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 stance as well as his keynote address. A huge round of applause for Mr. Venu Lambu, ladies and gentlemen.
I thought I am 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. This is also my first Investor Day as the CEO of the company. Incidentally, though I joined in January 2025 as the CEO designate, but officially, the switchover happened at the time of AGM, which happened on 30th of May. Just two days back, I also completed an official one year in this role. Thank you. In the next 30- 35 minutes, I am going to talk about our strategy.
You will see I mention about five years as the period in the strategy, but I want you to look at it as a strategy that makes sense to the point of today, because things change so fast, we keep improvising it as we go along. But it is important that we have a goal, what we want to achieve over the next five years, both in terms of revenue, profitability, employees, capabilities, and so on. A lot of those things are directional in the nature, but hard-coded in what we want to do today. Then we keep improvising it as we go along over the next five years. Let me take you all through that strategy. But before that, as Archana covered this, I will be joined by my leadership team for the rest of the evening here to talk about the respective sessions.
We also have the immersive showcase just outside of the break and in a separate banquet hall. We have our practice leadership, our delivery leadership, actually, we are all here in this venue. Not only you can interact at the showcase and this thing, it is also an opportunity for you to actually meet extended team of LTM, right? You have folks who leads interactive practices, data, and so on, right? Then also the vertical delivery heads are here as well. All right. Look, it has not been an easy year. I am sure you all will watch me on this point. When I say not been an easy year, from a macroeconomic standpoint or 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. You had war, right? I am not going to go to each one of the headlines, but it has been a year where I think a lot of us who have been in the industry, probably this is the new normal. That in a year you will have to, if not the external factors, the innovation is going to happen at shorter cycle. We should be ready to 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 is 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. We kept the engine chugging, right? We believe we did a decent job, right? Earned 6% year-on-year growth. I remember at the end of Q1 or in the beginning of Q2, I did mention in couple of calls that I would love to be near a double digit at the Q4, the exit, the Q4 run rate year-on-year for that matter. 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. I think what was fascinating about that order booking was we focused on large deals.
If you ask me, that is going to be one of my big moat of growth. We have shown that in FY 2026, 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. All these wins that we got led us to increase almost 100% year-on-year on our large deals compared to FY 2025. It was a massive fraction over there. Probably we were one of the first ones to launch a brand of our own for AI. It is not just about brand, it is about aligning to the concept that if you want to lead in the AI era, we need to develop an ecosystem. It is not about having tools or technology or a platform.
It is 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 you co-innovate and co-develop solutions along with customers in that ecosystem. That is the approach we took at the beginning of the year when we had the BlueVerse launch, and that ecosystem has developed beautifully. I am going to share with you some of the highlights of that in my presentation. Most importantly, you can actually see them in the breakout session outside. That ecosystem, as it keeps expanding, that is going to be the second biggest moat for us. It is not about having X capability or Y capability, it is about making it more all-inclusive capability. That is one of the things 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 is a completely new addressable market. We have a dedicated team which we set it up in FY 2026, and it is gaining a significant traction. Of course, I have shared with you in my various earnings call about the success of Fit4Future and sales transformation. That was reflected in our margin improvement both at the EBIT and PAT level. Of course, all this came without impacting how our customers feel, and we had a good rating on the CSAT as well.
It has 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 are performing? That is what it is. I am 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. Let us 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 will give my own example. I started my career with a company called Digital Equipment Corporation, which does not exist now. It was a company which used to make big computing machines called VAX/VMS and Alpha VMS systems. The Founder of that company said, "I will not 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 Boston-based $20 billion company. Ironically, that company was acquired by a PC company called Compaq.
I have seen personally the changes pretty early in my career. I should say I was fortunate enough to deal with all that. Then you moved away from client-server computing to an internet computing era, which started at the back of 2000. Then the internet computing led to a cloud computing and a mobile computing analytics era in the mid-2000s, more so after the financial crisis. The cloud computing journey when it started, it had so many questions from both the industry analysts as well as from the people who are in the industry. But now if you look back how each of this evolution transition, that led to a big opportunity of what we call as a digital transformation.
Digital transformation is big. There were times we were debating what is digital revenue, what is not a digital revenue. Personally for me, this looks like it is 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 two-three 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 till it reached the stage of maturity in each of the stages. But in AI, it is rapid. It is the shortest innovation cycle. I do believe that there is a huge opportunity for us in the tech services.
If I had to talk more so from LTM standpoint, I think AI adoption at scale is going to revolve around three big Cs: context, cost, and change. I will talk a little bit more on what the context is, but just to give you a quick reference, then I will 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 does not distinguish between experienced employees, it does not distinguish between where you work from, but the access is democratized. But applying of that needs a context. So that is where there is a huge successful criteria about the context.
Second thing is about change. As much as AI is about tools and technology, it is also about change management. How do you think I have known customers who have told me that, "Venu, I invested $30 million-$40 million in technology, but I am unable to see the productivity because people are using it as a shadow tool." They do their work, check it with on the AI tool and say, "Okay, it is working fine. Okay, let us move on." So the actual impact of that is the change management. It is a mindset change that is needed to make sure that AI adoption happens that directly gives the productivity benefit. Last one, we have been talking about this cost aspect, at least over the last two months.
At least I and Vipul in various conversations, we have been talking about it, but I see a lot of buzz over the last two weeks about the impact of cost. Nothing comes free. Enterprises are going to look at total cost of ownership, right? What is going to be my ROI on anything that I do on the adoption of AI? That conversation are getting serious, and they are getting real. All these three Cs is one aspect of it, which is a huge opportunity for us, right? How do I help customers in context, change, and cost is a huge opportunity. Second is, one 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 is the way model 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 wholes of our company, agents are going to be the best buddies for our human employees. Of course, the local talent is becoming a lot more important now because of sovereignty and the context aspect of it. I will talk a bit more on that. Output mindset is improving with clients, and I see that as a big opportunity, decoupling the effort. Once that changes, the opportunity just skyrockets, right? Because then you are getting into the space of spend, which is not traditionally addressed by us. Of course, I spoke about AI is democratized, which does not belong to one or two frontier model players.
It belongs to a lot more archetypes of the players who have their own intelligence play in the AI era. 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? 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. 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 lot many things. We do lot many things on data, SaaS, infrastructure, IoT, and so many things we do, right?
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, right? We acknowledge that there is a shrinkage of at least 25%, if I may say, 20%-25%, definitely a shrinkage of these services of the traditional model. We saw that in our business, right? The 6% growth that I spoke about in FY 2026 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. This was the net growth after all that. That means that the shrinkage happened, and then we grew.
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. 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. That business is going to go up. The fact that every technology opens up a new opportunity. I can give this examples in many times on cloud and digital transformation, right? 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. Same thing is going to happen on AI, we believe. 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 job reductions, if you can't call it's pretty much there in the core operations area or in the general operations area. Because that is where there is 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. 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 AI-led engineering. All the application development or product development will be AI-led engineering construct. That's going to be a huge opportunity. If we don't do AI-led engineering, there are no tokens to be consumed.
