Technology does not give competitive advantage;
it is leadership…
Jerry Jose
Chief – Human Resources | ICICI Lombard
The most important lesson from the rapid developments in AI is that business models are being disrupted. The value chain is getting re-engineered and there are entirely new ways of doing business. The rate of change makes it so difficult to predict the future. As the saying goes, the good thing about the future is that it comes one day at a time — and that gives you a sense of control.
At ICICI Lombard, our approach has been to keep long-term objectives firmly in focus while building short-term and medium-term capabilities that help us get there, constantly course-correcting as we go. The first big question we keep asking is whether we have adequate in-house tech and digital talent — and how well they are integrated with the business. We deliberately build data scientists and integrate them with business teams because the people inside the business are the ones who will spot opportunities and understand what is truly possible.
The second imperative is raising digital fluency across the entire organisation. Most people are not yet adept in the new digital environment, and we have to make it natural for them to communicate, collaborate and do their day-to-day work using digital tools and data. A business team that sits on a mountain of data cannot outsource the thinking to someone else. The third area is about the irreplaceable value that humans bring — strategic thinking, critical problem-solving, and the ability to connect dots. And underpinning everything is integrity and ethics. We manage other people’s money in a regulated economy; that responsibility cannot be delegated to a machine. These are time-tested skills that remain relevant at any point in time.
Ultimately, the future belongs to what I call a co-pilot economy — talent, leadership, and technology working hand in hand. Technology per se does not give you competitive advantage; it is leadership that builds a clear point of view on how technology can be leveraged for differentiated outcomes. I would always place talent and leadership as the fountainhead, with technology as the essential enabler. It is like a lemon-and-spoon race — you need to win the race, but with the lemon balanced in your spoon.
Does AI output translates into the right success
measure?
Parvathi Karthik
Senior Consultant | Tata Consultancy Services,
Chennai
At TCS, our CEO has articulated our aspiration to be the world’s largest AI-first tech services company, and we are well on that journey. To understand where talent is headed, it helps to distinguish clearly what AI will take over from what it cannot yet do.
Four skills that remain highly relevant today are already being overtaken by AI. The first is coding — AI is catching up extremely fast on development, maintenance, and support. The second is production support and ticket management; these execution tasks will be substantially automated. The third is data analysis — reports and dashboards will be comprehensively handled by AI. The fourth is content creation, synthesis, and compilation. None of this means opportunity is shrinking. Opportunity is becoming a force multiplier.
The skills of the future are three: managing ambiguity, because business and customer systems are large and partially integrated and the ability to look at complex architecture and arrive at simple technical constructs will become the mainstay; judgment, because when sophisticated AI tools hand you data, the journey from inference to insight to decision is a distinctly human capability; and systems thinking, because if AI is improving your value stream, you must be elite by design and capable of crafting the right experience for the end user. Computer science graduates will still be central — but their role will be to question AI, review AI-generated code, and demand explainability. On the question of how organisations preserve human judgment in an AI-assisted world, the answer is to shift focus from output to outcome. AI can deliver a near-perfect output; the question is whether that output translates into the right success measure. At TCS we ran an innovation contest where AI shortlisted applications, but our CTO instructed that the AI output not be shared with the jury in advance. The jury evaluated independently. Only when both sets of results were on the table did we bring mismatches to dialogue. That is how you use AI as an aid to sharpen the process while keeping human judgment as the final checkpoint before a decision is taken.
Career as a safety net has disappeared
Shilpa Rangaswamy
Consultant | Egon Zehnder
I started my career at TCS writing code more than twenty years ago, and for the longest time going back to code was my backup profession — something I was good at and enjoyed. Over the last couple of years, that safety net has disappeared, which is a question I suspect many in this room are sitting with.
When we talk to boards about the kind of organisations and leaders they are looking to build, the phrase we keep coming back to is sensing organisations. The industrial revolution built organisations for efficiency. The strategic era demanded people who could predict the future. Today, every day is genuinely new, and the ability to sense what is around the corner and adapt to it quickly is the defining organisational capability. That is one trait.
The second trait is eternal curiosity — the push to keep the learning muscle constantly engaged, both on things that make you effective at work and on things entirely unrelated to work. The era of finishing your formal education and considering your learning complete is gone. Every few years, deliberately pick something you are quite terrible at and get better at it. Research consistently shows that learning outside your domain makes you perform better inside it.
On whether the well-rounded leader is overrated, I think deep technical or subject-matter expertise will remain essential — AI as we define it today will take some time to match it. The T-shaped leader model still holds and matters even more now because that depth is where you generate genuine delta as a leader. But the second horizontal bar that matters now is people leadership in its fullest sense — empathy, servant leadership, stress management, the ability to rally people around a goal. That emotional capacity is what AI cannot replicate, and it is where leaders create disproportionate value.
Why tend toward refining, repeating what works?
Hariharan Srinivasan
Chief People Officer | Hexaware
Having relocated from the United States to India eight months ago, the most striking observation I can share is that India’s adoptability is significantly higher than what I have seen in other parts of the world. Three reasons stand out. First, there is a deep cultural instinct for learning — we are raised with it from childhood. Second, the curiosity quotient here — the hunger to learn, adopt, and explore new things — is exceptionally high. Third, the demographic reality: the talent pool currently in employment is young, energetic, and genuinely excited by new possibilities.
The blind spot I would flag is the mindset shift from services to innovation. When you walk into the Bay Area, you find teams in every coffee shop brainstorming new products, animated by a hunger to build something from scratch. I want to see more of that in India. We tend toward refining and repeating what works, when the moment calls for original creation — across every industry, from insurance to manufacturing.
On how we build talent at Hexaware, we have moved away from hiring for niche skills at the point of entry. What we look for is the right aptitude to learn and the willingness to adapt — that is the critical hiring metric. Once people are in, we invest substantially in development. Our programme called Clash of Titans rewards associates who learn not just within their domain but in areas they do not ordinarily use. We attach both monetary recognition and career opportunity to that cross-domain learning. The output is then channelled through our Customer Value Awards, which recognise learning that directly adds value in the customer environment — ensuring knowledge translates into action.
Q&A
Q What are the most common blind spots boards and CEOs have when planning leadership for an AI-driven future?
— Shilpa Rangaswamy
Two blind spots stand out consistently across sectors and geographies. The first is underestimating the pace of change — leaders frequently misjudge how quickly AI will alter the competitive landscape, and more often than not they underestimate rather than overestimate it. The second is failing to do the deep thinking required to separate the bells and whistles of AI from what it will genuinely enable in a differentiated way for their specific organisation. There is also an inherent structural challenge: most leaders are wired to operate in a definitive if-then-else mode, and when two or three layers of ambiguity enter the picture, there is a real risk of paralysis — an unwillingness to commit capital to a bet they cannot fully map. Building comfort with ambiguity, and doing so visibly from the top, is the third capability gap we most often see.
Q What core skills will remain future-proof despite rapid technological disruption?
Several skills are being reinforced rather than eroded by the AI era. Adaptability and resilience are paramount — as Darwin observed, survival and success belong to those who adapt, not simply to the strongest or most intelligent. Architecture and core engineering domain skills will endure; they will be assisted by AI tools but the conceptual depth required to evaluate integrated cloud architecture, identify loopholes, and drive legacy modernisation is distinctly human. The ability to apply data in new scenarios is another durable skill, as organisations work through transforming operational models. Underlying all of this is the organisational capability to sense change early and respond with disciplined purpose — to not merely survive disruption but to win through it.
