How Infosys Drives Enterprise AI Transformation and Innovation

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For our first session on tech transitions, why is the AI transition different? Please welcome Nandan

Nilleani, chairman of the board Infosys. Uh thank you and uh great to have you all here in these tumultuous times. Uh

today I'll talk about you know tech transitions. Uh I have had the fortune or misfortune of being in this industry

for more than 40 years and I've seen a lot of transitions. So I thought I'll talk about less about that and more

about why this time it's different and what are the implications of of this transition.

Now safe harbor clause now we have seen technology shifts you know for centuries whether printing press or telegraph but

over the last you know 60 70 years we have seen a much faster change and uh PCs cloud gen AI agentic AI and so on so

it's so change of technology and the speed of change has been a constant for many decades now and each time there's a

change the way we address that change has been different so we went from mainframes to mini computers to PCs,

client server, LAN, web computing, mobile, enterprise apps, big data and each time we had to think of it in

different ways. How do you think of it in terms of making it globally available through internet or how do you do

enterprise IT? So each time there was a tech transition, it had certain implications for us and firms like

Infosys had to deal with what was new. So we are used to the fact that each time there's something different.

This time the AI transition has been much faster than earlier transitions. If you look at the number of years it took

to reach 1 billion users. You know internet took more than 10 years. Smartphones took 5 years. AI is taking

couple of years. Now you have to realize that the AI speed is because of the first two things. The internet was

already ubiquitous. smartphones were already ubiquitous. It therefore allowed people to distribute a chat GPD or

Gemini or cloud very easily. So in some sense the speed of AI is also because of the infrastructure of the previous era.

Now what has happened this time is that this is a much more fundamental change to the way businesses will operate. So

this is not a layer of technology. When we had uh you know when smartphones came we could build applications where the

instead of having a PC you did it on the phone. It was like putting a front end to an existing application. When cloud

came we could do a shift lift and shift you could take the app from your inrem and move it to the cloud. So you could

do a lot of things to get going. But this time it's not that. This is a fundamental change in the way we do

things. Obviously there's a technology dimension and it's all about having AI native architecture but there's a whole

business dimension to this. We cannot run business the old way and businesses have to change the customer journeys

have to change all those things have to change. It's a huge challenge for talent. Talent will have to deal with a

world where writing code will not be the goal. It'll be actually making AI work orchestration and those kind of things.

So the jobs will change and operating model. How do we make this at scale? How do you get a firm with hundreds of

employees to change all the things and make it work? And of course our mental models have to change because we worked

you know technology was always deterministic. You said a plus b equal to c. So no matter how many times you

said a plus b, the answer was c. In this AI world, you know, every time you give a prompt, you'll probably get a

different answer. And therefore how do you deal with this non-deterministic world but how do you make sure the what

you build has the robustness reliability and resilience of the deterministic world. That's what the challenge is for

everybody. So this is a fundamental root and branch surgery of the way business is done which is why this technology

transition is so dramatically different from anything else that we have seen. Now one clear learning we have is

modernization of legacy systems cannot be deferred anymore. What happened over the last 60 70 years is people would not

replace the legacy systems. They just added to it. So if you go and look under the hood of a large enterprise they will

have mainframes from 1960. They'll have mini computers from 1980. They'll have LAN from 2000. They'll have all kinds of

things and all coexisting in silos. That is over. We if you really want a firm to take advantage of AI, you have

to fundamentally clean this up. So this is a massive massive cleanup job which everybody's dealing with. There are

reasons for that. One is the financial drain. Many large companies are spending 60 to 80% of their IT spend on

maintaining systems. There's no business value out of that. They want to go from 60% or 70% maintenance and 30% new

systems to 30% or 40% maintenance and 60 70% new system. They want to flip the way they spend money. But they can't do

that with that fundamental cleanup they need. Moreover, many of these systems were designed in an era before you could

have online attacks and so on. So security breaches which you see every day are just going up everywhere and

there more state and non-state actors who are getting better at it using AI. So security is a huge problem for

everyone. We have seen so many cases in the last few months and because the data is all in silos, you can't even innovate

fast. You don't have to. So there are fundamental structural issues today we have. So demand side is absolutely

demanding modernization. But the good news is for the first time because of AI we have the tools now to do

modernization fast and very quickly and and you know at a much more economic way. So we have a huge demand and we

have the ability now to do it and perhaps our team will talk about that. So fundamentally accumulated tech debt

over decades must be paid. You have no longer have the option to defer this and this is a huge huge requirement and

obviously it's a huge opportunity for us. Now the other thing which which is there

is as AI becomes bigger part of the spend the balance of advantage is moving towards build rather than buy and that

is actually what is if you see some of the concerns about what will happen to SAS companies and all that it's because

of this that building applications has become so simple that very often you may just build or you may replace something

that you have which you which to bought and with something to be built and so we we are see and that again actually

benefits folks like us because about building who's going to build it for them it's going to be us only who will

build it for them so fundamentally it's good for us and the other thing which is there is that our view is that

system foundational systems will increasingly become systems of record but the interface the interface will be

agentic because agentic interface makes a lot of sense agentic interface allows people to produce something which is

designed pro- consumer or pro-ous user and agentic interface enables you to take out the complexity and hide it

behind the agent. So the agent is simple to use. It's it's a very simple idea. Now enterprises will therefore want to

put agentic layers on top of all their applications even if they leave the system of record the same and that is

something which will be a combination of bought out agents as well as building their own agents because finally the

agents have to be composable in a customer journey which is seamless which is a mix of agents which are your own or

from somebody else again that requires orchestration and you work which somebody has to do. So there's a huge

amount of work required once they go towards uh build rather than buy. Now the other thing is the pace of

change is something which obviously we have not seen. We all know about the trillions of dollars being spent and all

that but even the technological change I mean you know 2023 foundation frontier model had 100 billion parameters. Today

it has 1 trillion parameters. There are only 10 to 12 agent networks. There are 60 agent networks. So this is only going

to go up. In the US alone, there are at least five frontier models. In China, there are big four or big five. So this

is only going to go up. In India, we have seen so much action and you'll see some big announcement this week on

