Not cheaper, just better: How AI is remaking India’s GCC industry

Manu singh chauhan

9 min read

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Aug 2, 2026

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AI was going to kill outsourcing to India. Instead, it’s multiplying it.

I keep running into the same headline in a dozen different forms: AI is coming for outsourcing, agents will do what analysts and coders do today, and the whole India offshoring story is about to unwind.

I don’t buy it. Not because I’m rooting for India, or I work in the indian Software industry, but because when you actually look at what’s happening on the ground, it’s closer to the opposite. The same AI that’s supposed to kill the GCC model is rebuilding it on a different foundation. Companies used to set up shop in India because it was cheap. Increasingly, they’re doing it because India has people who can actually do the work well, and they can’t find enough of those people anywhere else fast enough. That’s a different business, running on a different logic, and it happens to be growing faster than the old one ever did.

I went digging through the 2026 data to see if this thesis actually holds up, or if it’s just the story the industry likes to tell about itself. It mostly holds up. But there’s a real, uncomfortable undercurrent to it too, and I don’t want to skip past that part.

First, what’s a GCC anyway

If you’re not from this world: a Global Capability Center is an office a big multinational sets up abroad that it owns outright, staffed by its own employees, doing real work for the parent company. Not a vendor, not a call-center contractor, it is simply like a US bank or a hospital-tech startup or an airline literally opening its own engineering or data team in Bengaluru or Hyderabad.

India has been the biggest destination for these for about twenty years. What’s changing isn’t whether companies are doing this; it’s what they’re asking these centers to actually do once they’re there.

The scoreboard

I went through the Nasscom-Zinnov report published in May 2026, and here are some facts from that. Before I get into what any of it means, here’s the raw growth, five years ago versus now:

• Number of GCCs in India: ~1,600 in FY2021 → 2000+ in FY2026
• People employed across them: ~1.6 million → 2.3 million
• Fortune Global 2000 companies running a center here: 506
• Centers with real AI/ML work built into what they do: 1,200+
• Dedicated AI centers of excellence: 250+
• AI professionals working inside the ecosystem: roughly a quarter million

The number that actually stopped me, though, wasn’t any of those. It’s this: nearly half of the GCCs founded since 2021 were built with AI at the center of their mandate from the very first day.

Why is this happening now, specifically? A mix of things: the US/overseas hiring costs going up, general economic and political uncertainty, and a growing instinct among big companies that AI work involving their own data and models shouldn’t sit with an outside vendor. If a model is going to touch your customers and your reputation, you want it built inside a place you fully own. GCCs are exactly that. AI has made that kind of control feel less optional, and a lot of that work is landing in India.

How a GCC actually grows up

Not every center in India is doing cutting-edge AI work. Most of them are somewhere on a journey, and there’s a useful way to think about the stages of it.

You start as what researchers call an Outpost: a small, new, low-risk, basically a test to see if setting up in India makes sense at all. If it works, you become a Satellite: a real, functioning office running one or two solid functions, maybe finance ops or IT support, but still just carrying out a plan someone else designed. The next stage is a Portfolio Hub(Domain ownership model) — the center now owns whole products end to end, not just tasks handed down from headquarters. And at the top is a Transformation Hub, where the center’s leadership is actually shaping company strategy globally, not just running the India office well.

For most of this industry’s history, going from that first stage to genuine product ownership took about five to ten years. You had to earn it, slowly, year after year of proving you could be trusted with more.

That timeline has roughly halved. More than a quarter of new centers now reach real product ownership within five years. And it’s not just speed it’s the starting point. Look at where centers sit today across the whole landscape: only about 13% are still stuck in that old cost-cutting mode with barely any ownership. Another 43% have built solid delivery capability but are still mostly executing someone else’s roadmap. But 39% already own products end to end, and a genuinely surprising 5% have crossed into “setting company-wide strategy” territory. (* Data from internet)

So a center opening in India this year is starting somewhere much further along than one that opened ten years ago, and it’s likely to earn real trust within a few years instead of a decade. That compression, years of proving yourself collapsing into months, might be the single biggest structural shift in this industry right now, bigger than any one AI headline. One more data point that backs this up: nearly two-thirds of GCC site leaders in India now also carry responsibility for a global business unit, not just the local office. Decisions about AI strategy and product direction are increasingly being made in India, not handed down to it.

Cost, then skill, then capability

The industry has a name for its own evolution, and it’s easiest to understand through one example carried across all three stages.

Picture a US bank opening a Bengaluru office in the early 2000s. In its first form, that office exists purely to process claims paperwork more cheaply than doing it in the US. The work is repetitive, the mandate is narrow, and the whole pitch is: same output, lower cost. That’s cost arbitrage.

A decade later, the same office has grown into a genuine center of technical skill. It’s not just processing claims anymore; it’s building fraud-detection dashboards, running the analytics behind underwriting decisions, doing real engineering. It’s good at what it does. But it’s still executing a plan designed somewhere else. That’s skill arbitrage.

