A £200m AI portfolio, risk-assessed.
Enterprise-wide governance, established.
A Fractional Head of AI built the policy, the risk process, and the tooling a global automotive manufacturer needed to govern AI at scale — then trained the business to actually use it.
risk-assessed
governance framework
enabled
delivered
Client
A global automotive manufacturer — a multi-billion-pound enterprise with distributed product and engineering teams across regions, operating in a highly regulated sector.
Goal
Establish a Global AI Governance Framework that enabled responsible AI adoption at scale and prepared the organisation for emerging regulation, including the EU AI Act.
Nobody could say what AI the business was already running.
The business wanted to move fast on AI. Leaders were pushing for education and adoption, but the culture was risk-averse, and there was no enterprise-wide oversight of AI tool use, development, or procurement. That gap left pockets of unmanaged technical and regulatory risk scattered across business units, with no inventory, no consistent risk categorisation, and no standard way to assess AI impact.
The EU AI Act added urgency. Product teams needed to know how to classify their systems, what to document, and what compliance actually required — and without a single coherent framework, there was no way to prioritise remediation or investment with any confidence.
Three constraints were non-negotiable:
- No enterprise-wide AI inventory or risk-tiering
- Appetite for AI adoption, but a risk-averse culture
- EU AI Act compliance obligations closing in
Governance that goes from policy to practice.
Our Fractional Head of AI authored the Data and AI Ethics Principles and the enterprise AI Policy, then established and chaired an AI Steering Group to give the business senior oversight and a forum to approve high-risk initiatives. A Risk Management Framework brought AI-specific risk into the company’s existing enterprise risk processes, with clear escalation paths and accountability.
Policy alone changes nothing. This paired every principle with a process, a tool, and the training to use it.
To make governance operational rather than theoretical, the team built an automated AI impact and risk assessment process, backed by tooling, so teams could evaluate privacy, safety, fairness, and regulatory risk early in a project’s lifecycle. For EU AI Act readiness, they rolled out an AI system inventory and risk-tiering process, trained product managers to identify and classify their own AI initiatives, and defined lifecycle controls covering design, validation, deployment, and monitoring.
None of it worked without adoption, so training was treated as core delivery, not an add-on. The programme ran AI training sessions, workshops, and immersion days, plus role-based enablement and playbooks for executives, senior leaders, and product managers — grounding everything in trustworthy AI principles the teams could actually apply.
£200m of AI risk, now accounted for.
The result is enterprise-wide AI governance with clear accountability — unmanaged AI risk materially reduced, and executive confidence up because leaders can finally see what AI the business is running and how risky it is. Trustworthy AI practices are now embedded in delivery teams through training and playbooks, and the Steering Group gives the organisation a standing mechanism to keep it that way.
* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.
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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.