Indian-based sovereign models. So I think this is something now there are certain implications of this because if

I'm a businessman and I have to choose my technology how do I make sure I don't make the wrong choice because something

which I invest in today may be may have fallen behind tomorrow already people are facing this reality and therefore

how do you architect your technology so that you can deal with this rapid change is a very fundamental and structural

need for enterprises and again they need help on that from somebody who has done this in 2,000 locations and understands

the pros and cons of every approach. But the main thing is that the technology is far ahead of

its deployment because of this race and spending billions and some AGI and all that. the

technology is moving faster than the ability of enterprises to deploy it. If you look at this chart,

you can see that the the model performance is going up, but the progress in implementing is not really

because implementing this is hard stuff. Fundamentally, it's about organizational change, business change, train,

retraining your people, thinking about nondeterministic approaches, uh, uh, changing your data. So, it's no longer

in silos. So, fundamentally, we have a situation where there's a deployment gap between the power of the technology and

the capacity of businesses to use this. So, if you guys think that some better product has come, some nothing's going

to happen because the problem is here, not there. You get it? It's about how fast companies can implement. So you

have to look at that. And this is we call this the deployment gap. But this is actually a concept by professor

Clayton Christensen at Harvard 25 years back. He called it technology overshoot where technology gets ahead of the need.

And in fact he argues that that's how newcomers come because newcomers can then launch new products that are not as

sophisticated but good enough for customers. And Satya in his recent blog talked about model overhang which is the

same idea. Fundamentally the tech will keep getting better and better because billions are going to be poured into it.

There's massive competition but enterprise deployment is not going to go up and this deployment gap is what we

can help to address. So again it's a very important point. Now I think talent transformation is

huge. It's not that you will not need you will need talent but it'll go from you know QA testing or development we

have all kinds of new roles AI engineers forward deployment engineers AI leads forensic analysts data an so

fundamentally the challenge will be how do you take your workforce and make sure that they are reskilled

and ready for the new new new business and that's really the challenge that all the firms will phase and this is uh

something so there will be roles now the way you hire will change the way you train will change the way you deploy

will change all that is going to happen and I think we'll have sessions on that but fundamentally there will be a need

for people but they'll be doing different things also a lot of the talk of productivity

is green field writing green field is not a big deal I can take a tool and you know give it to

a kid and he'll generate a million lines of code. But that's not the real world. The real world is the fact that

companies have trillions of dollars invested in their systems. They have technical debt. They have data silos.

They don't have documents. Somebody was telling me the other day that there there are some old systems and on

contract they have guys as old as me, 70 75 year old guys because nobody else knows what the hell is going on. And

then you know when when there's a crisis to be sorted out they're pulled in from Phoenix or Florida or wherever they are

and they have to solve problem and nobody else knows how to solve. So we have that kind of situation out there

undocumented dependencies. So taking brownfield systems and modernizing them is a hell of a lot more difficult than

doing green field development. And a lot of us get biased because all the guys who talk about productivity are talking

about green field development. And therefore getting the

large enterprise organizations productivity going also AI implementation requires laser

focus. The very fact that you can generate stuff means you can generate slop.

You know in fact five years from now there'll be more AI legacy system than any other legacy system. All kind of

stuff will have been generated and we'll have to clean that up also. So and if an organization is not or you

know you can have this fake productivity let's say there are two guys and they are having a fight one guy will draft an

email which will be one paragraph he will give give it to AI to make it into a 10 paragraph email because he wants to

impress the other guy the other guy will take the 10 paragraph email and summarize it to one paragraph so both

have used AI but what have we achieved nothing so how do we make sure AI is used and therefore you need usage age

guidelines, you need quality gates, you need explanability. So how do you make sure that AI investments lead to real

performance in productivity and not just some makebelieve stuff? This is something which is very important.

So what still matters first principle thinking one of the things when we train people

is they have to learn to do this without tools because all of us learn to do this without tools. So when we got the tools

we knew how to use those tools better. But if you start by teaching them tools then everything is a black box. Then you

know it's like the guy who never knows how to calculate because he was born with a calculator. So first principle

thinking is very important. All the more important as you think about strategic transformation of large enterprises.

