This article was first published on The Economic Times GCCWorld.com. April 23 2026. Please click here to read the article.
GCC leaders have been asking us a question with increasing urgency: If AI can handle most of what our centre executes today within three years, what is the higher-value mandate only our team can own – and are we building toward it fast enough?
We call this the Redefined GCC not a leaner or optimized version of today’s centre but a fundamentally reimagined one. It is defined not by headcount but by the value each person creates. AI handles execution while humans own judgment and accountability. Governance becomes a competitive advantage. And every AI deployment produces a reusable pattern that makes the next one faster, cheaper, and more defensible.
The Redefined GCC is not a destination. It is a deliberate, phased transformation.
The ecosystem’s growth reflects expansion through new GCCs, new mandates, and new industries entering the model, but within established centres a different equation is emerging: if AI can deliver the same scope of work with 25 to 35 per cent fewer people in three years and 55 to 65 per cent fewer in five to ten years, what does your centre actually look like on the other side?
Drawing on our work with GCCs across multiple industries and geographies, a 20,000-member centre could deliver its current scope with 13,000-15,000 people by year three and 7,000-9,000 by year ten. These ranges vary by industry and functional composition. For example a BFSI centre with large transaction processing will likely compress faster than a technology product centre with predominantly engineering work. And this ten-year timeline may be conservative as every major AI capability milestone in the last three years has arrived ahead of predictions.
The critical addendum is that the transformed workforce could generate three to five times the economic value per person. This is not a story of decline, nor should these projections be read as a mandate for reduction. They describe a transformation in the nature of work. Centres that treat this shift purely as a cost cutting exercise may ultimately compromise their strategic relevance.
The 20,000-person illustration represents the mega GCC – centres that constitute just 5 per cent of India’s GCCs but employ nearly half the total workforce. The equation applies across the spectrum. A 10,000-person GCC follows a similar trajectory – roughly 9,400 by year one, 7,200 by year three, 3,800 by the decade mark at four to five times the value per person. A 5,000-person mid-size GCC, typically more engineering-heavy, compresses less dramatically: to approximately 4,800, then 3,800, then 2,200 at four to four-and-a-half times the value. An emerging 1,000 person GCC, often built lean from the start, adjusts gradually to 950, then 800, and ultimately 550, while delivering three to four times the value.
The common thread is structural. The roles that endure are judgment intensive. The roles that emerge, such as AI governance, agentic orchestration, and domain AI translation, are required regardless of centre size. The ratios may change. The architecture does not.
The transformation inevitably raises a question most centre heads would rather not confront. That is if a centre can deliver the same output with 55-65 per cent fewer people, why not bring it home? The thesis breaks down for the GCCs that matter. India already has over two million professionals in GCCs and millions more in its broader technology sector. As AI becomes embedded in enterprise work, this workforce will accumulate a depth of AI deployment experience that will be difficult for other geographies to match. And as AI compresses current execution, it expands the frontier governance across jurisdictions, continuous model evaluation, cross-enterprise AI orchestration. But these arguments do not make themselves. A leader who has not articulated them to headquarters risks having the conversation shaped without their input.
Use this foundation to take a clear pitch to headquarters, stating that the centre can deliver twice the output at 70 per cent of the cost within three years, provided it receives the mandate to architect rather than merely execute. Over a three year horizon, build three talent pipelines in parallel. Reskill talent from within through structured twelve month rotational programs. Hire selectively for capabilities that cannot be built internally, such as AI platform architects, agentic systems engineers, and AI governance specialists. Invest in workforce evolution with the same seriousness and discipline traditionally reserved for external hiring.
One dimension is more concerning than any other. Historically, every profession developed senior judgment through years of junior work. AI is now automating precisely that junior work, the repetitive tasks that once served as essential training grounds. If the bottom 30 percent of knowledge work is automated, the question becomes what the pipeline for the judgment workers that global capability centres will need in 2030 actually looks like.
The centres that deliberately design new apprenticeship models will hold a decisive advantage. Structured rotations, mentorship explicitly focused on judgment, simulated scenarios, and intentional exposure to complexity can replace what automation erodes. Those that do this well will build a talent advantage that compounds not just for years, but for decades.
These views are grounded in our work with GCCs across BFSI, manufacturing, technology, and life sciences. The shifts we describe reflect what is already happening, with AI tools automating the majority of routine operational tasks, developer productivity gains from coding assistants measured in production environments, and AI agents moving rapidly from proof of concept to deployment. Every major technology transition, from mainframe to client server, on premise to cloud, and manual to automated manufacturing, has followed the same three phase pattern of absorption, compression, and reconstitution. AI is moving faster because it affects cognitive work directly, but the pattern holds. Our near term projections reflect changes already in motion. The longer term outlook is deliberately illustrative, with the direction clear even if the precise pace is not.
Lastly, the GCC workforce of the future will be more specialised and dramatically more valuable per person, and the centres that lead this transformation will define what the next generation of global capability looks like. Every GCC in India was built on a promise of scale and efficiency. AI is now poised to fulfil that promise, while fundamentally reshaping the human talent required to deliver it. The GCCs that move first will rewrite their mandate, shifting from execution at scale to intelligence at the core. They will become the strategic brains of global enterprises.
The window to lead this transformation, rather than react to it, is measured in quarters, not years.
