Future-ready skills: How GCCs are accelerating learning in AI and advanced analytics

By: Vishal Rajani

Many GCCs in India started with a narrow mandate comprising a small team, a specific function, and a cost rationale. Over a decade or more, some of those centres have grown into genuine strategic hubs, handling the most complex, high-value functions in their parent organisations. Others have stayed exactly where they started, just larger. The difference rarely comes down to investment or headcount. It almost always comes down to how seriously the organisation treated capability — building skills the business would need before it urgently needed them. 

That question has never been more urgent than it is in 2026, with AI and advanced analytics reshaping what GCCs are expected to deliver.

The Scale of What Is Being Built

India's GCC ecosystem now employs close to 2 million professionals across over 1,850 centres, and industry reports project that the workforce will reach 2.4 million by the end of the year alone — an 11% increase driven almost entirely by AI-native roles. The ambition is real: nearly 70% of GCCs are already investing in Generative AI, and over 60% plan to establish dedicated AI safety and governance teams by the end of 2026. However, ambition and execution are different things. There are supply gaps of up to 43% in AI, Data and Analytics roles, and 38% in Platform Engineering. The centres moving fastest are not the ones with the biggest hiring budgets. They are the ones who built learning cultures before they needed to.

Learning cannot be a Side Programme

The most common mistake organisations make is treating upskilling as a bolt-on. A course catalogue, a few workshops on prompt engineering, and an annual learning target are all examples of strategies that produce superficial familiarity, not real competence. More than 75% of GCC leaders say they are continuously supporting AI upskilling across their organisations, but the quality of that investment varies enormously. The centres doing it well have stopped treating learning as a programme that runs alongside work. They have made it inseparable from work itself.

The Domain Knowledge Problem

One dimension consistently underweighted in conversations about AI capability is domain expertise. Technical skills are undoubtedly necessary but not sufficient. A data scientist who can build a clean model but has no grasp of the competitive dynamics of a specialised B2B market will produce technically correct outputs that are strategically irrelevant. The most durable capability-building happens at the intersection of deep domain knowledge and advanced technical skill and that intersection takes years to develop, not sprint cycles.

This is borne out in hiring data. GCC hiring grew 4–6% quarter-on-quarter in Q3 FY2026, but companies are increasingly shifting toward "precision over volume", meaning investing in specialised functions rather than expanding headcount broadly. Demand is strongest for professionals who combine domain fluency with technical depth: GenAI engineers who understand the business context they are building for, analysts who can translate data into commercial decisions, not just dashboards. That combination cannot be hired quickly. It has to be grown.

What High-Performing GCCs Are Doing Differently

The GCCs building real, lasting capability share a few observable characteristics. They treat skill development as a leadership priority, not a delegated HR function. Over 72% of GCC leaders now cite talent management and skill-gap analysis as a key strategic priority, but the centers where this translates into actual capability are the ones where leadership is aligned on which specific skills matter and why — not just that skills matter in the abstract.

They build from within rather than hire from outside. The top upskilling models in 2026 include role-specific reskilling journeys and micro-credentials (18%), corporate academies (17%), and embedding AI skills directly into career frameworks (16%). The most valuable AI and analytics professionals in any GCC are rarely the ones hired away from competitors, but the ones trained and encouraged internally over time, given complex problems with real stakes and trusted to own them.  

Finally, they build understanding alongside tooling. Generative AI platforms amplify what skilled professionals can produce, but they equally mask capability gaps that become critical failures at the wrong moment. The priority has to be foundational judgment, not just interface fluency.

The Real Test Ahead

India's GCC trajectory is not in question. What is in question is whether the capability being built inside these centres is real or cosmetic. Whether it compounds over time or resets with every attrition cycle. GCCs that view learning as infrastructure embedded in real work, aligned with strategy and built on domain expertise, will secure meaningful mandates. Those that treat it as a programme will stay where they started.