These are not incremental advances within an existing technology; they signal the emergence of a general-purpose technology on the scale of electricity or the internet—one whose effects will reshape productivity, labor markets, and competitive dynamics across every sector of the global economy. Recent academic and policy work suggests that AI could support growth in Sub-Saharan Africa and other developing economies, but that the gains are likely to depend heavily on complementary conditions—especially skills, connectivity, reliable power, institutional quality, and the capacity to absorb and adapt the technology—whereas weak infrastructure and capabilities could also limit adoption and widen gaps.
The global implications are already visible. The five largest technology companies—Google, Microsoft, Meta, Amazon, and Oracle—are projected to spend approximately US$700 billion on AI infrastructure in 2026. And although this investment could raise productivity growth materially over the next decade, the gains are unlikely to be evenly distributed. In advanced economies, AI is already augmenting professional services, automating routine tasks, and restructuring entire industries. In Sub-Saharan Africa, by contrast, adoption capacity remains constrained. Critical inputs, such as reliable electricity, high-capacity connectivity, technical skills, and functioning institutions, are often absent, suggesting that AI-related productivity gains may remain limited. At the same time, AI could help bypass (leapfrog) existing bottlenecks, like mobile phone technology in the past.
For Sub-Saharan Africa, the main risk is not disruption but irrelevance, with economies better positioned to exploit AI pulling further ahead, whereas less-prepared countries fall ever farther behind. This report examines how Sub-Saharan Africa could achieve a high-growth path under accelerated AI adoption. The constraints across the region are substantial, but they are not immutable.
In the past, the region has demonstrated that structural disadvantage does not preclude rapid adoption when enabling conditions are in line. Policy can relax the binding constraints in skills and institutional capacity; investment can build the power, connectivity, and compute infrastructure that AI requires; and regional coordination—on data standards, shared infrastructure, and harmonized regulation—can achieve the scale that no single country in the region can reach alone.
The sections that follow discuss the region’s AI readiness and quantify the potential effect of AI adoption on productivity and growth, taking individual countries’ current circumstances and readiness into account. After providing an overview of the barriers, risks, and opportunities that currently confront the region’s policymakers, the paper then considers the gains that could be realized if existing obstacles were addressed. Finally, it sets out a pragmatic policy agenda grounded in a simple premise: The region’s preparedness deficit is largely a policy problem, not a capability constraint.
However, with long implementation lags in energy and digital infrastructure, each year of inaction risks entrenching a productivity divergence that may prove difficult to reverse. Importantly, the key to benefitting from AI is not (necessarily) to be at the frontier of AI development but to be able to adopt AI quickly and broadly.
A Guest Editorial