India’s Global Capability Centres (GCCs) are increasingly positioning themselves as strategic transformation engines that can embed artificial intelligence (AI) into corporate strategy, but many remain unable to move pilots into full production owing to weak technical foundations, a joint report by Dell Technologies and Zinnov has said.
GCC growth, scale and financial footprint
The report, titled "India GCCs 2030: From Capability Centers to Agentic Transformation Engines", draws on input from more than 50 senior enterprise leaders across sectors including banking, financial services and insurance (BFSI), retail, manufacturing and software. It describes a rapidly maturing ecosystem that is already a significant commercial pillar for India.
Key headline figures from the report include:
- Over 2,100 operational GCCs in India.
- 2.36 million professionals employed by these centres.
- $98.4 billion in annual revenue in FY26.
- 28% of the world’s GCC AI talent resides in India.
- More than 1,200 facilities running active machine learning capabilities.
| Metric | Reported figure |
|---|---|
| Operational GCCs | Over 2,100 |
| Employees | 2.36 million |
| FY26 revenue | $98.4 billion |
| Share of GCC AI talent | 28% |
From proofs of concept to enterprise production
Despite the broad adoption of AI roadmaps—about 70% of centres have a defined AI plan—the report cautions that a large majority remain in a so-called "pilot trap". The study attributes this not to a lack of ambition but to shortcomings in architecture: data silos, legacy systems, governance that is bolted on after the fact, and talent structures that predate generative AI approaches.
While many GCCs are experimenting heavily with machine learning and AI proofs of concept, the authors argue that the next phase will belong to centres that can operationalise AI at scale and tie it directly to top-line outcomes. The report notes that two-thirds of centres now prioritise direct revenue impact over classic cost-saving objectives.
Another measure of accelerated maturity: 27% of newly established GCCs reach what the report calls "Portfolio Hub" maturity in under five years—a milestone that previously took up to a decade.
Technical and economic friction points
The study highlights several technical and economic frictions that slow down the transition from pilot to production:
- Persistent data silos and legacy architectures that inhibit model training and deployment.
- Post-hoc governance approaches that struggle to keep pace with rapid AI adoption.
- Talent models created before the AI era, which may not align with needs for model ops, data engineering and responsible AI practices.
The report also draws attention to operational economics changing as systems move from simple chatbot-style prompts to more complex, autonomous agent-style workflows. It gives an example that a standard chatbot query consumes on the order of 1,000–2,000 tokens, while more complex agentic workflows require substantially larger token consumption, increasing compute and cost considerations.
Leadership and governance
According to the report, leadership patterns are evolving: about 64% of India-based GCC leaders carry dual global mandates, overseeing both the India centre and broader enterprise functions. This indicates that Indian GCCs are not merely delivery centres but increasingly influence global strategy and product development.
For Indian companies and multinational enterprises operating GCCs here, the implications are practical. To convert AI experimentation into durable business impact, organisations must:
- Invest in foundational architectures that enable data sharing and model lifecycle management.
- Embed governance and compliance into design rather than as a retrofitted control.
- Reshape talent models to include roles such as machine learning engineers, MLOps specialists and responsible AI officers.
The report positions India’s GCC ecosystem at an inflection point: the ingredients—scale, talent concentration and growing investment—are present, but success will depend on systemic technical upgrades and governance reforms that allow AI to move from isolated proofs to enterprise-grade, revenue-generating systems.
Author note: The findings are based on the Dell Technologies–Zinnov report and input from senior enterprise leaders as cited in the study.
