NTT Data’s Rajeev Singh on AI Agents and Driving Global Business Transformation

Hey everyone. Welcome back here to Tech Trunk tv. Our next guest is Rajiv Singh Ra Rajiv is the Senior Vice President for NTT Data.

Joining us today, Rajiv, welcome to Techstrong tv. Thank you, Alan. Thanks for having me here.

Looking forward to Talking with you. I have time. Pleasure.

Thank you. So Rajiv, let's talk a little bit about you first, right? You, as I mentioned, you're SVP over at the NTT data.

Is there a particular portfolio that you're in charge of or, you know, and give us a sense of how you came to be here. You know, not not here on tech drunk TV, obviously, but you know, in your role at N-T-N-T-T data. What, what's kind of your journey been like?

Yeah, so Ellen, thanks for asking that. I'm, I'm actually the group senior vice president for, uh, what we call as applications and BPS. It's a global practice, uh, in entity.

As you know, entity is a $30 billion company. Uh, we've got 200,000 employees and we've got a portfolio which spans the entire stack. So that's why we call ourself as full stack company.

And out of that full stack, I run the go-to market solutions and offering for what we call as a global practice. And the global practice that I'm representing is application and BBPS business process services. Sure.

We are on the upper end of the stack if you, if you want to visualize that way. Um, my journey, I've been in this industry for, um, over 25 years now. Uh, I'm an electrical engineer by, by, um, by education.

Um, I did my electrical engineering and MBA back in India. Um, I have worked with, um, many companies outside of IT systems as well. I started my career in shop floor and other, other places, uh, have been in IT industry for well over 20 years now.

Uh, been with NTT data since 2011. I came in as part of the Dell Services acquisition. Um, and then, you know, have been with applications for, um, gosh, about 10 years now.

Right. So, um, so that's my background and I'm really looking forward to this conversation because while I'm here talking as an application, BPS, uh, senior Vice president, I mean, we obviously, the entire ecosystem that we operate is full stack in nature. And as you know, we are one of the largest, uh, backbone providers.

We've got about 45% of internet traffic that goes through us. We are second largest IT managed services player in the market. We are the third largest data center provider.

I mean, I can go on and on. Uh, when we talk about a full stack, we've got 200,000 employee base. Uh, we operate out of 50 plus countries.

So massive scale that you're talking about. And we are presenting most of the vertical industries that you can, that we can think of, right? So that's our global scale, and with that comes a lot of opportunity to, uh, play in this new world of ai, which is actually, uh, having a profound impact.

So that in short, is, is where we, where I'm placed in the organization. Love it. I love it.

So, Rajiv, I, I hope that gives people a sense of, you know, a lot of people, uh, look, I'm of a certain age, right? NTT where I grew up in the world, where at and t was the mabell, right? Was the US telecom provider.

NTT was Japan's telecom provider. Deutsche Telecom was West Germany back then, right? There was no just Germany, it was West Germany, east Germany.

And now, you know, obviously the world's changed at and t is not what it was, but it's yet greater than it was perhaps NTT, you're still the national telecom provider for Japan, but NTT data, as you said, operates in 50 company in 50 countries, right? It's a global powerhouse providing bandwidth, internet traffic, data centers, everything else. And now we come to maybe the next great era in it and in maybe, maybe as profound as the internet itself, uh, right?

Having that kind of effect on, on humanity, on civilization, and that's this AI thing. Um, and the race is on, right? It this, you know, in the, in the internet era, Rajiv, we had the dot coms, right?

com business. In the AI world, there's a lot of that. There's a lot of people starting AI businesses.

But when we look at where a lot of the action is in ai, it's distinctly with the big guys, with the NTT data of the world, with the Googles and the Microsofts and and so forth. Um, and the reason is, is it seems to have, well, I think one of the reasons is there's a high bar of entry. You've gotta put a lot of money, a lot of resources into having a state-of-the-art AI type of, of of infrastructure, though, you know, the people at Deep seek the, the Chinese have shown it, well, maybe you could do it, and, and they, you know, jury's out on that, on whether you could do that cheaper or not.

So we look at NTT data and say, that's a company that has the, the horses to play here. You guys recently announced a whole AI agent ecosystem, and you are not the only one. Salesforce has announced it.

