The UK has long punched above its weight in the development of artificial intelligence, providing much of the academic and conceptual groundwork for techniques that are now deployed worldwide. In Making AI Work for Britain, Alan W. Brown sets out to examine how the UK might build on that legacy and translate it into real, sustained economic and social benefit. The central question of the book is not whether the UK can lead in AI, but how it can turn proven expertise into effective practice at national scale.
Following a foreword by Lord Kulveer Ranger - Vice Chair of the All-Party Parliamentary Group on AI - the book is structured around the past, the present, and the future. However, rather than feeling like a chronological tour, the argument develops as a conversation between where the UK has come from, the dilemmas it now faces, and the choices it must make if AI is to serve the nation well.
Digital Sovereignty, The Five Paradoxes of AI Adoption and the Pilot Trap
Brown begins by setting out the conditions that underpin what he terms ‘digital sovereignty’. Rather than framing sovereignty in narrow political terms, he defines it practically, arguing that any nation seeking to benefit from AI must secure three core levers: sufficient compute capacity, access to appropriate data, and effective systems of oversight. This framing is helpful both for its clarity and for grounding often abstract debates about AI leadership in tangible capabilities. In the same early chapters, Brown provides accessible definitions of the main categories of AI, striking a careful balance between technical accuracy and readability for those without deep specialist knowledge.
One of the most compelling contributions of the book emerges early on, when Brown introduces the five paradoxes confronting UK leaders attempting to adopt AI at scale. Among these, the tension between centralisation and local adoption stands out. The balance between national consistency and local accountability - often caricatured as the choice between coherence and the ‘postcode lottery’ - is a familiar problem in public policy, and Brown articulates it with clarity. These paradoxes recur throughout the book, acting as a lens rather than a checklist, and they help explain why well‑intentioned initiatives so often struggle to move beyond initial success.
Brown’s exploration of the UK’s digital past reinforces this point. Drawing on a range of examples, he highlights the persistence of what he calls the ‘pilot trap’: the tendency to celebrate high‑profile demonstrators while failing to deliver equivalent capability at scale. The lesson is not that pilots are misguided, but that scaling is a fundamentally different challenge, requiring institutional commitment, procurement maturity, and sustained leadership. This section is particularly effective in drawing practical lessons rather than offering retrospective criticism.
Making AI Work: Institutions, Skills and Governance
Turning to the present, Brown shifts his focus to what must change if AI is to deliver real value now. He groups the core challenges into three interconnected themes: institutional change, workforce upskilling, and governance. Importantly, ‘institutional’ here extends beyond government. Brown treats business, industry, and civil society as active participants in the national AI ecosystem, each needing to adapt if progress is to be collective rather than fragmented.
The discussion of workforce upskilling is especially strong. Using concrete examples from the NHS, financial services, the creative industries and government, Brown shows how AI skills requirements differ by sector while sharing common obstacles. These case studies exemplify one of the book’s key strengths: its grounding in real‑world experience rather than abstract aspiration. Brown does not shy away from the barriers to upskilling, including structural inequalities related to gender, ethnicity and geography, and he situates skills development firmly within a broader transformation strategy rather than treating it as a standalone intervention.
Governance and ethics are addressed with similar pragmatism. Rather than rehearsing familiar warnings, Brown draws on practical experience to suggest specific actions that align oversight with innovation. The result is an approach that recognises risk without allowing it to become an excuse for inertia.
A Strategy for What Comes Next
The final section, ‘What comes next’, is framed not as speculation but a proposal, even an exhortation. Brown compares the UK’s position with that of other countries - supported by a useful appendix - but repeatedly stresses that imitation is not a strategy. The UK, he argues, must develop an approach that reflects its own institutional strengths and constraints. He then sets out a phased programme covering governance, procurement, infrastructure, skills, and international positioning, before returning to the human dimension by examining what leaders themselves must do differently.
Who Should Read This Book?
I found the book both accessible and grounded in hard‑won experience. It would serve well as a primer for leaders, advisers and informed members of the public who want to move beyond slogans and understand how AI transformation happens. In bringing together clear analysis, original insights and practical recommendations, Brown offers a persuasive and constructive roadmap for making AI work for Britain, and I commend it to anyone concerned with how the nation will navigate its AI future.
- About the author
- Professor Edward Rochead, M.Math (Hons), PGDip, CMath, FIMA is a mathematician employed by the government, currently leading work on STEM Skills and Data. Ed is chair of the Alliance for Data Science Professionals, a Visiting Professor at Loughborough University, an Honorary Professor at the University of Birmingham, Chartered Mathematician, and Fellow of the IMA and RSA. Copyright and licence
- © 2026 Royal Statistical Society
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