Artificial intelligence is moving from the margins to the centre of global economic and political power. Governments everywhere are scrambling to understand, regulate, and use it. Africa is no exception.

As we argue in our new paper on sovereignty in the Age of AI, the real challenge for governments in Africa is not building end-to-end AI systems but retaining the agency to shape their development and use.

AI sovereignty does not mean absolute control over every layer of the AI stack. It is agency under constraint. It shows up in the unglamorous details: whether governments can protect the public interest, shape markets, grow local capability, and keep credible options if access is restricted or withdrawn.

Right now, ambition is outrunning foundations. AI strategies are emerging faster than the institutions, budgets, and skills needed to deliver them. Councils are announced without clear authority or sustained funding. Pilot projects, often donor-backed, prove the concept, then stall at scale. Governments procure AI-enabled systems they cannot audit, do not fully understand, and may never be able to adapt to or exit.

This is not a technical gap. It is a political one.

AI is being introduced into sensitive parts of government, from revenue systems and social programmes to service delivery and security. Yet the infrastructure that makes advanced AI possible — compute power, cloud platforms, foundation models, reliable energy, and specialised talent — sits largely outside the continent.

No African country will be fully self-sufficient in AI. Advanced AI demands expensive compute, steady power, and scarce expertise that can leave as quickly as it arrives. Fiscal space is tight. Long-term investment in digital capability competes with debt servicing, wage bills, and basic services. Fragmented markets further weaken negotiating power.

The opportunity, therefore, lies less in building frontier AI systems and more in how effectively AI capabilities are deployed within the economy. This includes investing in applied use cases across public services, supporting smaller and specialised models tailored to local languages and sectors, and building the data, skills, and institutional pathways that allow AI to raise productivity at scale.

These constraints sit within a global AI economy where capital, compute, data, and rule-setting power are concentrated in a few places. The leaders in AI set standards, shape markets, and define the default rules others must follow. For African states, the danger is not dependence as such. It is asymmetric dependence, where exit is costly, bargaining power is weak, and policy space shrinks over time.

Sovereignty rarely disappears in a single moment. It wears down through routine choices: procurement contracts that hard-wire vendors, outsourcing that drains internal capability, and political incentives that reward speed over resilience. When systems cannot be audited, contested, or replaced, governments inherit risks they cannot explain to courts, parliaments, or citizens until the crisis arrives.

AI sovereignty does not mean eliminating dependence. It means choosing it deliberately. It means knowing what you rely on, why you rely on it, and what you do if it fails. It means controlling the terms: who selects the systems governments use, who can inspect and modify them, and who can switch providers.

If African governments are serious about AI sovereignty, three things must change.

First, invest in state capability, not just strategy. AI strategies without delivery capacity are political theatre. Governments need in-house expertise, tough procurement and contract-management functions, and regulators who can interrogate systems, not just approve them. For presidents and finance ministers, this is the real test: treating AI capability as core state infrastructure, no different in principle from energy or transport. This means funding it, staffing it, and building AI capability into the state itself, so it survives elections, reshuffles, and the end of pilot funding.

Second, stop confusing speed with progress. Outsourcing can be fast, and it can also be a trap. Proprietary systems may deliver short-term efficiency while steadily eroding long-term control. Interoperability, open standards, and diversified partnerships are not ideological preferences; they are practical tools for preserving policy space and keeping credible exit options.

Third, use regional scale as leverage, not rhetoric. No single African country can shape the AI ecosystem alone. But Africa can shift the balance through coordinated procurement, shared standards, regional compute and energy infrastructure, and aligned data governance. If regional integration is meant to underpin economic sovereignty, then AI must be treated as a regional public good, not a collection of national prestige projects.

The political temptation to outsource complexity, announce progress, and defer the hard work may deliver immediate political rewards, but the costs land later — on future governments, future budgets, and citizens who will live with systems they never chose.

AI sovereignty means choosing institutions over pilots, leverage over spectacle, and resilience over hype.

The most consequential AI decisions will not be made at summits or launch events. They will be made quietly in procurement contracts, budget negotiations, regulatory exemptions, and partnership agreements.

Africa is not late to the AI conversation. But it is early in the decisions that matter most. The choices are difficult, but they are still choices — ones that will shape who holds power, and on what terms, for decades to come.

Hilda Barasa is Senior Policy Adviser, Government Innovation, at Tony Blair Institute for Global Change.

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