India’s GCCs Face AI Shift as Nearly 70% Remain Stuck at Pilot Stage: Report

Indian GCCs are moving towards a more strategic role in enterprise AI, but nearly 70% remain stuck at the pilot stage due to gaps in data, infrastructure, governance and talent, according to a new report by Dell Technologies and Zinnov.

India currently has more than 2,100 GCCs employing 2.36 million people and generating $98.4 billion in revenue in FY26. The report found that 70% of GCCs have a defined AI roadmap or charter, while more than 1,200 centres have already built AI and machine learning capabilities. Indian GCCs account for around 28% of global GCC AI talent.

However, the report said the rapid adoption of AI is exposing structural gaps within these centres. Fragmented data, legacy systems, unclear governance, immature security controls and workforce models designed for pre-AI operations are preventing many AI proofs of concept from reaching production.

The economics of moving from pilots to production are also changing with the rise of agentic AI. According to the report, agentic workflows can consume between 10,000 and 500,000 tokens per workflow, compared with 1,000 to 2,000 tokens for a standard chat interaction. This makes decisions around compute, infrastructure, tooling and workload architecture increasingly important for GCCs.

The report identifies four areas that could reshape the GCC operating model: embedding AI into core business functions, planning infrastructure and architecture ahead of production, taking ownership of products and markets, and redesigning roles as AI automates repetitive work.

Workforce transformation is expected to be another major pressure point. Around 55% of routine GCC work is exposed to AI-driven automation, while 60% of the workforce will require reskilling by 2030, according to the report. It argues that GCCs will need to move beyond incremental AI training towards broader changes in roles and responsibilities.

The report also recommends that GCCs make infrastructure decisions based on the nature of individual AI workloads. Sensitive data, regulatory requirements, business-critical processes and predictable high-volume workloads may require greater infrastructure control, while lower-risk or exploratory workloads could be handled through leased or managed environments.

“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,” said Manish Gupta, President and Managing Director, Dell Technologies India.

Dell Technologies and Zinnov have also proposed a ‘Sovereign Sandbox’ model for GCCs experimenting with regulated or proprietary data. The model is intended to provide a controlled environment for experimentation before workloads move into production.

“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,” said Sidhant Rastogi, President, Zinnov.

The report also noted that the GCC maturity cycle is shortening, with 27% of new GCCs reaching Portfolio Hub maturity within five years, compared with nearly a decade historically.