First principle thinking is very important. Second

understanding enterprise context. Every company is different. Every company has a different legacy. Every

company has different systems. Some of them have come from acquisition. Some have come because they have five

business units all buying their own version of technology. All kinds of reasons. Everybody has a complex estate

of systems and context is essential to being successful at AI deployment and each context is different and it's the

dealing with these contexts that is the hard part which is where again you know we believe we have a way of doing that

I'll give you an example the self-driving cars the first DARPA challenge was 2004

the first time they rolled out was 2007 7 20 years back then everybody say oh yeah by next year we'll have

self-driving car it's 20 years later data all we have is a few cities in America where there's self-driving cars

because the context is different every city is different every road is different and by the time they come to

Bangalore it'll be 2047 because they're dealing with Bangalore traffic will be a different level of uh context so

enterprise context is so important and that is something which cannot be done by a tool it has to be done by capturing

implicit knowledge knowledge and making it explicit agnostic design again what I mean here

is don't let locked into a tool because that tool may no longer be obsolete in two years so how do you design for

agnostic that can choose any system getting the house in order we talked about removing technical debt we have to

go make the house in order for this whole thing and then massive change management you're changing organization

business sector people. I mean this is like unbelievable change management. So unless you have leaders who can do

effective change, nothing is going to happen. It's also about strong collaboration across firms. So firms

have to because all the knowledge is implicit in the heads of different people. How do you make that explicit in

one customer journey focus on productivity? It's not about using AI tools. It's about productivity out of

those tools. Otherwise, you'll get false productivity which leads to more complications. And then this is an

engineering game. AI engineering is a whole way of doing things and that is part of your uh change management and

and transition that you have to do and that's a big thing. So my view

is there is no opportunity gap. If anything the opportunity is bigger

than ever before. So don't get distracted by that. You can, you should still ask the question, what is the firm

doing to take advantage of this? What is the firm doing to transform its talent for this new world? What is the firm

doing to design the services and products for this new world? What are they doing to tell the customer in a way

that it resonates? what are they doing to make sure that the front- end conversations with clients are done

properly. These are all these are all the issues and I'm sure everybody will not execute the same way. So there is an

execution risk in doing that. So it is not an opportunity risk, it's an execution risk. You get it? And

therefore the balance of assessment is how do we make how do we know that each firm has the execution plan ready to to

get to where they they have to get. Are they are they able to do it well? Are they able to do it with speed? Are they

able to do it with scale? Are they able to do it with new mindsets? That is really the question of the day that all

of you need to ask. So I'm hoping that today you will hear from our team and it'll give you some reassurance that we

are on the right track. Thank you very much. Thank you Nandan. For our next session

on the AI services opportunity, please welcome Salil Parik, chief executive officer and managing director Infosys.

So good morning and welcome. I think with that session from Nandan uh I'm sure you have a lot of clarity and a and

a vision of where the industry is going where AI is going and what are the real issues that one needs to look at. uh I

want to share in the next few minutes where we see the opportunity today uh in AI services and how we are planning to

go after it. In fact already how we are going after it and give you examples of that.

So first one of the things we've learned again and again is what we see from our clients

is that our clients trust Infosys in driving and delivering AI work and throughout the day we want to share with

you several client examples. Here are a few quotes. uh the first one from the CEO of a large uh telco where we're

doing extensive amount of AI work and how he sees the value that Infosys brings another one from a COO

another one from a CIO and these are the sorts of examples that drive how we have built our approach how we are executing

on the AI journey. So today we are doing AI work for 90% of our large 200 clients. So this is not

something which is just here and there in pilots in terms of the scale. It's across many

things. It's small and large parts of large programs with large clients. What we have now done is introduced a

way to look at what we are doing through a AI first value framework and this framework we are putting together in

this hexagon which I will share in a few minutes in some detail what we are driving through with it.

We essentially see six large areas where there's growth opportunity for AI services. And I'll go through this in a

little bit. We will also have each of our different leaders from their different perspectives give you specific

examples in each area. First, AI strategy and engineering. This is really the area where we do a lot of strategic

work. You will in fact see one of the client examples I shared where CEOs engaging with us. There are several

others we will share later and you'll see some videos of this as well. So we are doing a lot more AI strategy work.

Infosys is a lot more engaged at that level because AI is central in that sense to all of those conversations. But

the building of agents, the orchestrating of what the platforms are, what the agents are, which are agents

which are built by Infosys, which are agents built by clients, which are agents built by third party and how to

make all of that come together. Second is data for AI. Data is absolutely critical as I'm sure most of

you already have seen. Each large enterprise is protecting its data. No one in the enterprise world unlike in

the consumer world is sharing data broadly with the AI foundation models. Everyone is building their own data and

there's a lot of work to be done to make enterprise data ready for AI in this new era.

The third is process where a lot of business process that exists today whether it was technology enabled or

whether it was not is being driven by agents into new worlds. One of the biggest areas we see here of course is

customer service and the customer service business case is driving huge change in how process is going to change

with the AI world. The next again you heard from Dundan the the legacy modernization piece. This is

a massive large opportunity that we are seeing uh in addition to customer service one of the largest opportunities

where we are essentially taking large legacy organizations. We'll actually share a couple of examples of this of

the work we're doing at scale and bringing them into away from the legacy landscape into the modern landscape.

Physical AI is something quite new where AI software is embedded into devices and that is becoming a growth area because

everything will have AI built into it and then AI trust which is both about the trust and cyber but it's also about

responsible AI which is something where Infosys is leading in making sure that things are built with a view to keep

responsibility in mind in the scaling of IT systems. So these are the six new areas that we are seeing. This is a

large opportunity set and this is where we see a lot of the growth coming. Now here are some examples of clients we

are already working with throughout the day uh in a little bit more depth. Each of the leaders from Infosys will share

one or two client examples. We will also have some videos to sort of have the client share from their perspective how

Infosys has supported them in this AI journey. So what I'm showing you in the hexagon in the AI value framework is not

theoretical. This is things that are actually happening on the ground with Infosys. This is what we are executing.

