A New Digital Development Strategy
On October 20, 2026, the World Bank Group launched its first comprehensive digital strategy, outlining a plan to help developing nations harness technology for growth. A central pillar of this strategy is a significant pivot towards what World Bank President
Ajay Banga calls 'small AI'—artificial intelligence for the people. Instead of focusing on building trillion-dollar, all-purpose models that require immense server farms and constant connectivity, the Bank is urging countries to adopt and adapt smaller, low-cost AI tools. This approach is designed to deliver practical benefits in places where electricity and internet access are often limited, democratising a technology that has so far been the domain of a few wealthy nations and corporations.
Big AI vs. Small AI: What's the Difference?
The AI that grabs headlines, like the large language models (LLMs) from global tech giants, are like encyclopedias—vast, general-purpose, and requiring a massive library (or data centre) to function. They are trained on enormous swathes of the internet. 'Small AI', or small language models (SLMs), are more like specialized field guides. They are trained on narrower, more specific datasets for a particular purpose. This makes them far more efficient, allowing them to run on local devices like a standard smartphone, even without a reliable internet connection. For businesses and public services, this means lower costs, faster performance for specific tasks, and greater control over data, as sensitive information doesn't need to be sent to a cloud server.
The 'Last Mile' AI Revolution
The true promise of this shift lies in its potential for real-world impact. With more than two billion people globally still offline as of 2025, 'small AI' offers a way to bridge the digital divide. In India, where internet penetration is growing but can be inconsistent in rural areas, the applications are immense. The World Bank highlights examples like farmers diagnosing crop pests using photos on a basic phone or community health workers using handheld devices for medical screenings without needing constant broadband. These tools can be tailored to local languages and contexts, delivering crucial information and services where experts are scarce. It's a strategy that aims to augment human capability, not just replace it.
Unlocking Innovation Across India
This emphasis on smaller, adaptable AI models is already gaining traction within India's tech ecosystem. Startups in fintech, healthtech, and agritech are increasingly turning to SLMs to navigate challenges like high cloud computing costs and data privacy regulations. By building models tailored for Indian languages and specific business needs, companies can create more accurate and efficient products. This trend empowers local developers and smaller enterprises, allowing them to compete and innovate without the massive capital investment required for large-scale AI. It aligns perfectly with national initiatives like 'Digital India', fostering a more inclusive and self-reliant technology landscape.
Challenges on the Road Ahead
Despite the enormous potential, the path to widespread adoption of 'small AI' is not without obstacles. The World Bank acknowledges that the foundational elements—reliable electricity, affordable internet, local data, and digital skills—are still lacking in many regions. In sub-Saharan Africa, for instance, a significant number of rural schools lack consistent power and internet access, a prerequisite for any digital tool. Furthermore, while small models can be developed locally, there's a risk of continued dependence on a few global firms that control advanced semiconductor and cloud platform technologies. Building a truly robust ecosystem will require sustained investment in this core infrastructure and in training a workforce capable of building and maintaining these new systems.
