AI in Developing Countries: Life-Changing Impact

Published Date : 11/09/2025 

While 'Big AI' often grabs headlines, 'Small AI' is quietly making a significant impact in developing countries. Affordable, accessible, and context-specific, Small AI is addressing immediate local challenges in agriculture, health, and education. 

When people hear “artificial intelligence (AI),” they often think of the headline-grabbing large language models—vast systems trained on huge amounts of data, running on supercomputers, and requiring immense resources to operate. These attention-grabbing forms of “Big AI” often spark important conversations about access and competitiveness. But an equally powerful, and perhaps more impactful, story is emerging in developing countries around the rise of “Small AI.”

Small AI is an approach that is affordable, accessible, and context-specific. Unlike Big AI, it doesn’t need massive infrastructure or cutting-edge servers. It flourishes on smaller datasets, runs on everyday smartphones or laptops, uses minimal resources, and is fine-tuned to address immediate, local challenges. Across agriculture, health, and education, Small AI is already delivering tangible solutions in communities where resources are constrained, but the need is urgent.

Agriculture: Cultivating Resilience and Productivity

Small AI is empowering farmers with simple, localized tools to help them make smarter decisions, boosting both productivity and resilience. In Kenya, for instance, the Nuru app allows farmers to photograph a diseased crop leaf and receive an instant diagnosis without needing continuous internet access. Tools like this can help farmers improve their yields and in turn increase income. In Senegal, a digital agriculture company uses farmer profiles and crop data to provide mobile-based advice on disease management and water needs, providing the right information when it matters most. In Ghana, a startup sends hyper-local weather forecasts via SMS, helping farmers decide when to plant, irrigate, and harvest—all using basic phones. These applications are practical, low cost, and nimble.

The power of these tools lies in their ability to build on what already exists. By using basic infrastructure, such as local farmer registries in places like India, Small AI can create new platforms that connect farmers to credit, new markets, and tailored advisory services.

Health: Expanding Access and Strengthening Systems

In health care, Small AI is already making an impact by delivering robust, low-bandwidth tools that expand access and are tailored to local needs. In the Pacific Islands, for example, AI applications are being piloted to support maternal care in remote areas where doctors are often out of reach. In India, mobile-based AI tools screen for tuberculosis and diabetic conditions directly on handheld devices, with no broadband connection required.

Crucially, Small AI also adapts to local cultures. An initiative in Peru is developing voice-based diagnostics in Indigenous languages, building community trust in health care and ensuring technology serves everyone.

Education: Bridging Gaps and Personalized Learning

Small AI is helping address one of education’s biggest challenges: Delivering quality, personalized learning at scale. In Ghana, the “Rori” AI math tutor—sent via WhatsApp and trained on just 500 micro-lessons—costs about $5 per student per year but produces learning gains equal to an extra year of schooling.

These benefits are expanding. In Costa Rica, the Dominican Republic, and Mexico, AI tutoring systems are extending personalized learning to remote and Indigenous communities. Meanwhile, platforms like India’s Diksha and Bangladesh’s Shikkhok embed AI tools into mobile applications that function offline and in multiple languages. These examples show how education technologies don’t need to be resource-intensive to make a real difference for students and teachers, expanding inclusion and opportunities everywhere.

Lessons from the Frontlines of Small AI

The success of Small AI hinges on a few principles. First, it works best when tackling hyper-local, clearly defined problems, such as a specific crop disease or a particular health condition. Second, it builds on existing infrastructure and networks—like farmer registries, WhatsApp, or local health worker systems—to expand their reach. Third, designing for mobile-first, offline functionality is crucial, since smartphones are typically the primary digital device in developing countries and connectivity is often unreliable. Finally, Small AI thrives on public-private partnerships, where governments provide enabling platforms, the private sector drives innovation, and communities shape solutions that truly work on the ground.

Building AI That Works for Everyone

Big AI will continue to push boundaries in research and industry, but Small AI is where we’re already seeing immediate impacts in communities. It is pragmatic, cost-effective, and sustainable. While it may have limits in scale, it enables developing countries to leapfrog traditional barriers and harness AI today. At the World Bank, we see this as a vital bridge to a more inclusive digital world tomorrow.

The promise of AI should not be a luxury for just a few nations. Small AI is revealing a new narrative: One of resilience, ingenuity, and opportunity, born in the very communities that need it most. And its most exciting chapters are still to come. 

Frequently Asked Questions (FAQS): 

Q: What is Small AI?

A: Small AI refers to affordable, accessible, and context-specific artificial intelligence solutions that run on everyday devices and use minimal resources to address immediate local challenges.

Q: How does Small AI differ from Big AI?

A: Unlike Big AI, which requires massive infrastructure and resources, Small AI operates on smaller datasets, runs on everyday devices like smartphones, and is tailored to local needs.

Q: What are some examples of Small AI in agriculture?

A: Examples include the Nuru app in Kenya for crop disease diagnosis, digital agriculture advice in Senegal, and hyper-local weather forecasts in Ghana to help farmers decide when to plant, irrigate, and harvest.

Q: How is Small AI impacting healthcare in developing countries?

A: Small AI is expanding access to healthcare by providing robust, low-bandwidth tools for maternal care, tuberculosis screening, and diabetic condition management, often tailored to local languages and cultures.

Q: What are the key principles for the success of Small AI?

A: The success of Small AI depends on tackling hyper-local problems, building on existing infrastructure, designing for mobile-first and offline functionality, and fostering public-private partnerships. 

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