ServiceNow is announced a similar ecosystem of agents. Uh, agent AI is some call it. Um, you know, a lot of the big guys are the big players.

What makes yours unique? What do you, what do you, what are you guys talking about with it? Yeah, no, Alan, that's a, that's a great point.

I mean, um, AI I think is a seismic shift in the entire ecosystem. com and the internet boom because of its profound impact across industries, right? Um, you know, we are playing AI quite deep inside first, obviously, like I said, we have 200,000 employee base and we had a lot of responsibility inside our company as well, right?

In terms of retraining some of the folks and building a workforce, which is, uh, AI ready. But let's talk about, you know, what is smart AI agent ecosystem that we have built and we have announced, uh, in this world of ai, you are right. I mean, a lot of other companies are doing a similar announcement.

What we are targeting basically is that, look, AI is going to be a big leverage for productivity and efficiency and all of that that you hear, but Agent AI in itself has the potential to rewire workflows and thereby, you know, um, evolve a new way of doing things. The potential is pretty big. And I think for us to make that real, we started obviously working, uh, inside our company couple of years back.

We started experimenting in different processes, for example, impacting our HR systems, impacting our marketing systems in a positive way. ai. Um, we, we've also entered into a deep alliance with open ai.

We are forming a center of excellence. We are building agents with them. We have relationships with Hyperscaler.

We have a deep relationship with Salesforce too. You mentioned the name, right? And we are building agency ecosystem over there.

So, uh, end of the day, the idea is to create an agent ecosystem which can help impact and make a positive impact to our customer base, our, our dear clients that we have, um, full stack. I mean, some of these agents are going to impact their business workflows. At the same time we have an agent ecosystem, which is going to impact the operational side of the it, uh, you know, as mundane as helping a service desk agent as looking at contact center from a different point of view.

So we are looking at all of that ecosystem for our clients because we believe that it is profoundly going to impact not just productivity, but even answers to global, uh, workforce shortage, for example, looking at reinventing business processes. So, so our entire ecosystem is built on that foundation, Alan, we've got, we've got an inventory of agents and we are also looking at relationship ecosystem around that. 'cause obviously this requires deep relationship with different, different partners, and that's what we are trying to offer to our customers, that there is a, there is NTT data, which is in known name in this industry, which has an arsenal of agents, which has the potential alliance and relationship.

And we are working with our customers from advisory all the way down to building as well as management of these agents in a very responsible manner where security and compliance is ski and is part of our philosophy, if that makes sense. It all makes perfect sense, Rajiv, perfect sense. You know, philosophically, I think one of the realizations that people need to come to is that not all your agents are going to come from one company.

I mean, you mentioned it, right? You call it an ecosystem. There's big companies, small companies, they're all producing agents.

And to me, the real prize is being the company that helps people manage all these disparate agents, right? You don't have to, no one company is gonna make the agents for everything. I think we're gonna live in a world where we all use agents at different companies, but you know, who, how do we manage them and who manages them?

I think that that's the big question is that NTT data's end game here. That is absolutely to help them manage. That's, thank you, Alan.

That's absolutely, uh, one of our key focus areas because as you rightly said, most of the hyperscalers, as you know, as you are, for example, they have their AI foundry, they have their open AI services. We have a deep practice which is just focused on building agent tech factory using these hyperscaler technologies. From a customer point of view, it is my personal belief that large enterprise customers are going to look at agents as loosely speaking.

We used to talk about custom apps, right? I mean, so you will have a portfolio of an environment where there are a lot of agents, there will be specialized agents coming from different areas and off late obviously you've got agent to agent protocols coming in for seamless communication, basically handling the interoperability portion. An entity is a, is a major player in that segment because we are going to operate this entire agent ecosystem for our enterprise customers trying to get them the right behavior.

Now, when you look at agent to agent interaction, it has profound impact on even a term like observability. You know, that term itself is going to change because you're A loaded Yeah, I mean, you're looking at an agent interaction, which is now you are looking at conversations and behaviors. I mean, we are, we have, we are, we are working on, um, accelerators to actually manage those kind of environment.