If there are no tokens to be consumed, you know who's going to be impacted a lot. There is going to be a huge push to adopt AI-led engineering in all our work. And we are already doing it, and I'll share with you some examples of how we are doing it. In fact, we have developed some products that you'll 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 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 three or four big customers.
While I leave you to read the text, but the headlines is, a client said, "I want you to help me to 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 three broad category of feedback that our customers said. And that's been in play whole of FY 2026, and it's going to be in play big, big time in FY 2027 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 two years back, right? 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- 24 months. And then you have on the left side, 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 6x . And look at where we were traditionally strong on. 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. That means our core position, we cannot move towards the platforms and models because that is not our strategy, and that requires a different. I do not think we want to talk about moving into that direction. That direction is where we want to partner. We have announced a lot of partnership throughout the year with our BlueVerse partnership, and you will soon hear us, we will be announcing a partnership with one of these two frontier models as well. Very soon we will share with you some updates on that. But that is where we will take the approach of partnership.
With 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 is where the partnership will play on the platforms and models. 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. My fellow speakers also will cover a lot more vertical depth. That is our core strength, and we want to strengthen that more on the domain side. Because we always addressed customers from the industry standpoint. We have a complete alignment right from the sales to delivery vertical-wise.
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. That is why we came out with a position saying that, "Look, if we 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 conversation will only accelerate. It is at the early stages, but it will accelerate a lot. We want to move. That is 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. We want to strengthen our tech domain convergence story. I will share with you a lot of examples, what it means in the tech domain convergence. Then from technology services to business creativity, because that is where I believe if you want to get into the big addressable spend area, our positioning has to move towards business creativity.
I did not have an opportunity to share the brand and the narrative earlier, apart from few updates on the earnings call. I thought I will spend some time in terms of what is the rationale went about in repositioning the brand and the company as LTM as a business creativity partner. I have got a very short video for you to watch that, and then I will come back again.
[Presentation]
All right. 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. It is 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. This is our Outcreate framework, right? The positioning is business creativity partner.
The markets we will address is, of course, it is a balanced portfolio that has been one of the observations and questions that has come from all of you in 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 will talk a bit more about that as we go along. Then the thing about how do we pivot, the strategic AI pivot. Whether it is about how do we strengthen our domain tech convergence so that it strengthens our business credibility position. You will hear it from Guru on that. Reimagine capabilities. We had probably 13 or 14 practices a year back. We have now realigned them into three simple lines of businesses that is positioned to deliver the outcomes.
You will hear it from Krishnan how we reimagine the capabilities, of course, how we reimagine the ecosystem as well. To enable all that, the talent has to be future ready. Chetana will talk about that. Of course, we need to bring more local expertise that complements with our global collaboration as well. Of course, the cost, right? We cannot continue to have the same cost parameter. We need to bend the cost curve a lot, and we have done that successfully in FY 2026, and the journey is going to continue. Vipul will share a bit more about what are the initiatives we are doing to bend the cost curve further. It is not just from a margin standpoint, it is to be competitive and also to reimagine services differently.
On the domain tech convergence, I am going to pick a few aspects of that framework, but you will hear it from others a bit more detail. On the domain tech convergence, I would strongly encourage you to see that in the booth. 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+ agentic solutions and lending operations, and Harsh will talk a lot more on that. Domain tech convergence is all about bringing an agentic solutions which is very verticalized and industrialized. That is what is going to be part of the BlueVerse ecosystem. Because intelligence is democratized, context and domain are premium, right? 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. If I leave that aside for a moment, we are going to make those partners, those frontier models successful by applying the context. That is where our clients need help on. That can be done only through this domain and tech convergence model. That is 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 very industry specific. Because you can have a tech domain convergence by having domain consultants and creating some frameworks and solutions.
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. These SLMs will be co-developed along with customers. We invested in first SLM model called Voicing, as some of you would be aware, and that is already doing great business for us. 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 is 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. All this will be done as part of our BlueVerse ecosystem. It is going to be outcome priced in non-linear model. Reimagine capabilities. We essentially had the run, how you run systems, infrastructure, application, data, and so on. Things was spread across the company because the practices were built as the technology evolved, and each of the practices had capabilities to run systems. Now we have brought all the capabilities that is required to keep the lights on for our customers and to move the run on a platform-based approach under one single line of business called iRun. Same thing, the capabilities that is needed to modernize technology systems and reimagine experiences, we brought it under a line of business called iTransform.
The third one, which I mentioned earlier, is about reimagining business process and agent-defined business process. Creating SLMs at the point of tech and demand convergence is what we call as a business AI. 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 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 three, five years back. The SLMs that I spoke about, we are going to develop with a partner called Uniphore, which we announced a partnership, which is a leading player in enabling and creating the SLMs.
We will also work with our other partners like Voicing AI and so on to create SLMs as an example. When we talk about partners, it is about value creation now rather than the focus on transactions. The needle has moved a lot about creating joint offerings. We will continue to announce more and more such strategic partnership in FY 2027 and as and when the opportunity comes up beyond that. The value proposition is not just about SIs. SI, whether if I do something on Copilot, if I do something on Gemini or any of the other models companies, it is not to go as an SI. It is to go as saying that look, how can we help you to align the services to the more business align. Then of course the GTM approach, is going to be leveraging the domain tech convergence with a joint investment.
The Uniphore is one classical example where we made the joint investment in the GTM as well apart from the SLM. Same thing will 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 is Microsoft, Google, ServiceNow, Salesforce, Databricks, Adobe, and so on. The talent strategy for reimagining partner ecosystem, and they are going to play a significant role, and I am grateful to that, in reimagining our talent skill set. A lot of certifications that we are driving within the company is done in collaboration with our partners, whether it is a hyperscaler, technology platform partners or the model partners.
In terms of geographies, very clear we are focused on four 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 under production. These are the four major segments or four segments, market segments that we operate. It is very clear we will lead in these segments. In fact, we have scale in some of the segments. Financial services and tech services are not very far off from being $1 billion business unit. 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 was in the consumer segment. The two of the largest deal that we announced was in the consumer segments.
Production, we have a very unique and differentiated capabilities both in our manufacturing area as well as the energy and utilities area. Some of the big names there are our customers. We will lead in these segments. When I say lead, we want to really dominate and lead in those segments. We also want to scale the geographies. We want to change the mix of the geographies. We want to scale the geographies. Europe is definitely a focus area. You saw an announcement of a deal. I will talk about that and Vipul will spend more time on that. Of course, being very focused in the rest of the world rather than spreading too thin. We will focus, double down our Europe focus, and we will be very focused in select few countries within the rest of the world.
That is 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 three capabilities being reimagined, spoke about the market segments. Now if you 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 us take this as an example. Investor onboarding Unitrax. This is a model that is already under development in our financial services vertical. We have a platform of our own where we support some of the major fund institutions in the North America region. We are working on the SLMs for that.
Same thing. We have an iNXT, which is our industrial AI capability team. Using that we are developing an SLMs for the smart plant operations. 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 is another example of how we will build contextual solutions, contextual models at the foundation of it. The assets that supports us are our BlueVerse platform, the studios. We are 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 will do it at most of the other locations.
