.jpg)
Most enterprises already have AI somewhere inside their GCC. EY's research on India's GCC sector finds 83% of centers are actively testing new AI technologies and 58% are already working with agent-based systems, according to EY's analysis of agentic AI in GCCs. What's rarer is a formal structure pulling that activity into one place. In a related finding, EY's GCC Pulse research shows only 7% of centers have a fully embedded Center of Excellence for cybersecurity, even though 42% already run advanced or automated security frameworks, according to EY's research on India's AI-native GCC shift. The same gap shows up with AI: plenty of activity, not much formal ownership. An AI Center of Excellence is what closes that gap, and a GCC happens to be the natural place to build one.
What an AI Center of Excellence actually does
An AI Center of Excellence is the team that owns AI as a capability for the whole enterprise, rather than one more project sitting inside a single business unit. It sets the standards other teams build against, maintains the shared platforms and models multiple functions draw on, and carries accountability for how AI gets governed, not just how it gets shipped. Done well, it turns scattered pilots across finance, customer service, and engineering into a single, coherent AI operating model the rest of the company can actually rely on.
EY's work with GCCs shows what this looks like in a live function: their analysis of AI-native customer service describes a CoE providing the global framework to design, deploy, and govern AI-driven customer journeys end-to-end, then scale them consistently across markets, unlocking value in a space where up to 40% to 50% of interactions carry genuine self-service potential once the right operating model is in place, according to EY's research on AI-native customer service in GCCs. That's the pattern worth borrowing for any function: a CoE isn't the team that builds one AI feature, it's the team that makes every future AI feature faster, safer, and more consistent to build.
Why a GCC is the natural home for this
Building an AI CoE inside a GCC is a talent and proximity decision. A GCC already sits close to the engineering, data, and operational teams doing the underlying work, and India's GCC ecosystem has been rapidly building exactly the skill sets an AI CoE needs, with EY reporting that 71% of GCCs are actively reskilling existing staff for AI-adjacent and future-ready roles. New positions have emerged specifically to staff this kind of function: AI and ML platform engineers who build the underlying infrastructure, AI governance and model validation specialists who manage regulatory and ethical accountability, and prompt and workflow engineers who design how AI actually gets used inside a business process.
EY's research also flags that 66% of GCCs report real difficulty attracting niche technical talent, alongside rising compensation costs for exactly these specialized roles. A GCC with an established local presence and employer brand is positioned to compete for this talent far more effectively than a headquarters team trying to build an AI CoE from a market it has no existing footprint in.
Building the operating model
An AI CoE needs a clear place in the organization chart, or it ends up as a working group with no actual authority, the exact failure mode behind that 7% cybersecurity CoE statistic above. Three structural choices determine whether it functions as a real capability or a symbolic one.
Centralized ownership with a federated delivery model tends to work best in practice. The CoE owns the platforms, the governance standards, and the model registry centrally, while embedding specialists into individual business functions to apply those standards to specific use cases. This avoids the two failure modes on either side: a fully centralized team that becomes a bottleneck every other function has to wait on, and a fully decentralized approach where every team builds its own AI stack with no shared standards at all.
A direct reporting line to a genuine executive sponsor, not just a shared services leader, is what gives the CoE authority to actually set standards other functions have to follow. Centers that reach real Center of Excellence status almost always have this kind of sponsor advocating for their mandate at the parent organization, rather than operating as a contained, locally-run initiative with no leverage beyond its own team.
A model registry and governance checkpoint built into the release process from day one is what separates a CoE that can demonstrate accountability from one that discovers its gaps during an audit. Tracking what each model does, what data trained it, its performance and fairness metrics, and whether it holds current governance approval, turns AI governance into infrastructure rather than a checklist someone runs through before launch.
Grounding governance in real standards, not an internal checklist
AI governance inside a CoE holds up best when it's built on established frameworks rather than an internally invented checklist, since internal-only standards tend to fall apart the moment a customer, regulator, or auditor asks for evidence. Two frameworks form the backbone most enterprise AI CoEs build toward today.
