Cutting through all the noise about AI, Vijay Guntur, chief technology officer and head of ecosystems at global IT consulting company HCLTech, talks about the inevitable pursuit of ROI and how he sees it not just in financial terms. In a conversation with Abhay Mital, a partner with McKinsey’s Technology, Media & Telecommunications Practice in India, Vijay opens up about deriving value from AI, the challenges of change management, going from pilots to scale-up, and ethical questions about responsible AI and cybersecurity.
The following transcript has been edited for clarity and length.
Everyone’s talking about AI
Abhay Mital: As HCLTech’s chief technology officer and head of ecosystems, you have a unique vantage point on the tech services industry. And, naturally, the buzzword these days is AI—everyone’s talking about AI. How are you seeing AI play out across industries?
Vijay Guntur: AI is front and center of the transformation that many industries and firms are going through. The fountainhead of this is the semiconductor industry, and then there are the hyperscalers and the graphics processing unit or GPU providers. You can see investments going into the capital expenditure of all these firms. They are betting on a return on that investment and investing ahead of time.
What’s being built are the foundational elements of the GPU infrastructure and the data center infrastructure required to do the AI inference and training. These are clear signs of expectations being high; early results are looking very encouraging. And enterprises are beginning to adopt AI in a significant manner.
Abhay Mital: How are you seeing AI play out at HCLTech?
Vijay Guntur: We are taking a two-pronged approach to this. One is enabling enterprises to drive higher productivity in their internal business operations and functions. Second is to enable them to use AI to create value from new businesses and opportunities. Both are important for firms. We are seeing very early moves by many enterprises and investments in both efficiency as well as creating new business value.
Apart from efficiency, many product and platform companies are focused on driving faster innovation and accelerating it, and AI is enabling that both in the front office and in the product development life cycle, or application development life cycle. It is pretty comprehensive and across the board in terms of efficiency as well as new business value creation. It’s a very exciting time for the industry, and I see a lot of optimism in the industry for adopting AI to make this difference to businesses.
Benefits that go beyond ROI
Abhay Mital: You spoke about ROI and big investments. We see Fortune 500 companies investing millions, if not billions, of dollars in this trend. So, naturally, this question of ROI keeps coming up. Do you have any real-world examples that illustrate how this is playing out?
Vijay Guntur: I’ll talk about two examples of ROI: One is a very financial view of ROI, which most people think about as a financial [metric]. Then I’ll talk about going beyond financial metrics.
For the financial view, an example is one of the largest providers in the US that we worked with to build a health advisory AI platform that helps clinicians do their job much more effectively and deliver higher-quality healthcare outcomes. This firm has a network of about 20,000 physicians. Even if we save them five minutes in diagnosing patients’ needs and giving them more accurate medication or prescriptions, that alone could create $100 million in value for this firm. To build this and to run this probably will cost a few million dollars, maybe $10 million or so, but the ROI is very clear. So these are instances where ROI is very clear on what outcomes it can produce for an organization.
Regarding benefits to patients, that’s beyond the ROI calculations here. In some cases, ROI is still evolving. Cost dynamics, inference cost, and token cost are all decreasing significantly, which will make many more business cases viable and return on investment much more attractive.
You have to look at financial returns as well as broader returns to society.
I’m keen to talk about the ROI beyond financial metrics. We are working with a not-for-profit healthcare provider in India. It has a system to figure out in advance whether patients might have a cardiac episode, allowing providers to intervene and provide care and possibly extend life. So how do you put an ROI on something like this? I don’t know. What’s the value of human life?
You have to look at financial returns as well as broader returns to society.
Abhay Mital: Are there specific verticals where you see the business ROI from AI being particularly attractive?
Vijay Guntur: Yes. I talked about capital expenditure by the hyperscalers and GPU providers—they are seeing returns already. You can see the valuations of these companies go up significantly because of these investments, and then there are accruing returns from those investments.
Going beyond the cloud providers or the people providing the infrastructure for AI to work, looking at the enterprises, I see healthcare and financial services adopting AI and seeing ROI from it.
We have already seen how enabling and accelerating the legacy transformation (with AI) is super critical for companies, helping them to be more agile and better serve customers.
One thing I did not mention earlier is about legacy modernization in the context of financial services. These systems are really old, and you don’t have people with the knowledge to manage them, and the tech debt on that is very high. So even solving that single problem of legacy modernization with AI has a significant benefit and return on the investments required to be able to get those returns. When you don’t have people, you don’t have the knowledge, so your ability for the business to change is very low. Creating that agility is invaluable for a business.
Finally, we are seeing good investments in manufacturing with edge AI and physical AI. That’s the next frontier I think that will come in. And we have some experience with distributed cloud, but I think edge AI is going to become a reality.
Abhay Mital: I really like the point you made about technical debt, because if you’re able to unlock resources in the legacy areas, that really unlocks the path forward for innovation and for investments.
Vijay Guntur: Absolutely. If you cannot do that, you’re slowing down the firm from progressing. I think enabling that legacy transformation and accelerating it is super critical for our customers. We’ve already seen how it enables firms to be more agile and better serve customers.
Managing productivity, scale, and change
Abhay Mital: What do you believe, based on your learnings, is the recipe for driving productivity improvement to scale?
Vijay Guntur: In software development and marketing front-office functions, productivity is significantly improving. The tech is there. Think about Copilot, AI Force—which is going beyond just coding—and beyond that to the entire development life cycle. We have agentic AI in IT operations, efficiencies. You can easily get productivity improvement in the mid-teens to the high twenties. It is probably a little lower in certain functions, but it is happening.