And this for us today represents 5.5% of our revenue in Q3 and it's growing at a robust pace. And this is something which

is extremely dynamic. It is something that is working extremely well with our clients.

Now this at the high level of six big blocks is something that we have as a visual of hexagon. within the company.

We then break it down to 30 offerings and then those get broken down to 100 sub offerings and each of those are

things that are being enabled with our engineers and with agents built on topaz which we will talk a little bit more

about later on and with partnerships. You saw one of the announcements this morning, but we are working with each of

the large AI players in very close coordination to make our clients more successful. And all of this is the way

we are now going to our clients. So each client discussion today uh is focused on the hexagon plus the 30 plus the 100 and

what is it that we can now work with you as a client executive to make it real for you the AI benefit whether it's for

revenue growth whether it's for cost optimization whether it's for innovation so our entire go to market team all of

our segment leaders all of our practice sales leadership are working with clients with this format at to make sure

that the AI becomes more and more embedded into the work that we're doing with clients

and this is sort of pulling back a little bit. We've always talked about navigate your next. This is how we are

looking at the journey. The navigate your next has changed because technology changes and this next is about AI. It

was different in the previous navigate your next but the navigate your next concept the client relevance remains the

same where we are coming from today the the issues that Nandan shared where we are going in the future what the

opportunities are and what are our strengths our strength is absolutely clear the understanding of what the

client landscape is there are some clients that we work with where we have probably as good an

understanding of their landscape as some of the client teams have and this makes a huge difference in how we navigate

through that through our AI toolkit our domain knowledge our engineering talent which with our training I've

always believed is one of the best that there is and the platform and IP that we have built which again we will highlight

a little bit later today. Now there is a dynamic in AI services which all of you know and we understand

one there's a huge opportunity the one that I mentioned the six areas with an external analysis we understand that the

opportunities between 300 and 400 billion uh in in the year getting to 2030. So

over that time frame at that time in 2030 at the same time we have several

entities have made estimates we have our own views the AI productivity leads to compression in IT services.

However today we have a clear view that the opportunity is massive and large and that that will become the driving force

of what we will grow and drive through in the next coming years. Now in putting all this together we have

created our own playbook and this is essentially what we want to share with you in depth today in each segment of

the day. Of course my vision and objective is that we unlock all of the AI value for our clients and we are

absolutely on our path to drive that. There's one set of discussions on the new services what we are calling AI

first. So with our delivery leadership, with our segment leadership, we will share with you what it means, where we

are doing it, what are the examples, what are the benefits. Then we have AI augmented services where

we are taking all of our services and making sure that AI is infused into it to become even more relevant for

clients. And again, our delivery leadership will share with you some examples of where that's working and how

we are doing that. And then we have foundational components our platform uh our IP what we built in Topaz fabric

which we want to share with you in a little bit more detail. We have a set of agents that we've already built that are

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

a partnership ecosystem. This is extremely critical in this new era and the partners are not only the partners

that existed in the past. There are some new partners both large and small that are extremely relevant and we already

have a very strong ecosystem and a way to go to market with those partners, a talent and culture which we think is

critical and how we are reshaping it. We are going through a huge reskilling process. As you have seen from the

announcements over the last several quarters, our approach has always been on reskilling and making sure that our

team that we have builds up to the new and we are recruiting. We continue with our recruitment. We have recruited

20,000 college graduates this year and through March that will be the number. And next financial year we also have a

plan to recruit 20,000 college graduates. But we wanted to talk about the talent and culture approach we have.

And then on the brand, we have a leading brand in the market. Our brand is one of the fastest growing. And what are we

doing to keep that or do even better in the brand in the AI world. That's how we are actually getting a lot more connects

with the CEO uh client base, which is what you need to succeed in the AI world. So that's our playbook and we

will showcase that to you throughout the day. With that uh I will close uh and I will

pass it on to our delivery leadership team to go ahead with the with the rest of the presentations.

We'll of course come back at the end of the day Jes and I to discuss your questions and give you some more views

on where I see things are going. Thank you for that. >> Thank you Salin. For our next session on

the AI services playbook which is by three leaders, I would like to welcome Satish Etsy, chief delivery officer for

the first part. Let me set the context uh before we deep dive into our emphasis uh AI playbook.

Every tech shift whether it is PCs, internet, cloud uh digital each one of them led to uh

rewiring of enterprise work and workflows and AI is the next rewrite. Here's a typical U enterprise landscape.

Sounds complex. This is one of the simpler ones. A typical enterprise will be far more complex because of the

scale, because of the fragmentation internal and external, the heterogenity and the divergence of the operating

model and the regulation with which it operates in which is by design by the way and of course technology debt. So AI

in enterprise is not just about lowhanging fruits like localized efficiency or user

productivity. Integrating AI in an enterprise is not just a software upgrade or a

plug-in. If it was that simple in such a huge complexity, no surprise that AI projects fail.

Then what is it about? It's about harnessing the full potential. It's about reimagination of systems,

processes, and deeply embedded ways of working and rewiring legacy power structures.

So, Infosys has done this enterprise rewrite with each of the past tech shifts and we're now doing the next

enterprise rewrite with AI. So let's look at how is the enterprise tech stack changing. Typically it

consists of the systems of record which deliver deterministic programmatic capabilities which are

codified structured processes and enforce policies and this delivers governance

accountability and compliance. We have systems of intelligence which facilitate engagement,

collaboration, transactions. But usually it is the humans who engage with data and the workflows.

