India’s Global Capability Centres (GCCs) are rapidly pivoting towards artificial intelligence, but nearly 70% remain stuck at the pilot stage due to foundational gaps in data, infrastructure, and governance, according to a joint report released by Dell Technologies and Zinnov on Wednesday.
The report, titled ‘India GCCs 2030: From Capability Centers to Agentic Transformation Engines’, noted that while ambition remains high, approximately 55% of routine GCC work is currently exposed to AI-led automation, necessitating that nearly 60% of the workforce undergo reskilling by 2030.
The report’s findings are based on surveys and interactions with more than 50 senior GCC leaders across the banking, financial services and insurance (BFSI), retail, manufacturing, and software sectors.
According to the study, India currently hosts more than 2,100 GCCs employing 2.36 million professionals and generating USD 98.4 billion in revenue in FY26. Indian centres account for approximately 28% of global GCC AI talent, with over 1,200 units having established dedicated artificial intelligence and machine learning (AI/ML) capabilities.
Furthermore, 70% of GCCs have instituted a defined AI roadmap or charter, and 66% of leadership now ranks top-line business impact as a top priority for enterprise AI adoption.
However, scaling these initiatives into enterprise-wide production remains a major bottleneck.
“…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.
“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 report stated.
The report pointed out that agentic AI workflows consume significantly higher compute resources, requiring between 10,000 and 5,00,000 tokens per workflow, compared with 1,000 to 2,000 tokens for a standard chat interaction, making early infrastructure and cost-modelling decisions critical.
“The most influential GCCs of 2030 will not be measured by the number of AI initiatives they launch, but by their ability to industrialise AI responsibly and at scale… robust foundations across data, infrastructure, and governance will become the bedrock of enterprise innovation,” said Manish Gupta, President and Managing Director, Dell Technologies India.
