Google Cloud Next '24 | Siva Ganesan, Krishna Mohan & Nidhi Srivastava | TCS

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Krishna Mohan

VP & Global Head, Cloud Business Unit

Tata Consultancy Services

Nidhi Srivastava

VP, Google Cloud Business Group

Genpact

From exploration to action: Trends and best practices in AI adoption

When a breakthrough technology matures, companies compete to carve their niche through innovation. Today’s example is generative artificial intelligence, and names such as OpenAI and Google LLC are staking their claim as global frontrunners.Expanding partnerships is crucial to driving AI’s advancement alongside accelerated research and development. Google is doing so through an active ecosystem with partners such as Tata Consultancy Services Ltd.“We are a premium partner with Google Cloud, and we have built deep capabilities with talent, impeccable delivery and a strong track record,” said Nidhi Srivastava (pictured, left), vice president and global head of AI.Cloud offerings at TCS

play_circle_outline Google CloudNext event in Las Vegas, Nevada

play_circle_outline Partnership between TCS and Google Cloud

play_circle_outline Exploring the Wide Adoption of Generative AI Across Industries with an Emphasis on Enterprise-scale Implementation

play_circle_outline TCS's talent, platforms, and industry expertise as key factors for partnering

play_circle_outline Future expectations: AI consumption in personal and work lives, realization of generative AI's promise.

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Siva Ganesan, Krishna Mohan & Nidhi Srivastava | TCS

Krishna Mohan

VP & Global Head, Cloud Business Unit Tata Consultancy Services

Nidhi Srivastava

VP, Google Cloud Business Group Genpact

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(industrial music)

>> Good evening, brilliant nerd fam, and welcome back to Google CloudNext, we're here in the beautiful desert in Las Vegas, Nevada, day one of three wrapping up our last segment, a very exciting segment with some customer veterans. But before we get there, I'm Savannah Peterson, joined by Rob Stretcher. You have sat in almost every single interview today, if not all of them. How are you feeling?

>> Not all of them. You guys had your segments in there. It was awesome. I think it's been fantastic. I think the thing that keeps me going is really Google Cloud Energy to 30,000 people here and really all of the innovation that's going on, especially with the partners. And I think that's what's been tons of fun.

>> For being a branded event, it is really partner focused, and I think that's a beautiful thing. It's a culture of collaboration. Not surprising that we got TCS here with us. You're our neighbors. You are Cube veterans. Thank you so much for being here, Krishan. This is fabulous to have you. I can't think of a better duo to close out our day. How is the show going for you? Fun, busy?

>> Yes.

>> All of those things?

>> All of those combinations, Lot of excitement. Fun. This is my first Google Next.

>> Me too. Yeah, so.

>> Yeah.

>> But you're a veteran, so yeah we're veterans-

>> Tell us what's going on.

>> And we actually get to be on stage together this time, which is much better. It's always be, but tell us about the partnership and where you're at. I mean, it's been eight months, you know, we haven't heard in eight months. How are things going with you and Google and the partnership with TCS?

>> Things are going really well. We are a premier partner with Google Cloud, and we have built deep capabilities with talent, strong, impeccable delivery track record. And most importantly, with the generative AI, we've done a really good pivot in terms of building up capabilities on what XAI, Gemini, we have been a launch partner. So I think the relationship is going really well. There is a lot of energy on both sides, and the relationship is strong at all levels of the organization, which helps, you know, when your CEOs are well aligned, it really-

>> Helps, is pretty critical, I would say, actually, especially for scaling innovation at the AI pace that we're currently at.

>> Yes. So you can see the excitement right next door if you just turn around and see the number of people from TCS.

>> Oh, hello everyone, wow! Oh my gosh, this is our biggest live audience of Cube history, but, you all look fabulous. Love to see it.

>> Yes.

>> I mean, I think that to me is really, is kind of been the story this week but also, it's been the story of the journey of AI adoption and where people are on and what customers are doing. What can you tell us about that from your perspective? I mean, again, your first one, but you've been working with customers, so you get this, you know, every day with the excitement.