We are going to have AI labs which are very focused in innovating with our partners. Whether it is Copilot Labs 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 one of the two frontier models, I will give an update very soon on that. That is 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 are part of the employee ecosystem, and you have a mentor who takes care of that. So that is our assets. Where do we make money from in this? We make money from here.
AI-led engineering, data for AI, integration, governance, and assurance. It is going to be big. Assurance, especially post-MyPath, it is going to be huge. We are already seeing an awesome conversation on that. FDEs. We have identified thousands of our best talent to be trained on FDEs, apart from doing lateral hires on the power deployment engineers. We are going to make money on all this. While we focus on delivering reimagined capabilities on our core, which is especially iRun and iTransform, and on these segments. This is how I will summarize all the elements that I spoke about. All this is a strategy, but it requires an execution mindset. In FY 2026, we have demonstrated that whether it is in Fit4Future, sales transformation, our execution muscle only got strengthened in the year.
We have a framework for execution. I call it as a New Horizons program, and the New Horizons is essentially whatever is good today is not good enough. We have to challenge ourselves to create a new horizon for ourselves. It is going to be focused only on three 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 is going to be a lot more pivot that will come in our journey. You need to have a strong execution mindset to deliver the pivot. That is our Horizon 2. 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.
This is how we look at the execution framework on Horizon 1, Horizon 2, and Horizon 3. Let me take 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. 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 two levers evolved into a wholesome 360-degree relationship. We spend good amount of money on our sub-cons. 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 where we want to scale. That is one part of the partnership. The second part of the partnership is enabling the GCC for Randstad, which I spoke about. We already started the initial ramp-up planning for that. The deal one, we had to wait for the regulatory approvals and the consultation process to go through before we announce the closing of it. The tenets of that is very clear. Focus geographies, and these are the geographies where we are not present. The one thing that I am taking away, taking the risk out of this is no integration risk. I am absolutely looking to integrate.
Let us take Australia as an example. We have seven salespeople. We are getting more than $100 million revenue over there. Integration risk is away. We did not 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. Also in the verticals, which will only add up to our production segment and we can scale a lot more 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. Of course, very differentiated capabilities on the service line as well.
I think the first slide, I had shown it when we announced it. Let me give a little bit more color on few aspects. I am 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+ years. In fact, the top five is almost 15+ years relationship. Great customer CSAT. The other interesting thing that really attracted us was, is the security clear? To work in aerospace, defense, sovereign cloud solutions, you need to have the security assurance roles and certifications that is available. 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 is huge. Just looking at the addressable spend, not their overall IT budget, just looking at their addressable spend as a cross-sell and upsell opportunity that we can bring in. We are bringing our SAP, Oracle Cloud data, and so on. The average tenure, it is 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. 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 and cross and upsell a lot more.
All right. Before I do, there is also one more new topic. The new topic is: how are we going to innovate commercially? 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. We want to be ready. We want to be ready, and we want to apply where it is applicable. We already started proposing that. 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 one or two elements.
How they 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. 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." We should be able to give a unified currency to our customers, which integrates one or two of these components, and that's what BlueVerse Currency is all about. We have done extensive research about it, taken external help as well in redefining this. We tested with few customers. As we speak, we're putting a bridge in few client organizations.
I'll take you a few examples of that. Two 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 in how do we move from a class decoupled effort, right? Decoupled effort. While there could be some fixed price, 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 runs side of the engagement. In the agent engineering factory, if you want to deploy agents at scale and through AI at scale, we can't do SOW to SOW. We need to set up an agentic factory.
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 developed for them, whether it's a small, medium, and complex agents. The third example is about business AI, which is completely business outcome-based, right? For the travel and hospitality customer, your conversation is around how do we charge you on a per member retention based on the business operations that we reimagine and agentify. 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 last years.
As I said, we started the journey. The readiness always lacks from terms of audio readiness, but I would expect over the next year or two you'll see more examples that I can share with you in 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. I want to tell you that it's a bold ambition.
I do believe that as a team, we are all very excited. We have the right mindset to accept a bold ambition of ours, which is in five 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? That is 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.
With this, let me stop here and invite Harsh for the next session. Thank you for listening. Thank you.
To market focus. Now we will have two 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 have 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. I have had this incredible privilege of building a very large part of this unit. I take immense amount of pride in what we have done. More importantly, I feel as excited as day one today, because I think the environment is absolutely fascinating. We have never had these kind of conversations. I talk to a lot of CIOs and we are having a whole lot of conversations.
I feel that I am going to make an honest attempt to distill all of that and give my perspective today. I sincerely hope that helps us begin to answer this whole big question that is in the room. What is AI going to do to our industry or to coding jobs? If we were to go by social media, folks, five people, 20 prompts, 100,000 a year. It is a frictionless world, and we can run an enterprise IT unit. Nowhere is this debate on sci-fi versus friction more pronounced than in the BFSI space. I think we understand what friction is because our landscape is like a bowl of spaghetti. I am sorry for the food reference, and you can probably figure out why. It is more like the [khichdi away] .
It's got more complexity to it's got more texture to it's got more nuances to it. But one thing is clear. The pace is accelerating like I have never, ever seen before. If you see, a whole lot of enterprises, almost all leading enterprises have announced a CIO or, sorry, Chief AI Officer or an AI Czar. 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 Czar who works in technology as such. Then I feel there is 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're 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. What this is doing, it is creating multiple conversations across the spectrum. 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. Instead of doing one large project, we are doing 35 smaller projects in different shape or form, which have higher impact.
I feel that it is important for a partner to know the ecosystem well, know the application landscape well, know the domain well. There is a level of homogeneity in the conversation in the room. But more importantly, there is issue around governance. The partners will be so deeply embedded, they're finding it difficult to govern. I think 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. Or actually, I would call it three buckets. The bucket one is where you're reimagining your business.
So when 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. For every dollar we spend in this industry on technology, we spend $2- $3- $4 on operations. I think that's one area ripe for disruption. 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. We've also verticalized top three 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 is the promise of ROI on AI spend, right? You can lower risks faster or quicker ROIs on what you do. I think this model, in our view, this model lends itself much better to this kind of an orientation. 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 have now kind of zeroed down on a model where we are 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 sub-product-specific SLMs will be built in.
There is an orchestration layer that essentially governs how these SLM talks to industry LLMs on compliance, on regulation, even from a cost perspective. We are starting to build a whole lot of this for our clients. Our thinking today is that we have one account with +100 million accounts, two are in the cusp. From there, to about six to eight, $100 million accounts by the time we are done with our Lakshya plans. Because we feel that we are an engineering-only firm. That is our core DNA. We understand architecture really well steeped into it. This is our moment to really partner and add more value. As I said, there are spend happening in three broad buckets. I just want to talk about few of the things that we are seeing in this space. I think 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. We have taken AI-first approach on this and built 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. What is the good part? We are resellers on this. So, 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 are doing for an insurance company. Again, we are reimagining their businesses.
Their thought process is, can we use AI to really upsell, cross-sell to our existing customer base? Again, and also underwrite a product if we define, do a custom product, and if we can define that product for them. We are building about 35, what do we call, data lakes for them for different product size products. Putting a SLM on top, which essentially collates data from here and a bunch of unstructured data, creates intel, develops a, I would call it, a far 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. The thought process probably is that, as we see, the decisioning is a lot more closer to business. Business is really trying to harness the power of this technology.