The ability to bring a lot of data together from different systems and make sense of it is driving this efficiency and productivity. I think the tech is not the issue. The key aspect of getting productivity is about how you manage change.
There are two kinds of change. One is the process change, because you should rethink the process and redo it with these tools. But beyond process efficiencies and redoing the process, it’s about people. How do you upskill people? How can they learn new skills? Some of these are hard skills, and some are soft skills.
How do you manage the productivity change? What do you do with those productive hours that you’re going to be saving? How do you put them to good use? I think those are important aspects. So while the tech has been able to prove and solve the efficiency puzzle, I think the bigger challenge in unlocking productivity will be change management, both on the process side and the people side of change management.
The bigger challenge in unlocking productivity will be change management.
By and large, when companies do not get what they want, it means they are not doing well on change management processes. Firms that have done it well have benefited from improved efficiencies. More than that, they have happier employees because their work is much more enriched. They are not doing the same things they were doing earlier because much of that has been automated.
Abhay Mital: Vijay, you spoke about change management and how that’s pretty much at the core of driving an AI-led transformation. How do you think about changing mindsets, behaviors, and capabilities within a given organization?
Vijay Guntur: See, when you are successful, it’s even more difficult to be able to change, right? We’ve been running this almost linear model for several decades, and the industry itself has succeeded and grown well using it.
That linearity is no longer going to be true in the future. So the mindset shift that organizations need is to break that linearity. It has worked in the past, but it’s not going to work in the future. So getting our organization ready, and, for that matter, any organization ready, to think about how to break this linearity and get to nonlinearity is super critical.
And part of change management is to think through that process and design incentives, behavioral modifications, interventions, and organizational capability to drive toward that nonlinearity in the business. I think it will take time, a few years, but I think we are on the path of getting it done.
Abhay Mital: In technology services, we regularly see very high numbers quoted on productivity improvement in pilots. So in some ways, the holy grail is how we build on that and deliver at scale. Are there any lessons that have helped you to do this at HCLTech?
Vijay Guntur: We are in the initial stages. We have done well with about six customers—improving productivity and methodically baselining the gains to show the productivity improvement at a smaller scale in pilots. We are scaling up now in those six, and then scaling to another hundred more such customer situations or accounts.
It’s easy to see returns in pilots because you have smaller teams of 50 to 100 people. But when you think of scaling this to a few thousand people, there are challenges.
Scaling up is feasible but requires a lot of hard work. We need to build people’s ability to look at the value chain, look at where the improvements are, and focus on that and get it to scale. So I think organizations require a lot of capability building. We have black belts, experts in value-stream management, in analysis, and in management coming in, building train-the-trainer programs to scale up the capability and capacity in the organization.
It’s easy to see returns in pilots because you have smaller teams of 50 to 100 people. But when you think of scaling this to a few thousand people, there are challenges. Education and capacity building are a large part of it; commitment and incentives are the other parts.
So it’s definitely possible. We are on the way. And we are very confident that we can build this.
Embedding responsible AI through the life cycle
Responsible AI is fundamental to bringing and deploying systems that can be trusted by users.
Abhay Mital: Vijay, responsible AI is a theme that comes up quite often when we discuss AI with various stakeholders. How do you think about it?
Vijay Guntur: Responsible AI is fundamental to bringing and deploying systems that can be trusted by users. Fundamentally, these systems think about aspects like security, ethics, and bias, and make sure that the right guardrails are put in place when we build.
When we test for them, we test for all the abilities to meet the requirements, some of which are within acts and legal requirements and some beyond as well. When you actually deploy them, monitor their usage, and check, are they working the way we designed them? It is through the life cycle of building the systems that responsible AI has to be considered and put in place.
Abhay Mital: I imagine one of the biggest challenges of the AI era that we’re living in is the implications on cybersecurity. Because if cyber was a priority in the past, now with the next level of data being unlocked for making decisions, it’s going to become even more important. How are you thinking about cybersecurity?
Vijay Guntur: When we think about responsible AI, cyber is part of that. How do we build, deploy, and monitor systems for cyber? Sophisticated tools are coming in as you deploy the systems, and many of our partners are building such tools and the technology. However, I also think AI is helping cyber become much more resilient. AI-infused cyber tools that monitor, report, and detect are growing much more advanced and sophisticated.
Across the spectrum, it’s always the one-step-ahead game that you play in cyberspace. AI is cutting it two ways: It is creating more risk, but it is also enabling cybersecurity to become more resilient and robust.
Across the spectrum, it’s always the one-step-ahead game that you play in cyberspace. AI is cutting it two ways: It is creating more risk, but it is also enabling cybersecurity to become more resilient and robust.
Abhay Mital: Vijay, thank you so much for this detailed interaction. HCLTech is certainly one of the companies leading the charge on AI, and we’re very grateful to you for joining us today. I wish you all the best for the exciting times ahead.
Vijay Guntur: Thank you for the opportunity to talk about how we are progressing with our journey on AI and helping our customers get the most value out of AI.
“Our firm is designed to operate as one—a single global partnership united by a strong set of values. We are equally committed to both sides of our mission: attracting and developing a talented and diverse group of colleagues and helping our clients create meaningful and lasting change.
From the C-suite to the front line, we partner with clients to help them innovate more sustainably, achieve lasting gains in performance, and build workforces that will thrive for this generation and the next.”
Please visit the firm link to site