>> Yeah, yeah. No, I think the excitement in the market by the customers on AI and specifically generative AI, has been really, last 12 months has been like crazy in a, in the right way of craziness. In the initial six, eight months, it was all about euphoria, about what's art of possible.

>> Yeah.

>> And, you know, every customer has thousands of use cases across the business functions and ID functions that they've actually developed. Now we see at the, you know, turning point where they're focusing on experimentation is fine. So how do I take them now to production? How do I scale it at an enterprise level? How do I measure the value? So I think that is the phase, and to me that's the real adoption phase at the, especially for the customers that we saw with Fortune 2000.

>> Do you feel like we're at a hinge point, 'cause we've talked about this a lot, a lot of different proof of concepts, everyone's excited, all these little baby MVPs, but are, do you feel like customers are at the stage in their journey where they're starting to hinge towards really making it real at scale? Or is that something that you're really helping customers navigate a lot? Nidhi, I'll go to you for this one to start.

>> So I will say that there is a lot of pragmatism that has come in into the enterprise context. Everybody is keen to figure out the right use case to take to production because AI is not inexpensive. And while the tons of use cases which are out there in terms of chat bots, how to build a new recipe, they're great. They prove the point from a technology perspective. But for an enterprise, it has to make business sense, it has to make commercial sense.

>> Got to hit that P and L or it's just fun and games.

>> Yes, so to answer your question, I will say our clients are looking for TCS to help them to that path, to-

>> To see the light towards that actualization, exactly.

>> Yeah, so.

>> What are some of the top use cases that you are hearing from your customers?

>> So there are three that are particularly relevant, I would say. The first one is on developer productivity. So tech is like, drink your own lemonade, or eat your own dog food, whichever way we like to go. But the point is that

>> I'll take the lemonade, just in this particular circumstance. I'll actually take a cocktail if we're at it.

>> Yeah, so maybe drink your own champagne, let's say that. So the point over here is that being able to use AI to reimagine the software development lifecycle, that's the use case that has really come to the top in terms of interest from our clients, in terms of interest within TCS because it leads to a lot of productivity and overall developer satisfaction. And the other two are on sales and marketing, personalized campaigns. You saw the creative agents this morning in the keynote and also customer service. So agents, customer agents, all over. So those are the top use cases.

>> Right, makes total sense.

>> Yeah, it really does. I'm curious though, obviously you touch a lot of different verticals and a lot of different jar issues, I mean, you kind of have your hands in probably everything a little bit that everyone's doing right now, which is awesome. Are there any trends you're seeing or any verticals that are really accelerating faster perhaps than others with no, you know, no diss on any of them. Krishna now I'll start with you with that one.

>> Yeah, no, I think the adoption, as you said, broadly across the industries and-

>> It seems like everyone's talking about it.

>> Everyone's talking about, so if I actually look at it, probably the adoption itself in the customers before I come to the, what are the industries who are taking lead, the adoption of generative AI, the way the TCS looks at is three phases or three, you know, approaches. What we call it is assist, augment, and transform. Assist is where generative AI is actually assisting making human look more intelligent. And-

>> I mean, we could all use that. I mean, I'll say for myself at least, I'll take anything that looks me more, it makes me look more intelligent.

>> Augment is where if you will, both human and generative AI kind of work together, but a subset of the work is actually done by generative AI. And then the transform, I think is where the value that we see maximum is where you are reimagining the value exchange and the value cycle of the overall value chain. Often each industry or subsegment, not only at industry level. So for example, if you take BFSI banking, financial services and insurance, at a capital market level, what is that you can generative AI can actually improve at an insurance, how you can improve the claims processing and reimagine the claims processing.

