Nidhi Srivastava and Krishna Mohan, TCS | Google Cloud Next ’24

[Savannah Peterson]

Introduction

Good evening, brilliant nerd fam, and welcome back to Google Cloud Next. 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.

Introducing the Guests

But before we get there, I’m Savannah Peterson, joined by Rob Strachey. You have sat in almost every single interview today, if not all of them. How are you feeling?

[Rob Strachey]

Not all of them. You guys had your segments in there, and it was awesome. 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.

[Savannah Peterson]

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’ve got TCS here with us.

TCS Partnership

You’re our neighbors. You are CUBE veterans. Thank you so much for being here, Krishnan.

[00:01:00]

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.

[Krishnan]

All of those combinations. A lot of excitement. Fun. This is my first Google Next. Me too. Yeah. Oh, okay. Yeah. But you’re a veteran. Yeah, we are veterans. Yes, we’re veterans. Tell us what’s going on.

[Rob Strachey]

Tell the show. We actually get to be on stage together this time, which is much better. Tell us about the partnership and where you’re at.

Partnership Progress

It’s been eight months. We haven’t heard in eight months. How are things going with you and Google and the partnership with TCS?

[Nidhi Srivastava]

Things are going really well. We are a premium 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 Vertex AI, Gemini, we have been a launch partner.

[00:02:11]

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.

[Savannah Peterson]

Importance of CEO Alignment

I mean, it’s pretty critical, I would say, actually, especially for scaling innovation at the AI pace that we’re currently at.

[Nidhi Srivastava]

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

TCS Audience

Oh, hello, everyone. Wow.

[Rob Strachey]

Oh, my gosh. This is our biggest live audience of humans going back.

[Savannah Peterson]

Y’all look fabulous.

[Rob Strachey]

Love to see it.

[Savannah Peterson]

Love to see it.

[Rob Strachey]

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.

[00:03:07]

AI Adoption Trends

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.

[Krishnan]

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 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 turning point where they’re focusing on experimentation is fine. So how do I take them now to production?

[00:04:02]

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, especially for the customers that we serve with Fortune 2000.

[Savannah Peterson]

Hinge Point for AI Adoption

Do you feel like we’re at a hinge point? Because we’ve talked about this a lot, a lot of different proof of concepts. Everyone’s excited, all these little baby MVPs. But 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.

[Nidhi Srivastava]

So I will say that there is a lot of pragmatism that has come 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 chatbots, how to build a new recipe, they’re great.

[00:05:09]

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&L or it’s just fun and games. So to answer your question, I will say our clients are looking for TCS to help them to that path to see the light towards that actualization, exactly.

[Rob Strachey]

Top Use Cases

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

[Nidhi Srivastava]

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 if we’re choosing it, just in this particular circumstance.

[00:06:01]

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.

[Rob Strachey]

Makes total sense.

[Savannah Peterson]

Trends and Verticals

Yeah, it really does. It really does. I’m curious, so obviously you touch a lot of different verticals and a lot of different geographies. 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 diss on any of them?

[00:07:08]

Krishnan, I’ll start with you with that one.

[Krishnan]

Yeah. No, I think the adoption, as you said, broadly across the industries.

[Savannah Peterson]

Yeah, it seems like everyone’s talking about it.

[Krishnan]

Everyone’s talking about it. 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 approaches.

Three Phases of Generative AI Adoption

What we call it is assist, augment, and transform. Assist is where generative AI is actually assisting, making human look more intelligent. Mm-hmm.

[Savannah Peterson]

I mean, we could all use that.

[Krishnan]

Yeah. I mean, I’ll say for myself at least.

[Savannah Peterson]

I’ll take anything that makes me look more intelligent.

[Krishnan]

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.

[00:08:02]

And then the transform, I think, is where the value that we see maximum, is where you’re reimagining the value exchange and the value cycle of the overall value chain, often each industry or sub-segment, not only at the 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?

[Savannah Peterson]

We all know that can be better, too, if you’ve ever dealt with an insurance company.

[Krishnan]

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 kind of, in the morning keynote and the afternoon keynote, I think TK very validly identified that generative AI adoption is not only happening at technology level, it’s actually the business units, the LOBs, who are actually seeing the value more, and they are encouraging.

[00:09:16]

So, it’s the perfect combination of the businesses and the technology teams working together, and that’s where we see the maximum traction.

[Savannah Peterson]

Synergy Between Business and Tech Units

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. 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 used to be separate, and now it’s basically one unit now. This kind of cross-business collaboration is hypothetically going to accelerate the innovation cycle, right?

[00:10:02]

Maybe? Are we making that up? Yes.

[Krishnan]

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 benefits. And we’re working very closely with our industry expertise, and really driving. Probably later, we can probably take a couple of examples also, where we actually see from a business perspective what the use cases we are seeing.