Above the enterprise stack lies a vast non-deterministic or non-programmed flows.

These are unique, unstructured. They need novel problem solving. It needs experience, gut feel

and is usually handled by humans. This is the layer which is underserved today and this is ripe for AI transformation.

So it's a myth that enterprises need just two layers which is AI and data. is enterprise and algorithm.

How should we harness AI? Then AI is not the endgame of tech transformations is just another one but

it's a giant leap in raw capability and it's not system complete. So we need a multi-layer transformational approach

and purposeful orchestration uh to harness the potential of AI. This is why AI diff diffusion in an

enterprise lacks the rate of AI adoption and it needs time. So how is AI transforming the enterprise stack? What

is this enterprise rewrite about? The co- intelligence of humans plus machines will be seamlessly shared across the

layers so that every layer gets reinforced uh within the enterprise stack.

The systems of record need acceleration so that the business and operational processes uh can become more efficient.

The systems of intelligence needs a seamless integration of structured and unstructured data so that intelligence

can be wired into user journeys. business processes, transactions and decision making. It is estimated that

60% of effort in an AI project goes into doing this. The models will come in later

and this requires deep industry knowledge and context of the enterprise. encoding um intelligence

and AI in the flows leads to more automation and autonomy

and this is leading to the development of a new layer within an enterprise stack which is what we call as the

systems of cognitive work within an enterprise. as an outcome. Humans will shift from acting on data

directly to you know a governance and oversight role on flows and decisions.

All the new AI tools that keep coming at us at fast pace will get plugged into the systems of new cognitive uh work and

it'll accelerate the reimagination of uh an enterprise or its flows and decisions. Infosys will unlock tech debt

and complexity and harness the power of AI to be enterprisegrade and bridge the evolution adoption gap

and expand our addressable market. So how do we monetize this? Our playbook reflects the structural changes

necessary in our industry so that we drive value at the intersection of intelligence, engineering, and domain.

With AI, capability is a commodity because it's available to all.

Sustainable mode for an enterprise can be created by deep integration in specialized workflows and unlocking

unique organization knowledge and context. So every enterprise has got unique data

processes risks and complexity and AI will not unlock uniformly across the enterprises because

of this variance. So our client intimacy and deep understanding of our uh of our client will help us or context will help

us mitigate this and unlock value and also drive culture and change. So we have built an engineering approach in

our delivery where we can codify enterprise context which will help accelerating scaling of

AI and this also enables enterprises to retain and protect this unique enterprise context within their you know

four walls of the enterprise so that they can keep their competitive differentiation and this is not diffused

into the AI models. Our depth in engineering um and frameworks on IP and patents will accelerate AI

readiness and adoption uh with our Topaz fabric and our specialized talent uh in the form of

full stack and FDES for a financial services client. They were looking for an AI partner. When we started talking

to them, we realized that they have a very strong, you know, enterprise AI platform that they have built. But then

what we realized was they had an adoption gap and we pulled out our agent control framework as we call it which

would address the quality of code that is being generated which would address the AI slop which led to a poor adoption

within their organization. So now we are working with them on taking our framework and fortifying their

enterprise uh AI platform so that we can accelerate that journey. We have embarked on a talent transformation

journey to build an ambidextrous workforce which is deep in engineering and creating in creative in reimagining

uh work and workflows from first principles. We see new opportunities with

domain stack. You know we have over 25 years of industry focus experience and when co- intelligence connects with

agentic economy the play of AI elevates from you know how work is done to new

outcomes that are possible. So we are invested in building the domain stack powered by our depth and domain and

knowledge of how to deeply integrate AI tools and plugins into the flow. We have created strong differentiation in our

services track. AI is now integral to how we deliver every service. Our services stack is powered by Topaz

fabric. We now have an approach to productize and reimagine work and workflows that will lead to a human plus

agent model. We're also seeing momentum with new deal archetypes, legacy modernization with

reduced risk, uh higher predictability and cost and accelerated timeline, large deals with integrated ops and tech and

transformation wired in organization transformation encompassing enterprise stack and people.

You know this includes the AI first GCC approach which we have pioneered in the market. We have also elevated our play

to take end to end accountability from strategy to actionable road map to execution and eventually outcomes. We

have expertise in delivering both above the line which is business value and below the line which is efficiency.

Infosys is best equipped to deliver enterprise AI ambition with the power of our client intimacy and our AI playbook.

A quick example here is a client a CPG who had an ambition of clocking about growing the revenue to 7 billion. They

came to us to bridge their ambition and deliver a AI operating model so that you know we'll have an actionable road

map and execute to it with executive AI value office along with managing risk governance and

assurance. We used our Infosys IP and built their unified data foundation. We built their enterprise u agentic AI

platform with the requisite guard rails. This enabled them to rapidly innovate and diffuse AI across their functions.

