Strategy Saturday

Why Your AI Center of Excellence Will Fail

Published April 18, 2026 — 3 min read

TL;DR:

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The CoE Trap

Enterprise playbook: Create a dedicated AI team. Hire PhDs. Build infrastructure. Announce initiatives. Watch the organization stay exactly the same.

The problem isn't the team—it's the structure. Centers of Excellence are by definition separate from the work. Your CoE researches "the future of AI adoption" while product teams ship features the old way. They become advisory bodies. And nobody listens to advisors.

The parallel everyone misses: companies that dominance distribution (Amazo Logistics, Tesla Manufacturing, Netflix Encoding) didn't create Centers. They embedded the capability into every team that touches customers. AI should work the same way.

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Why Teams Build CoEs (And Why They Regret It)

Centers of Excellence exist because:

  1. Fear of spreading incompetence — "If we let product teams touch AI, they'll waste money"
  2. Org structure inertia — easier to create a new department than restructure existing ones
  3. Resume-building — executives want a VP of AI on the org chart

But the result is predictable:

The companies winning with AI aren't the ones with fancy CoE structures. They're the ones where a product team can integrate Claude or GPT in a sprint, iterate with real users, and own the outcome.

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The Embedded Alternative

Skip the Center. Instead:

  1. Hire 2-3 AI engineers (not 20). Put them inside product teams.
  2. Give product leaders budget for AI experiments. Not gated through a committee.
  3. Build internal tooling (monitoring, cost tracking, safety checks). Make it self-serve.
  4. Create async docs (not weekly sync meetings). How to integrate APIs, common pitfalls, what works.
  5. Let teams fail fast. Most AI bets won't work. That's feature discovery, not failure.

This isn't "no governance." It's governance that moves at product speed, not academic speed.

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What Actually Works

The pattern we see in enterprises shipping AI fast:

One company went from "CoE explored 5 AI ideas in 2 years" to "product teams shipped 30 AI features in 1 quarter" after embedding engineers directly.

CoE: nice org chart. Real progress: messy, distributed, team-owned.

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What We've Learned

If you're building AI capability in an enterprise:

  1. Don't create a separate team. Embed the skill.
  2. Give product teams autonomy to experiment.
  3. Build infrastructure, not ideology.
  4. Measure success by shipped features and revenue impact, not research papers.

Your best AI strategy isn't a Center of Excellence. It's making every team excellent at AI.

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