AI can help governments help citizens

Résumé détaillé

In developing economies, the AI debate has focused less on frontier risk and more on how to ensure that the technology actually serves people. That will require not only designing AI tools to work within government, but also investing in the state capacit

Many countries are trying to do more for their citizens, albeit often with outdated systems. That means accessing government services can feel like a second job.

A mother walks half a day to a clinic, only to be turned away because she needs a form from an office in another town. A farmer who qualifies for financial support gives up after being passed from queue to queue. AI, if used correctly, could help close these gaps, expanding a capable state’s reach.

For low- and middle-income countries, the stakes could not be higher: AI could be a great equaliser, or it could deepen inequality. While AI’s likely contributions to agriculture, health care, and education hold great promise, the World Bank’s World Development Report 2026 estimates that AI’s productivity benefits could be more than twice as large in advanced economies if current trends continue.

The technology’s future trajectory depends on the decisions that developers, investors, and governments make today about how it is built, funded, and deployed.

As Kristalina Georgieva, managing director of the International Monetary Fund, explained at the IMF-World Bank Annual Meetings in October, AI adoption depends on electricity access, digital connectivity, and workers equipped with the right skills.

But after decades spent in development finance, I am convinced that access, while a legitimate concern, is not the biggest barrier. Above all, AI adoption requires building the state capacity to put these tools to use.

In advanced economies, the AI debate has largely focused on safety, security, and the threat of job displacement. But in developing economies, the more immediate question is how to ensure that AI tools actually serve people. They must work in local languages, train on local data, and be built for local realities.

And they must be coupled with investment in the institutions and people closest to the problem, who understand where AI tools can deliver real benefits and can ensure that they do.

But even the…

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