Today they have 10 agentic AI products in their in the business across different functions from R&D, sales,

marketing. above the line with the agent that we developed in research and R&D for product formulation they now have

line of sight to 50 million revenue which they didn't have before below the line we've been able to unlock 25

million cost savings through just optimizing operational work and then beyond this we were also able to deliver

40% of business productivity improvement in functions like procurement and marketing

Thank you. >> Thank you Satish. For the next part of this session, I would like to invite DH,

chief delivery officer. >> Thank you, Siman. I'm audible. Thank you, Siman. And good morning, ladies and

gentlemen. Thank you very much for sparing your time here today. As we navigate the landscape changes

with respect to AI, I think our priority in the last 6 months to an year has been how do we accelerate our customers from

experimentation to really looking at an AI scalable at industry level. Now AI as a technology is as good as what it can

really understand from orchestration across systems of record which Satish alluded to ability to really understand

the complex business processes and navigate and most importantly how does it even get to understand a deepseated

legacy systems that are there and frankly looking at all the complexities that we had with respect to some of the

estates that we've been working as well as with the customers we decided decided to codify our entire services across six

strategic pillars and these six strategic pillars are all integrated in a way. A customer who wants to start the

journey of AI adoption has to look at each one of them and see how does it and then our intent is to take this

strategic pillar to walk them through this entire journey of scaling the AI. Now let me start with the pillar number

one the AI strategy and engineering. Nandan alluded to it. So as sil organizations are extremely complex one

needs to really look at at a top down looking at how do I really create an AI blueprint a need of really setting up an

AI transformation office what would that mean I need to first understand which business units which business processes

that I would be able to unlock the AI value you can't really be spraying across the AI in multiple different

business unit now that is a very key thing because that's where the value value realization framework of Infosys

comes to bear to look at how do I map the process to the value that it actually arrives with. Now the second

key thing is how do I change the ways of working because you have so many models so many platforms that have come by one

needs to really put together a very technology centric uh AI platform with the models that it needs to go with the

uh the governance that it has to happen and how do we make sure that we diffuse this particular platform across multiple

layers of the organization so that we have one standard way of really looking at how do I scale the AI. The third

layer obviously is to really looking at the entire governance model that you need to put together in in a way this AI

is gone very strongly. The last one obviously is to really look at a purposeful selection of an AI

infrastructure where you also need to continuously keep an eye on what would happen to my AI ops.

Very recently we've been working with the CEO as well as at the board of Dansky Bank where we really started

working with them to looking at how do I enable this bank towards the journey of being AI first and digital first. So we

set up an AI office along with the board working along with the CEO to really put together a strategy all the way to

engineering augumented that with innovation labs as well as identification of core processes like

KYC the fraud detection credit and whatnot. So in a way the journey is just not that I would start doing software

development but it needs to have a top-down view of really setting up the strategy for an organization. Now the

second pillar my friends data is everything that AI needs today organizations have multiple pools of

data sitting. It is just not structured data. It is in fact even unstructured data multiple modes in terms of videos

in terms of speeches and whatnot to an extent of about 60 to 80%. And today the most of the time is spent on data in all

our projects. Data is one which accelerates your AI journey or potentially could also decelerate your

entire velocity of how you going to do it. So our frameworks here today is to really look at help our customers try

and harness the data, transform the data, bring them all under one uniform data fabric and it just doesn't sit

there. At the end of the day data also have to drive intelligence. The intelligence can be driven only by

connecting the semantics and the ontology on top of it. And that's a very elaborate process one needs to go

through because the processes are different by region by business units and it has been all codified in systems

of records as well as in systems of experience and that's a humongous job. The third layer there is to really look

at how do I govern the data because the data fingerprinting is extremely critical as you really look at who would

get the access of data. So that you know in in some sense there is a a framework if you really look at so in one of the

large industrial manufacturers where it was 10 pabyte of data that we had to really bring in all together harness it

as well as create the semantic ontology today help them actually drive the supply chain optimization by over 20 to

30%. Now the third pillar Nandan alluded to this this is all of re-imagining the entire business processes. Most of the

large enterprises today have pointto-point solution. One need to really look at in the context of domain

and reimagine the entire business processes and these processes have to be reimagined with respect to how a human

intuition works along with the AI and agent and it is extremely critical that you know every workflow by persona has

to be reimagined and has to be codified the way AI would actually come by. Now this also has to be contextual to the

business and the regions where you really work by. You know you just can't take as sourcing and a procurement

process and say that it is a domain that it could be applied to every industry. It has to be really be contextualized to

the industry and the business that you are really looking at. And most importantly, since it's going to change

and touch every one of us, it has to also be looked at how do I operationalize the entire workflow, the

technology, and the change management all put together to really help realize the end-to-end value. We've been now

with Toyota Motors Europe working through a supply chain transformation process where we took our industry asset

of automo uh on top of it the entire agentic playbook really looked at by persona of a buyer a planner or a

customer service agent and really double click to look at what does this transformation really mean and just to

look at one critical process of drop ship which is so critical for uh an automobile um um um uh is to really

agent bring in agent and orchestration there to really take away all the manual work and bring in much better inventory

visibility. Now the fourth pillar obviously is modernization and everybody has talked about it. It's actually seal

of our large organizations today. There is so much of tech debt sitting there. The code is obsolete. There is no

written documentations. There are no availability ofmemes today. And it's not that the customers did not really try to

do the legacy modernization, but the ROI and the time it took never stacked up in the current in the past technologies. So

what AI models as well as powered by our Topass fabric today enables is to make sure that we we transition some of these

large legacy both on the data as well as on process side into the most modern cloud-based microservices architecture.

