As healthcare enters the age of artificial intelligence, data is becoming as strategically important as physical infrastructure. For Africa, this shift is profound.

The continent holds some of the world’s most diverse and underrepresented health data — critical for building robust global health systems — yet much of the infrastructure to analyze, govern, and benefit from that data remains external. Without deliberate action, African countries risk repeating a familiar pattern: supplying raw inputs while the value is captured elsewhere. To break this cycle, these countries must domesticate AI in health.

Domestication means ensuring African health data is not merely extracted, but governed, stored, processed, and translated into economic and public health value within African systems. That requires investment not only in governance frameworks that protect sovereignty, privacy, and equitable benefit-sharing, but also in the digital infrastructure, research institutions, and technical talent needed to move from data generation to AI development and deployment. It also requires regulatory ecosystems capable of validating AI tools for African populations and ensuring their ethical and clinically relevant use.

The institutions to undertake these actions already exist. The African Medicines Agency and Africa CDC provide a foundation to shape the governance of digital health and AI-enabled technologies. But there needs to be urgency because AI diagnostics are entering African health systems faster than regulatory frameworks can adapt.

This raises risks linked to algorithmic bias, poorly validated tools, and limited local value capture. Without intervention, these technologies could deepen inequities. With the right approach, however, African countries can collectively leapfrog legacy systems and help define global standards for ethical AI grounded in equity and public health needs.

Progress across the continent remains uneven. While countries including Rwanda, Kenya, South Africa, and Nigeria are advancing digital health and AI strategies, many health systems still face fragmented records, limited computational capabilities, and weak regulatory capacity.

Infrastructure remains one of the most critical constraints. Africa accounts for less than 1% of global data center capacity, and much of its data is stored or processed abroad. Estimates suggest that 70%-90% of African cloud traffic is handled outside the continent.

This is not simply a technical issue. In an AI-driven health economy, whoever stores and processes data is often best positioned to develop the models, set the standards, and capture the commercial value. Reliance on external infrastructure therefore risks leaving African countries dependent not only on foreign technology providers, but on foreign legal frameworks governing sensitive health information.

For Africa, traditional global health partnerships used to focus on service delivery, disease control, and financing. Increasingly, however, health partnerships are also centered on data ecosystems that carry long-term strategic and commercial value, prompting growing scrutiny over how health data is shared, governed, and monetized across borders.

The issue is not whether Africa should engage in global partnerships — it must. The question is on what terms. If African countries do not build the capacity to govern, store, and use its own data, it risks ceding control over its health future. Rising to this challenge will allow African countries not merely to participate in the AI revolution, but to help shape the rules, standards, and economic value that emerge from it.

Francisca Mutapi is Professor of Global Health Infection and Immunity at the University of Edinburgh.