‘World’s Largest’ AI Hiring Hub, Yet No GCC in India is Actually AI-Native
By Sanjana Gupta ·
India’s global capability centres (GCCs) are building AI systems for the world, hiring AI talent at scale, and reshaping enterprise operations. The country now has 2,117 GCCs employing 2.36 million people and generating $98.4 billion in revenue in FY26.
More than 2.5 lakh AI and machine learning professionals work in these centres, giving India nearly 28% of the global GCC AI talent pool.
Yet, none of them qualifies as AI-native.
But ‘AI-native’ does not simply mean using AI tools within workflows. “It is a centre whose operating system has been redesigned around AI,” says Nilesh Thakker, President for GCC Business at Zinnov. “It rethinks the work itself: how decisions are made, how products are built, how risks are governed, how talent is organised, and how value is measured.”
This idea sits at the centre of the Nasscom-Zinnov GCC Landscape Report 2026, released this month. The report states that “0% of GCCs in India are AI-native yet”. Only 13% influence enterprise AI strategy beyond execution. Most still operate as delivery engines rather than decision-making centres.
Thakker says the bigger issue is no longer technical capability but organisational authority. “The real question is whether enterprises are ready to move decision-making closer to where the capability now sits,” he adds.
For an ecosystem often positioned as the backbone of enterprise AI transformation, the gap is notable.
The report describes GCCs as organisations “measured on outcomes delivered, not processes owned”. Yet, most Indian centres remain tied to execution mandates, even as AI becomes central to global business strategy.
The report estimates that nearly 75% of India’s GCCs could reach portfolio or transformation-hub status by FY2030. But that depends on changing the value proposition itself.
The Talent Paradox
India currently leads the world in AI hiring intensity among GCC markets. More than 1,200 GCCs have AI and ML capabilities, while over 250 run dedicated AI or ML centres of excellence.
The ecosystem has also changed faster than before. According to the report, 27% of new GCCs now reach portfolio maturity within five years. Historically, that transition took a decade. Nearly half of post-FY21 GCCs launched as AI-first operations from day one.
Yet, the ownership structure has not evolved at the same pace.
According to Zinnov, most GCCs were originally designed as execution engines built around engineering, analytics, and operational scale. AI changes that structure because it cuts across product strategy, cybersecurity, compliance, customer experience, and business decisions simultaneously.
The report notes that India remains strongest in “applications, model engineering, chips”, while capabilities around model research, infrastructure, and energy still lag global peers. That has created a layered AI economy. India builds and deploys enterprise AI systems, but foundational research and strategic control still sit elsewhere.
Nasscom and Zinnov mention in the report that “becoming AI-native is an operating-model challenge, not a technology one”.
That distinction matters because many GCCs already possess the technical workforce needed for AI deployment. What is lacking is the authority over enterprise-wide AI governance, platform ownership, and business outcomes.
“The biggest gap is authority,” Thakker says. “Today, many GCCs are building the AI platforms, data systems, digital products, and automation engines that global enterprises depend on. But in several organisations, the big calls on architecture, standards, governance, and commercial priorities are still made elsewhere.”
AI is Moving Faster
The report also points to growing strain inside GCC workforce structures.
About 73% of Chief Human Resources Officers surveyed said AI disruption is outpacing workforce readiness. Technical skill half-life has collapsed from 10 years to between two and five years. Around 60% of workers will require reskilling by 2030, while nearly 18% may not receive training at all.
This pattern is already visible in hiring trends.
Several GCCs have slowed junior hiring and increased redeployment of existing staff. AI-focused roles have grown as a share of open positions in the past six months, while non-AI hiring has shrunk. “Backfilling requirements using existing talent, supported by AI tools, is the new default,” the report states.
The operating structure is also changing rapidly. GCC adoption of hybrid AI operating models rose from 35% to 70% between August 2025 and February 2026.
Thakker believes enterprises are moving towards hybrid AI structures because centralised AI teams have become too slow for enterprise-wide deployment. “Central teams continue to own governance, security, platform standards, and risk controls, while GCCs and business units build AI solutions closer to where the operational context and domain expertise actually exist,” he says.
That pace suggests companies are redesigning workflows around AI faster than organisational systems can adapt. Moreover, leadership roles are shifting. Around 64% of site leaders now hold dual mandates that combine global business ownership with local centre leadership.
Still, the report argues that many GCCs continue to operate as capability centres rather than transformation hubs.
Offshore Support to Enterprise Nerve Centre
The report frames the current phase of the GCC industry as an inflexion point.
“What began as a pursuit of cost arbitrage and operational support has evolved into a sophisticated orchestration engine,” the report states. It added that Indian GCCs are “increasingly positioned as primary architects of enterprise strategy”.
That transition appears visible in business structures and commercial models.
Traditional time-and-material contracts are giving way to value-share and gain-share models. New GCC service-provider structures, such as embedded pods, co-innovation setups, and value-based managed services, are also emerging.
Structurally, GCCs are also shifting from siloed delivery teams towards cross-functional pods that combine product engineers, data scientists, cybersecurity teams, governance leaders, and business owners. According to Zinnov, the most advanced GCCs are increasingly owning enterprise platforms, reusable AI assets, governance frameworks, and model performance systems.
The ecosystem around GCCs has widened, too. Between 90% and 95% of leading GCCs now partner with universities, while up to 85% work with skilling platforms. Around half co-innovate with startups.
At the same time, physical expansion continues. GCC office space is projected to grow from 263 million square feet to more than 350 million square feet over the next few years. GCCs accounted for 40% of India’s office leasing over the last decade.
The report calls for a shift from “talent volume to expertise density” and from “an offshore node to the locus of enterprise architecture”.
That gap explains the paradox shaping the GCC sector today. According to Thakker, the larger shift is philosophical as much as technological. “GCCs can no longer be treated as downstream execution engines in an AI-first enterprise,” he says. “They are increasingly becoming part of the enterprise brain itself.”