And today it does stack up and you would hear one of the uh examples very soon. Now physical AI is something we believe

is is at the cusp of really accelerating the AI journey. Whatever intelligence that we all thought and built as a part

of the digital workflow is finally now moving towards the actual uh physical objects and this friends helps the

acceleration of AI in lot of the products that we really look at. Some of the key cases that we really look at is

um in in terms of u uh data first the new data new product introduction wherein the entire process would be

reimagined as well as infused with AI the products have to be defined or designed with AI in the front and with

more and more products with the software bomb being larger than actually the physical and the mechanical we have a

huge play in terms of embedding the AI as we look ahead. The second case is the intelligence today the real-time

intelligence is moving from cloud to the edge. Now this will help accelerate the decision making at the edge which means

vehicles the industry operations running infrastructure all of this would actually increase the uh advancement and

usage of AI and lastly the autonomous systems today the prevalence in lot of industries as well as areas is

continuously keep you know increasing towards that we see that we accelerate the journey of actually infusing the AI

in the physical and here I also want to draw your attention to the two acquisitions that we did. One in semi

which meant the silicon design as well as validation. The other one in intech on the automobile directly fits exactly

into this particular pillar where it would help us bring more context as well as acceleration in terms of enabling the

physical AI. The last ladies and gentlemen is not the least and the most important is the trust and the

governance. If I today ask everybody here who has used the AI, I'm sure that all of you would raise your hand. But

how many of you really trust the output that came by? I'm not sure whether everybody would raise your hand because

there are hallucinations, there are model breaches and there are also governance issues with respect to the

new AI act and etc coming in. So in a way the trust has to permeate through all the other five layers for us to

really make sure that we have the output that comes in an enterprise which is trustworthy. To me the trustworthy AI in

every enterprise would be a huge differentiator. So we want to build the trust to our customers and we want to

monetize the trust for our customers. Now having looked at all these six important strategic pillars right I just

want to dwell um um on one of the cases Nova chemicals. They are a large prochemical manufacturer based out of

Canada and US and it's a very uh asset intensive as you know the industrial operations is extremely complex. If an

asset goes down they would have an impact on the top line and the bottom line. In the current context most of

what they were doing with respect to maintenance was all manual logs and etc. So we were invited to a program in smart

maintenance where we actually brought in the data across their machinery, their uh OEM manuals, their maintenance

manual, the historical data, the log data and etc. to help a planner to really with a simple NLP on a chatbot

would would be able to guide them on what part of the industrial operations have to really go through a maintenance

and the most importantly we were also able to actually bring in orchestration with agent AI where the OT systems and

the IT systems come together. So seamlessly we were able to really move towards actually creating the entire

work order process the planning process which actually moves over from OT systems to the SAP or the ERP that we

have and we see the impact of bringing huge planning efficiency asset utilization and etc. of course here we

partner with Microsoft and we used all the stack of the entire Azure to really make it come to life. The last case I

want to really dwell is about herds. I'm sure that all of you know it's a very large mobility organization where we are

today as we speak embarking on really modernizing their entire reservation their fleet management their pricing and

the whole thing which today is approximately 3 million lines of code actually sitting on a tandem uh uh

computer and I would like to hear play the video let's hear from the customer on what their experience has been and

what we've done with respect to this journey >> so where are we right Now we are in a

journey to modernize legacy platforms and let me tell you a story about 6 months back my CIO at that time and we

traveled about four four cities in 5 days in three different locations each and every we actually met four

partners and each and every partner was given the same problem take this 10 pieces of cobalt program and tell me how

are you going to convert it this co cobalt programs were running on the non-stop right and every I think you

might have heard about HP non-stop or it's also called a tandem These programs were running on the

non-stop. Three three partners, three vendors gave me the typical, oh, we will do analysis,

we will do design, we'll do unit testing, we'll do I'm like, no, that's not of interest. The day we walked into

Infosys office in the first one hour, they showed me a working prototype, working model of the

converted code, right? No presentations. I was not interested in PPS. I wanted to see a working model, right? And that is

what actually impressed us to partner with Infosys. So if you think about what we did, they have a platform called

IELD. IELD helped us do the documentation. This programs have been existing for the

last 20 25 years, right? As you can imagine, a non-stop has been in operation for the last 25. Documentation

doesn't exist. Nobody knows why a program was written. Nobody knows how to test a migrated cobalt program, right?

Nobody knows what scale to test it at. So the documentation actually helped me big time to say program A talks to

program B talks to program C. It also showed me the database dependencies, the table dependencies. Right? So in one

sheet of paper I could now see that a given program what it was dependent on as well as the tables. So obviously we

had a part of a journey where we are migrating uh we are about 6 months into a journey. Without the platform I think

we were taken about four years to convert. with the I platform I think we are targeting about 18 months.

So the important point to note is models are there. I think workflows are there but our context of herds in terms of

what their processes are, how the code has been written, how the existing architecture is and what is the new

modular architecture that we need to really help them migrate to is the context that this lead brings to bear

and that is very critical as we really look at this entire legacy modernization. So that ladies and

gentlemen summarizes the six value pools that we're talking about and thank you so much.

>> Thank you so much DH. For the last part of this session I would like to invite Balak Krishna Dr.

