale Europe and other geographies, and the segments that Venu spoke about, that is what will be driving the growth. And on the margin front, our focus on bending the cost curve, which I spoke about in the New Horizons third pillar is going to continue. Our AI-led LOBs will lead to a productivity boost, which will contribute to the margin expansion. And the business AI revenues that we are targeting not only will contribute to the revenue line, but also given the new pricing dynamics that we are working on and the pricing models we are working on will come at higher EBIT than the traditional business that we have been getting so far. This is how we expect to complete our journey towards our Lakshya target. My presentation cannot be complete without at least talking about ESG. So our ESG goals, I think we have talked about that in the past also, but some of the progress that we have made. On the water positivity side, we have already exceeded the goal that we have set for 2030; on the environment side, on the emissions trajectory, our Scope 1 emissions are down by 70%. Scope 2 emissions are down by 55% versus FY '19. Renewable energy side, we are already using 75.56% electricity, which comes from renewable. And the target is to reach 85% by 2030. CSR side, our spend last year was around INR 95.21 crores. This year, it's going to be a bit higher. I'm not going to talk too much about it because I'm going to show you a small video on the CSR parameters. And in terms of the governance side, our Board is -- sorry, I'm also having a bit of a sore throat. On the governance side, our Board is constituted of 67% independent directors. And I think on the governance side, you can give us better feedback on how do you find our governance by reading our reports and the investor communications that we do regularly. So I'll not talk too much about that. I'll go on to the video. [Presentation]