>> We all know that can be better too. If you've ever dealt with an insurance company,

>> We see maximum traction in financial services, life sciences, healthcare, retail, and CPG, and then probably life sciences and healthcare. These are the maximum that we actually see the traction. It also, you know, kind of in the morning keynote and the afternoon keynote, I think TK very, very, you know validly identified the generative AI adoption is not only happening at technology level, it's actually the business owners, the LOBs, who are actually seeing the value more and they're encouraging. So it's the perfect combination of the businesses and the technology teams working together. And that's where we see the maximum traction.

>> That synergy. I'm so glad you brought that up, that synergy has been a little bit of a theme. It's kind of like the classic marketing, engineering and product teams. There's always a little bit of healthy tension. Same things with business and tech units for obvious reasons, cost center and achievement centers. But I do, so you are seeing across the board the trend of these units working together. I mean, we were even talking to McKinsey about it with their digital, and AI used to be separate, or cloud and AI is to be separate. And now it's basically one uni now, this kind of cross business collaboration is hypothetically going to accelerate the innovation cycle, right? Maybe? Am I making that up, yeah.

>> Yes, no, you're not making it up, it is very perfect. In fact, as TCS, we both are part of AI cloud unit, both the AI as well as cloud together. So that's where we see maximum, you know, benefits. And we work really closely with, you know, our industry expertise and really driving, you know, probably later in the, we can probably take a couple of examples also where we actually see from business perspective what the use cases we are seeing.

>> Yeah. Yeah, I mean, I think again, it's a, you talked about a vertical approach and different industries have different reasons for going down this path. You're really focused in that as well and really helping different industries with their transformation. I mean, you mentioned, you know, in a little, your insurance use case. What else are you seeing in that and how are you instantiating it?

>> So one of our fundamental beliefs is that AI adoption will be industry led on the cloud, and it will be enabled by an ecosystem of partners. And that's something that was spoken about in the keynote as well when TK spoke about, you know, the Google cloud ecosystem of partners, and most importantly, the model will be fine tuned with data for the enterprise context. So keeping those, so I'll go to the first cardinal principle that adoption will be industry led. So the way we are approaching this is that we are looking at an industry and we are looking at the core value chain, the business process. And then within that value chain we are identifying key personas, and then building a smart agents, AKA co-pilots, or you're basically intelligent agents for the key persona. The, so we are taking a human-centric approach so that AI becomes more easily consumable and you are also able to break it into bite sizes or chunks because for manufacturing, if you look at the value chain, so you will have product design, you'll have supply chain, you'll have sales and marketing distribution, and most importantly, you'll have plant operations. So if you do a intelligent agent for the plant operator, so you look at the day in the life of a plant operator and you see how gen AI and AI interventions change their life. And then so that's the human-centric approach, you know, we are moving forward with, and the other thing we've noticed is that AI is getting in consumed, you know, by personas, by use cases. It's not that one mega transformation roadmap that anybody's biting because the, you know, business cases to be, you know, determined. So it's, you know, by personas, by use cases that we are seeing the adoption of AI.

>> Well, and what you're getting at is a really important point. It's the nuance, it's the ability to have that real time inference as super fast experience that's customized to you to customize to the customer or to the end user or to an agent or to anything that's happening, and making sure that that feels as wonderful as a normal human interaction or some of the other less data heavy interactions that we're having. Let's talk about data for a second. Data's obviously a big challenge with making sure that customers get the AI solution that they want. What are some of the best practices that you're advising for your customers to navigate that? Because it's kind of a data hygiene moment. It's the less sexy part of this whole game, but yeah, Krishna, I'll start with you if you want.

>> Yep. No, I think data is where the real differentiation is.

>> Right. And it's, especially at an enterprise scale, it's not easy to bring in the data, curate the data, and make the data ready for driving any outcomes. But I think with generative AI, the best part of it is you can actually take the structured data as well as unstructured data, and can still make intelligence out of it. So I think that's the difference. If you look at it, the cloud world versus AI world versus generative AI world. So the ability to look at unstructured data, combine them to drive an outcome, is where the difference is, they would like to add few-

>> And to add to the points Krishna just made, what we find is that many of our clients, they need help because the data is a state per se is very fragmented. So you have to look at different data sources, work on de-duplication of data, de biasing, data labeling. All of this has to be done to create a data pipeline, which will ground your model well. So you know, that whole data readiness for AI is a very critical nuance to enterprise grade AI adoption.

