An Africa AI Strategy Is a Means. Competitive Advantage Is the Goal

By Roger Jantio

The first in a three-part series, this article argues that the first generation of African AI policies focused on readiness. The second must focus on productive adoption and competitive advantage.

Africa no longer faces an artificial intelligence awareness problem. Governments across the continent are publishing national strategies, creating advisory bodies, drafting regulations, and consulting businesses, universities, and civil society. This is progress. But an AI strategy is not an outcome.

A country does not become competitive because it has published an impressive document, established an AI council, or convened another summit. It becomes competitive when its firms produce more, its entrepreneurs build valuable companies, its institutions work better, and its solutions succeed in domestic and international markets.

African countries definitely need AI strategies. The key issue now is whether those strategies will help make their economies more competitive. That distinction should define the second generation of African AI policy.

From readiness to competitiveness

The first generation has understandably concentrated on readiness: electricity, connectivity, computing capacity, skills, data, regulation, and institutional preparedness. These are necessary foundations. They are not the destination.

Kenya illustrates both the progress made and the transition required. Its draft Artificial Intelligence and Other Emerging Technologies Policy addresses governance, infrastructure, human capital, innovation, public trust, and strategic autonomy. These are serious priorities. The harder question is what measurable economic advantage these foundations will produce: higher farm yields, more efficient industries, and local AI firms capable of scaling across borders. These are questions of competitiveness, not readiness.

AI competitiveness is a country’s ability to use, adapt, and produce AI-enabled solutions that raise productivity, strengthen institutions, create competitive enterprises, and capture value in domestic and global markets.

It does not require every country to build a frontier foundation model or compete with the United States and China across the entire AI stack. Countries can build competitive advantage in selected sectors where they already possess knowledge, demand, data, or difficult problems worth solving (agriculture, healthcare, mining, logistics, education, financial services, or trade).

Michael Porter taught that national advantage is created rather than inherited. Countries become competitive by improving productivity, fostering innovation, building capable institutions, and continuously upgrading their industries. AI changes the tools available to nations, but not that underlying logic. 

AI may broaden the range of countries able to create such advantage. A small economy need not control global computing infrastructure to develop an excellent agricultural advisory system, a trusted trade-intelligence platform, or a diagnostic application designed for resource-constrained health systems.

AI does not eliminate differences between nations. It changes some of the sources from which competitive advantage can be built.

Productive adoption, not passive consumption

Africans need little encouragement to experiment with AI. Workers, students, entrepreneurs, and businesses are already adopting tools that save time, lower costs, or improve decisions. As those tools become cheaper and easier to use, adoption will accelerate.

The policy challenge is not merely to increase the number of people who have tried an AI application. It is to encourage productive adoption.

Passive consumption occurs when individuals and firms use imported tools without materially changing what they produce, how they compete, or where the resulting value accumulates.

Productive adoption, by contrast, occurs when AI strengthens the user’s own capabilities, such as a farmer improving yields, a manufacturer reducing waste, a bank developing a better credit product, a health provider reaching more patients, or a startup turning local expertise into an application that can be sold elsewhere.

This is not an argument against foreign AI technology. An African company can use an American, Chinese, European, or open-source model and still create significant local value. The important question is whether its use leaves African firms more capable, more productive, and better able to compete. A continent of AI-enabled producers and problem-solvers would be building economic power.

The informal economy will be the decisive test. Too many technology policies implicitly treat the economy as a collection of ministries, banks, large companies, universities, and registered startups. Yet millions of Africans earn their livelihoods through small farms, markets, workshops, transport businesses, and informal commercial networks.

For a trader, AI could improve inventory management and demand forecasting. For a farmer, it could provide advice on weather, pests, and market conditions. A mechanic could improve diagnosis. A transporter could optimize routes. An artisan could identify customers beyond the immediate neighborhood.

If AI adoption remains concentrated among large corporations and central governments, Africa may become more digitally sophisticated without transforming its underlying economy. If AI reaches smaller and informal enterprises, the effects could spread much further.

Better records can improve access to credit. Digital transactions can strengthen commercial identities. More productive businesses can join formal supply chains and contribute more consistently to public revenue. Formalization would then be driven not only by enforcement, but by the commercial value of becoming more visible.

The IMF’s recent report, Unlocking the Potential: AI in Sub-Saharan Africa, documents the continent’s infrastructure, skills, and institutional constraints. It estimates sharply different outcomes between current conditions and faster, broader adoption, while acknowledging that its lower figures are diagnostics rather than forecasts of Africa’s technological potential. It also recognizes that wider diffusion, especially into agriculture and other sectors traditionally considered less exposed to AI, could produce much larger gains. 

Government enables; businesses compete

Governments cannot create competitiveness by decree. Businesses, entrepreneurs, investors, universities, workers and customers will make most of the decisions that determine how AI is adopted and where economic value is created.

Governments should therefore focus on what only governments can do: provide reliable infrastructure, establish trusted rules, strengthen institutions, open useful public data, improve access to regional markets, and use procurement to create early demand for effective solutions. Coordination should connect the agencies responsible for education, infrastructure, trade, investment, and regulation around a limited number of economic priorities, not centralize innovation inside another bureaucracy.

Outreach also matters, but it must be practical. Professional associations, cooperatives, and small businesses need to understand how AI can solve real problems, where it cannot be trusted, and how it can be used safely. The objective is not to persuade people that AI exists. It is to reduce the distance between technological capability and productive use.

The Atlantic Council’s Commission on AI offers a useful lesson: innovation and integration must advance together. Frontier innovation creates little national advantage if it does not diffuse through the economy. For Africa, that requires institutions capable of planning, procuring, managing data, evaluating vendors, and learning from implementation.

An actionable strategy should therefore answer four questions: Who is responsible? What must be delivered within the next year? How will it be financed? How will progress be measured?

Long-term visions can provide direction, but they cannot substitute for annual delivery. The African Union’s Agenda 2063 should be a horizon, not a permission slip for delayed execution.

The first generation of African AI policy asked whether countries were ready. The second must ask where they can compete, what they can build, and how quickly they can execute. Success will be visible in businesses adopting AI as producers and problem-solvers, institutions delivering better services, entrepreneurs building scalable companies and technology reaching the productive heart of Africa’s formal and informal economies.

An Africa AI strategy is a means. Competitive advantage is the goal.

Roger B. Jantio is an AI investor and strategic advisor focused on artificial intelligence, development finance, emerging markets, and strategic capital. He is the founder and CEO of Sterling Merchant Finance Ltd, a Washington-based merchant bank active across Africa for more than three decades, and General Partner of its affiliated investment funds.www.linkedin.com/in/roger-jantio-3262a113