What we learned from the Prime Minister’s AI Exemplars programme – and how it is shaping the next phase
AI Summary
An evaluation of the UK government's AI Exemplars programme after ten months, detailing lessons learned from running a test-and-learn portfolio of public sector AI pilots. The initiative showed that departments can effectively pilot AI to improve productivity and public services, but scaling individual pilots is hindered by data readiness, procurement constraints, and parallel duplication. Consequently, the government is shifting from numerous standalone pilots to a smaller number of large, transformational, mission-driven programmes with centralized support for shared barriers like safety, assurance, and evaluation.
Key Takeaways
- Test-and-learn portfolios successfully identify meaningful AI use cases and demonstrate public value, such as summarizing probation meetings and speeding up diagnostic scans.
- Scaling standalone pilots is slow and resource-intensive due to common system-level barriers including data readiness, commercial constraints, and duplicated effort across departments.
- Centralized support delivers maximum value when addressing shared infrastructure needs—such as AI assurance, legal frameworks, and evaluation—rather than repeating work per project.
Why it matters
Understanding how large public institutions transition from isolated AI experiments to scalable, transformational production systems provides valuable architectural and management lessons for enterprise engineers tackling bureaucratic, security, and procurement hurdles.
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