The AI Center of Excellence (CoE) is the most common organisational structure for enterprise AI. When designed well, it accelerates adoption, standardises best practices, and builds internal capability. When designed poorly, it becomes a gatekeeper that everyone tries to bypass.
The Hub-and-Spoke Model
The most effective CoE structure is hub-and-spoke: a central team (the hub) that maintains platform infrastructure, governance frameworks, and best practices — with embedded AI specialists (the spokes) in each business unit who work on domain-specific use cases. The hub enables; the spokes execute.
Key Roles in the CoE
Essential roles: (1) Head of AI — sets strategy and priorities. (2) 平台 Engineer — builds and maintains the shared AI infrastructure. (3) MLOps Engineer — owns deployment pipelines and monitoring. (4) AI Ethics/Governance Lead — ensures compliance and responsible AI. (5) Business Partners — embedded in each BU to identify and prioritise use cases.
What the CoE Should NOT Do
The CoE should not be the only team that builds AI. If every AI project requires the CoE's involvement, you've created a bottleneck. The CoE's job is to enable other teams to build AI safely and efficiently — not to be the sole builder. Measure success by how many teams deploy AI independently, not by how many projects the CoE delivers.
Common Pitfalls
Pitfall 1: CoE becomes a gatekeeper — every AI project needs CoE approval. Fix: publish standards, automate compliance checks, let teams self-serve. Pitfall 2: CoE builds everything centrally — business units disengage. Fix: embed specialists in BUs. Pitfall 3: CoE focuses on technology, not business outcomes. Fix: CoE leadership reports to the business, not to IT.
核心要點
- The Hub-and-Spoke Model
- Key Roles in the CoE
- What the CoE Should NOT Do
- Common Pitfalls
總結
Pitfall 1: CoE becomes a gatekeeper — every AI project needs CoE approval. Fix: publish standards, automate compliance checks, let teams self-serve. Pitfall 2: CoE builds everything centrally — busine...
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