AI Essentials Workshop - April 2026

This report sets out a framework for UK leaders and policy makers on building robust AI systems, organised around four interconnected pillars: security and adversarial resilience, reliability and performance, data integrity and quality, and operational resilience. It explains why AI systems are inherently more fragile than traditional software due to their statistical nature, opacity, and expanded attack surface and provides practical, evidence-based guidance on addressing each risk area. The core message is that good practice reduces risk but does not eliminate it, and that ongoing monitoring after deployment is just as critical as pre-deployment testing.

written by DPA Research Director, Professor Alan Brown

The AI pilots look promising, the strategies read well, and yet adoption stalls. The problem is not what most people think it is — and the good news is that we have solved something very like it before.

Across the DPA network I hear the same story, whether in boardrooms, public sector teams, or consultancies. The AI pilots look promising. The strategies read well. Yet adoption numbers stall, value never quite lands, and the enthusiasm of recent years is giving way to something more anxious in 2026.

If that picture feels familiar, I want you to know two things. First, you are not alone. Second, the problem is not what most people think it is. 

This is the argument a new book, Making AI Work for Britain, written by DPA Research Director, Professor Alan Brown, available via Open Access for free download.