Africa Can Grow Faster With AI—If It Moves Now

AI could help deliver the next leapfrog

A farmer in Kenya gets weather and planting advice on a basic phone. A teacher in Nigeria uses a chatbot to help students catch up in math. South Africa’s revenue authority uses data analytics to better target tax audits. These are not futuristic examples from Silicon Valley. They are early signs of the broader transformation that artificial intelligence (AI) could bring to sub-Saharan Africa.

AI will reshape the global economy. The question for Africa is whether it rides the wave or gets left behind.

Transformative potential

Our research shows AI’s promise, but it also points to significant risks and challenges. At current levels of preparedness, we estimate that AI will add just 0.2 percent to the region’s GDP over the next decade—little more than a rounding error. However, if countries can put the right foundations in place to accelerate adoption and extend the impact of AI beyond today’s digitally connected firms, the gains could rise to about 4 percent over the decade—nearly half a percentage point of additional growth a year.

That extra growth is critical given Africa’s vast jobs challenge. By 2030, sub-Saharan Africa will account for roughly half of new entrants into the global labor force. But the issue is not only the number of jobs needed—it is also their quality. Most workers are still in informal microenterprises or smallholder agriculture, where productivity is far below that of formal firms.

For the region, AI’s main promise is not about replacing office workers, but boosting productivity across the economy—helping informal firms manage inventory, enabling farmers to increase yields, and supporting mid-sized firms to transition to formality and export readiness.

The risk is that the opposite happens. AI adoption in sub-Saharan Africa currently lags well behind every other region. If richer economies race ahead while African firms and governments lag, the productivity gap between the region and the rest of the world will only widen.

The largest gains from AI may come in places people least expect. Much discussion today focuses on coders, consultants, and call centers. But, in Africa, the key question is whether AI can reach farms, schools, clinics, small businesses, and tax offices.

Agriculture is the biggest test. It employs a large share of the region’s workforce, but crop yields remain well below potential. AI tools can give farmers practical, low-cost advice—when to plant, how much fertilizer to use, how to spot pests, and how to cope with weather shocks. Kenya’s Agricultural Observatory Platform, for instance, shows how real-time weather and crop-management data can help inform farmers’ decisions. Trials in Ghana, Nigeria, Rwanda, and Uganda suggest that digital advisories can lift yields, especially when paired with better inputs. Similar results with AI-enabled crop monitoring in South Africa shows that technology can boost yields while cutting waste.

The same potential extends beyond farming. In education, AI tutors and even simple SMS-based learning tools can support students where teachers are in short supply. Recent pilot programs in Nigeria show that well-designed chatbot tutoring can deliver sizable learning gains. In Rwanda, digital-skills initiatives and expanded school connectivity show how AI can support a broader skills agenda.

In healthcare, AI will not replace Africa’s overstretched nurses and doctors, but it can help them do more by supporting triage, diagnosis, and follow-up care.

In public finance, AI-driven data analytics are already helping governments—from Kenya to South Africa—to strengthen tax compliance and mobilize revenue for development.

Africa has leapfrogged before. Mobile money succeeded by not waiting for every household to have access to a bank branch. Instead, it used a technology people already had—the mobile phone—to reach people traditional banking had left behind. AI could help deliver the next leapfrog; if it is affordable, useful, and trusted in the African context.

Delivering on AI’s promise

Two priorities for AI adoption stand out.

First, countries must build the foundations for broad adoption.

AI depends on reliable electricity, affordable broadband and data infrastructure, and workers with digital skills. That means investing in power and connectivity, supporting regional data infrastructure where viable, and strengthening digital and AI literacy through education and training. Countries in Africa do not need to develop the world’s most powerful AI models. But they do need the capacity to adopt, adapt, and scale AI quickly.

Second, build trust—and scale.

AI can widen inequality if its benefits are concentrated among large firms, skilled workers, and urban hubs. It also creates risks around privacy, cybersecurity, misinformation, and dependence on foreign providers. Governments need clear and practical rules on data, competition, consumer protection, cybersecurity, and the public sector’s use of AI. Regional cooperation will also be essential. Many African economies are too small to build AI ecosystems alone. But together they can create the scale needed for infrastructure, data standards, regulation, and markets.

AI in Africa is not just a technology policy issue—it is central to the region’s growth strategy. Africa does not need to win the race to build cutting-edge AI models, but it must find ways to use AI widely, cheaply, and safely. The window is narrow. Over the next debate, Africa’s young and growing workforce will either find more productive jobs, or watch the global productivity gap widen further. The outcome will not be shaped in Silicon Valley, but in the choices made across governments, schools, farms, and firms from Dakar to Dar es Salaam.

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Martin Schindler is an advisor, Nikola Spatafora is a senior economist, and Andrew Tiffin is a deputy division chief, all in the IMF’s African Department.

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This blog is based on the departmental paper, “Unlocking the Potential: AI in Sub-Saharan Africa ,” prepared by an IMF team led by Martin Schindler and Andrew Tiffin, under the general guidance of Andrea Richter Hume and comprising Kamil Dybczak, Marwa Ibrahim, Youssouf Kiendrebeogo, Jacinta Bernadette Shirakawa, Nikola Spatafora, and Solo Zerbo.