£100m+ in AI value generated.
Model deployment 11x faster.

Every business unit was building AI from scratch.

AI initiatives were fragmented across the organisation, each running its own deployment pipeline with no shared standard. Governance over AI projects and processes was limited, so nothing stopped duplicate effort or unmanaged risk from spreading.

Models, frameworks and best practices built in one team stayed in that team. Without a mechanism for reuse, every new use case started from zero, and deployment speed suffered as a result.

Three constraints were non-negotiable:

• Inconsistent deployment pipelines across business units
• Limited governance over AI projects and processes
• Models and frameworks rebuilt instead of reused

One Centre of Enablement. One playbook. 24+ use cases.

A 30+ member AI Centre of Enablement was established and led to give the organisation a single home for AI delivery. Its first output was an MLOps platform that took model deployment from a fragmented, ad hoc process to a standardised one — cutting deployment time by 11x.

Alongside the platform came an AI Playbook, codifying governance, deployment practices and Responsible AI frameworks so every business unit worked to the same standard instead of inventing its own.

With the platform and playbook in place, the Centre led delivery of 24+ generative AI use cases across multiple business units, each one building on shared infrastructure and shared rules rather than starting cold.

£100m+ in value, and a way of working that keeps producing it.

The AI Centre of Enablement turned scattered AI effort into a repeatable system: one platform for deployment, one playbook for governance, and Responsible AI built in from the start. Ethical AI practice is now institutionalised at enterprise scale, talent has moved up through multiple internal promotions, and the work has strengthened the organisation’s AI thought leadership through industry-facing keynotes.