How to Build an AI Center of Excellence That Actually Delivers
The problem with most AI CoEs is that they often fail due to declining relevance and a lack of actionable results. The key to success is designing the CoE as a product team rather than just a policy team, providing measurable value to the organization.
Why Most CoEs Fail: The Advisory Trap
The default CoE model centralizes AI expertise into an advisory function, which often fails in practice for several reasons:
- No Delivery Accountability: Advisory functions lack responsibility for implementation outcomes.
- Create Bottlenecks: Centralized reviews slow down AI initiatives.
- Attract Wrong Talent: Governance-focused CoEs struggle to recruit top technical talent.
"The measure of a successful AI CoE is not how many standards it published or how many projects it reviewed. It is how many production AI systems the organization is running."
- Source Name, Report Title
The Product Team Model
The CoEs that deliver treat themselves as internal product teams, building AI alongside business units. They establish a shared AI platform as an internal product and embed engineers into projects not just as reviewers but as active builders.
This model aligns the CoE's success with business unit accomplishments, focusing on shared goals rather than just governance documentation.
The Four Functions of an Effective AI CoE
- Platform Engineering: Create and maintain a shared AI/ML platform for business units.
- Embedded Delivery: Deploy engineers into business units for technical engagements.
- Governance as Code: Automate governance requirements into the platform.
- Community and Capability Building: Foster an internal AI community to spread AI literacy and support new AI champions.
Staffing and Reporting
An effective CoE for a mid-market enterprise requires 5 to 12 people, including technical and product management roles. Reporting to the Chief Data Officer, Chief AI Officer, or CTO ensures a broad mandate and avoids being pigeonholed into IT or a single business unit's agenda.
Conclusion
The success of an AI CoE is measured by the number of production AI systems and the speed of moving initiatives from concept to production. It thrives when business units choose to engage with the CoE because it accelerates their goals.
Building your AI Center of Excellence? Talk to Flynaut about AI team structure, platform design, and operating model.
Related Reading
- AI-Powered Personalization: Moving Beyond Recommendation Engines
- Building a Data Quality Framework That Actually Gets Adopted
- Building Your First Enterprise AI Strategy: A Practical Roadmap
Ready to take the next step? Explore Flynaut AI & Data Services to discuss how we can help your organization.
For an AI CoE to deliver meaningful outcomes, align it as a product team that actively contributes to building AI initiatives, rather than just creating policy documents.