Digital Economy Dispatch #285 -- Find. Fix. Finish

This is not the book I thought I would be reading. Not by a long way. When I picked up Katrina Manson's Project Maven, I was expecting a rather dry, technological review of AI applied to military applications and an analysis of the lessons learned about technology adoption. Instead, what I found was over 300 pages of something that reads more like an episode of “Yes, Minister” than an edition of “Tomorrow's World”. It lays out in compelling detail why AI adoption is yet another example of that well-known adage: 1% inspiration, 99% perspiration. In this case, the effort involved convincing the US military establishment that it had to wake from its institutional slumber and revolutionise. Fast.

The Team in the Basement

Katrina Manson’s book tells the story of how, in 2017, Colonel Drew Cukor gathered a small team inside a windowless Pentagon room with a mandate most of his colleagues considered either impossible or undesirable: put artificial intelligence at the heart of how America fights wars. Cukor was not a Silicon Valley evangelist. He was a Marine Corps officer who had experienced the deaths of several colleagues and was convinced that an AI-equipped China was closing the gap with American capability faster than anyone in the military wanted to admit. His response was to behave like a startup founder inside one of the most bureaucratic institutions on earth.

What followed, as Manson documents through more than 200 interviews with insiders and opponents, was not a smooth technology deployment. It was a decade-long battle of wills, budgets, procurement rules, ethical objections, and competing interests. The Maven team fought with Pentagon bureaucrats and each other. They enlisted a reluctant Silicon Valley and triggered a revolt among thousands of Google employees who refused to have their work used in targeting algorithms. They brought in Palantir, Amazon, Microsoft, and others to field AI systems in live combat zones. They learned, often painfully, where AI fails.

A Story about Institutions, Not Algorithms

Manson is a Bloomberg reporter and former Financial Times correspondent who covered US foreign policy and defence. She writes with the confidence of someone who has spent years watching powerful institutions resist the very changes they publicly claim to want. The result is a book that is, at its core, not really about AI at all. It is about institutional change: how organisations convince themselves that transformation is urgent while doing everything possible to prevent it.

The pattern will be familiar to anyone who has watched the UK's public and private sectors grapple with AI adoption over the past decade. The technology is rarely the limiting factor. The limits are structural: procurement systems built for a different era, risk cultures that reward caution over capability, leadership teams with the authority to commission pilots but not the appetite to scale them. Project Maven had impact not because the AI was perfect, it frequently was not, but because Cukor and his team refused to treat the organisation's resistance as a reason to stop. They treated it as the problem to be solved. And were relentless about it.

Find. Fix. Finish.

The phrase comes from military targeting doctrine, and it organises much of what Project Maven was trying to do. Find the target. Fix its position. Finish the job. As a framework for thinking about AI adoption more broadly, it is uncomfortably precise.

Find

Britain has never had a problem with this step. We have more pilots, proofs-of-concept, and AI research projects than most comparable economies. The NHS, HMRC, local government: each has its collection of AI experiments, many of them technically impressive. Project Maven started here too. The initial brief was to use computer vision to analyse video footage from military drones, processing at a speed and scale no human team could match. The technology worked. That, in the end, was found to be the easy part.

Fix

The harder step was fixing the institutional conditions that would allow the technology to move from experiment to operation. For Project Maven, that meant creating a demand signal strong enough to bring Silicon Valley off the fence, building procurement routes that could actually accommodate AI vendors, and persuading a chain of command that deploying systems whose decisions they could not always explain was a risk worth accepting. Cukor spent years on this step. The Pentagon's bureaucracy pushed back at every turn. Most organisations never seriously attempt it; they mistake running another pilot for making progress.

Finish

Project Maven did eventually finish. Today, its AI-enabled systems operate in every branch of the US military. But Manson is careful not to make this feel like a triumph. Finishing, in the Project Maven sense, meant accepting that the AI was imperfect, that it would make mistakes, and that the organisation had to build the internal capacity to understand, oversee, and challenge what it had deployed. That is the smart-buyer model in its most demanding form: not passive consumption of capability, but active stewardship of it.

The one recurring obstacle in Manson's account, appearing at almost every stage of the story, was data. Not the absence of it. The US military generates more data than almost any organisation on earth. The problem was making it usable. Finding relevant datasets scattered across siloed systems. Securing permission to use footage and intelligence records carrying their own legal and classification constraints. Labelling thousands of images so that algorithms could learn to distinguish a vehicle from rubble, a person from a shadow. Applying those labelled datasets to real-world conditions that never quite matched the training environment.

Each of these was a solvable problem. None of them was a technology problem. They were organisational, legal, and human problems dressed in technical clothing. Anyone who has tried to build an AI system inside a large UK public body or private company will recognise every line of that.

What cut through, ultimately, was a refusal to wait for conditions to become comfortable. The Project Maven team went where the problems were hardest and stayed until something worked. They embedded with operational units, deployed imperfect systems, and iterated in the field rather than holding out for certainty that the lab would never deliver. They bent rules. They circumvented procurement timelines. On occasion, they acted first and sought permission afterwards. This was not recklessness. It was a deliberate choice to treat inaction as the greater risk.

The organising principle throughout was value creation, and the speed at which it could be demonstrated to sceptics who needed to see a system working in conditions they recognised before they would commit. That principle, at least, requires no adaptation before it travels. But it is not the only universal lesson we should be taking away.

Project Mason’s Lessons for the UK

The Project Maven story is an American one, shaped by American institutions, American procurement culture, and the particular urgency that comes from facing a near-peer military adversary in China. It would be easy to conclude that its lessons do not travel. I don’t think that’s right.

The structural challenge Cukor faced, convincing an established institution that it needed to change faster than its own processes allowed, is not uniquely military, nor uniquely American. It is the challenge facing every public body and large private organisation in the UK that is trying to move AI from the edges of the organisation to its operating core. The UK is exceptionally good at Find. It has a patchy record on Fix. It rarely gets to Finish. The result is what I have elsewhere called pilot purgatory: technically interesting, strategically irrelevant.

In Making AI Work for Britain, I make a simple argument that the answer lies in consolidating demand and diversifying supply: creating the institutional structures that send a clear, sustained signal to the market while simultaneously opening the supply side to genuine competition.

The US Department of Defense did not approach AI vendors with a list of discrete departmental requirements. It created a focal point, a programme, a mandate, around which commercial capability could coalesce. That is how you get from Find to Finish. Project Maven, for all its controversy and its contexts that many will rightly find troubling, is one of the most instructive case studies available in what that actually looks like. It is also a reminder that AI success at scale requires so much more than the algorithms.