I think that no conversation in this room is complete without talking about the [Mythos] impact. I do not know where to put it because I think in my view, it belongs in a category of its own. I have 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 D.C. 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 team, there is other things that they are working on, and the industry will find some solutions. I feel this should also point out a tons of vulnerability in the application in the data space, and we are preparing for it.
We have created a 10-star program partnering with top AI companies for each of those stars. Watch this space, you should hear a lot from us. We have also become, I think, probably the only service provider who is a part of FS-ISAC, partnering with large banks in U.S. and regulators to build a comprehensive solution. As I said, the debate on build versus buy is raging. It has shown us some early promises, some even bigger failures, but I think the number of conversation in this space has just exploded. I will talk about two very different case studies. One is a completely a new build. A large bank, which is trying to build its wealth business all over again, has decided to build two big rails on its own. It is bespoke rails.
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, just like a swivel chair, 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. You guys probably know how complex the tech landscape behind a wealth management business is. Earlier, we would have done this on a Salesforce, Pega, ServiceNow, lot of custom code, private licensed products. 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.
I think that threshold of reduction of risk is what probably is driving a lot of this conversation. Similarly, for a large insurance company, we are doing a global rollout of a Duck Creek platform. Earlier, because of lack of multi-tenancy or the way the product was designed, the cost of implementation would be huge. Reduce 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. There is a phenomenal reduction in cost because some of this can be very expensive for smaller countries. We are seeing these kind of conversations pick up significantly in our business. I will just throw a random statistics.
Earlier, we would have work on 10, 15 proposals, 20 proposals in a week. We are talking about it in hundreds right now because everybody is trying to ideate something. A lot of conversations are in these three buckets. Operations transformations. Nowhere the promise of AI driving saves is more prominent than this. Again, as I said, for every dollar we spend on technology, our clients spend about three to four on operations. Apart from cost, it also does two things. It limits the experiences that people deliver to their clients. I think that is probably a bigger value proposition because I sometimes, we as a partner, sit on bids that custodians make to asset managers, and the biggest question is, "Oh, we want a better experience for our clients." I think that is going to become prominent.
I just want to talk one case study, because I think it is really massive. A very large bank is looking to spend about $200 million-$300 million to take out billion dollars of annual spend on operations, and just radically change the entire experience. We are looking at about 1,100 odd processes in 30 odd, 35 odd products in about 50- 60 countries. It has been the details on this, we have really gone up to the detail of course, cost, but experience at every stage. Who touches it? How do we work on regulations? This is very different because the assumption is we can build infinite amount of software to work through some of these challenges. This is what I find fascinating.
I think we are 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, the last percentage of what we do is largely application development engineering. We feel excited, and we feel that we are the right kind of people to do this kind of work. I feel that we as a company are in a very interesting zone. We have the right kind of capability, which is data, domain, architecture, even delivery depth. The right set of clients where we have the right to play. We work with almost seven of the top 15 banks. We have a very large, 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 needs to be done in these changing times. Excited to be a part of this journey and happy to have conversations with you guys as we go along. Thank you very much. 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.
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. I do not want to preach the choir, because you all know very well what is happening in the tech industry. With that, basically what I wanted to cover today is that, okay, what are the client priorities, what we are seeing because of our associations with them over the last few years, and also because a few of the relationships are there over the decades. Our strategies evolved from our dealing with the clients, and at the same time, I think we also understand the client priorities. 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 it well.
How are we ready to capture those opportunities and also the proof points? Because that is my outline of my presentation today. 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 this is a change which has happened over the last few quarters and maybe slightly more than a year. If you look at all these investments which they are doing, at least many of my clients and also all the generative AI build outs, that infra build outs. This 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 that is the one which is driving. 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 is the context engineering and the surface in the context. Especially when the SaaS providers are also leveraging their tool knowledge and okay, they want you to drive the outcomes. 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, with major part of our clients. For all these investments which we talked about it, and of course, there are going to be a lot of optimization on the OpEx.
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. With this sort of an addressable market, what we have got, and okay, probably if we just look at what are the opportunities for us. Some of the opportunities and okay, which has evolved over the last one and slightly more than one year, and look at they were not the opportunities which we used to address it, and I think those are the opportunities which are coming in our way. Especially the hyperscalers and the chip manufacturers, both of them wanted to try to manage both the upstream and the downstreams. That is like a client spend and our revenue, which was not there before, and okay, which is the one which is coming up.
And of course, the strategic partner on the client growth initiatives. I will take this opportunity to say that when 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, we always look at it, and I think that is what all the tech companies look at it, how we can drive their revenues. First thing is the buying, next thing is the adoption, and second thing is the consumption. So how do we drive those things? And of course, you know about the future of the tech disruptors, and I think Venu has covered it nicely about those partnerships, 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. Previously, what used to be the size and the scale of our engagements, I think they are increasing exponentially and creating more demand on which we need to address that. And of course, I do not 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-led land and expand with our clients, and I think this is differentiating us 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. With that, these are the opportunities, right? But how prepared are we and how capable are we to expand that?
We came with the Silicon 2.0 services offerings. The bottom-most of the pyramid which you see is that was not our revenue source 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 standalone 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. The one what you see lab as a service, I think that was an offering which has started roughly around six quarters ago. And now today, we scaled it up to such an extent, I think it is 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. 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 hyperscaler and gone live six weeks ahead of the schedule, and which really, really increases their revenues a lot for that sort of a CapEx. In these AI infrastructure build out, conventionally, we know the CPU center build outs and we have drawn things from there, and I think that how we need to do that on the AI infrastructure, especially on the integrated offerings. 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. The automation, if you call about a one-click deployments and on such a huge AI data centers, I think this is one which we have started and which sort 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 used to be around at least, if not more, three months. Now the fastest time to market of the feature development and the problem management and also the life cycles of our customers to be managed.
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. That AI-assisted product development to the keeping AI at the center or AI natives, whatever you give it, and thus, how do they scale up? There is 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 few years ago. Thus, we also included the, because you know very well about all the stuff, the security part of it. That is the one which we almost rehashed our product engineering stack and the platform development.
With that, now I am sure that most of you would have been seen that one, what sort of the supply chain disruptions. Because we used once and the aggressive timelines and geopolitical tensions and tariffs and whatnot. 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 is improving even our clients' revenue streams as well. Now, the last part of it is services. I am 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.
I think with that AI-infused, the professional services or the support what we are doing, the standard TPAs, we are not measuring it. Of course, the average handling time, resolutions, and stuff like that. Because what is the thing? What it is generating the revenue for our clients by the cross-sell and up-sell or while doing the support. Thus, I think this is a offering stack which we came with. Offering stack is there and. 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 of this one, I think, covering each of the layer and, I just picked a few of the proof points there in that.
If you look at it, the first one which I was telling about the AI data center sort of a stuff. Here is the one. We do actually do the qualification testing of the silicon, what it gets into the equipment, how many of the qualifications what we do. Before we infused AI, our throughput used to be around 250+ qualifications in a year. These qualifications are like a memory chip to the CPU to GPU to anything. Now with AI, if we increase the throughput by 4x, 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.
