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Across the mutual sector, AI ambition is progressing ahead of practical adoption. Building societies are testing new tools, colleagues are finding their own ways to use them, and more AI is arriving quietly through existing suppliers. Change at this rate raises fundamental questions: what is already in use? Who owns the outcomes? And where should the boundaries sit?
This is where governance earns its keep. Done well, it gives people a safe route to test ideas, with controls that tighten as the risk grows. Beyond Experimentation: Building AI Governance for the Mutual Sector makes the case for letting real business problems lead.
We challenge the idea of waiting for perfect data, instead encouraging teams to define data standards around each use case. We highlight why accountability should remain with the people expecting to realise the benefit, while a central governance forum sets standards and provides oversight. The hidden AI estate can also be easy to underestimate. Browser tools, internal pilots and supplier-enabled features can all influence decisions or expose data. If they remain out of sight, they remain difficult to govern.
The report brings these ideas together in a five-part framework, helping building societies take an honest look at where they are now and decide what to tackle first. Download the report to move beyond scattered experimentation and build an approach to AI that people can use and leaders can stand behind.
