An AI Center of Excellence earns its place by connecting decisions that would otherwise happen separately.
A business team sees an opportunity. Technology evaluates a platform. Risk asks who is accountable. HR develops training. Finance asks when the investment will produce a return. These are parts of the same conversation, yet an organization can treat them as separate workstreams until something gets stuck.
I would start the design of an AI Center of Excellence with those connections. What needs to happen between an idea and a reliable capability that people can use? Where do decisions stall? Who has the authority to resolve them? The structure should make those answers clearer.
Its purpose is to help the enterprise identify valuable opportunities, deliver reliable solutions, and develop the ability to use them well. That requires attention to business value, engineering, responsible use, and how work changes. Strength in one area cannot consistently compensate for gaps in another.
A hub-and-spoke model is a useful starting point. The central team provides shared expertise, platforms, and standards. Business units own their priorities, domain knowledge, workflow changes, and outcomes. That arrangement gives teams access to capabilities they would struggle to maintain independently while keeping accountability close to the work.
At the center, I would establish four connected capabilities: AI Strategy and Value; AI Engineering and Platforms; AI Governance and Assurance; and AI Adoption and Workforce Enablement. These are responsibilities that need clear ownership. The size and maturity of the organization should determine whether they require dedicated teams or capacity from existing functions.
Strategy and Value connects the portfolio to organizational priorities. It helps leaders decide which opportunities deserve investment, identify the business owner, and define evidence of progress. Its scope should include both efficiency and opportunity: improving existing work and exploring what the business could do that was previously impractical.
Engineering and Platforms makes those choices deliverable. It connects systems and information, establishes reusable components, evaluates performance, and supports reliable operation. The work includes maintenance, cost, and recovery when something fails. A convincing demonstration leaves many of these questions unanswered.
Governance and Assurance makes acceptable use and accountability explicit. It establishes proportionate requirements for evaluation, release, monitoring, and changes in scope. Security, Privacy, Legal, Risk, and Quality bring their specialist requirements and independent challenge. Their oversight needs to remain meaningful when delivery teams are under pressure to move.
Adoption and Workforce Enablement connects the solution to everyday work. It examines which tasks and handoffs change, what employees need to understand, and how managers support the transition. Training belongs here, alongside workflow redesign and the ability to question an output or recognize when to escalate.
The reporting relationships matter. An executive sponsor gives the head of enterprise AI the authority and support to coordinate this work. An executive steering committee resolves investment priorities and competing demands. Business-unit AI leads remain accountable to business leadership while working with the CoE on shared standards and delivery. Enterprise control functions retain their oversight responsibilities.
The central team also needs boundaries. Business ownership should include responsibility for results after deployment. If a business leader wants a capability, that leader must help define success, commit operational capacity, and address the changes needed to realize the benefit. Otherwise, the CoE can become the owner of everybody else's unfinished transformation.
Consider an assistant supporting a customer-service team. The business owner identifies where employees struggle and establishes a baseline for resolution time and service quality. Strategy and Value helps test whether this is a worthwhile investment and what improvement would justify expansion.
Engineering connects approved information and builds evaluation and monitoring into the service. Governance and the relevant control functions establish requirements for access, accuracy, human review, and escalation. Enablement helps employees incorporate the assistant into their work and practice recognizing recommendations they should question.
After launch, the operating team tracks what happens. Faster drafting may leave resolution time unchanged if another handoff remains slow. Employees may save time but spend it checking unreliable recommendations. Those findings need to travel back across the same connections that supported delivery.
Leadership should make the operating agreements explicit before those situations arise. Who prioritizes investment? Who approves release? Who can accept the remaining risk, and within what limits? Who can suspend a system? Those decisions may sit with different people, and the path between them needs to be understood.
Funding needs the same clarity. Shared platforms, individual solutions, adoption, and ongoing operation all require capacity. Each solution needs owners for evaluation, incidents, improvement, and eventual retirement. A launch date should never be the point at which responsibility becomes ambiguous.
I would assess the CoE through the outcomes it helps the enterprise achieve, alongside quality, adoption, cost, and risk. Estimated hours saved need a credible path to useful capacity or financial benefit. More experiments and more users tell us something about activity; leaders still need evidence that the work is improving.
If you traced one AI initiative through your organization today, where would ownership become unclear? That is where I would begin designing the CoE.