An AI Center of Excellence done right is a reuse and governance function that makes every team's AI work cheaper and safer. Done wrong, it's a committee that blocks the work it was meant to enable.
The reputation of "Center of Excellence" is deserved — too many become gatekeepers that slow shipping. The useful version is small, opinionated, and built to make other teams faster, not to approve their work.
The Business Model of a Useful CoE
Canvas the CoE as a service to the rest of the org. Its customers are product teams; its product is shared infrastructure, standards, and reusable components. If the CoE's "customers" can't ship faster because of it, the CoE is a cost center pretending to be a function.
- Shared evals and benchmarks.
- Reusable context, prompts, and guardrails.
- A governance path teams can actually follow.
Synergize, Don't Centralize
The CoE that wins is the one that multiplies other teams' output — shared memory, shared tooling, shared standards — not the one that hoards the right to build AI. Centralize the invariants (security, audit, eval). Distribute the work (the actual applications).
What to Build First
Don't start with a charter. Start with the one thing every team rebuilds: an eval harness, a context store, a guardrail pattern. Ship that, let teams adopt it, and the CoE earns the standing to set standards.
How FACTA Frames It
FACTA's enterprise work is built around shared standards — compliance, audit, ownership — that every team inherits rather than rebuilds. That's the CoE pattern: the invariants are central, the applications are distributed, and the shared layer is what makes the whole thing affordable.
Conclusion
A useful AI Center of Excellence is a reuse and governance function, not a committee. Build the shared layer teams would adopt anyway, and the standing to set standards follows.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
Ask us what your AI CoE should ship first.
We'll point at the one shared component every team is currently rebuilding. See AI governance for startups for the governance frame that makes it work.
Explore AI Strategy