Dell Technologies, in partnership with Zinnov, released a report at the Dell Technologies Forum 2026 that charts the next phase of India’s Global Capability Center evolution. Titled “India GCCs 2030: From Capability Centers to Agentic Transformation Engines”, the report draws on surveys and interviews with more than 50 senior GCC leaders across BFSI, retail, manufacturing, and software sectors, concluding that the GCCs poised to lead by 2030 will not be those running the most AI pilots, but those that have built the foundations to scale AI into measurable enterprise outcomes.
“The most influential GCCs of 2030 will not be measured by the number of AI initiatives they launch, but by their ability to industrialize AI responsibly and at scale. As they take on greater strategic ownership, robust foundations across data, infrastructure, and governance will become the bedrock of enterprise innovation. The GCCs that build these capabilities now will define how their organizations harness AI globally, and Dell Technologies is focused on enabling that journey from foundation to transformation,” said Mr. Manish Gupta, President and Managing Director, Dell Technologies India.
“The GCC model is reaching an inflection point. For the last two decades, the conversation was largely about scale, talent, and capability. The next decade will be about ownership. As AI and agentic systems become embedded into enterprise workflows, GCCs will increasingly be expected to own products, platforms, markets, and measurable business outcomes. Those that build the right data, technology, governance, and talent foundations now will move from being capability centers to becoming true transformation engines for the enterprise,”said Mr. Sidhant Rastogi, President, Zinnov.
A Sector at an Inflection Point
India hosts over 2,100 GCCs employing 2.36 million people and generating $98.4 billion in revenue in FY26. 64% of GCC leaders hold dual global mandates, running the India centre while owning a global function. 70% have a defined AI roadmap or charter, and Indian GCCs account for approximately 28% of global GCC AI talent, with over 1,200 centres having built AI and machine learning capabilities. Significantly, 66% of GCC leaders already rank top-line business impact as a high priority for their enterprise AI strategy, signalling that the conversation has moved well beyond cost and delivery.
The maturity curve is also compressing. 27% of new GCCs now reach Portfolio Hub maturity within five years, compared with nearly a decade historically. AI mandates are arriving earlier in the journey, and capabilities once expected at advanced stages are now becoming requirements at earlier ones.
But scale and ambition alone are not enough. Nearly 70% of GCCs remain stuck at the pilot stage, unable to consistently move promising proofs of concept into sustained enterprise adoption. The constraint is foundational: fragmented data, legacy systems, unclear governance, immature security controls and talent models built for a pre-AI world.
The Pilot Problem and What It Will Take to Solve It
AI pilots stall for structural reasons: production data is messier than controlled environments, governance is addressed after the fact rather than built in, and use cases developed outside common enterprise platforms are difficult to integrate at scale. The economics shift significantly once token consumption, compute, tooling and reskilling costs are factored in at production volume. Agentic workflows can consume between 10,000 and 500,000 tokens per workflow, compared with 1,000 to 2,000 for a standard chat interaction. GCC leaders who do not make workload-level infrastructure decisions early often find themselves managing a budget problem rather than a business outcome.
Four Forces Reshaping the GCC Operating Model
The report identifies four levers that will define the next phase of GCC evolution. Building functional AI capabilities means moving beyond scattered experiments to repeatable, AI-enabled workflows embedded into core business functions. Planning AI architecture ahead of production means treating data readiness, compute, security, governance and economics as one integrated decision rather than a sequence of separate ones. Owning markets means taking genuine responsibility for products, regions and business outcomes rather than supporting them from a distance. And redesigning the workforce means restructuring roles so talent moves from repetitive execution toward engineering, product and business problem-solving where human judgment creates lasting value.
With 55% of routine GCC work already exposed to AI-driven automation and 60% of the workforce requiring reskilling by 2030, the imperative is not incremental AI training but a fundamental reinvention of work itself.
Own It or Lease It: Making the Right Infrastructure Call
One of the report’s most actionable contributions is a framework for deciding which AI workloads to own and which to consume through leased or managed environments. Workloads involving sensitive data, regulatory exposure, business-critical processes or high and predictable usage increasingly require greater control. Lower-risk and exploratory workloads may be better served through flexible, leased models. The report also introduces a Sovereign Sandbox model for GCCs that need to experiment with regulated or proprietary data in a contained environment before those workloads progress toward production. As agentic AI moves from experimentation to always-on enterprise operation, getting these infrastructure decisions right early is what will determine whether GCCs can run autonomous workflows at scale or remain constrained by the foundations they failed to build.
Covered By: SMART SOLUTIONS WORLD / Dell Technologies
If you have an interesting Article / Report/case study to share, please get in touch with us at editors@roymediative.com, roy@roymediative.com, 9811346846/9625243429
