Study of four governments finds five foundations that make AI actually work

[Dan96]

A new Global Government Forum study, drawing on interviews with senior public servants in Australia, Spain, the UK and the US, examined what separates AI pilots that produce lasting improvements to public services from ones that quietly stall out. Real examples cited include the US Department of Health and Human Services using AI to help prevent fraud, waste and abuse across Medicare and Medicaid, the Department of Energy applying AI to accelerate nuclear energy rollout toward a 2050 target of 400 gigawatts of capacity, and the UK's HMRC using AI in fraud detection and compliance casework, which it says has already helped protect and recover 10 billion pounds in tax between 2025 and 2026

Spain's contribution centers on building reusable applications across government rather than one-off tools, including legislation analysis, machine translation and citizen-facing virtual assistants meant to serve multiple departments rather than getting rebuilt separately each time. Research editor Sarah Wray said that across this diversity of applications, governments are learning the technology itself is rarely the hardest part, the harder work is organizational, data readiness, workforce skills, governance and genuine leadership buy-in

The study reflects a broader shift the research describes as governments moving beyond simple digitization toward a second era of transformation, where digital platforms now generate the data that actually fuels AI, enabling more proactive and integrated public services rather than just faster versions of existing paper based processes. Curious what people think is the hardest of these foundational requirements for governments specifically to get right, given how different public sector constraints, procurement rules, workforce structures, political accountability, are from a typical private company deploying the same technology

AI that delivers: Practical foundations for government successwww.globalgovernmentforum.com

[WWEPete45]

Curious how directly transferable these five foundations actually are to governments outside these four specific wealthy, well resourced countries, capacity constraints in lower income countries likely change which foundation matters most first

[Patrick94]

Building reusable applications across departments rather than one-off tools is honestly the harder and more valuable discipline here, government IT has a long history of duplicated, siloed systems that this approach directly pushes back against

[MondayMoan]

Monitoring crocodiles being mentioned alongside nuclear energy rollout and tax fraud detection shows just how broad the actual application range already is, government AI use is clearly far more varied than the flashier headline use cases suggest

[Reacher Erin]

Workforce skills and genuine leadership buy-in are probably the two hardest foundations to actually build given how political appointee turnover and civil service structures work, sustained multi-year initiatives don't always survive a change in leadership

[Christopher_27]

10 billion pounds recovered through AI-assisted fraud detection at HMRC is a genuinely concrete, measurable result that cuts through a lot of the usual vague government AI success claims, that's real money with a clear before and after

[DataProphet53]

The technology being the easy part while organizational readiness is the hard part tracks with basically every large scale government IT initiative in recent memory, this isn't a uniquely AI specific lesson, it's just a fresh reminder of an old one