Schindler et al., “Unlocking the Potential: AI in Sub-Saharan Africa”

An IMF paper frames sub-Saharan Africa’s central AI risk less as immediate technological disruption than as failing to adopt, adapt, and scale the technology quickly enough to share in productivity and growth gains. Using country-level estimates, adoption scenarios, and emerging African use cases, the authors identify unreliable and insufficient electricity, limited digital infrastructure, scarce technical skills, and gaps in regulatory and institutional capacity as the main constraints on adoption.

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Analysis byAIImpactLab Editorial Desk

PublishedJuly 21, 2026

Reading time3 minutes

Why it matters

The argument shifts AI policy from model procurement to systems capacity. Electricity reliability and digital connectivity determine whether services can run; education and technical skills determine who can build and adapt them; and regulatory capacity determines whether deployment earns public trust.

Treating the region as one market would hide large differences in readiness and need. The useful policy question is therefore where adoption foundations are weakest and which locally relevant applications can produce measurable gains without deepening dependence on external infrastructure and providers.

Primary trail

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International Monetary Fund — Unlocking the Potential: AI in Sub-Saharan Africa

Why it matters

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