AI Center of Excellence
From Ad Hoc to Structured: How a 200-Person Tech Company Built Its AI Center of Excellence
The Problem
A 200-person B2B software company had ambitious AI product ambitions but was paralyzed by internal confusion: which team owns AI? What standards do we apply? How do we decide what to build vs buy? The AI Readiness Assessment showed 58/100 (Advancing) with strong technical capability but weak governance and strategy alignment. Three separate teams — Product, Engineering, and Data Science — were each running independent AI initiatives with no coordination.
The Approach
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Results
AI CoE launch time
Before
No CoE
After
60 days
Initiatives governed
Before
0
After
17
Engineering capacity freed
Before
£0
After
£280k/yr
AI Readiness score
The Outcome
AI CoE operational within 60 days. All 17 initiatives now governed. 6 deprioritized initiatives freed up £280k in engineering capacity that was reallocated to top 3 priority use cases. AI Readiness score improved to 74/100 (6 months post-CoE launch). Two of the top 3 use cases shipped to production. AI is now a visible element of the company's product roadmap — previously it was scattered across team backlogs.
Key Lessons
- 1
The federated model was the right structure. A centralized AI team in a fast-moving product company would have been too slow and resented. Embedded practitioners with central standards gave speed and consistency.
- 2
Scoring existing initiatives was immediately valuable and immediately controversial. Teams whose work was deprioritized needed careful handling — the scoring framework provided objectivity that made those conversations possible.
- 3
Hire the governance person before the technical people. The AI Governance Lead being in place from day one meant policy decisions didn't bottleneck technical progress.
Playbooks & Tools Used