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AI strategy & adoption
From a first credible use case to AI agents running inside core, regulated processes, with the governance to keep them there.
Past the pilot
Most large organisations are not short of AI experiments. They are short of experiments that became part of how the business runs. Closing that gap is rarely a modelling problem. It is a question of which use cases deserve funding, who owns them, and whether the platform and controls exist to run them safely.
What this looks like in practice
For an automotive manufacturer, I drove agentic AI solutions across three lines of business and more than fifteen use cases, and developed the data platform operating model that keeps them compliant with the EU Data Act. Elsewhere, I have industrialised AI delivery through regulated AI factories with standardised agent interfaces, serving both business-specific and shared enablement use cases.
Adoption is the result that counts
A platform nobody uses returns nothing. On one internal agentic AI platform, a designed programme of onboarding, playbooks and walkthroughs brought adoption to roughly 48% within three months of go-live.