Case 02 /
AI governance working group operating model
A case study on creating practical AI governance rhythms for use-case intake, executive oversight, and regulated adoption.
- Outcome as stated
- A clearer operating rhythm for evaluating AI use cases, surfacing risks, and keeping human accountability explicit.
01
Challenge
AI interest was rising faster than the governance model needed to classify use cases, set evidence expectations, and maintain executive control.
02
Operating context
A senior data leadership setting with regulatory sensitivity, practical AI exploration, and a need to separate useful adoption from theatre.
03
Intervention
Chaired a Data and AI Governance Working Group, established structured intake, clarified accountability, and connected AI adoption to data quality and control requirements.
04
Board-level lesson
AI governance is credible when it gives executives usable decision rights, not when it adds another abstract policy layer.
The useful governance question is not whether a model looks impressive. It is whether the organisation can explain the use case, the data, the human control point, the evidence, and the decision it supports.
Evidence
What the programme produced
01
Identify, capture, monitor
Microsoft Purview deployed across the critical data estate.
Similar challenge
If the same pattern is showing up in your organisation, start with the decision at stake.
A useful first conversation clarifies the operating context, evidence gaps, ownership model, and practical 90-day sequence.