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. If you look at it is quite a significant one. I do not want it to delve a lot into the details about it, but this is a new service which we've incubated. I think it is applicable for all our clients, lab as a service and qualification as a service, which was not our revenue stream few years ago. Coming to the 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.
How do we make that platform and the production ready and whatever code comes from whatever the GitHubs to anyone, how do we 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 back to that planet-scale threat detection and malware analysis. You know very well about this geopolitical stuff, with that, the threats in all the devices is so much. I just want to tell that from the 2 billion devices, we really safeguard them with the threat signatures, what we generate and what we put into them. Any of those assets, if it gets into their stores, then we do certify that I think that it is safe.
These are all the things which we started it, that is the sort of the numbers what we talk about it. Of course, I leave that contact center operations part of it. Previously, what we used to do as the average handling time. Now, we've reduced the human intervention almost around 85% of the conversational AI accuracy which we get it. What does it mean? I think we are not even allowing those 85% of the staff to be raised as a ticket for someone to work on that. Of course, just look at the scale and the campaign operations and also the content operations. We do almost around 3 billion emails per year campaigns which we run. Also we run those content operations by doing closely around 250+ markets we serve it, and we do those many impressions.
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 good amount of our nonlinear revenue. 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 is close to around a billion of the Bluetooth devices which you use has got the LTM IP in that. By the way, any Bluetooth manufacturer who wanted to get it certified and get that logo of Bluetooth, that will be tested and validated on the platform which LTM has built.
That is all I have got. With that, this is for my business talk here. I definitely. If you have got any questions you wanted to know more, I have rushed through the presentation, I think, during those booth visits or otherwise, and I think you can just talk to us.
Archana, over to you.
Thank you. Now we will have our CDO and CGO, Mr. Gururaj Deshpande and Krishnan Iyer, walk us through our capabilities and delivery models, how we operationalize the strategy at scale.
All right. Good afternoon. Welcome to all again. My name is Gururaj Deshpande. I am usually based in Bangalore. I am the Chief Delivery Officer at LTM.
My name is Krishnan. I am the Chief Growth Officer based in Hyderabad. We are Chief Guru.
All right. We thought we will give you a quick view of what it means to Outcreate on our delivery and on our capabilities. You all heard Venu speak about the three horizons. Horizon 1 was growth. That is 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. Last but not the least was Horizon 3, which was really all about excellence and the importance of doing, outdo, as we called it. 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.
We have been in this business for a while. We have been delivering significant outcomes to well over 700+ enterprise clients at scale. We understand scale, we understand complexity. If I were to talk about a few data points to just drive home this aspect. 14.5 billion transactions that we process per year for a single client. You can imagine the complexity behind all of that. 125+ million 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. Again, there are many more to talk about. Then talking about the millions of support tickets that our teams look at and solve. Many of them using AI in the course of what they do.
We support millions of SKUs as part of managing the e-commerce estates for our clients, and likewise, thousands of online properties that we take care of. 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. Of course, the enablement that we are trying to do for our people, again, running into millions of hours. 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. What I am going to talk about leads me into context. Context, how it is becoming super important. It is becoming really a super asset for us as we kind of pivot into our next part of the journey. That is the Outcreate diagram that you saw.
I am going to zoom into the domain and the tech convergence part for a few minutes. Domain and tech convergence, Venu already gave a bit of a context around what it means, and I am going to really instantiate this in the context of the verticals that we operate in. 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 is a given. There are tool sets coming in on a daily basis. This space is changing by the day, by the hour. Tech is almost a given. It is 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.
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 will talk about a few examples there. That is where the art of the possible is in terms of process re-imagination. Process re-imagination has been, you could say, the holy grail for many enterprises for several years. I think this technology is really helping in sort of ride that wave of process re-imagination and cut down the cycle time. I think that is the super important point. You already heard from Harsh about financial services. I am not going to cover that in detail. There was enough example in there about how we are really reimagining this business for our clients.
Likewise, Vijay spoke about the whole tech services piece, so I am not going to talk about that as well. On the consumer side, again, we have four broad sub-segments that make up consumer for us. We have retail CPG. We have travel, transport, hospitality. We have healthcare life sciences, and we have MedTech, tech, media, and entertainment. Again, we have production, as Venu spoke about it. Manufacturing and energy and utilities make up the production segment for us. What our teams have been doing in the last, you could say almost 18- 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.
Looking at every segment, sub-segment, sector, sub-sector, every large account that we play in, and looking at the entire value chain, talking to clients about it and saying, "How can we reimagine this business?" That is the work that our delivery teams have been up to. If I zoom in, let me also add that in the process of that 18- 24 months, our teams harvested well over 1,000 domain-centric use cases. Using AI as a lever. How can we reimagine this part of the business? How might we? That is the leading statement that our teams used to start to reimagine some of these things. I will give you a few examples 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 when the journey from data to insights would take months, if not years. 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. That is what AI is doing today. The journey has moved from months into days into almost being real-time. That is one big trend. The second big trend in retail is consumerism is no more about humans. Look at this interesting thing unfurling in front of us. The consumption is all now digital. We are most likely selling to a bot. We are most likely pleasing an algorithm than pleasing a human.
That is how things are changing. 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 Venu 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. The so-called black swan events unfurling at very short notice. What does it mean for retailers? 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 to that. The sense part of the whole value chain. We are trying to look at this process and saying for particular customers, how to convert inspiration into a purchase.
There are times when somebody is scrolling an app, or it could be an Instagram page or TikTok, and somebody is looking at a recipe. How do you convert that moment of inspiration into an actual sale? That is 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 is the whole AI commerce engagement agents, almost engaging people in a conversational style all the way from product discovery till the point of making a sale. So many more such examples. There is the whole supply chain aspect I spoke about. How do you manage inventory? How do you get the signals, converting it into replenishment cycles?
Again, the next best offer as well. That is 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. We are trying to break all of that down into multiple areas that we have started to look at. Again, I do not 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 TTS space. Again, if you look at what is happening out here, clearly loyalty is such a big part of the whole hospitality business. 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. In the airline space, frauds, chargebacks are a very big part, and customers lose billions amount of dollars as part of the whole process. How do you really bring in AI, bring in agents, bring in decisioning into the whole value chain to solve for these billions of dollars? That's what we're doing on the TTS piece. If I look at M&E, 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. If you look at the value chain from, as they say, script to screen, that's a very apt term that we use for the M&E industry. If you look at it from script to screen, there's a whole lot you could do to bring an AI into making this more efficient, more accurate, and so on and so forth. If I talk about a couple of examples, again, a lot of what I'm saying, you can go to the booths and fully appreciate the power of what I'm saying. If I spoke about hospitality on the earlier slide, you could go there and look at reimagined travel, for example. Or you could look at intelligent commerce.
That's another showcase we have as part of the booth. 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 Media Cube. Again, Media Cube 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 this space. I'll pick a couple of examples. If you look at content creation. 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? How can you establish dubbing? How can you get dubbing in regional languages in a matter of short time?
