As technology giants race to build ever more powerful artificial intelligence systems, the World Bank is promoting a different approach: smaller, affordable AI tools designed to solve everyday problems in countries that cannot afford to join the global technology arms race.
In a rural community in Kenya, a farmer photographs a damaged leaf with a smartphone. An artificial intelligence application examines the image and helps identify a crop disease, even when internet connectivity is unreliable. Thousands of miles away, a student in Ghana practices mathematics through a WhatsApp-based AI tutor. In Nigeria, doctors are experimenting with software that records clinical notes without requiring a continuous internet connection.
These applications have little in common with the enormous AI systems being developed by Silicon Valley’s technology giants. They do not require massive data centers or extraordinary computing power. Their ambitions are narrower, their costs are lower, and their potential impact on everyday life may be considerable. This is the emerging world of “small AI,” an approach the World Bank believes could help developing economies overcome some of their most persistent challenges.
The central question is whether such technologies can genuinely narrow the gap between wealthy and poorer nations—or whether the artificial intelligence revolution will simply create another form of global inequality.
A Different Kind of AI Revolution
For years, the public conversation about artificial intelligence has been dominated by increasingly sophisticated chatbots, powerful language models, and the enormous investments required to develop them. Companies compete to build systems capable of performing more complex tasks, while governments worry about falling behind in a technological race that demands capital, infrastructure, and highly specialized expertise.
For most developing countries, however, competing directly in that race is unrealistic. The World Bank’s World Development Report 2026: The Promise of Artificial Intelligence argues that the most immediate opportunity for poorer economies may not be creating the world’s most advanced AI models. Instead, it may lie in adapting existing technologies to local needs.
The report estimates that 16.2% of jobs in developing economies could experience meaningful productivity improvements through AI. That figure is not dramatically different from the 18.7% estimated for high-income countries. The potential for improvement exists across the world, even if the resources available to realize it differ enormously.
The distinction is important. In many developing economies, AI’s most valuable role may be to assist workers rather than replace them: helping a nurse evaluate a medical image, giving a farmer timely agricultural advice, or allowing a teacher to provide more individualized instruction. In this vision, artificial intelligence becomes less a futuristic replacement for human labor and more a practical instrument for extending scarce expertise.
A Farmer’s Smartphone Instead of a Supercomputer
Agriculture provides some of the clearest examples. Small farmers often lack access to agricultural specialists who can identify diseases, recommend treatments, or advise on changing weather conditions. A problem that could be solved quickly with expert assistance may instead destroy a harvest.
In Kenya, the Nuru application uses computer vision to help farmers identify crop diseases from photographs. Its ability to function without continuous connectivity makes it particularly relevant in areas where internet service is expensive or unreliable. Other initiatives provide localized weather forecasts, irrigation guidance, and advice on managing crop diseases through mobile phones or basic messaging services.
The technology is not necessarily revolutionary in itself. Image recognition, predictive models, and automated recommendations have existed for years. What changes is their accessibility and adaptation to the circumstances of the people using them.
A farmer does not need a general-purpose AI system capable of writing poetry or programming software. The farmer needs reliable information about a diseased plant, delivered at the right moment and in a language they understand. That is the practical philosophy behind small AI.
Health Care Where Doctors Are Scarce
The possibilities are equally significant in health care, where shortages of medical professionals and diagnostic equipment can have life-changing consequences. The World Bank has highlighted mobile-based tools in India that assist with screening for tuberculosis and other medical conditions. Some applications can operate on handheld devices without depending on constant broadband access.
In Nigeria, a startup has developed an offline, voice-based system that transcribes and organizes clinical notes. By reducing administrative work, such a tool could give overstretched medical professionals more time with patients. These examples illustrate a crucial difference between AI designed for wealthy markets and AI designed for resource-constrained environments.
A sophisticated hospital system may assume reliable electricity, high-speed internet, expensive equipment, and a large technical staff. A rural clinic may have none of those advantages. An effective solution must therefore be designed around the limitations of the environment, not merely imported from a wealthier country.
