Building an AI Center of Excellence: Structure, Roles, and Common Pitfalls

The article argues that organizations setting up an AI Center of Excellence often focus first on staffing and reporting lines, when the more important question is which AI practices must be uniform, which should be shared defaults, and which teams should decide for themselves. Getting this governance split wrong can leave a CoE either bottlenecked by reviews or existing only as an unenforced policy. The author cites a global industrial automation company whose regional CoE spent nine or ten months stalled on one workflow, held back by cloud AI skills gaps and by rebuilding infrastructure already available as a company platform service. The piece describes the core purpose of a CoE as preventing fragmented AI efforts, improving investment decisions, building organization-wide AI skills, and reducing duplicated spending. It also links these failures to the trust and identity issues that can stall an AI CoE even when its structure looks correct on paper.