>> Yeah, I mean I think this is a big key to it is that you got to be ready for it. You have to do all of this work upfront and getting started sometimes, it can seem overwhelming. What do you think from a, you know, TCS partnership perspective, you know, really brings those customers to TCS to partner with you on that journey? Where do you think, like why is it that they start there with you?

>> I think two, three things. One, as Nidhi said, we are number one in the talent, right? In specifically Google, sitting at the Google Next. We are number one with 35, 40,000 certifications on Google by distance as a partner. So we have the core technical competency at scale across the, you know, geographies and industries. So that's one aspect of it. The speed with which we make our, you know, at A TCS level, 350,000 people are trained on AI and generative AI. So out of the 600,000 strings that we have, so that's one. I think the second thing is, as an engineering, core engineering company, we built platforms that actually makes the adoption of generative AI and AI, because we strongly believe if you want to solve a business problem and provide a business solution, it cannot be generative AI alone. It has to be AI, generative AI and data and cloud altogether. So we have built platforms which actually makes these adoption enterprise scaling that I said, where as you look at the journey from use case to value case, value being the business value outcome delivery, these platforms where what we have built is actually core differentiation. That's why they actually come to us. And the third, I think the important one is our industrial knowledge and the industry knowledge that we have on the, you know, verticals, deep domain knowledge and the contextual knowledge that we have. So we believe generative AI, the real value is in the transform use cases where you reimagine the, you know, business process, the value chain and value exchange that TCS is very deep. We are being aligned verticals as an organization and captured all the, that knowledge into knowledge graphs and knowledge, you know, repository if you will. And that gets supplied to drive the things. So these are the three things, the talent, the platforms that we have built for enterprise scaling and our industry experience and expertise.

>> Well, you, that's what you're known for. You curate the best bits so that your customers can come in and adopt these new huge solutions, no matter their vertical at scale. Very exciting. So you and I chatted at AWS reinvent two years ago, I think it was now, you guys chatted eight months ago. Since you're obviously Cube alumni and superstars at this point, what do you hope that you can say the next time we sit down, we'll sit down, we'll call it Google Cloud Next, just in case, who knows what happens at the end of this year, that you can't currently say right now? Nidhi, I'll start with you.

>> So, well, if you could say that one more time for me? So what is this?

>> Yeah, so what do you hope you can say a year from now? That hasn't yeah, see, say some achievement, anything.

>> Okay.

>> Where do you hope we are a year from now?

>> So in a year from now, I think we should have a few agents, you know, at the Google Cloud Next. So I think, you know, we could do this show with agents as well, avatars, who knows? That's, I'm really being a little futuristic over there.

>> You're allowed to do that. I grant you permission.

>> Yeah.

>> But I will say that I would expect to see a lot more AI being consumed in our personal lives, as much as it'll be in our work lives. So I, that's what I see coming. And I also will refer to a cloud study that we did at TCS. It's the AI study. And it said that in three years we are anticipating everybody to use AI, you know, for 50% of the work that they will do. So I think that's the trend we are headed towards.

>> Yeah, I love that. Alright, let's go for Krishna.

>> I actually see right now it's the promise of generative AI for next one year, probably. It's realize the promise of generative AI, so that's the way I actually see it. It's evolving, every two weeks, there's a new company that is coming up, every two weeks.

>> Every two hours, I feel like at this rate, I'm not even kidding you.

