The World Bank’s World Development Report 2026 made a bold claim, stating that artificial intelligence (AI) is a historic opportunity for developing economies like Nigeria and the rest of Afr
The World Bank’s World Development Report 2026 made a bold claim, stating that artificial intelligence (AI) is a historic opportunity for developing economies like Nigeria and the rest of Africa if they choose the right path.The study, titled The Promise of Artificial Intelligence, arrives as development progress hits its weakest pace in 75 years. Growth in low- and middle-income countries has slowed to levels not seen in three decades. Against this backdrop, AI offers a chance to compress decades of development into years. The catch is that success will not come from racing to build the most advanced models but from adapting what already exists to local realities.The technology is spreading faster than any previous general-purpose technology. The steam engine took about 80 years to reach lower-income countries. Electricity needed 40 years, while the internet required 20. ChatGPT, by contrast, drew half its global traffic from middle-income countries within just six months of launch. This speed creates both urgency and possibility.
Expertise that once took generations to develop can now reach farmers, teachers, doctors, and administrators on a compressed timeline.The report’s central message is that developing economies do not need to build trillion-dollar, all-purpose AI models to reap the benefits of this revolution. Instead, the focus must be on “Small AI,” low-cost, highly tailored tools that can bypass the infrastructural bottlenecks of the developing world.
African map Yet, it warns, adopting ready-made AI tools from Silicon Valley and Beijing is not enough either. The real gains lie in adapting them to local realities: languages, infrastructure, weak connectivity, and the specific challenges their countries face.
AI practical use cases in developing countriesConsider the three forces that shape AI’s value. First, its capabilities. Artificial intelligence can perform cognitive tasks that usually demand scarce human expertise, diagnosing disease, forecasting weather, designing lessons, or managing public records. In places where specialists are few, this expands the effective supply of skilled support.
Second is concentration. A handful of companies in a few countries control the most advanced chips, models, and infrastructure. This creates dependency risks, yet it also means poorer nations can customise existing systems without starting from scratch.
Third are the complements. AI works best where electricity is reliable, the internet is available, schools produce capable workers, institutions function, and local data exist. Many developing countries still lack these foundations. Without them, the technology’s promise remains limited.
The World Bank therefore recommends a sequenced approach: adopt, adapt, and only later advance. Adoption of existing tools is the practical starting point.
Doctors can use diagnostic aids. Farmers can receive better weather guidance. Businesses can improve operations. Yet mere adoption falls short. Adaptation unlocks the largest benefits. Tools must work through text messages or voice calls for people without smartphones. They must respect local farming practices, educational curricula, and administrative systems. Building frontier models, the “advance” stage, is the most expensive and least realistic option for most nations in the near term. It requires vast computing power, massive datasets, and top talent that remain concentrated elsewhere.Evidence of early adaptation is already emerging. In Bangladesh, AI-powered medical imaging has increased the number of patients screened daily for diabetes-related eye problems by 40%. In India’s Telangana state, artificial intelligence weather forecasts have delivered savings of up to $560 per small farmer. In Ghana, a tutoring system designed for basic phones and weak connections produced nearly a full year of mathematics learning gains for as little as $5 per student. These are not abstract possibilities. They show how modest, context-aware models can stretch limited expertise further.The labour market picture reinforces the case for adaptation over disruption. Only 4.5% of jobs in low- and middle-income countries face high automation risk from generative AI, compared with 14.2% in high-income economies. At the same time, about 16.2% of jobs in developing countries could see meaningful productivity gains, nearly matching the 18.7% figure for richer nations. Because many workers in the Global South still perform manual or less cognitive tasks, artificial intelligence is more likely to amplify their efforts than replace them. The technology can lend a hand rather than take a job.
Governments hold the decisive role. As enablers, they must build the basics: reliable power, affordable connectivity, foundational education, and better data systems in local languages. Initiatives that expand electricity access, such as those targeting hundreds of millions in Sub-Saharan Africa, become even more critical. As users, governments can deploy their purchasing power to test and scale solutions in health, education, agriculture, and public administration. As regulators, they should start with voluntary industry standards, apply existing laws to clear harms, and cooperate internationally to avoid fragmented rules that block access.The risks of inaction are real. Without stronger complements, the technology could widen gaps between countries and within them. Dependency on a few foreign providers may grow. Trust could erode if systems embed bias or compromise privacy. Energy demands may rise. Yet the report remains measuredly optimistic. Even under cautious assumptions, AI could lift potential growth rates in developing economies above the dismal averages of recent years.The window is narrow; the technology is moving faster and proves more context-specific than earlier technologies. Countries that treat it as a plug-and-play import will capture limited value. Those that treat adaptation as the core strategy, reshaping tools for their people, languages, and challenges, stand to solve problems that have resisted solutions for generations.The World Bank’s 2026 blueprint is not a call to join an expensive global race for the most powerful AI models. It is a practical invitation to make the technology work where it is needed most. For Africa and other developing markets, the smartest move is not to advance at all costs but to adapt with purpose, urgency, and clear-eyed focus on local realities.