Today, that office might own the bank’s entire global AI-driven fraud-prevention platform: setting the roadmap, building and training the models, answering for business results rather than just uptime. This is capability arbitrage, and it’s where companies like Cohere Health, Walmart, McDonald’s, Vanguard, and DAZN already sit with their India teams. Four completely different industries, all using their India centers to own something core to the business rather than just support it.

A recent industry survey found only 15% of centers still operate in that first, pure cost-cutting mode. Half sit somewhere in the middle, still building skill. About 35% have already crossed into genuine ownership-driven work, which is roughly a full reversal of where the industry stood ten years back.

Why India, and not some other cheaper country? Because very few places can offer the whole range in one location: basic back-office work, deep engineering, AI research, which lets an organisation consolidate its entire value chain in one country instead of scattering it across five.

Why India, specifically

If AI is acting as a “multiplier” here, it’s worth being precise about what’s actually multiplying.

The West has a genuine shortage of senior technical people, and it’s getting worse, not better. Demand for people who can build with generative AI has grown roughly 300% year over year globally, and the hardest group to find anywhere in the world is the 8-to-15-years-experience band, people who can own an AI roadmap and actually talk to leadership about it. Everyone is fighting over the same small pool.

India’s own AI talent pool has grown large enough to be a real second base for companies, not just overflow capacity. There are now something like 500 thousand+ AI professionals in the country, making India the world’s second-largest employer of enterprise AI talent after the US, and its senior AI hiring is growing faster than America’s own.

Companies also want to start measuring value per person rather than just headcount. That’s a genuine shift in ambition. Whether the industry can actually prove this is happening yet, with real numbers, is a separate question, and it’s one worth getting into.

What’s actually true

Before AI, the only lever a GCC had for growth was hiring more people. Centers mostly executed what headquarters designed, success was measured in cost saved, and a GCC job was a wide-open door into stable, high-skill work for anyone coming out of engineering college.

That’s changing. Growth and headcount are coming apart, with AI absorbing the routine work while a smaller, more senior team owns much more. Centers increasingly hold real product responsibility, and most site leaders now carry genuine global authority. Success is meant to be judged by outcomes rather than headcount, even if the tools to prove that are still catching up. And that open door for fresh graduates is narrowing at the same time the door for experienced, AI-capable people is wider than it’s ever been.

So is the original thesis right? Mostly, yes, if you’re looking at the overall market, its money, and its most talented people. But it’s not evenly true across the whole industry or the whole workforce. I’d describe AI here less as a multiplier of the market as a whole, and more as a multiplier of the gap between the centers and the people who climb this ladder quickly, and everyone else.

What this means if you’re actually trying to act on it

If you’re running a GCC

Don’t let your center sit comfortably in the skill-building middle. That’s the layer most exposed to automation, and comfort there is a trap.

Fix your measurement problem before anything else. If your organization can’t tell whether its AI investments are working, that has to be the first thing you solve, and it means tracking how fast work actually ships and how many AI projects reach real production, not how many pilots got launched. Account for the real product or business impact, customer satisfaction score, system health, ideation-to-release window, etc.

Fight to keep your 8-to-15-year people. That band is the scarcest resource in the entire industry right now, and losing them is expensive in ways that don’t show up on a spreadsheet until it’s too late. Give them real ownership and a visible path to global roles.

Take AI governance seriously, not just AI tools. As rules around AI tighten worldwide, being known as a trustworthy, well-governed place to do sensitive work is going to matter as much as being fast or cheap.

Build real relationships with the software ecosystem and network. It’s not charity. It’s where your next good idea and your next good hire are probably going to come from.

If you’re building your career here

The skills worth chasing right now are more specific than “learn AI.” Generative AI and LLM engineering, MLOps (getting models to actually run reliably once they’re live), AI observability (knowing when a model is quietly failing), data engineering, and solid cloud and security fundamentals. These are the roles companies say they simply can’t fill fast enough.

If you already know a domain well- healthcare, finance, insurance, whatever- learning to work with AI inside that domain is probably a faster path forward than trying to become a generic AI engineer from scratch. Someone who understands both the field and the tooling is genuinely hard to replace.

Don’t overlook AI governance and oversight roles. Someone has to supervise these systems, check their work, and be accountable when they’re wrong, and that rewards judgment and communication as much as raw technical skill. It’s a real opening for people who don’t think of themselves as engineers.

For a senior engineer working in the industry for a long time, think beyond the code; understand the industry and customers; work on the challenging areas at your system scale; and try to use your best friend, AI, to solve genuine problems, improving SLOs, customer query lead times, system efficiency, etc.

If you’re fresh out of college, chase real project exposure over just a placement letter. Fewer entry-level roles exist now, and the bar for the ones that do exist has gone up, so internships and hands-on work matter more than they used to.

If you’re mid-career, treat reskilling as urgent rather than optional. A lot of people privately worry their current skills won’t hold up in a few years. That worry is worth acting on now instead of sitting with it.

Sources: Nasscom-Zinnov GCC Value Orbit report (May 2026); Hindu Business Line coverage of the FY26 Nasscom-Zinnov numbers; Financial Express, Economic Times, and Fortune India reporting on GCC displacement and hiring trends;