Indian GCCs are entering a new phase of enterprise relevance. They have already proved they can deliver at scale. What their enterprises need now is help making the right decisions on AI, data, infrastructure, governance, talent, and market accountability before the next wave of mandates arrives.
That window is narrowing quickly. GCCs are reaching higher levels of maturity faster than before: 27% of the ~500 GCCs set up in the last five years already operate at Portfolio Hub maturity, the stage at which a center owns entire processes, software platforms, and commercial products.
Historically that took nearly a decade. AI is accelerating the curve further. Indian GCCs already account for ~28% of global GCC AI talent, and 1,200+ GCCs have built AI/ML capabilities, giving them a stronger platform to shape enterprise AI agendas rather than simply execute them.
Scale, however, is not the same as readiness.
Most AI adoption today sits at Individual Productivity, where AI helps people perform existing work faster without changing the underlying workflow. The larger opportunity lies at Enterprise Productivity, where AI redesigns end-to-end workflows, and beyond it at Frontier Capability, where AI enables products and operating models that were not previously practical. Each step makes the requirements for running AI in production significantly more demanding.
This is why Global Capability Centers (GCCs) are being asked to do more than run pilots.
They are expected to industrialize AI, embed it into core business processes, and support production-grade systems that are secure, cost-efficient, resilient, and measurable.
The relevant measure is therefore not the number of pilots or the volume of AI activity, but the business outcome delivered and the full cost of delivering it reliably at scale. Yet nearly 70% of GCCs remain stuck at the pilot stage, unable to move promising proofs of concept into sustained enterprise adoption.
The constraint is almost always the foundation. AI pilots stall when the underlying environment is not ready: fragmented data, legacy systems, unclear governance, immature security controls, insufficient compute planning, and talent models designed for pre-AI ways of working.
At the same time, AI is already reshaping the work GCCs perform, with ~55% of routine GCC work such as QA, standard coding, and routine delivery exposed to AI-led displacement. That makes workforce redesign a leadership priority rather than a downstream HR intervention.
As usage scales, the economics change as well. Reasoning models executing a multi-step chain can consume 10-1,000X more tokens than a standard chat interaction. Model selection, overlapping AI tools, infrastructure utilization, monitoring, support, compliance, and reskilling all materially impact the cost of running AI at scale.
Leaders must match models to workloads, avoid tool sprawl, and continuously optimize workflows.
The next phase of GCC leadership will therefore be defined by a more disciplined operating question: which AI workloads should GCCs own, which should they lease, which should they test in controlled environments, and where should each workload run as its usage, sensitivity, and business criticality change?
GCCs will need to reassess workload placement continuously as these factors evolve. These choices, rather than a generic AI adoption agenda, will determine how GCCs balance speed, control, cost, risk, and enterprise value.
For GCC leaders, five priorities stand out:
By 2030, the most influential Indian GCCs will be the ones that made the right foundational choices early, scaled the right workloads deliberately, and became trusted co-authors of enterprise transformation. Those choices are being made now, in centres where the mandate has already arrived and the foundation is still being built.
This whitepaper sets out what that foundation requires at each stage of maturity, why AI initiatives stall between pilot and production, and how to decide which workloads to own, lease, or test.
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