Why Africa should sequence, not rush into AI

At a small community hospital in Gabon, the patient register is still handwritten. Nurses flip through paper ledgers, sometimes misplacing entire patient histories. The contrast with global headlines about artificial intelligence (AI) could not be starker. While the world debates how generative AI will transform industries and accelerate global growth, many African economies remain stuck in paper-based systems that constrain productivity, inclusion, and competitiveness.

Across the continent, internet penetration stands at 38%, far below the global average of 68%.¹ According to the Africa Data Centres Association and the World Economic Forum, Africa accounts for less than 1% of global data center capacity—and far less of the GPU infrastructure that powers AI.² The continent produces under 1% of global AI research output and faces significant energy constraints for AI computing.³ This divide is not merely technological; it is a divide in opportunity, productivity, and participation in the emerging intelligence economy.

This paradox underscores Africa’s central challenge: The greatest risk is not missing the AI revolution, but joining it too early. Just as many countries once industrialized prematurely—importing factories before developing skilled labor, supply chains, and domestic markets—Africa now risks  premature automation: Adopting AI technologies before building the digital foundations to harness them productively.

History offers cautionary lessons. Ghana’s rapid state-led industrialization in the 1950s and 1960s—marked by ambitious factories and major hydroelectric investments—ultimately faltered because these industries had weak domestic linkages and depended heavily on imported inputs.⁴ Ricardo Hausmann’s work on economic complexity reminds us that countries grow by accumulating productive capabilities—skills, institutions, and interconnected sectors that enable more sophisticated production.⁵ AI can accelerate this trajectory, but only if it is layered onto economies already building those underlying capabilities. Dani Rodrik’s idea of premature deindustrialization reinforces this warning: Globalization and laborsaving technologies have narrowed the traditional industrialization ladder, eroding the employment and capability-building benefits Africa cannot afford to lose.⁶

The risk of a digital dependency cycle

In advanced economies, AI complements aging and high-cost workforces. In Africa, it could undercut the continent’s greatest asset: Its young and cost-competitive labor force. With nearly 12 million Africans entering the job market each year but only about 3 million formal jobs being created,⁷ unemployment and underemployment remain structural.

If deployed hastily, AI could displace workers in key sectors—such as call centers in Kenya and Rwanda, logistics operations in South Africa, or financial back-office services in Nigeria—before alternative employment opportunities emerge. Without sequencing, automation could deepen social vulnerability and instability.

Proponents argue that Africa cannot afford to wait. Indeed, AI holds immense promise for  agriculture  (precision mapping of yields),  health care  (diagnostic imaging and disease surveillance), and education (personalized learning tools). Yet the question is not whether to adopt AI, but how—and when.

Without sequencing, Africa risks becoming the world’s  raw data mine—exporting information, importing algorithms, and capturing little of the value. The echo with history is sobering: once raw minerals, now raw data. The danger is a digital dependency cycle, where AI models, platforms, and governance systems are designed elsewhere, while Africa remains a consumer rather than a producer in the digital economy. The result would be a replay of extractive development—only this time, in code.

A sequencing strategy for AI

Africa’s late-mover status is not a disadvantage if used wisely. By sequencing deliberately, countries can design guardrails before diffusion accelerates—avoiding the mistakes advanced economies are now scrambling to correct. Four priorities stand out:

1. Rule the data or be ruled by it

Data governance is now industrial policy. Regulatory frameworks must mandate digitization, interoperability, and data sovereignty. Interoperability is not just efficiency—it is power. When governments and local firms  own, analyze, and control data, they shape the AI economy rather than surrender it. Gabon’s directive on digitalization,⁸ Rwanda’s National Data Strategy,⁹ and Ghana’s Digital Economy Policy¹⁰ are early steps toward this sovereignty.

2. Invest in digital foundations

Digital public infrastructure—payments, digital IDs, e-signatures, and local data centers—is today’s equivalent of roads and power grids. In Gabon, linking small enterprises to regional payment rails like GIMACPAY¹¹ expands market access while generating structured datasets essential for AI. In Kenya, the combination of M-Pesa mobile payments and digital IDs has created a data ecosystem that now underpins fintech innovation. Without these building blocks, AI will remain a promise without a platform.

3. Regulate the pace of change

AI must be introduced at a pace economies can absorb—tested in sandboxes, piloted, and refined through feedback loops. In Gabon, a forthcoming Start-up and Digital Enterprise Act could create such pathways for innovation while keeping guardrails in place. This approach ensures that AI systems are trained on local data, refined by local feedback, and deployed for local needs—without destabilizing markets or displacing jobs prematurely.

4. Turn late-mover status into first-mover advantage

Africa can lead where others faltered. Brazil’s Pix  shows how a latecomer can set global benchmarks in digital payments, now serving over 160 million users.¹² Likewise, South Africa’s Artificial Intelligence Institute, co-led by the University of Johannesburg, is pioneering governance models that balance innovation with sovereignty and inclusion—¹³ something aging economies with entrenched systems struggle to achieve.

Getting the sequencing right

AI is not a disruption to resist, but a transformation to prepare for and shape. If unregulated, it could become Africa’s  premature automation trap—deepening unemployment, dependency, and inequality. Given the region’s rapidly growing population and the millions of youths entering the labor force each year in search of jobs, the margin for error is thin.

But sequenced wisely—anchored in infrastructure, regulation, ecosystems, and regional coordination—AI can unlock productivity, expand services, and accelerate Africa’s structural transformation. The path forward is not about slowing innovation, but about synchronizing it with Africa’s development goals.

Africa does not need to win a race it never signed up for. It needs to chart its own course: digitize before automating, secure data before exporting it, build capacity before importing platforms. This is not delay—it is strategy.

The lesson from Gabon is clear: When sequencing is right, digital foundations create transformative value and prepare systems for responsible automation. Done in this order, Africa can bridge the AI gap on its own terms—turning late entry into durable advantage while navigating the geopolitics of the emerging intelligence economy. This matters profoundly, because the global AI landscape is increasingly shaped by U.S.–China competition over data, chips, standards, and cloud infrastructure. Sequencing becomes a tool of sovereignty, enabling African countries to adopt AI on strategic terms, engage globally, and shape technology according to their own development priorities rather than absorbing systems designed elsewhere.

In the age of AI, sequencing—not speed—is Africa’s greatest competitive edge and the key to turning technological promise into shared prosperity.