Things like that, resulting in much lesser cost per finished hour of footage, for example. Or we could look at content management. 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? Or you could even look at voice-based discovery of content. 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?" AI enables all of this at scale. It's no more proof points, but certainly enables this at scale.
And then things like rights clearance and all of that you could start to talk about. That is what we are doing on M&E. I will also switch quickly to a couple of our other sectors as part of production. If you talk about manufacturing as a segment, 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. 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?" Again, a couple of examples I will talk about.
If you look at demand generation here and broadly look at inventory management, 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 measure, you can start to think about who looks at inspections, quality inspections. How do you inspect a welding joint in an automobile company, right? Can we completely reinvent that through AI? What models can I use? Do I need 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? Likewise, if you look at things like service.
Service is a very big part of the manufacturing industry. We do not realize it, but service can be a very large revenue stream for manufacturing clients. It is never really just about the shop floor. So if you look at service, and that is aftermarket revenue that we speak about, how do I bring in asset intelligence and maintenance intelligence? For example, if you are 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? Things like that. Then again, many other functions that could be around DSO improvements and things like that. The good news is, coming from a heritage of our parent company, this is space we understand well. It could be manufacturing, it could be energy and utilities as well.
We understand the domain, and that sort of gives us an edge as well with our heritage, where we come from. Then finishing off with energy and utilities as well. Again, it is a space which is very asset heavy. It is a space that is slow to change. It is a space that is also very regulated. So in the context of all of that, we are trying to see how do we really solve for some big, hairy problems in the energy and utilities space. 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, how do I use the basic data to establish AI-driven well data estimation and bring down essentially the cost of exploration? That is 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. 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? How do I really get many legal people out of the loop? 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 Studies. These are things we would not do in the past.
We are bringing in talent that sort of understands the domain, hits the ground running on day one, and can be productive. I was saying before I hand it over to Krishnan, who will talk about the Outcreate capabilities part, and I will come back eventually to talk about what is the enablement that is happening. How are the roles changing in the context of delivery, in the context of AI coming in? I will 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, which kind of brings a completely new set of opportunities which wouldn't have existed if it were not for AI. Thanks to Guru. I am going to talk more about the reimagined capabilities Venu highlighted in his speech. It revolves around three things. One is iRun, second is iTransform, and third is the business AI. What I am going to give you is a few peekaboo into how we are reimagining some of these capabilities, what are the markets that are opening up because of that. While I do understand the compression because of AI, I am also going to talk about the myriad of opportunities that have got unfolded in each one of these areas.
Let us talk about build. Now, we do almost 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%. That is one part. Secondly, there are almost 100 human AI interactions per day per developer. What it means is that the efficiency of the developers have gone up almost a factor of 30%. This is what some of the high-end developers work at from a level of interaction with AI to get it right. That is what is happening. Clearly, AI delivers, therefore, at almost 30%-40% faster speed, enhancing the quality and the accuracy.
Interestingly, what is also happening is there is a lot of legacy modernization, which is again, a huge market. I will 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%-70% of the IT budget of many companies is going to managing legacy. If you really look at it, how can you de-risk 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 and obviously understands all the interactions that go between the various systems, and it develops new code.
What this has done, what AppIQ has done actually, is significantly reduce the time it takes to modernize legacy applications. As I said, 70% of customers' systems are legacy. When you are talking about a 50% time reduction, also 50% cost reduction to do it. Imagine, the more faster you are able to modernize, the more business you get. The clients will be able to do the same business, multiply more effectively. For example, they have a $10 million budget. If you modernize one system, now suddenly you can modernize two systems and also in the same amount of time. That is what things like AppIQ has brought to the table. The second thing that is happening is if you look at agents run the life cycle end-to-end. This is something which is enabled by our AgentIQ.
Everything right from planning to detecting to building, everything, the whole thing can be done by agentic AI, with obviously some human in the loop. Then there is FusionIQ, which is another tool that we have developed, which significantly shortens the time it takes to test, automates the test script generation, the whole testing process, and so on and so forth. We will see a significant opportunity ahead of us, ahead of companies like LTM, and we have actually deployed this whole thing in many of our customers. 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 combined the whole operations management of apps, infra, and the platform apps all together.
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 is 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 have won, and we are really going aggressive in this space because it meets the customer's hyper-productivity requirements, and this is real. We have got the assets to go behind this. 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 is both human + AI, but soon there is a very high degree of autonomous agent that is possible here. Similarly, there is a very significant amount of triaging that goes on between text and voice and to enable auto solve.
What is interesting while we are doing this, the scattered intelligence. I will just go back. I think there is somebody speaking at behind, which is disturbing me. I do not know who. 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. This, eventually, you can extend it to even business operations. But right now it is all about apps, infra, and the platform apps. This is the knowledge fabric that we are building, which is key, which is powered by our LTM BlueVerse. Then also the whole agentic orchestration, so many of these tickets can get solved autonomously.
Obviously, it is shifting more from SLIs and SLOs and SLAs is the typical old ways of managing to outcome ownership. We are guaranteeing outcomes for our customers. Okay. Now, there is somebody talking at the back. If you can tell them to stop, that will be helpful because it is significantly interrupting my. There is somebody talking at the back. Interactive experience. This is something which is a phenomenally big market. 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%-5% on martech. They allocate on marketing. Basically, 3%-5% is marketing spend. Out of that, 25%-40% goes on martech spend. The martech spend actually is growing by around 17% per annum. This is a huge market.
The market that we are going behind is almost a $ 700 billion+ market, the market spend. This is something which 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. This is something new we are launching, which is under the CraftStudio banner, which is BlueVerse Craft Studio, which is where technology meets creativity. That is a big, big thing, where we are looking at generating content through AI. Not only generating content, but also driving more personalized experiences. This goes across all kinds of medium. This is what our CraftStudio is. I will show you a quick video of how we are looking at spreading CraftStudio across and capturing more and more of the marketing spend.
[Presentation]
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 are going behind. The last, not the least, we are talking about the autonomous enterprise. Industrial AI. 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. If you really look at it today, even we are already supporting a lot of industrial workers and digitizing a lot of physical assets. This is the area of IoT. In fact, if you recollect the slide Venu showed, that is 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, it is just becoming much more powerful. A combination of both enterprise data and the industrial data is what will drive much more real-time intelligence for our clients. Therefore, if you really look at it, the whole physical AI business, again, it gives you a lot of closed-loop autonomy. You have edge intelligence and self-healing industry operations, huge cost saver. Expert AI, again, we are talking about industry co-pilots. Physical AI, here we are talking about digital twins. Although me and Guru are delivery twins, but these are digital twins in the industry segment.
And this is, again, a huge hit with some of our manufacturing clients, where you can actually test out what is happening from an AI perspective without having to make a lot of investments, et cetera. And then, obviously, the whole vision AI. 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. It runs into these opportunities, runs into billions and billions of dollars. And now we come to our last page, which is more about how the autonomous enterprise is unfolding. If you really look at it, every company has SAP or Oracle or Microsoft Dynamics. We call them systems of record. You will have some systems of engagement, which is all being ServiceNow, et cetera. And you have all the data platforms.