There are important safeguards to consider. AI-assisted screening is not the same as a medical diagnosis, and mistakes can have serious consequences. Medical tools require clinical validation, appropriate regulation, protection of patient information, and human oversight. Making a technology affordable does not automatically make it safe or effective.
A Mathematics Tutor for Five Dollars
Education may offer another particularly compelling opportunity. In Ghana, a WhatsApp-based mathematics tutor called Rori has been used to provide personalized instruction at approximately $5 per student per year, according to the World Bank. The institution reports learning gains comparable to an additional year of schooling in the program it describes.
The example suggests that educational AI does not always require expensive computers, elaborate classrooms, or the latest generation of software. For students who have access to a basic smartphone, a familiar messaging platform may be enough to deliver explanations, practice exercises, and individualized feedback.
Yet educational technology also presents difficult questions. Can these systems work effectively in local languages? Are their lessons aligned with national curricula? How do schools evaluate their accuracy? And can governments ensure that the poorest students are not excluded because they lack devices or connectivity? AI tutoring can supplement education, but it cannot replace qualified teachers, functioning schools, or sustained public investment.
The Digital Divide Has Not Disappeared
The greatest obstacle to this vision is not necessarily the intelligence of the technology. It is the unequal world in which that technology must operate.
According to the World Bank, approximately 2.2 billion people remain offline. Millions more face expensive connectivity, unreliable electricity, limited digital skills, or inadequate access to devices. The institution’s research identifies four foundations for meaningful AI adoption: connectivity, computing capacity, locally relevant data, and human skills. Without these foundations, even inexpensive applications may struggle to reach the people who need them most.
Language presents another problem. AI systems trained primarily on information from wealthy, English-speaking societies may perform poorly when confronted with local dialects, unfamiliar agricultural conditions, or cultural practices that are poorly represented in their training data.
There is also a question of ownership. If developing countries become dependent on foreign technology providers for essential services, they could exchange one form of inequality for another.
Who controls the data collected from farmers, patients, and students? Who determines the cost of the software once communities depend on it? And who is accountable when an automated recommendation causes harm? These questions deserve as much attention as the technology’s potential benefits.
Can Small AI Create Economic Opportunity?
Beyond agriculture, health, and education, the World Bank sees opportunities for local entrepreneurs and small businesses. An AI tool embedded in a familiar payment application could help a merchant understand sales patterns or manage inventory. A localized language assistant could support a small business that cannot afford specialized administrative staff.
Such applications may also create opportunities for domestic software developers, researchers, and technology companies to build products tailored to their own markets. But that outcome is not inevitable.
The most successful innovations may still depend on foreign cloud services, imported computing infrastructure, or proprietary AI models. Local startups may struggle to secure financing, while governments may lack the expertise needed to evaluate vendors and protect public interests. The challenge is therefore not simply to distribute AI applications. It is to develop the economic and institutional capacity to sustain them.
The Promise—and the Limits—of a Smaller Revolution
There is something appealing about the idea that artificial intelligence’s most important contribution to development may come from relatively modest technologies. A farmer receiving an early warning about a crop disease, a nurse gaining access to diagnostic assistance, or a student obtaining affordable mathematics instruction represents a more tangible benefit than many of the spectacular demonstrations produced by the world’s largest AI laboratories. But technology cannot substitute for sound public policy.
Small AI will not repair failing electrical grids, eliminate poverty, train enough doctors, or resolve longstanding educational inequalities. At best, it can make existing resources more effective and expand access to services when appropriate infrastructure and safeguards are in place.
The World Bank’s argument is ultimately about priorities. Developing nations do not necessarily need to win the race to build the largest artificial intelligence systems. They need the capacity to choose, adapt, and govern technologies that address their own problems.
The future of AI may be shaped by trillion-dollar companies and enormous data centers. But its value to much of humanity could be measured in considerably smaller terms: a healthier harvest, a more accurate medical screening, a student who finally understands a difficult lesson. The real test of artificial intelligence will not be how powerful it becomes, but how widely its benefit