>> New capability coming in. After probably one year things will settle. So you would have a mature players. You know, ecosystems will be some kind of settle. And we'll all focus on delivering value to the customer and, but more and more at scale. So that's what I actually hope to see. And we will still be probably in person, not AI robots. (people laugh)

>> A robot is not taking our job yet. I feel, I feel good. Maybe I'm just arrogant, but I feel like we'll be all right. Yeah, well this was absolutely-

>> You might be maybe not me. (people laugh)

>> Hardly, hardly, hardly. I'll defend all of our jobs at this point. Krishna, Nidhi, this was so nice. Thank you both for being here so much, this was fantastic. Thanks for being our neighbors. Another shout out to everyone over there. Give us a holler. Fabulous TCS team, it's absolutely fantastic. Really love having the, and I can like feel the glow of their smiles behind me right now. It's awesome, Rob, always a pleasure to share the desk with you. And thank all of you from tuning in from around the world to our live coverage here from Google Cloud Next for the first three days, well, for the entire three days of the show in Las Vegas, Nevada. I'm Savannah Peterson. You're watching the Cube, the leading source for enterprise tech news. (industrial music)

Siva Ganesan, Krishna Mohan & Nidhi Srivastava | TCS

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(industrial music)

>> Good evening, brilliant nerd fam, and welcome back to Google CloudNext, we're here in the beautiful desert in Las Vegas, Nevada, day one of three wrapping up our last segment, a very exciting segment with some customer veterans. But before we get there, I'm Savannah Peterson, joined by Rob Stretcher. You have sat in almost every single interview today, if not all of them. How are you feeling?

>> Not all of them. You guys had your segments in there. It was awesome. I think it's been fantastic. I think the thing that keeps me going is really Google Cloud Energy to 30,000 people here and really all of the innovation that's going on, especially with the partners. And I think that's what's been tons of fun.

>> For being a branded event, it is really partner focused, and I think that's a beautiful thing. It's a culture of collaboration. Not surprising that we got TCS here with us. You're our neighbors. You are Cube veterans. Thank you so much for being here, Krishan. This is fabulous to have you. I can't think of a better duo to close out our day. How is the show going for you? Fun, busy?

>> Yes.

>> All of those things?

>> All of those combinations, Lot of excitement. Fun. This is my first Google Next.

>> Me too. Yeah, so.

>> Yeah.

>> But you're a veteran, so yeah we're veterans-

>> Tell us what's going on.

>> And we actually get to be on stage together this time, which is much better. It's always be, but tell us about the partnership and where you're at. I mean, it's been eight months, you know, we haven't heard in eight months. How are things going with you and Google and the partnership with TCS?

>> Things are going really well. We are a premier partner with Google Cloud, and we have built deep capabilities with talent, strong, impeccable delivery track record. And most importantly, with the generative AI, we've done a really good pivot in terms of building up capabilities on what XAI, Gemini, we have been a launch partner. So I think the relationship is going really well. There is a lot of energy on both sides, and the relationship is strong at all levels of the organization, which helps, you know, when your CEOs are well aligned, it really-

>> Helps, is pretty critical, I would say, actually, especially for scaling innovation at the AI pace that we're currently at.

>> Yes. So you can see the excitement right next door if you just turn around and see the number of people from TCS.

>> Oh, hello everyone, wow! Oh my gosh, this is our biggest live audience of Cube history, but, you all look fabulous. Love to see it.

>> Yes.

>> I mean, I think that to me is really, is kind of been the story this week but also, it's been the story of the journey of AI adoption and where people are on and what customers are doing. What can you tell us about that from your perspective? I mean, again, your first one, but you've been working with customers, so you get this, you know, every day with the excitement.

>> Yeah, yeah. No, I think the excitement in the market by the customers on AI and specifically generative AI, has been really, last 12 months has been like crazy in a, in the right way of craziness. In the initial six, eight months, it was all about euphoria, about what's art of possible.

>> Yeah.

>> And, you know, every customer has thousands of use cases across the business functions and ID functions that they've actually developed. Now we see at the, you know, turning point where they're focusing on experimentation is fine. So how do I take them now to production? How do I scale it at an enterprise level? How do I measure the value? So I think that is the phase, and to me that's the real adoption phase at the, especially for the customers that we saw with Fortune 2000.