Head head of global services. Thank you Simra. So my colleagues um DH and Satish uh talked about AI first

services which is new value pool that is being created out of AI. What I'm going to be talking about is AI augmented

services. What we mean by that is taking our traditional services and inducing AI in that and we want to be a leader in

this space as well and I will talk about how we are doing that. So we have taken each of our services that we actually

traditionally provide whether it is application development, testing, modernization, migration,

uh engineering services, um uh right operations, business operations. So 20 plus services that we

actually provide and we have created detailed playbooks of how we can use AI in transforming the way we deliver these

services. When we are doing this, we are partnering with the best of the technology that is out there. We are

working with models, leading models from Anthropic, from OpenAI, from Gemini, from Amazon Nova. Um, and in fact, we

have also working with open source models, right? Open source models like DeepS, Llama, and we have even created

our own coding model in this space. perhaps the only JSI that has actually created a coding model. So we use a

combination of these models that uh in our engagements. So in the Hertz example that you actually saw, we in fact use

two models. We used a cloud model that actually generates the code and one of the things about LLMs are they better in

critiquing output than generating output. So we used a open AAI model to critique that output and that's how we

actually improved the accuracy. So one is the models, the second is the tools and in the tools we are working again

with the leading tools. GitHub copilot was the first and it had almost 100% of the market share. We uh two years back

we set up a GitHub COE that was inaugurated by the CEO of GitHub and then we are still ranked the number one

gsi in terms of GitHub adoption. Just few months back we got this award as the leading gsi for uh right working on

GitHub but just not GitHub we are working with anthropic we are working with Gemini we are working on uh uh

right we are working on the models uh from anthropic cla we are working on the new age models right whether devon from

cognition or also uh right we are working with cursor.ai AI that you would have recently seen. So we're working

with the leading models, we are working with the leading tools. But then again as my colleagues also talked about a lot

of these models don't understand the enterprise context. They don't understand the standards that are there

in the enterprise. They don't know other libraries that are there, other programs that are there. So we have to do a lot

to actually bring that context into the tools uh that we are actually using. Right? So we do that by creating uh MCP

registries. We create a knowledge graph of the enterprise context and we combine that. In the recently the release that

we just did with anthropic you hear Dario talking about that they need Infosys to bring the enterprise context

onto the models and that is what we actually do. In addition to that we have actually created agents specifically for

each kind of services. So in application development we have taken the life cycle and said we need agents for

requirements, design, architecture etc. And we have created 100 such agents that we are using in each of our service. In

addition to that, we have to create other tools. Uh, right. Uh, you saw in the Herz example, the LLM models don't

can't digest huge pieces of code at one time, right? Some of these enterprises that work with have millions of lines of

code. If you give that to the LLM, they start to hallucinate. So, you have to chunk the code. You have to create

graphs, call graphs on how these are actually associated. And that's when you get better output from LLM. So we have

created all of these assets that are part of our topass fabric which my colleague Rafi will actually talk about

in more detail in the next section. uh in spite of doing all this we need talent right and sometimes people ask me

if LLMs are generating code why do you need people why do you need developers anymore right and I think they as the

same question to Boris Churnney from Anthropic because Anthropic is continuing to hire developers so

somebody on X asked him why does Anthropic if uh right if cloud code can actually generate code why are you still

hiring software developers and His response was that engineering is changing but great engineers are a

requirement still and in fact the most important requirement going ahead and that is something that we also actually

believe right the way that we actually deliver code or or the way we support applications may change but still you

need uh great talent and so what we are doing is to take each of our developers and training them on AI so we have 100

90% of our developers that have been trained on AI, it'll never become 100% since we are always hiring new people

into the stream. But our intent is to actually have everybody be able to use AI in their daily work. In addition, we

need specialized roles, right? Forward deployment engineers that will create the platforms that the teams will

actually use and then we have created COE's for each of the partnership that we have. So it is a combination of all

of these that actually helps us deliver our AI induced services. It is the blueprints. It is the technology our own

technology plus the leading technology that is out there and the people that we actually create. The way

we are going to market is also as you saw in the herz example it is not about PPS anymore. It is about actual demos

and that is what we see creates the impact and that's how we actually go to market in all of our large deals. I will

talk about a couple of examples of how we are using it in actual programs that we are executing right. What better

example than Microsoft who is in the leading edge of this kind of technology adoption creating the technologies and

also adopting this technology. So in Microsoft we have a 360 degrees partnership. What we mean by that is

that we go to market with Microsoft. We are one of the big customers of Microsoft. We also are Microsoft is our

customer. We provided services to Microsoft and we do multiple engagements with them. But I'll give you a couple of

examples of what we are doing. Microsoft themselves are going through a big transformation. They are going with u

they're going to from enterprise agreements they're going to what they call MCAS, master customer agreements.

What this means is that through the master customer agreement they want to eliminate all the paperwork that they

had to deal with in the enterprise agreement. They also are talking about evergreening

the licenses. So enterprise agreements had only two years time frame. This is perpetual agreement that you can use for

multiple years. Enterprise agreements had a minimum seat count of 250. Uh this actually has no minimum seat count. In

addition in MCAs you are able to monitor your usage, adapt your usage. You also you bill based on your usage. You get a

very flexible billing. So multiple advantages for the customers of Microsoft by using this agreement and

also for um Microsoft itself it actually eliminates because you are not having papers and documents anymore. It

eliminates and accelerates the way they go to market and also the operations that they have right on this engagement

they have to build the IT system to manage all of this. So, Infosys is actually working with them to build

that. We used uh all the uh technologies that I actually talked about and we are getting 2x developer velocity and 35%

improvement in the time to market. The other engagement that we are working with Microsoft is on their intelligent

cloud right. Um so as you know this includes Azure and Microsoft office they carry a lot of mission critical systems