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Let us begin with a simple question: Does the arrival of powerful algorithms automatically rewrite Africa’s development script, or does it simply add a new set of tools to old problems? The answer matters because the continent is already running both experiments and experiments-at-scale, from national AI strategies to corporate deals, while basic enablers remain thin.

Yes, AI can be transformational as seen elsewhere, but only when it is accompanied by policy, infrastructure, and requisite talent to move from slogans to sustained action. Otherwise, it risks concentrating gains in well-positioned firms and capitals while delivering marginal improvements for most people — if at all.

Consider scale and ambition first.

Estimates place AI’s potential economic gain for the continent at roughly $2.9 billion to $4.8 billion by 2030, an eye-catching and promising outlook, but modest in the face of Africa’s needs and population growth. At the same time, major commitments continue to be announced at regional gatherings, including a proposed $60 billion Africa AI Fund, which points to political will and headline-grabbing pledges.

However, funding announcements and delivery are different things; the practical question is whether pooled capital will be structured to finance basic enhancements in data, computation, and skills or mainly to underwrite commercially attractive startups.

Now examine the real bottlenecks. Only about 3 percent of the world’s AI talent resides in Africa, despite the continent’s youth and demographic growth. Data infrastructure is even thinner: African data centers accounted for less than 1 percent of the global total in 2024. Translating policy into capability therefore requires sustained investment in people and hardware, from GPUs to uninterrupted power and secure storage, not just a slew of strategy documents.

Some progress does exist in deals such as the Cassava-Nvidia collaboration (circa $720 million) aiming to build regional AI capacity across several countries. Still, analysts estimate more than $7 billion is needed to patch critical shortfalls in data, computing, and skills across the continent, a humbling reminder that a few marquee projects will not by themselves close systemic gaps that are key to unlocking the continent’s potential via AI.

Policy choices will also decide who captures value.

Do countries build open, shared infrastructure and public datasets that enable many local firms, or do they become markets for opaque, imported models that extract data and revenue? Several African governments have moved quickly to adopt national AI strategies; roughly a dozen national strategies and a handful of policies now exist.

But strategy documents without institutions are fragile. Permanent regulatory authorities, audit mechanisms, and budgets are the missing links that turn plans into durable systems. Modern governance must therefore answer questions about algorithmic liability, data classification, and cross-border flows, not just issue aspirational plans.

Sovereignty is not a slogan here; it is a practical constraint. When most data and computation live on foreign servers and most models are trained on non-African data, local needs, local languages, local health, and agricultural patterns get ignored. Integrating African languages into models is not cultural window-dressing; it is a technical requirement to reduce bias and increase accuracy for local use cases. Otherwise, the alternative is clear: relying on imported models that perform poorly where they matter most, while funneling value offshore.

Africa should stop treating AI as a novelty and start treating it as an infrastructure problem; invest in affordable computing, distributed data stores, and resilient power.

Hafed Al-Ghwell

There are real, measurable wins to point to. AI-driven mobile finance systems have already broadened access to payments and savings across East Africa, delivering concrete inclusion gains where mobile penetration is strong. Policy briefs and field studies show how AI in health screening, crop diagnostics, and flood prediction can multiply scarce human expertise. But those wins are uneven. When infrastructure or skills are missing, pilots with great promise remain just that, pilots.

The continent also faces distributional dangers. Historical patterns show technology often concentrates wealth; the firms that control models, compute, and focus on customer relationships capture disproportionate rents. The rise of AI may reinforce industrial concentration unless regulators act to preserve competition and broaden access to data and compute.

In parallel, automation risks are real: OECD analysis flags that roughly 27 percent of jobs globally are in occupations at high risk of automation, a blunt reminder that AI’s disruption is not only about unlocking productivity enhancements but also about confronting less than desirable social outcomes.

So, what should Africa do?

First, stop treating AI as a novelty and start treating it as an infrastructure problem; invest in affordable computing, distributed data stores, and resilient power. Second, treat data as a public good in many contexts, curated, accessible and governed so researchers and small firms can build relevant models.

Third, fund talent at scale; basic digital literacy, mid-level machine learning skills, and incentives to keep researchers building locally. Programs that train tens of thousands of citizens within compact; accredited pipelines are the high-yield option. Fourth, build institutions; independent AI authorities with audit powers, regional standards that allow economies of scale, and transparent procurement rules that prevent vendor lock-in.

Is AI ultimately a game-changer for Africa?

The honest answer is: yes, but only conditionally. It can be game-changing in sectors where cheap computation plus local data enhance productivity such as in financial services, basic diagnostics in health care as well as weather-smart agriculture, and where adoption complements, not replaces, local human skills. Yet without disciplined public investment, regional coordination, and legal frameworks that protect data and competition, the technology risks delivering big headlines and minimum impact in reality.

In short: AI is a powerful set of tools with demonstrable use cases in Africa, but it is not a substitute for institutional development. If African countries, regional bodies, and the private sector treat model access, data, compute and skills as national and continental public goods, AI could tilt growth, inclusion, and resilience in measurable ways. If they treat AI as simply another market to be serviced by global providers and headline deals, outcomes will concentrate and disappoint.

The goal should not be to mimic the AI race as defined by others, but to redefine the finish line by building intelligent systems that respond to local realities, reflect cultural diversity, and serve the public good. However, the window for action is quickly narrowing, and the proliferation of artificial intelligence will not wait for Africa to catch up. How the continent responds today, be it building institutional capacity, retaining talent, and firewalling digital sovereignty, will determine whether AI becomes a tool of emancipation or just another form of technological dependence.

• Hafed Al-Ghwell is senior fellow and program director at the Stimson Center in Washington and senior fellow at the Center for Conflict and Humanitarian Studies. X: @HafedAlGhwell