India’s 2,100+ Global Capability Centres are struggling to scale AI projects, with 70% stalled at the pilot stage. Despite generating $98.4 billion in annual revenue as of FY26, technical bottlenecks and high compute costs are delaying enterprise-wide adoption. For investors, this indicates that efficiency gains from AI integration may take longer than initially expected to impact bottom lines.
India’s Global Capability Centres (GCCs) are currently at a critical turning point in their digital evolution. With over 2,100 units currently operating in India, employing 2.36 million professionals and generating $98.4 billion in annual revenue as of FY26, these centers are vital components of the global operations of multinational corporations. However, a new report from Dell Technologies and Zinnov highlights a significant gap between ambition and reality: 70% of AI initiatives in these centers remain trapped in the initial pilot phase.
Infrastructure and Data Bottlenecks
The inability to scale these initiatives is largely due to deep-rooted technical challenges. Many of these centers rely on legacy infrastructure that was not originally designed to support the data-intensive requirements of modern AI models. Fragmented data silos, where information is stored in isolated systems, prevent the seamless data flow required for enterprise-level applications. Furthermore, the lack of robust security frameworks is acting as a major roadblock for full-scale implementation. For parent companies, this means that modernizing existing IT architecture is no longer optional but a prerequisite for AI success. Investors should note that this transition may require higher capital spending on technology infrastructure before the company can see significant productivity returns.
Operational Costs and Workforce Realities
The move toward agentic AI—where AI agents execute autonomous, end-to-end workflows—is creating new financial pressures. These workflows are significantly more compute-intensive than simple chat-based applications, often requiring up to 500,000 tokens per workflow. This jump in resource intensity forces companies to rethink their cost modeling. Additionally, there is a massive shift expected in the workforce composition. According to the report, 55% of routine tasks currently performed in GCCs are prime candidates for AI-led automation. This poses a long-term management challenge, as approximately 60% of the current workforce will require comprehensive reskilling by 2030 to stay relevant in an automated ecosystem.
The Path to 2030
The focus for GCC leadership is now shifting from the volume of AI projects to the industrialization of those projects. By 2030, success will be measured by how effectively companies integrate AI into their core operations rather than just testing new tools. For shareholders, the key monitorable will be how effectively these centers manage the balance between investing in high-cost infrastructure and optimizing their existing human capital. Companies that successfully clear these technical hurdles and reskill their workforce are likely to gain the most in terms of long-term efficiency and operational resilience.