World Bank: Small AI Could Transform Developing Countries 2026

Small AI could be the key to unlocking economic transformation in developing countries, according to the World Bank. The institution is urging emerging economies to embrace specialized AI models that run on mobile phones with low connectivity, rather than relying solely on the sophisticated consumer products developed by Silicon Valley giants.

Paschal Donohoe, the World Bank's chief knowledge officer and former finance minister of Ireland, emphasized that AI can accelerate development—but only if it is adapted to local needs. "We argue that economic development in the countries that we serve and partner with that could have taken centuries to make happen, the potential is there for that economic development now to happen within decades," Donohoe said.

What Is Small AI and Why Does It Matter?

Small AI refers to specialized models trained on narrower datasets for specific purposes. Unlike large language models that require massive computing power and constant internet connectivity, small AI can operate offline on a mobile phone, in local languages, and with minimal infrastructure.

This approach contrasts with the trend among big tech firms, which are focused on ever more sophisticated consumer products. For developing countries, where connectivity and computing resources are often limited, small AI offers a more practical and accessible path to leveraging artificial intelligence for development.

How Small AI Works in Practice: The AgriConnect Example

Donohoe cited the example of Indian farmers engaged in the World Bank's agricultural scheme, AgriConnect, who needed to access soil analysis in their own language. "There's not going to be enough scientists and agricultural experts to do that for every small farm in India," he noted. "And this is where what we describe as 'small AI' comes in."

By deploying small AI on mobile phones, farmers can receive tailored advice on soil health, crop management, and weather patterns without needing constant connectivity or expensive hardware. This democratizes access to expert knowledge and enables real-time decision-making at the local level.

Key Benefits of Small AI for Developing Economies

• Low connectivity requirement: Small AI models can run offline or with minimal internet access, making them suitable for rural and remote areas.
• Local language support: Models can be trained in local languages, ensuring that users can interact with AI in their native tongue.
• Mobile-first design: Small AI is optimized for smartphones, which are increasingly ubiquitous even in low-income regions.
• Cost-effective: Training and deploying small AI requires fewer resources than large-scale models, lowering the barrier to entry.
• Tailored to local needs: Models can be customized for specific sectors such as agriculture, healthcare, and education.

Challenges and Considerations

While small AI holds promise, several challenges remain. Data availability and quality are critical for training effective models. Many developing countries lack comprehensive datasets, especially in local languages. Additionally, building local capacity to develop and maintain AI systems is essential for long-term sustainability.

Governments and organizations must also address issues of digital literacy, privacy, and ethical use. Without proper safeguards, AI could exacerbate existing inequalities rather than reduce them.

Comparing Small AI and Large AI Models

The Road Ahead: What to Watch

The World Bank's push for small AI aligns with broader efforts to bridge the digital divide. As mobile penetration continues to grow in developing countries, the potential for small AI to drive economic growth becomes more tangible. However, success will depend on collaboration between governments, tech companies, and local communities.

Investors and policymakers should monitor how small AI models are deployed in sectors like agriculture, healthcare, and education. The AgriConnect example shows that targeted AI solutions can address specific pain points and deliver measurable impact.

For those tracking global financial flows, the conversation around AI and development is part of a larger debate about how the global financial system serves developing countries. As Ed Miliband has argued, the global financial system fails developing countries, and innovative solutions like small AI could help redirect resources more effectively.

Moreover, the economic potential of small AI must be viewed in the context of financial stability. When bond market turmoil hits developing nations hardest, access to affordable technology becomes even more critical for resilience and growth.

Beyond economics, the adoption of small AI can improve daily life in tangible ways. Just as expert tips can transform a small outdoor space into a functional area, small AI can transform limited digital infrastructure into powerful tools for development.

Similarly, thoughtful design matters. Whether you're looking to transform a small outdoor space with rugs and lighting or deploy AI in low-resource settings, the key is adapting solutions to local constraints and needs.

This principle extends to indoor environments as well. Techniques to transform a small bedroom into a cozy place show how small, targeted changes can yield significant improvements—a lesson that applies directly to the design of small AI systems. The background to this story is set out in World Bank: Small AI Could Transform Developing Countries.