Celebrating Tech @ BLR DC 2026 - From Insight to Impact - AI at Scale

Guruprasad NV:

AI in the hands of an engineer who understands coding is far more effective than in the hands of an engineer who does not.

Manjunatha Kukkuru:

The most important skill, I believe, is to be entrepreneurial.

Anantha Radhakrishnan (Radha):

The key thing is what mix of human and AI to use. I call this humanware.

Pragya Rai:

AI is changing not only the technology we build, but also how we work, solve problems, and create new ideas. For engineers, that creates a big opportunity to learn new skills, work differently, and help shape what comes next.

Pragya Rai:

Welcome to the Infosys Knowledge Institute Studio, and welcome to Celebrating Tech at Bangalore DC 2026, the premier tech event for our development center, bringing together the biggest names in engineering and innovation at Infosys Bangalore DC. This is our inaugural fireside chat, and I'm Pragya Rai from Infosys Knowledge Institute, joined by three leaders with very different perspectives on technology and innovation. Guruprasad NV, or as we call him Guru, he is an associate vice president and technology architect focused on AI, cloud, and platform engineering. Then we have Manjunatha Kukkuru, who is a vice president focused on the Infosys Business Incubator, Deep Tech Startup, and Enterprise Innovation. Then we have Anantha Radhakrishnan, or Radha, he is the CEO and managing director of Infosys BPM, with experience across technology, transformation and operations. So today, we will talk about what AI means for engineers, our clients, and the future of work. Let's get started. Guru, my first question will be for you. AI is changing engineering very quickly. In simple terms, what is changing most about an engineer's job today, and what opportunities does it create for engineers at Infosys?

Guruprasad NV:

Software engineering as a discipline has evolved over the last 20, 25 years. But the advent of generative AI has significantly changed the landscape, especially when it comes to velocity. People are able to do things much faster. Historically, we capture requirements, we wrote user stories, we then converted them into high-level design, low-level design, then we focused on coding, subsequently testing, and then eventually deployment. So fundamentally, these steps have not changed. They've remained more or less the same, but the approach has radically changed. Today, a lot of these activities that I described have either been fully automated or they've been augmented with AI. And there are some activities where human involvement is still needed. An average engineer today has a lot more time to focus on higher order or higher value activities such as design, architecture, and focusing on solving the problems rather than just looking at adhering to 10, 15 different processes. So that's the fundamental shift that we are seeing. So how does this really impact an average engineer in Infosys? The desirability quotient was always very high. But when it comes to feasibility and financial viability, things were always challenging. Not everything that we thought about was easy to realize. But with AI, that has changed significantly. Sky is the limit today. Anything that you can imagine can be realized using AI, and that presents a lot of opportunities for engineers in Infosys. Given that they work so closely with customers, every problem that they witness in the customer's environment today potentially has a solution, and our engineers have the ability to prototype that quickly and put that in front of the customer. So that, in my view, is a tremendous opportunity for us.

Pragya Rai:

Second question is for you, Manju. AI makes it much easier to turn an idea into a working solution. So how can an Infosys engineer with a good idea turn it into something useful for clients or their business?

Manjunatha Kukkuru:

It's a very fundamental question that the entire industry is asking. As you can imagine, billions of dollars have been poured into this idea of what AI can do. But at the same time, the adoption at enterprises is relatively limited compared to the potential that AI holds. And the key question is, what do I do with this technology? Where is the solution that I can realize? So to be able to discover these solutions, in Infosys, we have created three different programs. One is called BTN V(ai)bing. So what we encourage here is for all Infosys employees to proactively look for customer challenges, customer constraints, and ideate along with their team members to come up with a set of solutions for these problems. And to be able to relatively quickly demonstrate that solution is where we use Topaz Fabric and then turn around and create a vibe application. Then you can quickly show to the client and to the stakeholders the potential solution that is likely to make a difference for the client. And as we speak, there are thousands and thousands of such ideas people are working on. I think the latest that I heard was every 97 seconds, a v(ai)bed app is getting created in Infosys. So that is the power of AI, where it is able to quickly translate your idea into something that is demonstrable. But then this is the beginning of the journey. How do you make it real for the clients, for businesses? That is where you need a much more deeper capability to translate a v(ai)bed application to a real functional application. And that is where from the business incubator, we have an offering called exponential engineering. Here, it is about making sure the entire software development life cycle is agentified. So pretty much the life cycle that Guru spoke about, how do we leverage the agentic way of doing software development, design, testing, and implementation? Is the question this team has asked, and they come up with a solution, methodology, tooling, et cetera, which kind of gives you multiple X productivity when you leverage this technology. But at the same time, the next generation of AI, the impact you will see is around physical AI. Now, physical AI is where you have physical and digital systems coming together, and for which you need to have a different set of skill set, different set of tool sets, because you're looking at things like robots, drones, satellites to be able to work on. And that is where we have something called as Be a Maker, where tens of projects across Infosys teams are working on interesting ideas. For example, one of the teams is working on an idea called Helmet, essentially trying to transform a helmet into a sensory environment which can sense the environment around you and alert you of the threats and the no-go areas, a lot of our clients interested in mining places, working in dangerous areas are using this kind of an approach. Another example is an interesting idea from our DX team, which has come up with converting a dumb surface into a smart interactive surface. It's called Haptik Beam, which uses AI and computer vision to translate it. So like this, you're bringing these physical and digital solutions together, come up with next set of ideas to implementation.

Pragya Rai:

Wow. Very nice, very interesting. Coming to you, Radha. What are clients expecting from engineers and technology teams now that they were not expecting a few years ago? What has changed in client expectations?

Anantha Radhakrishnan (Radha):

See, client expectation is very simple. They want business value. They clearly want improved efficiency, improved effectiveness, and improved experience for the stakeholders who are experiencing AI. For me, if I look at clients, their three broad priorities are revenue growth. Can any of our business use cases help them accelerate their revenue growth? Second, can they improve margins, profitability? Which means can we reduce costs by eliminating the need for human effort, transactional human effort, while keeping more intelligent human decision-making and insights in-house. The third area is, can we innovate faster and help them differentiate in the marketplace with the rest of their competition? Connecting the dots from design, architecture, vibing, exponential engineering, physical and digital, and creating this value for them is really the important step.

Pragya Rai:

So with value faster, the ask has become to deliver faster.

Anantha Radhakrishnan (Radha):

Yeah, correct. Accelerated value delivery. What took months, they want it in days and hours. And clearly, they're looking at business metric realization, free cash flow improvement, revenue growth improvement. If you take areas like pharma, clinical data, can we do things faster, better with the power of compute and processing and storage of data? Can we do things which took years? Can we discover a new molecule in weeks or months with the same level of robustness and safety?

Pragya Rai:

Guru, no company builds everything on its own. Infosys engineers work with major technology companies and new AI platforms. How do these partnerships help us build better solutions for clients and give engineers access to new technology?

Guruprasad NV:

Access to technology was never a problem for us, but early access to technology certainly makes a difference, and that's where partnerships come in. Having strategic partnerships helps us get early access, which means we can try them in our environment and be ready before the technology is out there in the market. So the last six to 12 months, we've focused significantly on strengthening our existing relationships, mostly with the hyperscalers. We've signed up with most of the emerging AI natives, and what that has done is, one, it has allowed us to gain early access to these technologies. We're also focusing on building a large pool of talent, certified talent, which is now market-ready. So fundamentally, that is how partnership is helping. In addition to that, given that we have extensive presence in the enterprise landscape, we have a better understanding of how to scale AI in organizations.

Pragya Rai:

Well said. Moving to Manju. We have more about services as software. In everyday terms, what does that mean and why should an Infosys engineer care about this?

