GCCs are using AI, but are they gaining an advantage?
Here's why AI adoption alone is not enough for global capability centres to create measurable enterprise value.
AI adoption is rising fast. Advantage is moving slower. Global capability centres, or GCCs, have become central to how multinational companies build technology, run operations and access specialist talent.
In India, many GCCs are now experimenting with generative AI, automation, data science and agentic AI across software engineering, finance, customer support, supply chain and analytics.
Yet the more important question is not whether GCCs are using AI. It is whether they are earning the trust, authority and discipline needed to create measurable business value from it.
Why adoption is not the same as advantage
Using AI tools can improve speed, productivity and experimentation. However, adoption alone does not automatically create a competitive edge. A GCC may have multiple pilots, coding assistants and AI-enabled workflows, but still remain outside the core decision-making process of the parent enterprise.
In that case, AI becomes an efficiency layer rather than a strategic advantage. Research discussed by Nasscom AI, Zinnov and Tiger Analytics highlights this gap clearly. Based on inputs from more than 75 GCC and AI leaders, the study suggests that most GCCs can execute AI, but fewer are trusted to own the AI agenda.
The difference is not simply size. Larger centres are not always more influential. What matters more is whether a GCC has a clear mandate, decision rights, governance, domain depth and change management built into its AI programmes.
The real advantage lies in ownership
For GCCs, the next stage of AI maturity is not about running more proofs-of-concept. It is about moving from execution support to enterprise ownership. This means helping define AI roadmaps, selecting high-value use cases, building reusable platforms, setting responsible AI controls and measuring outcomes against business goals.
GCCs are well placed for this role because they often sit close to enterprise data, core processes and technology platforms. Many also have strong engineering and analytics talent. When these strengths are combined with business context, GCCs can build AI solutions that are more relevant than generic tools.
For example, an AI model designed for claims processing, fraud detection or supply chain planning becomes more valuable when it is trained around real workflows and clear business outcomes.
What holds many GCCs back
The biggest blockers are rarely just technical. Weak data foundations, fragmented systems and unclear accountability can limit the impact of even advanced AI tools. If teams cannot access reliable data, or if different business units run disconnected pilots, AI remains difficult to scale.
Governance is another major issue. AI systems must be transparent, secure and compliant with privacy and regulatory expectations. Without clear rules around bias, accountability and human oversight, enterprises may hesitate to give GCCs greater control.
Talent also matters. Teams need more than tool familiarity. They need skills in data quality, prompt design, model evaluation, risk management and business process redesign.
How GCCs can move from activity to impact
To gain a real AI advantage, GCCs need to focus on outcomes rather than tool usage. The first step is to identify business problems where AI can create measurable gains, such as faster product development, lower operational costs, improved customer experience or better risk detection.
The second step is to create repeatable frameworks. Instead of isolated pilots, GCCs should build shared data platforms, reusable components and clear governance models. The third step is to secure stronger enterprise alignment. AI programmes need sponsorship from business leaders, not just technology teams.
The bottom line
GCCs are certainly using AI, and many are progressing quickly. But true advantage will belong to those that move beyond adoption and become trusted owners of enterprise AI transformation. The winners will not be the centres with the most pilots. They will be the ones that combine domain expertise, responsible governance, strong data foundations and measurable business impact.