Divesh Agarwal, Founder & CEO, Aumni .
India's pitch to global corporations was almost embarrassingly easy to make for almost a decade. Deep talent pools, cost efficiency, and engineers who would outwork and out-deliver their counterparts almost anywhere else in the world. Global capability centres grew steadily, headcounts scaled, and somewhere along the way, India quietly became the backbone of the world's enterprise technology infrastructure.
That story is not wrong. It is just no longer the whole story. The organisations still competing on the same value proposition they used in 2018 are now losing ground, not dramatically or all at once, but steadily, and in the places that matter most.
The 400K Number Nobody Is Fully Celebrating
Reports project that India's GCC ecosystem will add over 400K jobs in 2026, and on the surface, that sounds like a continuation of a success story. But spend any real time inside the hiring cycles of mid-to-large GCCs and a very different picture starts to emerge.
The roles that are genuinely hard to fill, specifically AI engineers, cloud architects, platform engineering leads, and ML infrastructure specialists, are not sitting in a readily accessible pipeline waiting to be discovered. They are being contested simultaneously by Big Tech, well-funded AI-native startups, and global consulting majors who have long figured out that the best technical talent in India does not make career decisions on cost alone. Across the industry, the average time-to-close for senior AI roles has increased meaningfully compared to two years ago. Not because demand has softened, but because the supply of truly AI-native engineers who can operate inside complex product environments from day one is genuinely thin. Every organisation competing for that talent is fishing in the same small pond.
What AI-First Actually Demands
The phrase "AI-first" has been used so liberally that it has started to mean almost nothing. An AI-first organisation does not hire a data scientist and ask them to also explore some GenAI use cases on the side. That is AI-adjacent thinking dressed up in AI-first language. What it actually requires is a complete reconception of what a role is, what problem it exists to solve, what outputs it is accountable for, and what tools it is expected to work with natively from the very first week.
Tools like Claude, GitHub Copilot, AI-assisted testing, and modern automation frameworks aren't experiments to introduce after the first quarter. They're standard practice from day one. The way a good engineer shows up to work.
The engineers worth building pipelines for aren't the ones who can explain how a large language model works on a whiteboard. They're the ones who can scope an undefined problem, pick or adapt the right tools, ship something meaningful under a compressed timeline, and course-correct fast when the ground shifts.
The Talent War GCCs Are Quietly Losing
Most GCCs are still running hiring processes designed for a different era. Timelines are long, compensation benchmarking lags the actual market by six to twelve months, and the career narrative being offered lands poorly with engineers who have the option of joining an early-stage AI company where their work is more visible, their scope is wider, and their sense of ownership is real.
What tips it is harder to quantify. Industry data shows senior AI candidates hold an average of three to five competing offers at the point of a GCC interview, a dynamic that fundamentally shifts the negotiation power in the room.
The real question these candidates are asking is not about the salary band. It is whether the work will matter. Whether they will be close enough to the actual business problem to see their decisions land. Whether they will have genuine agency over how something gets built, or whether they will spend their first year executing a roadmap that was finalised before they joined.
These are not unreasonable demands. They are exactly the questions the best engineers should be asking. The trouble is that most GCC hiring processes are not designed to answer them. The job description talks about the role. The interview tests for the skill. Nobody in the room is making the case for why this particular problem, at this particular organisation, is worth the best years of a senior engineer's career.
The GCCs that are closing this gap are not doing it with better offer letters. They are doing it by moving AI mandates closer to the core business, giving their teams real scope, and shortening the distance between what gets built in India and what actually changes for the business globally.
The Window Is 2026
The organisations that will define the next chapter of India's GCC story are not going to be the ones that hire the most aggressively. They are going to be the ones that hire most intentionally, rebuilding job architectures around AI-native workflows, expanding talent pipelines beyond LinkedIn searches, and treating employer brand as a product that requires the same rigour as anything else they ship.
The AI talent market in India right now is a seller's market, and the best candidates are choosing organisations that treat AI as a core capability and not a cost-reduction exercise in disguise. India has the talent, the infrastructure, and the institutional momentum to lead the world's next wave of high-value technology work. Whether that potential is realised depends entirely on the choices organisations make this year.
Divesh Agarwal is the Founder and CEO of Aumni Techworks, a GCC solutions firm helping US, UK, and European product companies build dedicated AI-native engineering teams in India, with over 20 global clients and approximately 400 professionals in Pune.