>> Do you feel like we're at a hinge point, 'cause we've talked about this a lot, a lot of different proof of concepts, everyone's excited, all these little baby MVPs, but are, do you feel like customers are at the stage in their journey where they're starting to hinge towards really making it real at scale? Or is that something that you're really helping customers navigate a lot? Nidhi, I'll go to you for this one to start.

>> So I will say that there is a lot of pragmatism that has come in into the enterprise context. Everybody is keen to figure out the right use case to take to production because AI is not inexpensive. And while the tons of use cases which are out there in terms of chat bots, how to build a new recipe, they're great. They prove the point from a technology perspective. But for an enterprise, it has to make business sense, it has to make commercial sense.

>> Got to hit that P and L or it's just fun and games.

>> Yes, so to answer your question, I will say our clients are looking for TCS to help them to that path, to-

>> To see the light towards that actualization, exactly.

>> Yeah, so.

>> What are some of the top use cases that you are hearing from your customers?

>> So there are three that are particularly relevant, I would say. The first one is on developer productivity. So tech is like, drink your own lemonade, or eat your own dog food, whichever way we like to go. But the point is that

>> I'll take the lemonade, just in this particular circumstance. I'll actually take a cocktail if we're at it.

>> Yeah, so maybe drink your own champagne, let's say that. So the point over here is that being able to use AI to reimagine the software development lifecycle, that's the use case that has really come to the top in terms of interest from our clients, in terms of interest within TCS because it leads to a lot of productivity and overall developer satisfaction. And the other two are on sales and marketing, personalized campaigns. You saw the creative agents this morning in the keynote and also customer service. So agents, customer agents, all over. So those are the top use cases.

>> Right, makes total sense.

>> Yeah, it really does. I'm curious though, obviously you touch a lot of different verticals and a lot of different jar issues, I mean, you kind of have your hands in probably everything a little bit that everyone's doing right now, which is awesome. Are there any trends you're seeing or any verticals that are really accelerating faster perhaps than others with no, you know, no diss on any of them. Krishna now I'll start with you with that one.

>> Yeah, no, I think the adoption, as you said, broadly across the industries and-

>> It seems like everyone's talking about it.

>> Everyone's talking about, so if I actually look at it, probably the adoption itself in the customers before I come to the, what are the industries who are taking lead, the adoption of generative AI, the way the TCS looks at is three phases or three, you know, approaches. What we call it is assist, augment, and transform. Assist is where generative AI is actually assisting making human look more intelligent. And-

>> I mean, we could all use that. I mean, I'll say for myself at least, I'll take anything that looks me more, it makes me look more intelligent.

>> Augment is where if you will, both human and generative AI kind of work together, but a subset of the work is actually done by generative AI. And then the transform, I think is where the value that we see maximum is where you are reimagining the value exchange and the value cycle of the overall value chain. Often each industry or subsegment, not only at industry level. So for example, if you take BFSI banking, financial services and insurance, at a capital market level, what is that you can generative AI can actually improve at an insurance, how you can improve the claims processing and reimagine the claims processing.

>> We all know that can be better too. If you've ever dealt with an insurance company,

>> We see maximum traction in financial services, life sciences, healthcare, retail, and CPG, and then probably life sciences and healthcare. These are the maximum that we actually see the traction. It also, you know, kind of in the morning keynote and the afternoon keynote, I think TK very, very, you know validly identified the generative AI adoption is not only happening at technology level, it's actually the business owners, the LOBs, who are actually seeing the value more and they're encouraging. So it's the perfect combination of the businesses and the technology teams working together. And that's where we see the maximum traction.

>> That synergy. I'm so glad you brought that up, that synergy has been a little bit of a theme. It's kind of like the classic marketing, engineering and product teams. There's always a little bit of healthy tension. Same things with business and tech units for obvious reasons, cost center and achievement centers. But I do, so you are seeing across the board the trend of these units working together. I mean, we were even talking to McKinsey about it with their digital, and AI used to be separate, or cloud and AI is to be separate. And now it's basically one uni now, this kind of cross business collaboration is hypothetically going to accelerate the innovation cycle, right? Maybe? Am I making that up, yeah.