Manjunatha Kukkuru:

Again an interesting paradigm shift that is happening, and easily we can mix up between the software-as-a-service. That is a paradigm that we have seen for the last many decades, to service as a software, which is to transform the set of services that we've been delivering, as an example, into a reasonably well-defined productized way of delivering them. So anytime any service is delivered, it is a mixture of human activity, certain set of using some set of tools, some set of data, and all of this is kind of brought together by different humans, and then a particular service is delivered. An example could be you go to a bank and then you want to do some loan processing or something like that. So there are N number of processes that happen between humans, tools, places, data, et cetera. Now, the idea of this service as software is to bring all of these things together in a relatively autonomous way, driven by agents, and delivered in a meaningfully reliable and confident business outcomes. So you cannot have random outcomes here or unreliable outcomes. So that's the important aspect of productizing services. I'll give you two other examples where we are working on something like this. So there are two broad categories that we think of. One is tech services, the other is business services. So the tech services are like ticket resolution, or legacy modernization. Now that entire process is getting agentized and to be able to autonomously fix these problems. On the other hand, when it comes to business functions, you need a different set of mindset, understanding of the business process function to build those solutions. One example is for customer lending, commercial lending. So commercial lending is a complex process, hundreds of people getting involved, because millions of dollars are at stake, and there are tens of regulations, et cetera. And it takes a lot of time and effort and expertise. Now, if there were tens of agents which would deliver this collectively, it makes the whole process more efficient, more faster, highly accurate, and so on and so forth. For example, what Radha and team have been leading. And in fact, under his leadership, we are working with this idea of translating some of our F&A functions or F&A services to a productized way of delivering, which is broadly called as service as software.

Pragya Rai:

Okay. Now we know more about SaaS in very layman terms. Next question for you, Radha. AI agents can now do more than answer questions. They can carry out tasks and take actions. What does that mean for how people and AI will work together? And should engineers prepare for that change?

Anantha Radhakrishnan (Radha):

See, very clearly the opportunity to create business value is exponential. The key thing is what mix of human and AI to use. I call this humanware. The ability to get the boring, repetitive, transactional effort eliminated through use of AI agents. And then the decision data contextual insights and ensuring there is responsible AI with human in the loop or human in the lead, depending on how regulated that processes. So that combination to create business value responsibly is what the new world is all about. Clear example, Manju spoke about our finance and accounting. Our client CFOs want more robust book closures. If it took ten days to close a book at the end of a year or a period or a quarter, they now want it faster and better. If it is about collections, they want the free cash flow improvements to happen. Our own CFO, we do work on in Infy F&A, which has clearly led to improved free cash flows by presenting invoices much earlier than how we used to present before. If I look at industry process examples, whether we underwrite a mortgage, it took seven days. It now can be done much faster. If we are looking at supply chain use cases, the inventory turns can improve, the stock-outs can reduce, the order fulfillment rates can improve. All of this by powerful use of agents and humans together.

Pragya Rai:

Yeah, now we all know that human plus AI is the future. Let's finish with two quick questions for all three of you. First, looking ahead over the next few years, what is the one skill or mindset that will matter the most for engineers in an AI first world?

Guruprasad NV:

So I'd like to share not just one skill, but I probably believe there are two skills that people must have. The first one is having an open mindset, the ability to change and learn new things. Change is the only constant. So see AI as a companion, not as a competitor. With AI, you will not just survive, but you will thrive. So that's the first one. The second aspect is, as engineers, focusing on the fundamentals is extremely important. AI in the hands of an engineer who understands coding is far more effective than in the hands of an engineer who does not. So essentially, point is focus on the strength, focus on your core, which is coding. For everything else, there is AI.

Pragya Rai:

Yeah. Same question for you.

Manjunatha Kukkuru:

One of the biggest things with AI is the velocity with which it is transforming everything around us. And the most important skill, I believe, is to be entrepreneurial. You need to have that entrepreneurial mindset and skill set to be able to thrive, so that you're able to dynamically understand the challenges and dynamically find solutions for it.

Pragya Rai:

And what does an individual need to do when they say entrepreneurial?

