The AI-First GCC: Why New Centers Are Being Built Differently
Every new GCC now starts with an AI-first mandate. Digital-first, AI-first operating models are no longer a differentiator for new centers; they are the default. For mature centers, the challenge is different: transforming existing platforms, teams, and operating models so that AI can move from pilots to enterprise-scale impact.
Across the industry, we see three distinct generations of GCCs taking shape, each at a different stage of this shift, and each facing quite a diverse set of priorities. Understanding which generation, a center belongs to, and what that demands its leadership, has become essential to planning its next phase of growth.
The newest generation: AI-native by design
The centers being established today begin with a structural advantage, and it is worth being precise about what that advantage is. It is not superior to technology. The models, tools, and platforms available to a new center are available to every center. The advantage is the absence of legacy: no inherited processes, no organizational structures designed for an earlier era, and no workflows that assume large teams doing work AI can now do.
These centers do not add AI to an existing way of working; they design the way of working around it. Workforce plans, team structures, and delivery processes assume AI is part of the team from day one. A center established in 2026 will be built very differently from one established even three years ago, not because its leadership knows something others do not, but because it carries nothing it does not need.
The middle generation: centers in transition
Centers set up over the last five years occupy a valuable middle position. They have operational momentum, established teams and credibility with their global organization, yet the way they work has not yet hardened to the point where change becomes too expensive to attempt.
These centers are adapting rapidly. Across the industry, we see them reworking headcount plans, rethinking team structures, and pivoting toward AI-led ways of working before those earlier decisions become costly to reverse. Their advantage is timing, and it is a diminishing one. The cost of course correction rises with every year a center settles into its ways, which is why the strongest centers in this group are acting now rather than waiting for certainty.
The established generation: legacy centers and the operating-model reset
The longest-established centers hold real strengths: scale, mature teams, deep institutional knowledge, and years of earned trust with their headquarters. Those assets matter, and they will continue to matter. But these centers also carry the heaviest lift. A significant share of their work will be directly impacted by AI, and their operating models, built for a different era, need a fundamental refresh rather than incremental adjustment.
And the hardest part of that reset has little to do with technology. It means changing how thousands of people work, how teams are structured, how performance is measured, and how careers progress. Change management at this scale is among the hardest programs an enterprise can undertake, and it deserves the same rigor, investment, and leadership attention as any major transformation. Centers that treat it as a side effect of AI adoption will struggle. Centers that treat it as the core program will emerge stronger.
AI amplifies the operating model it is given
One principle connects all three generations. AI does not fix an operating model; it amplifies the one it is given. Applied to a well-designed center, it compounds the advantage. Applied to an outdated one, it compounds the friction. The meaningful divide between these generations is therefore not when they were built, but how much of their current design deserves to survive in contact with AI.
This suggests a useful test for any leadership team. If you were building your center today, from a blank page, how much of it would you build the same way? The gap between that answer and your current reality is your transformation agenda. New entrants are exempt from the question. Every other center must answer it, and the ones moving fastest are answering it deliberately, on their own timelines, rather than waiting for it to be forced on them.
The path forward
The GCC industry is no longer moving at one speed. AI-native entrants are setting the pace because they treat AI as the starting point of their design rather than an addition to it. Nothing prevents an established center from making the same choice. It requires conviction, a clear-eyed view of how the center works today, and a genuine commitment to bringing people through the change.
The centers that do this work will find that AI delivers exactly what it should: enterprise-scale impact, built on a foundation designed for it. The AI-first label will follow. The foundation must be earned.