>> Yes, no, you're not making it up, it is very perfect. In fact, as TCS, we both are part of AI cloud unit, both the AI as well as cloud together. So that's where we see maximum, you know, benefits. And we work really closely with, you know, our industry expertise and really driving, you know, probably later in the, we can probably take a couple of examples also where we actually see from business perspective what the use cases we are seeing.

>> Yeah. Yeah, I mean, I think again, it's a, you talked about a vertical approach and different industries have different reasons for going down this path. You're really focused in that as well and really helping different industries with their transformation. I mean, you mentioned, you know, in a little, your insurance use case. What else are you seeing in that and how are you instantiating it?

>> So one of our fundamental beliefs is that AI adoption will be industry led on the cloud, and it will be enabled by an ecosystem of partners. And that's something that was spoken about in the keynote as well when TK spoke about, you know, the Google cloud ecosystem of partners, and most importantly, the model will be fine tuned with data for the enterprise context. So keeping those, so I'll go to the first cardinal principle that adoption will be industry led. So the way we are approaching this is that we are looking at an industry and we are looking at the core value chain, the business process. And then within that value chain we are identifying key personas, and then building a smart agents, AKA co-pilots, or you're basically intelligent agents for the key persona. The, so we are taking a human-centric approach so that AI becomes more easily consumable and you are also able to break it into bite sizes or chunks because for manufacturing, if you look at the value chain, so you will have product design, you'll have supply chain, you'll have sales and marketing distribution, and most importantly, you'll have plant operations. So if you do a intelligent agent for the plant operator, so you look at the day in the life of a plant operator and you see how gen AI and AI interventions change their life. And then so that's the human-centric approach, you know, we are moving forward with, and the other thing we've noticed is that AI is getting in consumed, you know, by personas, by use cases. It's not that one mega transformation roadmap that anybody's biting because the, you know, business cases to be, you know, determined. So it's, you know, by personas, by use cases that we are seeing the adoption of AI.

>> Well, and what you're getting at is a really important point. It's the nuance, it's the ability to have that real time inference as super fast experience that's customized to you to customize to the customer or to the end user or to an agent or to anything that's happening, and making sure that that feels as wonderful as a normal human interaction or some of the other less data heavy interactions that we're having. Let's talk about data for a second. Data's obviously a big challenge with making sure that customers get the AI solution that they want. What are some of the best practices that you're advising for your customers to navigate that? Because it's kind of a data hygiene moment. It's the less sexy part of this whole game, but yeah, Krishna, I'll start with you if you want.

>> Yep. No, I think data is where the real differentiation is.

>> Right. And it's, especially at an enterprise scale, it's not easy to bring in the data, curate the data, and make the data ready for driving any outcomes. But I think with generative AI, the best part of it is you can actually take the structured data as well as unstructured data, and can still make intelligence out of it. So I think that's the difference. If you look at it, the cloud world versus AI world versus generative AI world. So the ability to look at unstructured data, combine them to drive an outcome, is where the difference is, they would like to add few-

>> And to add to the points Krishna just made, what we find is that many of our clients, they need help because the data is a state per se is very fragmented. So you have to look at different data sources, work on de-duplication of data, de biasing, data labeling. All of this has to be done to create a data pipeline, which will ground your model well. So you know, that whole data readiness for AI is a very critical nuance to enterprise grade AI adoption.

>> Yeah, I mean I think this is a big key to it is that you got to be ready for it. You have to do all of this work upfront and getting started sometimes, it can seem overwhelming. What do you think from a, you know, TCS partnership perspective, you know, really brings those customers to TCS to partner with you on that journey? Where do you think, like why is it that they start there with you?

