Africa Could Miss the AI Boom Unless It Fixes Power, Internet and Skills

Sub-Saharan Africa's AI future may be decided by something more basic than algorithms: whether farms, schools, clinics and small businesses have reliable electricity, affordable internet and the skills to use emerging technologies effectively.

The International Monetary Fund's report, "Unlocking the Potential: AI in Sub-Saharan Africa," argues that the region's economic gains from AI will depend on far more than access to technology. Reliable electricity, affordable internet, stronger digital skills and trusted regulatory systems will determine whether AI raises productivity across the wider economy or remains concentrated among large firms and well-connected urban users.

The report estimates that, at current levels of preparedness, AI may add only about 0.2% to sub-Saharan Africa's GDP over the next decade. If countries strengthen infrastructure, skills and institutional capacity while extending adoption beyond digitally connected businesses, the gain could rise to roughly 4% over the same period, equivalent to nearly half a percentage point of additional annual growth.

The real AI opportunity lies outside the technology sector

Much of the global debate about AI revolves around software developers, consultants, call centres and office workers whose tasks may be automated. In sub-Saharan Africa, the technology's greater economic value may lie in sectors that rarely feature in discussions about the digital economy.

For example, agriculture employs a large share of the region's workforce, yet productivity and crop yields remain far below their potential. AI-supported services could help farmers decide when to plant, identify pests, manage fertiliser use and respond to changing weather conditions. These tools do not need to arrive through expensive computers or sophisticated machinery. Advice delivered through mobile phones could place practical information within reach of farmers who have little access to agricultural extension services.

Kenya's Agricultural Observatory Platform demonstrates how real-time weather and crop-management information can inform farming decisions. Digital-advisory trials in Ghana, Nigeria, Rwanda and Uganda indicate that such services may improve yields, particularly when farmers also have access to appropriate seeds, fertilisers and other inputs. AI-enabled crop monitoring in South Africa similarly points to the possibility of raising production while reducing waste.

However, technology cannot compensate for every weakness in an agricultural system. A farmer may receive accurate information about rainfall or disease and still lack credit, irrigation, storage, transport or affordable inputs. The value of AI will therefore depend on whether it is integrated with wider investment in agricultural services rather than promoted as a standalone solution.

The same principle applies in education. AI tutors, chatbots and even basic SMS-based learning tools can provide additional support where teachers are scarce or classrooms are overcrowded. Pilot programmes in Nigeria suggest that well-designed chatbot tutoring can generate meaningful learning gains. Rwanda's investment in school connectivity and digital skills shows how such tools can form part of a broader strategy to prepare students for a changing labour market.

In healthcare, AI is unlikely to replace Africa's overstretched doctors and nurses. Its more immediate contribution may be to help them work more effectively by supporting triage, diagnosis, patient monitoring and follow-up care. The opportunity is not a fully automated health system but better use of limited professional capacity.

Africa's jobs challenge turns productivity into an urgent priority

The debate matters because sub-Saharan Africa is entering a period of rapid labour-force growth. By 2030, the region is expected to account for roughly half of all new entrants into the global workforce. Creating jobs for this expanding population will be difficult enough. Creating productive, stable and better-paid work will be even harder.

Most workers in the region remain employed in informal microenterprises or smallholder agriculture, where productivity is substantially lower than in larger formal firms. In this context, AI's most important contribution may not be the creation of entirely new technology industries. It may be its ability to improve the performance of businesses and workers already operating across the economy.

Governments also stand to benefit. Data analytics are already being used by revenue authorities in countries including Kenya and South Africa to improve tax compliance and target audits. More effective revenue collection could strengthen governments' ability to finance infrastructure and public services.

However, these gains are not automatic. AI adoption in sub-Saharan Africa currently trails every other region. If firms in richer economies use the technology to raise output and reduce costs while African companies remain constrained by weak infrastructure, the global productivity divide could grow wider. AI could then reinforce the economic inequalities it is often expected to reduce.

The deepest divide may be between connected and excluded economies

Electricity is the first foundation of AI adoption. Digital platforms, mobile services, data centres and connected public systems cannot function reliably where power supplies are unstable or unaffordable. Broadband and mobile data are equally essential. A service may be technologically available but economically irrelevant if households, schools and small businesses cannot afford to use it regularly.

Skills represent another constraint. Countries need specialists who understand data management, cybersecurity, procurement and AI oversight, but they also need wider digital literacy. Workers must be able to use new tools, recognise their limitations and adapt them to everyday tasks.

Without deliberate action, access may remain concentrated among large companies, highly educated workers and urban hubs. These groups have the capital, connectivity and expertise to adopt AI quickly. Rural communities, informal businesses and smaller public institutions could be left further behind.

The divide would carry social as well as economic consequences. Technology that raises productivity in a narrow section of the economy may increase national output while doing little for the majority of workers. It could also increase pressure on firms that cannot afford new systems, making it harder for them to compete.

Africa's previous experience with mobile money offers an important lesson. Financial inclusion expanded not because countries waited for every household to gain access to a bank branch, but because services were built around mobile phones that millions of people already used. AI could support another form of leapfrogging if applications are designed for basic devices, local needs and constrained environments.

The comparison should not be overstated. AI systems require large amounts of data, computing infrastructure, cybersecurity protection and continuing oversight. Reaching scale will be more complex than launching a mobile payment service. But the underlying principle remains relevant: adoption will depend on whether technology fits the realities of its intended users.

Trust will decide whether adoption moves beyond isolated pilots

Infrastructure may determine whether AI can function, but trust will shape whether people are willing to use it. AI systems raise concerns about privacy, cybersecurity, misinformation and opaque decision-making. These risks become especially serious when governments use automated systems in taxation, healthcare, education or the distribution of public services. Citizens and businesses need to know how decisions are made, how their data are used and how errors can be challenged.

Dependence on foreign technology providers creates another vulnerability. African governments and companies may have limited control over where data are stored, how models are trained, which languages are supported and how service prices change. Technologies developed elsewhere may also perform poorly when applied to local languages, institutions or economic conditions.

Governments will therefore need clear and practical rules covering data protection, competition, consumer rights, cybersecurity and public-sector use of AI. Regulation must protect citizens without creating costs that prevent smaller firms from experimenting or adopting useful tools.

Regional cooperation will be critical. Many African economies are too small to build complete AI ecosystems independently. Shared infrastructure, compatible data standards and coordinated regulation could provide the scale needed to attract investment and develop services suited to African markets.

The next phase should consequently be judged not by the number of AI strategies, conferences or pilot projects announced, but by whether successful applications can move into routine use. Governments and development institutions will need evidence that digital agricultural advice improves incomes, that education tools produce lasting learning gains, and that automated public systems are accurate, secure and accountable.