[Rob Strachey]

Industry-Specific AI Adoption

Yeah. I mean, I think, again, 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 a little insurance use case. What else are you seeing in that, and how are you instantiating it?

[Nidhi Srivastava]

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.

[00:11:14]

And that’s something that was spoken about in the keynote as well, when TK spoke about the Google Cloud ecosystem of partners. And most importantly, the model will be fine-tuned with data for the enterprise context. 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.

Human-Centric Approach to AI

And then, building smart agents, aka co-pilots, or basically intelligent agents for the key persona.

[00:12:02]

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 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 the life. So, that’s the human-centric approach we are moving forward with. And the other thing we’ve noticed is that AI is getting consumed by personas, by use cases.

[00:13:02]

It’s not that one mega transformation roadmap that anybody’s biting. Because the business case is to be determined. So, it’s by personas, by use cases, that we are seeing the adoption of AI.

[Savannah Peterson]

Nuance and Real-Time Inference

What you’re getting at is a really important point. It’s the nuance. It’s the ability to have that real-time inference, a super-fast experience that’s customized to you, customized 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.

Data Challenges and Best Practices

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.

[00:14:01]

Krishna, I’ll start with you if you want.

[Krishnan]

Yeah. 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, unstructured data, combine them to drive an outcome. Yeah. Is where the difference is. You would like to add a few?

[Nidhi Srivastava]

And to add to the points Krishna just made, what we find is that many of our clients, they need help because the data estate per se is very fragmented.

[00:15:08]

So, you have to look at different data sources, work on deduplication of data, debiasing, 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.

[Rob Strachey]

Data Readiness for AI

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 up front. And getting started sometimes, it can seem overwhelming.

TCS Partnership Value Proposition

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?

[00:16:02]

Where do you think, like, why is it that they start there with you?

[Krishnan]

I think two, three things. One, as Nidhi said, we are number one in the talent, right? Specifically Google sitting at the Google Next. We are number one with 35, 40,000 certifications on Google by a 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, the TCS level, 350,000 people are trained on AI and generative AI. So, out of the 600,000 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.

[00:17:05]

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 all together. 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, 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 re-imagine the business process, the value chain and value exchange.

[00:18:00]

That TCS is very deep. We have been aligned verticals as an organization and captured all that knowledge into knowledge graphs and knowledge repository, if you will. And that gets applied to drive the thing. So, these are the three things. The talent, the platforms that we have built for enterprise scaling and our industry experience and expertise.

[Savannah Peterson]

Well, 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.

Future Goals

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 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.

[00:19:00]

So, if you could say that one more time for me. So, what is this? What do you hope you can say a year from now? What did I say? Yeah, say, some achievement, anything.

Future of AI

Okay. Where do you hope we are a year from now?

[Nidhi Srivastava]

So, in a year from now, I think we should have a few agents at the Google Cloud Next. So, I think we could do this show with agents as well. Avatars, who knows. I’m really being a little futuristic over there. You’re allowed to do that. I grant you permission. But I will say that I would expect to see a lot more AI being consumed in our personal lives. As much as it would be in our work lives. So, that’s what I see coming. And I also will refer to a cloud study that we did at TCS.

[00:20:03]

It’s the AI study. And it said that in three years, we are anticipating everybody to use AI for 50% of the work that they will do. So, I think that’s the trend we are headed towards. Yeah, I love that.

Realizing the Promise of Generative AI

All right.

[Krishnan]

Let’s go for it. I actually see, right now, it’s the promise of generative AI. For next one year, probably, it’s realized the promise of generative AI. Yes. 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.

[Savannah Peterson]

Every two weeks? Every two hours, I feel like, at this rate.

[Krishnan]

I’m not even kidding you. There’s a new capability coming in. After probably one year, things will settle. So, you would have mature players. Ecosystems will be some kind of settled. And we’ll all focus on delivering value to the customer. And more and more at scale.

[00:21:00]

So, that’s what I actually hope to see.

Settling of the Generative AI Landscape

Yeah. And we’ll still be probably in person. A robot is not taking our job yet.

[Savannah Peterson]

Not yet. Hopefully. I feel good. Maybe I’m just arrogant, but I feel like we’ll be all right. You might be.

[Rob Strachey]

Maybe not me.

[Savannah Peterson]

Hardly.

Closing Remarks

Hardly. Hardly. I’ll defend all of our jobs at this point. Krishna and Nidhi, this was so nice. Thank you both for being here so much. This is fantastic. Thanks for being our neighbors. Another shout out to everyone over there. Give us a holler.

[Krishnan]

Fabulous.

[Savannah Peterson]

It’s absolutely fantastic. Really love having that. I can 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 for 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 theCUBE, the leaning source for enterprise tech news.