So when you look at, let's take a old school managed services deal, and that deal is going to morph where agent ecosystem is going to become part of the deal. So your entire observability is going to change, and we are actually working in those kind of nuances basically because it is an important ingredient of, you know, handling a com complex, multi-agent environment, if that makes sense. It may, it makes perfect sense.

And this, that, that is exactly the point. Yeah. You look at something like observability, what, what, you know, observability is gonna be turned on its head when, when AI is done with it, right?

Takes a lot out that Rajiv, as you can imagine, we talk a lot about agentic AI and stuff here at techron TV and on techron, you know, on our sites. And one of the things we hear from our audience is how much of this is pie in the sky? How much of it is real?

Right? Where, where, like what, what's available now, you know, and, and obviously this changes day to day, week to week, but you know, when you look at the NTT data, uh, this recent announcement you guys had with smart, a AI agent ecosystem, how much of it is real and available today? Yeah, Alan, it's a very good question and I'm, I will probably answer this question with a couple of client examples and, and talk about little maturity graph that we are going on.

So, um, we've got some interesting engagements. I'll give few examples. You know, we are working with highest rail, uh, highest rail materials or you know, from advisory all the way to defining a multi-agent ecosystem, which is outcome oriented.

But take an example. There is a global automotive giant that we are working with. They were having issues like on their assembly line, they used to have probably like 20 issues in a week.

And these issues were on the assembly line. We actually started building a, they, they called upon us because we have a relationship with them and we brought in what we call is a manufacturing AI solution, where we initially started with consuming all their artifacts, their operational document, their assembly line documents, their repair guidance. We built an ecosystem of a knowledge base, which was completely driven through vector databases and LLMs and all of that that you heard.

We got that to an accuracy level, and then we started layering a very intelligent chat bot, which is not exactly a chat bot. You can have a conversation where the workers can have a conversation to get repair guidance. Now you are talking about a mean time to repair really, really coming down because the SME knowledge is becoming a bigger, wider spread, right?

You're, you're sort of making that knowledge flow into the ecosystem. The next steps that we are working is on that repair guidance to trigger agent flows. For instance, if you have to order something, can it go and order on its own?

And those are becoming a reality. The second example I'll give you is as part of our smart agent ecosystem announcements, I, I'm pretty sure you notice that we have built an accelerator. What it does is it takes legacy bots and it converts them into agent applications, AgTech, right?

Um, the challenge over there is, you know, legacy bots follow a simple workflow, a linear workflow, a true AgTech system is going to follow sort of undefined processes. And that's the effort that we are doing to build those kind of ecosystems into our solution. Take an example of a Fortune 50, a large healthcare company.

They actually cater to a lot of these managed care products and government services like Medicare. Medicare, and we are operating with them where we have built a complete clinical and claims intelligence platform and we are building agents which drives things like pre-authorization drives, things like medication adherence. So it is starting to become reality, Alan.

We still have nuances where these agents have to be closely monitored and captured and monitored carefully, but there are simple workflows which are converting. We are trying to disrupt a contact center industry working with a partner where the partner is an expert in conversational AI from an agent tech point of view. So we, we, we, we've embedded ourself in insurance and health insurance and card payment industry.

What we are seeing is that as we are building these agents, these agents are, are very flexible in nature and they're able to handle a lot of different question. One agent can handle a lot of other activities, which you would probably code using multiple legacy bots. And we are starting to see a lot of success as this technology matures.

So that's an example of how it is going to start to disrupt this industry. An entity is playing in the forefront of that. So, Alan, it is coming.

I, I, the rapid expansion is taking place and people, there are folks who are still apprehensive and I'm playing this from inside. I actually went and took a degree personally three years back. You know, I, I live in Texas and I went there and did a degree.

It took me about nine months to really play from inside. And I can tell you that people are going to be really surprised if they're not on it. It is coming.

I love it. I love your passion. Yeah, that's great.

Rajiv. Hey, I promise I know you have commitments and we were, we're running up on the top of the hour. Thank you so much for coming on Techstrong TV and sharing with us.

I'm gonna hold your feet to the fire, invite you back on here in the next couple months as this more and more becomes real and keep us posted on this. Okay? Thank you Alan.

Appreciate you having me and I look forward to our next conversation. Alright, thank you. Rajeev s senior VP NTT data here on Tech Trunk tv.

We're gonna take a break. We'll be right back.