AI Centre of Excellence: When It Helps and When It Becomes a Bottleneck
An AI Centre of Excellence can accelerate adoption, but only if it builds reusable capability instead of becoming the team that must approve or build everything.
By Leila Haddad, Women in AI Editorial Fellow ยท 30 August 2026
The AI Centre of Excellence has become a common response to enterprise AI adoption.
The logic is straightforward. Scarce expertise is easier to build in one place. The company needs shared standards. Leaders want visibility into what teams are doing. A central group can accelerate early learning.
But the same structure can become a bottleneck if every experiment, procurement decision and deployment depends on one small team.
What an AI CoE should do
A useful Centre of Excellence typically owns capabilities that benefit from reuse across the organisation.
That may include:
reference architectures; model and vendor standards; shared evaluation tooling; AI governance patterns; security guidance; reusable code and agent components; training and enablement; support for high-value strategic projects.
Its job is to make other teams better at AI.
What it should not do forever
A CoE should not become the only team allowed to build AI.
If every business unit must hand its problems to a central team, the organisation will struggle to scale. Domain knowledge also gets lost because the people building the system may be too far from the workflow.
The better long-term model is usually federated: central capability with distributed delivery.
Start central, then spread capability
Early in adoption, a central team can move faster because expertise is scarce.
Over time, it should create standards and platforms that allow product and business teams to deliver more independently. The centre becomes a multiplier rather than a factory.
This requires deliberate capability transfer. Training alone is not enough. Teams need reusable tools, examples, office hours and experienced practitioners embedded in important projects.
Governance belongs close to the work
The CoE can define risk frameworks, but it should not own every risk decision.
Business owners still need accountability for how AI is used in their processes. Technical teams need responsibility for system quality. Legal, security and risk functions retain their own responsibilities.
The centre coordinates rather than absorbing accountability.
Measure whether the CoE is creating leverage
Useful measures include time from idea to production, reuse of shared components, reduction in duplicated tooling, number of teams independently delivering within standards and the proportion of pilots that create measurable outcomes.
A central team's headcount is not a measure of AI maturity.
For leaders designing these structures, the Women in AI Global Summit in London will bring together enterprise AI executives, transformation leaders and practitioners working through the same operating-model questions. Comparing how different organisations balance central standards with distributed delivery can save a lot of organisational trial and error.
Know when the structure has done its job
The strongest Centre of Excellence may eventually become less visible.
When AI capability is embedded across product, engineering and business teams, the central group can focus on advanced platforms, governance and difficult cross-company problems.
That is a sign of success, not decline. The goal was never to own all AI work. It was to help the organisation become capable of doing it well.