Beyond the AI Giants
The artificial intelligence most people know is the 'large language model' (LLM) variety—think of the massive systems from giants like Google and OpenAI that require immense data centres and computing power. A 'small language model' (SLM), in contrast,
is designed to be lean and efficient. These models are trained on smaller, more specialised datasets for a specific purpose. This makes them faster, cheaper to run, and capable of operating on a simple mobile phone, even with a weak internet signal. Their focused nature and lower resource needs mean they can be deployed locally, reducing costs and enhancing data privacy by keeping sensitive information on the device itself instead of sending it to the cloud.
A New Development Blueprint
The World Bank is pushing this strategy as a way for developing nations to reap the rewards of AI without the multi-billion dollar price tag of building advanced systems from scratch. According to Paschal Donohoe, the Bank's chief knowledge officer, this approach is about adapting AI to local needs. The goal is to accelerate economic development, enabling countries to solve long-standing problems related to productivity and public services. Rather than just consuming AI built in Silicon Valley, this strategy promotes digital sovereignty, allowing nations to create their own solutions and reduce dependency on foreign tech. This shift is part of the World Bank's first-ever comprehensive digital strategy, launched in October 2026 to help developing countries create jobs and improve services.
The Power of Going Local
The true strength of the 'small AI' strategy lies in its local application. These models can be tailored to the unique languages, dialects, and contexts of a community. For a country as diverse as India, this is particularly significant. Instead of a single, monolithic AI, imagine specialised assistants for micro, small, and medium enterprises (MSMEs), which account for a huge portion of India's GDP. An AI tool could help a small artisan predict demand for her products or allow a manufacturer to optimise inventory. Donohoe cites the example of Indian farmers who need soil analysis in their local language—a task where a small, specialised AI on a phone is far more practical than a global LLM.
From Theory to Practice
This isn't just theoretical. The World Bank points to tangible results already emerging from this approach. In India, AI-supported weather forecasts have reached over 30 million farmers, providing earlier and more accurate predictions for the rainy season. In other nations, small AI has helped screen for tuberculosis without reliable broadband and allowed healthcare workers to analyze patient data securely on a mobile device. To foster more such innovation, the World Bank has launched the AI Infrastructure and Readiness Initiative. This program aims to help governments in emerging markets access affordable computing power through shared, pay-as-you-go infrastructure, making it easier to put small AI to productive use.
Hurdles on the Horizon
Despite the promise, the path is not without challenges. For any AI adoption to be successful, fundamental infrastructure must be in place. IMF Managing Director Kristalina Georgieva notes the critical need for reliable electricity and digital connectivity, which remain significant barriers in many rural areas. A World Bank report noted that in sub-Saharan Africa, for instance, nearly a third of rural schools lack electricity. Furthermore, success depends on building local talent and digital literacy so that people and businesses can use these new tools productively and safely. Governments will also need to establish clear frameworks to manage risks, protect against misuse, and ensure that successful pilot programs can be scaled up effectively.