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. SAP Joule and Microsoft Copilot, and you see the Agent First and Amazon. So a combination of all of these. Now, what is happening is many of the enterprises already have, obviously, 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 is 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 a scale of AI adoption in many companies go up significantly high. Just to give you a sense of how much the platforms that we have mentioned here are growing, they are all growing at around 15%, and in some cases, the data platforms are growing at 30%. So that is a huge market that is available for us as long as we think platform plus data together. 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 is what we talk about all the integrated offerings splash. We build offerings splash to tackle most of the business problems that we have. We are building excellence across the enterprise business processes.
If you look at order to cash, record to report, procure to pay, across those, we are building excellence. Then obviously we have functional consultants. Then we are targeting the buyer personas. Historically, we have 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, or the Chief AI Officers are reporting into the business, and that is an untapped. That is a huge market potential. As long as we can combine everything together and go and solve for a business outcome. That is what is 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 to do all this and to execute all this, you require talent, and talent has to be reimagined. With that, over to you, Guru.
Thank you. I will use the next seven, eight minutes to quickly talk about the talent reimagination, and I am only 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. Talk about future-ready talent. Service reimagination, and I know this is a concept that you have started to hear across the industry as well. You may not be seeing it for the very first time. Let me break this up a bit for those of you who are seeing this for the very first time. The hypothesis and what we are seeing already play out in some of our clients, in some of our leading verticals. It is not everybody.
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. We do see that play out in our 700+ clients. I think where this is all heading to is the space is one of building with AI. The nudge across everybody, across all of our employees. The industry is talking about go out there and build. That is the thing. There are all these tool sets to go out there and build. Then, of course, in the context of the domain that we spoke about. We see a gradual shift. I am 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.
That is what we call the AI builders. There is always the big question out there that we keep hearing. People from the media ask us, and sometimes clients ask us as well, is, "Hey, what happens to entry-level workforce?" That is the question. We clearly see there will be a space for entry-level people, right? There will be a space for learners. Maybe it will shrink a bit. That is why you see a smaller box at the bottom. But there will be a space for learners. And of course, as you can see here, there will be digital employees augmenting every level, right? Within LTM itself, we talk about 1,500+ agents. Then there are all these toolkits available to produce lots and lots of code. But there will still be a need for learners. Clearly, that is not going to go away.
I will talk a little bit about what does it mean to the people coming in as well. At the top, there is, of course, going to be strategy and governance. Many a time in the context of a domain, sometimes across the whole delivery estate, right, across what we all do. That takes us into. If you then zoom into the middle, which is the AI builder box, right? What does that mean? We clearly see there are three tiers of people, right? There are three tiers of AI builders. One is what the industry has started to call as AI engineers, right? 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 we call it LLM engineer.
There is 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? Then there is a platform engineer, data engineer. Obviously, data is the entry point to AI, so there is a need for data engineers. 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 is one. Then, again, you have 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. If the question is, "Hey, can we have a fresh engineer become an FDE on day one?" Probably not. Probably they will start as an AI engineer and eventually become an 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. 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 three tiers.
Then I wanted to spend a minute or two 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 three sample roles, three big ones you could say, and start to talk about where is the shift, right? How will our people need to make the shift? We have broken it down into three dimensions of what we call a tool set, skill set, and mindset. 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.
Slowly, we are seeing the shift into what is called the exponential AI engineer. Exponential word comes from an expectation of hyper-productivity. Some people call it 10x. 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? An acute awareness of models and the APIs to deal with the models. Their skill set is changing into context design.
How do I design for the context that I am in? The mindset obviously is shifting to systems thinking. How do I connect the dots for value? It is no more about just one piece of a solution. It is about a connected solution. It is about getting the solution in the context of connecting several dots. Today's intelligence is in how you connect the dots. That is the system thinking. If you look at the FDE, the tool set, like I said, is an ever-learning playground. The skill set is hands-on. Everybody is a builder. This is a new thing. Everybody is a builder. If you are an FDE, more so. You are still building. You are not commanding AI engineers, but you are still building in the context of the client.
The mindset is slowly shifting to day one builds in client environments. If you were in the industry many, many years ago, you would spend many months on requirements, then there will be a design phase, then you will start to code, all of that. Today's mindset shift, if you are a forward deployed engineer, is to build day one in the client environment. You go with the tool sets, you go with your proprietary frameworks sometimes, and you start to build. You show evidence of the build working in the client environment. That is what FDEs do. 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 time. They relied on knowledge sitting in people's heads.
Today, it is 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. Essentially, these engineers will start to rely on tool sets like a knowledge graph. That is your assimilated knowledge across the enterprise. That is what will help you self-heal many tickets, auto-solve many tickets. It does not even get to a person. Maybe it gets solved by an agent or it gets deflected, then very rarely it gets into a human. That is the whole knowledge graph piece. The skill set, of course, is AI workflow orchestration. The mindset is Agent- First. Get agents to work. It is never about let me go solve it. Those are some of the key shifts we are starting to witness. There are many other roles as well.
Hopefully, this gives you an idea of the key shifts that we think are already beginning to happen. This is very much underway. I go back to this diagram that I showed and close with what are we doing about it as LTM. 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 beginner's mindset. We are always learning. Every day is day one. 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? 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? How do we sort of have a handshake in the creative space? That is the one with IICC. 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 vanilla 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 is so much happening in that space. Quantum is a space that we continue to invest in terms of research. May not be right here, right now, but we are already seeing client interest through quantum. There are many problems out there which cannot be solved through classical models, that they need quantum. That is still an emerging space, but we have invested significantly into quantum already. Adobe, Azure, these are some of the other partners that we work with very closely in terms of content, certifications, trainings. There is a handshake there. Then our own builders community. We invest a lot into community as a learning platform.
Hack for Future is something that we do. Sometimes we join hands with partners to do hackathons, builder events. Microsoft, obviously, is a big partner of ours. We do that. The last one that is very interesting, we actually sponsored an AI filmmaking hackathon with the International Film Festival of Delhi. This was phenomenal, and we had a chance to present this as part of the NASSCOM summit and everywhere else. This is clearly we are leading the way in terms of the creative economy and kind of showing how it is 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 out from colleges.
We think there is a need to shift left. What it really means is we work with the colleges themselves. We have a set of colleges that we hire from. In a given year, we hire about 6,000-7,000 fresh graduates. We do the shift left with respect to working with these colleges and really making what we 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 their curriculum. That is the shift left and what we call is Orchard in terms of our own program. That is the last slide. Time is up, just in time.
Very happy to state 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 am 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 is as much time as we had to talk about reimagination, out-creating, on capabilities, on delivery. Hopefully, we left you with a good sense of what is happening in our backyard. Thank you.
Moving ahead, we saw the people. 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.