Manjunatha Kukkuru:

Essentially, they need to be able to be proactively having a vision or an aspiration about a certain problem or a certain space to say this is what I want to achieve. Secondly, they should be able to convince a set of stakeholders to believe in that idea and then participate in the process. And the third is to make sure they're able to execute and deliver results, right? So if you can follow this life cycle, which is essentially to be like a startup entrepreneur, that's what they do every day, then I think each one of us will be much more better off. The other way to look at it is I call it the 5C mindset. The 5C mindset begins with curiosity. You need to be curious. If you're not curious, it'll be hard to understand the world around you. So going back to the basics, to your own childhood and staying curious and not being afraid is one of the Cs. The second C is client context. If you don't understand the context of a client's business process or the underlying industry, it's very hard to solve for problems. So understanding that client context. Third is you will not really be self-sufficient. You need to collaborate. You need to collaborate with business stakeholders, with partners, with startup ecosystems, anybody who brings that niche skill. Fourth one is consultative. You just need to not just prescribe, but clearly go through the steps of optionizing and studying what is the best solution for the client context. Last but not the least C is really creating business value. Anything you do, the first four steps will clearly have to get quantified, measured, and linked back to what the client's priority and business case for it is. And doing all of this responsibly. So five Cs is what I would clearly feel the change should be.

Pragya Rai:

Yeah. I'm sure that Infosys engineers have got a go-to checklist with these recommendations. And in this final round of Celebrating Tech at Bangalore DC, what is your one message for Bangalore Infoscions, for Bangalore engineers, and all engineers at Infosys that they can use this platform?

Guruprasad NV:

Yeah. So my message to all engineers and Infoscions in particular is, as an organization, we are heavily invested in AI. We have access to all the tools and technologies. So let's start by utilizing all of them to the fullest potential. And the fact that today we are celebrating the birth anniversary of Sir M Visvesvaraya, there can be no better way to pay our respect to him than by making use of the technology that's at our disposal. Further, we are also Celebrating Tech at Bangalore DC, and for those who have not yet started their AI journey, this is probably the best possible time to launch yourselves. So just go and start embracing this in a very big way.

Pragya Rai:

Well said. Manju, over to you.

Manjunatha Kukkuru:

Yeah. So the world is divided into two parts. One into celebrating the utopian world, another being very sorrow about a dystopian world. One thinks the AI is going to solve every problem from cancer to food to poverty. The other feels that humanity will be wiped out. Now, the people who are going to decide at the end of the day, the reality is going to be somewhere in between, and it's going to be the engineers who are going to make this happen. And these engineers, to be able to make a new successful world where AI is going to play the significant role, are the ones who take charge. And they cannot be doing this alone. They need to work with every other engineer in the organization. And Celebrating Tech at Infosys is a very good moment where they can come, meet, interact, and learn, and use this opportunity to exchange their views and build partnerships where we can build a better future with engineers.

Pragya Rai:

Very nice. Radha.

Anantha Radhakrishnan (Radha)

Well, Celebrating Tech at Bangalore DC is a phenomenal way for our software engineer colleagues to work closely with business stakeholders to create business value for our clients. So for me, the opportunity and the message to our engineers would be, we have AI, which can create exponential value. It is not a deterministic technology. It's probabilistic and stochastic. So it is important for you to practice to make it perfect. Don't be afraid. It's like driving and swimming. The best way to learn it is not by a manual, but actually practice it in your daily work life. Stay curious, stay connected with your business colleagues, and figure out context to create greater value for customers. That is the opportunity in front of us to Celebrate Tech at Bangalore DC.

Pragya Rai:

Very nice. So with this, we have come to the end of this interesting conversation. Very insightful and very informative. Thanks a lot. AI is changing the tools we use and the way we work, but engineers remain the people who turn ideas into real solutions. The opportunity is to keep learning, use AI wisely, and think bigger about the problems we can solve. Thank you Guru, Manju and Radha for joining us today. And to all the engineers across Infosys, happy Engineers Day.