Sequencing or Stagnation? Rethinking Africa’s Artificial Intelligence Strategy

By Sebaggala Richard (PhD)

 

 

In a recent Brookings commentary titled “Why Africa Should Sequence, Not Rush Into AI,” Mark-Alexandre Doumba argues that Africa’s greatest risk is not missing the AI revolution but joining it too early. Drawing on the work of Ricardo Hausmann and Dani Rodrik, the article cautions against what it describes as premature automation. The central concern is that without adequate digital infrastructure, data governance frameworks, and productive capabilities, rapid adoption of artificial intelligence could deepen dependency rather than accelerate structural transformation. It is a thoughtful intervention in an important policy debate and one that deserves serious engagement.

At the same time, the analogy underpinning the sequencing argument merits closer examination.  The thesis implicitly treats artificial intelligence as comparable to earlier industrial technologies such as factories, heavy manufacturing, or large-scale power infrastructure. In those historical periods, countries needed to accumulate domestic skills, supply chains, and institutional capacity before industrial investment could generate sustained returns. Where this foundation was weak, industrialization often produced enclaves with limited linkages to the broader economy.

 

Artificial intelligence operates in a different space. It does not primarily reorganize physical production; it reshapes how thinking and problem-solving are organized. It influences how research is conducted, how policies are drafted, how code is written, how diagnoses are made, and how information is processed. Its deployment is largely cloud-based and does not depend on ownership of heavy physical capital. More importantly, the use of AI tools itself contributes to skill formation. Individuals often develop competence through interaction, experimentation, and repeated application. Capability therefore grows partly through adoption rather than entirely before it.

 

This reality complicates the historical logic of waiting until foundations are fully consolidated. In earlier industrial waves, late entry sometimes allowed countries to observe pioneers, import mature technologies, and expand cautiously. In the current environment, the capability frontier moves quickly and continuously. Early adopters refine processes, accumulate institutional experience, and embed AI deeply into their systems. As experience compounds, catching up becomes more demanding.

 

The pattern is already visible at the individual level. Professionals who dismissed AI tools a few years ago often find that peers who experimented early have reorganized how they conduct research, prepare lectures, analyze data, and manage projects. The difference is not limited to marginal efficiency gains. It reflects changes in workflow, iteration speed, and analytical depth. When such shifts scale across institutions and economies, divergence becomes structural.

 

The labor market concerns raised in the sequencing argument are understandable. Automation can displace certain categories of routine work, particularly in service sectors. Yet many African economies have not developed large-scale industrial employment bases comparable to those that powered earlier development trajectories elsewhere. Informality remains widespread, and productivity gaps persist. In this context, the more pressing risk may not be premature deindustrialization but the failure to cultivate high-productivity knowledge and service sectors capable of absorbing a growing youth population.

 

Artificial intelligence should therefore be viewed not only as an automation technology but also as a productivity-enhancing instrument. It can strengthen agricultural advisory systems, support diagnostic processes in health care, enhance educational personalization, improve logistics coordination, and assist public administration. In environments where documentation remains paper-based and data fragmented; AI-assisted digitization and analysis can accelerate institutional modernization. In that sense, AI can contribute to building the very foundations that sequencing advocates consider prerequisites.

 

The concern about digital dependency is historically grounded. Africa’s experience with extractive development shows how exporting raw inputs while importing high-value outputs can entrench structural imbalances. A digital parallel could emerge if data is generated locally while algorithms, platforms, and standards are designed and controlled elsewhere.

 

However, dependency does not arise solely from early adoption. It can also result from disengagement. Global AI platforms will continue to expand regardless of cautious national strategies. Data ecosystems will evolve. Technical standards will consolidate. Countries that actively cultivate domestic competence are better positioned to negotiate terms, influence governance frameworks, and adapt systems to local realities. Sovereignty in the digital age depends not only on regulation but also on participation and expertise.

 

The labor dimension is equally nuanced. The relevant comparison is not between African workers and machines in isolation, but between workers who use AI effectively and those who do not. In global service markets, AI literacy is rapidly becoming a baseline expectation. Youth who master these tools strengthen their competitiveness in remote work, digital entrepreneurship, research support, and creative industries. Delayed exposure risks widening skill gaps that become increasingly difficult to close.

 

None of this diminishes the importance of governance, infrastructure, and regulatory design. Data protection regimes, interoperability standards, and digital public infrastructure remain essential pillars of a sustainable AI ecosystem. The question is whether these frameworks must be fully consolidated before meaningful adoption begins, or whether they can evolve alongside practical engagement. Institutional learning is often iterative. Policymakers refine regulatory approaches through exposure to real-world applications and emerging risks.

 

The strategic issue, then, is not whether Africa should move early or late. It is whether it will build the capacity to shape how AI is integrated into its economies and institutions. Artificial intelligence functions as a general-purpose technology that reshapes the production of knowledge and decision-making. Countries that embed it thoughtfully in education systems, research environments, entrepreneurial ecosystems, and public administration may realize productivity gains that conventional development models underestimate.

 

The debate should not be reduced to speed versus sequencing. It should focus on whether Africa approaches AI as a passive consumer or as an active capability builder. Postponement may appear prudent, but in a rapidly evolving technological landscape it carries opportunity costs that accumulate quietly yet persistently.

 

In this context, delay is not merely caution. It is a strategic position whose consequences deserve careful reflection.