Am I audible? Thank you. Once again, good evening to all of you. Thank you, Archana, for being a wonderful host throughout the afternoon and leading towards evening. We all heard Venu talking in the afternoon today about the New Horizons program of LTM, which has three 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. Our talent, our Lakshya strategy 2031, our talent is going to out-create in that process. I am going to take you through today as to how we are out-creating 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. Some of that I am not reading it was. I want to tell you that for every organization, the technology shift which the sector is going through, for us, it is not a technology shift only. It is 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 a differentiation at the marketplace. Let me take you the four leading indicators in this slide, which are about the fresher intake. 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 optic 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 how committed we are to the learning of our talent in the organization. All this reflects with the stickiness of our talent and the attrition coming down from 14.5% last year to 13.25% this year. While we saw the talent landscape at LTM, I will take you through the big shift which the industry is going through. Most of the organizations are seeing five 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 no more is going to be a training-based, classroom-based training programs or learning program. 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 build your organization at a similar level across boundaries and across geographies with a local context with a global mobility.
The fifth one, which is 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 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 how we are structured at LTM to face these shifts. The four pillar HR strategy of ours, the talent strategy of ours is already in motion. It is no more in the slide, but it is in motion. The strategy pillar one 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 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 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 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. 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 organization who are going through a pivotal change with AI as a technology. 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 that the work. The third pillar is about accelerating the talent readiness. It is all about how do we build an organization which will have a sustainable leader over a period of time.
Our talent continuity initiative, which talks about doing rigorous talent council across unit, across geography, across the organization, and also identifying critical talent and doing succession planning for each of the role holder, enabling with an individual development plan and mentoring and coaching, real-time mentoring and coaching when they are at work with stretched assignments, with rotation of roles, is actually helping us to build an organization for future-ready leaders. So while we are doing it for the critical talent, but we are also looking at the high potential in the organization who need an 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 how we are actually bringing the context with a local perspective, that while we are going to have a global mobility, 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 at the marketplace. At the heart of this four 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 slides. 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 has mentioned, but some of them are mentioned here, which are L&T EduTech, Coursera, Richardson for our sales enablement program, and SVA Bhakuni for our next generation project manager development program. When we are doing all these investments, what is that we are setting the goals for us for the next four to five years? Very clear. One, we want to have 100% AI fluency. That means we want to move from AI literacy to AI fluency. As our leaders have said about it, 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 workflow ecosystem.
The second outcome that 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 mod 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 EVP 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 a leadership stability. Therefore, 200+ 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. That's the fifth outcome that we are chasing for.
As I mentioned to you while doing this, I spoke to you about the four pillar of HR strategy and the foundation at the heart is the HR transformation journey. I am very proud to share with you that at LTM, two years back, we started the HR tech transformation journey, wherein we initiated every unit of HR with an 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 two hackathons in HR we have done so far. The AI initiatives are spreading across hiring to onboarding, to learning and development, to performance management system, to the employee engagement, to the employee query resolution, as well as to the attrition prediction, onboarding predictions, as well as at the analytics and dashboarding. Before I click on that, she's a super agent of HR who's called RAIma, and she's a digital agent.
Hey there. I'm RAIma, LTM's HR super agent. Human-centric, culture-aware, and AI-powered. I drive employee experiences and strategic HR outcomes at scale.
Thank you. Thank you, RAIma. While we developed this, I cannot miss to tell you that at every step of our transformation, we built the responsible AI into it. We made sure that it is while we scaling the transformation, but we also being scaling equally on the responsible AI part of it, governance part of it. So these are some of those data points wherein she's sitting on the contact center or the shared service desk, and 60% of auto 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 mentioned by Guru, but I want to pick up some of them which are not CII National HR Excellence Awards.
We achieved there is a significant achievement in HR excellence. Some of them from AI, some of them from Brandon Hall and talent management. Basically, these are few of the recognitions from the industry bodies which talks about mostly our talent management, our talent attraction strategy, and the usage of AI in talent management as well as in people's lifecycle 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 2031 strategy. I leave it with all of you to see the EP video of LTM.
Good evening, everyone. Thanks a lot for staying with us so far. I hope I do not bore you too much. I think, this next session I am going to focus on how our strategy, which Venu and all my other colleagues have already spoken about, is going to work towards Outcreate 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 a 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 is 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. 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 four strategic pillars that we were focusing on. One was sales transformation, large deals focus, AI pivot, and Fit4Future program.
I am happy to say that in all of these four levers, we did achieve a lot of success. In terms of the sales transformation, our focus was on focusing on key accounts and diving deeper into those key accounts. I am happy to say that we, as a result of those efforts, we have managed to add eight accounts in the $20+ million category, 12 accounts in the $10+ million 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 deals 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 100% increase in the large deal wins year-on-year, including two mega deals that we announced. We still have a strong large deal pipeline as we enter into FY 2027. 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. The BlueVerse ecosystem got launched last year.
The service line investments we had started making last year already, some of which you 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, 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. I will 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.
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, rebaselining the indirect costs, and it led to an overall improvement of EBIT by 90 basis points in FY 2026. What is 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 delinking 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. The reason why I am 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. 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 a 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 three pillars, each of the three pillars are important. That is the message which I wanted to leave with. Moving on, I spoke about Customer Zero initiatives. This slide captures some of the key areas in which we have introduced AI internally in the company. I have 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 is a straight-through processing, which we have been able to achieve with this. FP&A agent is a 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. Instead of having dashboards, you 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 and 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 redlining agent. I think talent side, Chetana has already covered, so I will not get into that. Again, in terms of some of the accuracy rates that we have been able to achieve on the SOW and MSA obligation extraction, we have been able to achieve almost 90% accuracy. Contract redlining agents, almost 60% acceptance of the markups done by the agent.
Overall, I think this slide basically showed you how we have been able to adopt AI internally. 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 customer's context and domain into that situation, but having the experience of doing it gives the confidence also. In terms of our overall Lakshya strategy, if I were to look at it from a finance lens, I am looking at it as five big blocks. One is an accelerated growth roadmap, 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 will 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. I am 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. Lastly, but not the least, is the resilience in the balance sheet that we are committing to maintain as we go along.
I think 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 is a 360-degree partnership which is starting. Let us 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. 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 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. 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 and the talent ecosystem that introduces efficiency in our talent ecosystem and generates some savings for us. 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 establish a global delivery model for the entire consolidated business once it gets consolidated. There is 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. Some of these are the synergy levers that obviously are there in this whole transaction. Coming to the financials, I think I have 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 targets and 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. As a part of that, they have also had some one-off costs which are not going to be recurring. 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%-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.
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. Overall, we expect that there will not be a material margin impact in FY 2027 from this transaction. From year two onward, revenue growth synergies and our New Horizons 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.
In summary, I will just conclude with this slide to say our target, which Venu spoke about between FY 2026 to FY 2031 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. If you continue to scale Europe and other geographies and the segments that Venu spoke about, that is what will be driving the growth. 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.
Our ESG goals, I think we have talked about that in the past also. 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 one emissions are down by 70%. Scope two emissions are down by 55% versus FY 2019. 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 is going to be a bit higher.
I am not going to talk too much about it because I am going to show you a small video on the CSR parameters. In terms of the governance side, our board is-- Sorry, I am also having a bit of a sore throat. On the governance side, our board is constituted of 67% independent directors. On governance side, you can give us better feedback on how you find our governance by reading our reports and the investor communications that we do regularly. I will not talk too much about that. I will go on to the video