The government must learn from years of experience introducing digital transformation if the UK is to make the most of the opportunities AI offers to the public sectorThe United Kingdom is currently in the throes of a transformative period, shaping the future of the public sector’s artificial intelligence (AI) capabilities for generations to come. The visible aspects of this transformation, including the rollouts of Copilot, the establishment of foundation model partnerships, and the securing of high-profile contracts, are already well underway. However, the less conspicuous part of this process is whether we are simultaneously building the purchasing capability, nurturing a diverse supply base, and setting shared standards that will allow us to adapt as the technology continues to evolve.
We have made significant strides in the first half of this task. However, it is the second half that will determine whether the initial efforts bear fruit. This is what we can refer to as the “silent lock-in” trap. It is the gradual build-up of AI capability on top of infrastructure, management practices, and governance approaches that are individually defined, poorly coordinated, and mismatched to the rapid pace of technological change.
Despite the diligent efforts of individuals and teams to procure and experiment with emerging AI capabilities, the pieces are not fitting together as they should. So, how can we learn from the experiences of the past decade’s digital transformation to accelerate the UK’s adoption of AI? This is the central theme of “Making AI work for Britain,” a book published by London Publishing Partnership and available for download at FutureOfAI.uk under an open-access license.
The book, which draws on several years of research into the UK’s AI strategy and ecosystem, as well as over a decade of experience working with the UK government on digital transformation, proposes a simple strategy for AI success: consolidate demand, diversify supply. The following is a summary of three key recommendations from this analysis: Build buyers who can push back; pool demand that is already shared; and keep the supply side plural.
The challenge of creating smart buyers is easy to articulate but difficult to solve. AI suppliers, especially the larger ones, now routinely make claims that require significant technical capability to evaluate. These claims pertain to training data provenance, model behaviour under distribution shift, security properties of the fine-tuning pipeline, and interoperability with alternative providers.
Most procurement functions were designed to assess claims like, “this system meets this specification” and “this supplier has these references.” They were not designed to assess claims like, “this model will remain useful as underlying capabilities change.”
The smart-buyer model does not necessitate building deep AI expertise in every department. Instead, it means having a small core of people in each organisation that spends significantly on AI, who can sit opposite a vendor and understand what they are looking at. This core needs three things: technical understanding, commercial acumen, and the ability to influence the wider organisation.
In conclusion, the UK is at a pivotal point in shaping the future of AI in the public sector. The success of this endeavour will not only depend on the visible advancements but also on the less conspicuous aspects such as building purchasing capability, nurturing a diverse supply base, and setting shared standards. By learning from past digital transformation experiences and implementing strategies such as building smart buyers, pooling shared demand, and keeping the supply side plural, the UK can ensure that it is well-positioned to adapt as AI technology continues to evolve.
