The World Bank is promoting specialised “small AI” models as a way for developing countries to expand access to useful artificial intelligence despite limited connectivity and infrastructure. Unlike large general-purpose systems, these compact tools target specific problems and can operate on basic smartphones, reducing dependence on continuous cloud access.
India’s AgriConnect initiative illustrates this approach by supporting locally adapted agricultural advice for farmers. Such applications could help users access information about crops, pests and farming decisions in their own languages. However, their wider impact will depend on reliable electricity, affordable devices, appropriate local data and access to trustworthy advice.
The opportunity comes with significant challenges. The International Monetary Fund estimates that nearly 40% of global employment is exposed to AI, although exposure does not necessarily mean job losses. Some workers could become more productive, while others may face reduced demand for their skills. Developing countries must also address gaps in digital infrastructure and workforce capabilities if they are to benefit from the technology.
Development finance remains another constraint. The World Bank Group is seeking to mobilise private investment alongside its own resources, but technology alone cannot compensate for weak infrastructure, limited institutional capacity or insufficient investment in education.
Small AI models could widen access to digital services by shifting some computing from remote data centres to devices already used by local communities. This reduces dependence on continuous internet connectivity and may lower operating costs for narrowly defined applications. However, local execution does not eliminate hardware, electricity, maintenance or data-quality constraints.
The key technical advantage comes from matching model size and capability to a specific task. Quantisation and other compression techniques can reduce memory requirements, allowing suitable models to run on existing smartphones and lower-power processors. Performance still depends on the model architecture, available memory, processing capacity and the accuracy required for the task. Advanced semiconductor manufacturing matters, but it is not the sole determinant of adoption.
For developing countries, the more immediate challenge is building systems that solve practical problems reliably. Agricultural advice, local-language services and basic public information tools need relevant data, field testing and mechanisms for correcting errors. They also require affordable devices and sufficient digital literacy.
For India, small AI could support agricultural extension and public services in areas where specialist assistance is difficult to access. The strategic priority should be to combine technical efficiency with local expertise, infrastructure investment and measurable improvements in service delivery.
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