India’s Global Capability Centres (GCCs) are no longer just low-cost execution hubs supporting global headquarters. Increasingly, they are becoming the nerve centres where enterprises are designing, testing and scaling artificial intelligence systems for global operations.

That shift, according to Nasscom president Rajesh Nambiar, is changing how multinational companies view their India operations, from cost centres to strategic growth engines.

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“AI is no longer operating as a standalone technology project inside these centres. It is being embedded into the core operating fabric of enterprises across products, internal workflows and customer-facing functions,” Nambiar told Business Today in an interview. 

The transformation comes as enterprises globally move from experimenting with AI to deploying it across core business operations.  

GCCs move from execution to ownership

Nambiar said three structural shifts are driving this transition.

The first is governance. “About 64% of GCC site leaders now hold dual mandates that combine global business ownership with India site leadership,” he said. 

The second is the nature of work itself. GCCs are no longer focused purely on back-office execution or technology support. Instead, they are managing enterprise platforms, AI governance frameworks and customer-facing outcomes.

Must read: AI is now the 'foundational operating system': Nasscom President Rajesh Nambiar

The third shift is the scale of India’s AI talent ecosystem. According to Nasscom, India now has more than 1,200 GCCs with AI and machine learning capabilities, supported by over 250 AI and ML Centres of Excellence and more than 250,000 AI professionals. That gives India nearly 28% of the global GCC AI talent pool, second only to the US. 

“As of FY26, India hosts an estimated 2,117 GCCs operating across 3,728 units, employing nearly 2.36 million professionals, with the ecosystem valued at an estimated $98.4 billion,” Nambiar said. 

According to Nambiar, what stands out even more is how newer GCCs are being designed.

“Nearly 50% were AI-first from day one,” he said, referring to GCCs established after FY21. 

Engineering layer of the global AI stack

While many global AI models continue to be developed by hyperscalers and frontier AI labs, Nambiar argued that India is increasingly becoming the application and deployment layer where enterprise AI gets operationalised at scale.

“The conversation inside GCCs has shifted from building AI to scaling it,” he told Business Today. 

According to him, AI capabilities are now deeply embedded into enterprise workflows ranging from fraud detection and compliance to pricing optimisation, customer operations, and demand forecasting.

Must read: India’s GCCs move from cost centres to AI-powered command hubs as policy clarity boosts enterprise confidence

“India is also increasingly becoming the application and engineering layer of the global AI stack, with strong capabilities in applications, model engineering, semiconductors, and enterprise AI deployment,” Nambiar told Business Today . 

He added that India-based teams are now contributing to enterprise-wide AI governance standards, platform strategy, and key technology decisions instead of simply implementing instructions from overseas headquarters. 

Nambiar pointed to examples across industries where India-based GCCs are taking end-to-end ownership of AI systems that are eventually deployed globally.

In banking and financial services, India teams are building AI-driven underwriting, compliance, risk analytics, and customer intelligence systems. In retail, GCCs are developing AI-led pricing and inventory optimisation platforms that are being rolled out across international markets. Meanwhile, telecom GCCs are building GenAI-powered transcript analytics and autonomous service workflows to improve customer operations globally. 

The shift is also visible in semiconductors and industrial engineering.

“India teams are contributing to AI-accelerated chip design, predictive maintenance systems, and smart manufacturing platforms that are increasingly becoming central to global product roadmaps,” Nambiar said. 

GCCs are no longer being measured only on cost

The rise of AI inside GCCs is also changing how multinational companies measure value creation.

Traditionally, India's GCCs were judged largely on cost efficiency and operational scale. But AI deployments are now tying these centres directly to business growth, product innovation, and customer outcomes.

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“GCCs are not being evaluated on how much work they execute, but on the enterprise value they create,” Nambiar told Business Today. 

According to him, mature GCCs are increasingly operating with scorecards linked to business outcomes such as productivity gains, customer retention, innovation throughput and speed-to-market.

“The more advanced centres are becoming strategic value creators and, in many cases, direct enablers of enterprise growth and competitiveness,” he added. 

AI hiring shifts toward specialised and flexible talent

The AI boom is also reshaping hiring patterns across GCCs.

Nambiar said enterprises are prioritising redeployment and AI-led productivity gains over aggressive linear headcount growth, even as demand for specialised AI talent continues to rise.

“AI hiring across GCCs continues to intensify with demand for AI-centric skills increasing to 1.5 percentage points in the past six months alone,” he said. 

Must read: Why India’s GCCs are hiring fewer full-timers and more AI contractors

The most sought-after roles now include AI and ML engineers, MLOps architects, prompt engineers, AI security specialists, RAG architects, and Agentic AI orchestration experts. 

At the same time, GCCs are increasingly moving toward flexible workforce structures that combine a stable core AI team with agile specialist talent deployed for fast-moving projects.

“This shift is less about uncertainty and more about adapting to the pace of AI evolution,” Nambiar told Business Today. 

“What is emerging is a more flexible workforce architecture, a stable core of strategic AI talent supported by agile specialist teams that allow enterprises to build, test, iterate, and scale AI initiatives much faster,” he added.