>> I think two, three things. One, as Nidhi said, we are number one in the talent, right? In specifically Google, sitting at the Google Next. We are number one with 35, 40,000 certifications on Google by distance as a partner. So we have the core technical competency at scale across the, you know, geographies and industries. So that's one aspect of it. The speed with which we make our, you know, at A TCS level, 350,000 people are trained on AI and generative AI. So out of the 600,000 strings that we have, so that's one. I think the second thing is, as an engineering, core engineering company, we built platforms that actually makes the adoption of generative AI and AI, because we strongly believe if you want to solve a business problem and provide a business solution, it cannot be generative AI alone. It has to be AI, generative AI and data and cloud altogether. So we have built platforms which actually makes these adoption enterprise scaling that I said, where as you look at the journey from use case to value case, value being the business value outcome delivery, these platforms where what we have built is actually core differentiation. That's why they actually come to us. And the third, I think the important one is our industrial knowledge and the industry knowledge that we have on the, you know, verticals, deep domain knowledge and the contextual knowledge that we have. So we believe generative AI, the real value is in the transform use cases where you reimagine the, you know, business process, the value chain and value exchange that TCS is very deep. We are being aligned verticals as an organization and captured all the, that knowledge into knowledge graphs and knowledge, you know, repository if you will. And that gets supplied to drive the things. So these are the three things, the talent, the platforms that we have built for enterprise scaling and our industry experience and expertise.

>> Well, you, that's what you're known for. You curate the best bits so that your customers can come in and adopt these new huge solutions, no matter their vertical at scale. Very exciting. So you and I chatted at AWS reinvent two years ago, I think it was now, you guys chatted eight months ago. Since you're obviously Cube alumni and superstars at this point, what do you hope that you can say the next time we sit down, we'll sit down, we'll call it Google Cloud Next, just in case, who knows what happens at the end of this year, that you can't currently say right now? Nidhi, I'll start with you.

>> So, well, if you could say that one more time for me? So what is this?

>> Yeah, so what do you hope you can say a year from now? That hasn't yeah, see, say some achievement, anything.

>> Okay.

>> Where do you hope we are a year from now?

>> So in a year from now, I think we should have a few agents, you know, at the Google Cloud Next. So I think, you know, we could do this show with agents as well, avatars, who knows? That's, I'm really being a little futuristic over there.

>> You're allowed to do that. I grant you permission.

>> Yeah.

>> But I will say that I would expect to see a lot more AI being consumed in our personal lives, as much as it'll be in our work lives. So I, that's what I see coming. And I also will refer to a cloud study that we did at TCS. It's the AI study. And it said that in three years we are anticipating everybody to use AI, you know, for 50% of the work that they will do. So I think that's the trend we are headed towards.

>> Yeah, I love that. Alright, let's go for Krishna.

>> I actually see right now it's the promise of generative AI for next one year, probably. It's realize the promise of generative AI, so that's the way I actually see it. It's evolving, every two weeks, there's a new company that is coming up, every two weeks.

>> Every two hours, I feel like at this rate, I'm not even kidding you.

>> New capability coming in. After probably one year things will settle. So you would have a mature players. You know, ecosystems will be some kind of settle. And we'll all focus on delivering value to the customer and, but more and more at scale. So that's what I actually hope to see. And we will still be probably in person, not AI robots. (people laugh)

>> A robot is not taking our job yet. I feel, I feel good. Maybe I'm just arrogant, but I feel like we'll be all right. Yeah, well this was absolutely-

>> You might be maybe not me. (people laugh)

>> Hardly, hardly, hardly. I'll defend all of our jobs at this point. Krishna, Nidhi, this was so nice. Thank you both for being here so much, this was fantastic. Thanks for being our neighbors. Another shout out to everyone over there. Give us a holler. Fabulous TCS team, it's absolutely fantastic. Really love having the, and I can like feel the glow of their smiles behind me right now. It's awesome, Rob, always a pleasure to share the desk with you. And thank all of you from tuning in from around the world to our live coverage here from Google Cloud Next for the first three days, well, for the entire three days of the show in Las Vegas, Nevada. I'm Savannah Peterson. You're watching the Cube, the leading source for enterprise tech news. (industrial music)