ISO/IEC 42001, published in 2023, is the first international, certifiable standard for an AI management system, giving organizations formal, auditable requirements covering the full AI lifecycle: risk assessment, control implementation, and continuous performance evaluation. The NIST AI Risk Management Framework complements it with four operating functions, Govern, Map, Measure, and Manage, offering a more flexible, risk-based approach that many organizations run alongside ISO 42001 rather than instead of it. Together, the two give an AI CoE a governance foundation that maps cleanly onto other regulatory regimes it may need to satisfy, including the EU AI Act's risk-based obligations for high-risk systems and India's own Digital Personal Data Protection Act.
A framework for building AICoE, in stages
Enterprises that build a genuinely functioning AI CoE, rather than a name on an organization chart, tend to move through the same rough sequence.
The first stage is proving value on a narrow, well-scoped function before asking for a broader mandate. A single business process, customer service self-service resolution, financial anomaly detection, or a specific engineering workflow, gives the future CoE a concrete result to point to rather than an abstract promise of enterprise-wide transformation.
The second stage is formalizing governance and the model registry before scaling to a second or third function. Enterprises that skip this step usually end up retrofitting governance under pressure once a regulator or major customer asks a question the team can't yet answer cleanly.
The third stage is securing an executive sponsor and a real reporting line at the parent organization, converting a locally successful pilot into a function with actual authority to set standards other teams are expected to follow.
The fourth stage is building the specialized talent bench, AI/ML platform engineers, governance specialists, and workflow engineers, deliberately rather than opportunistically, since these are exactly the roles EY's research flags as the hardest to hire competitively once demand across the market intensifies.
The pattern underneath
The GCCs building AI Centers of Excellence that actually function share a common thread: they treat the CoE as a genuine operating model decision, with real governance, real executive sponsorship, and a real reporting line, rather than a rebrand of whichever team happened to build the first AI pilot. The gap between centers running plenty of AI activity and centers with a formal Center of Excellence is exactly the gap between activity and ownership, and closing it is what turns scattered AI wins into a durable, enterprise-wide capability.
Curious what an AI Center of Excellence built specifically for your GCC's stage and mandate actually looks like?
At GCCBase, we help global enterprises design the AI operating model, governance framework, and talent strategy behind a genuine AI Center of Excellence, grounded in what's actually working inside mature GCCs today.
Book your free 15-minute GCC strategy call today.
FAQs
1. What is an AI Center of Excellence (AI CoE)?
An AI Center of Excellence is a dedicated team that owns AI as a capability for the whole enterprise, setting shared standards, maintaining common platforms, and holding governance accountability, rather than one business unit building its own AI initiative in isolation.
2. How do you build an AI Center of Excellence?
The strongest approach follows a staged sequence: prove value on one well-scoped function first, formalize governance and a model registry before scaling further, secure a genuine executive sponsor and reporting line, and then build out specialized AI talent.
3. Why build an AI CoE inside a GCC rather than at headquarters?
A GCC already sits close to the engineering and data teams doing the underlying work, and India's GCC ecosystem has been rapidly building the specific talent an AI CoE needs, including AI/ML platform engineers, governance specialists, and workflow engineers, with EY reporting 71% of GCCs actively reskilling staff toward these roles.
4. What governance frameworks should an AI CoE be built on?
ISO/IEC 42001, the first certifiable international standard for AI management systems, and the NIST AI Risk Management Framework are the two most widely adopted foundations, and together they map cleanly onto other obligations like the EU AI Act and India's DPDP Act.
5. What's the most common reason an AI CoE fails to gain real traction?
Lacking a genuine reporting line to an executive sponsor is the most common failure point. Without that authority, a CoE tends to function as an informal working group other teams can safely ignore, rather than a function with the standing to set standards the rest of the enterprise actually follows.
