India’s Global Capability Centers (GCCs) are approaching a defining transformation. By 2030, their success will no longer be measured by cost efficiencies, talent scale or the number of AI pilots launched, but by their ability to industrialize AI, own business outcomes and influence global enterprise strategy, according to a new report from Dell Technologies and Zinnov.
Released at the Dell Technologies Forum 2026, the report, “India GCCs 2030: From Capability Centers to Agentic Transformation Engines,” draws on surveys and interviews with more than 50 senior GCC leaders across BFSI, retail, manufacturing and software. Its central message is clear: ambition around AI is high, but the foundations required to scale it remain incomplete.
India’s GCC Story Enters a New Phase
India today hosts more than 2,100 GCCs, employing 2.36 million people and generating $98.4 billion in revenue in FY26. The strategic importance of these centres is also rising, with 64% of GCC leaders holding dual global mandates—managing India operations while simultaneously owning global functions.
AI is accelerating this transition. Around 70% of GCCs already have a defined AI roadmap or charter, while India accounts for approximately 28% of global GCC AI talent. More than 1,200 centres have developed AI and machine-learning capabilities.
Importantly, 66% of GCC leaders now identify top-line business impact as a major priority for enterprise AI strategy. This signals a fundamental shift from using India primarily for efficiency and execution towards positioning GCCs as engines of innovation and business growth.
The AI Pilot Trap
Despite this momentum, nearly 70% of GCCs remain stuck at the AI pilot stage. The problem, according to the report, is not lack of ambition but inadequate foundations.
Fragmented data, legacy infrastructure, unclear governance, immature security controls and outdated talent models frequently prevent successful proofs of concept from becoming enterprise-scale deployments.
Production environments are considerably more complicated than controlled pilots. Data quality deteriorates, governance requirements increase and standalone AI applications become difficult to integrate with common enterprise platforms.
The economics also change dramatically at scale.
A standard AI chat interaction may consume approximately 1,000–2,000 tokens, while agentic workflows can require anywhere from 10,000 to 500,000 tokens per workflow. Compute, token consumption, tools, security and workforce reskilling therefore become critical business considerations.
Four Forces Will Reshape GCCs
The report identifies four major forces that could determine which GCCs emerge as transformation leaders.
First, GCCs must move from isolated AI experiments towards repeatable AI-enabled workflows embedded within business functions.
Second, AI architecture needs to be planned before production. Data readiness, infrastructure, compute, security, governance and economics can no longer be treated as separate decisions.
Third, GCCs must increasingly own products, markets, platforms and measurable business outcomes, rather than merely supporting global operations.
Finally, workforce models need fundamental redesign. With around 55% of routine GCC work exposed to AI-led automation and 60% of the workforce requiring reskilling by 2030, employees will increasingly need to move from repetitive execution towards engineering, product development and complex business problem-solving.
Infrastructure Becomes a Strategic AI Decision
One of the report’s key recommendations is that GCCs carefully determine which AI workloads they should own and which they should lease.
AI applications involving sensitive information, regulatory requirements, business-critical processes or predictable high-volume workloads may demand greater infrastructure control. Experimental and lower-risk workloads could remain better suited to flexible cloud or managed environments.
The report also proposes a Sovereign Sandbox model, allowing GCCs to experiment with regulated or proprietary data within controlled environments before moving those workloads into production.
From Capability Centres to Transformation Engines
“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,” said Manish Gupta, President and Managing Director, Dell Technologies India. He emphasized that data, infrastructure and governance will form the foundation for enterprise innovation.
Sidhant Rastogi, President, Zinnov, sees the GCC model reaching an inflection point where the next decade will be defined by ownership rather than simply scale, talent and capability.
The message for India’s GCC ecosystem is significant. The next competitive advantage will not come from launching another AI proof of concept. It will come from turning AI into secure, governed and economically sustainable production systems.
By 2030, the leading GCCs could therefore look fundamentally different from today’s delivery centres—evolving into Agentic Transformation Engines that co-create enterprise strategy, orchestrate autonomous workflows and deliver measurable global business outcomes.
