By Olamide Adeyeye, Country Head of Programmes, Jobberman Nigeria
Artificial intelligence is rapidly becoming the language of ambition. In boardrooms, classrooms, policy circles, and startup communities, AI is now spoken of as though it were the one unavoidable road to relevance. The pressure is immense: adopt it, integrate it, teach it, scale it, or risk being left behind. But amid all the excitement, Africa must pause long enough to ask a more important question than whether AI is powerful. We must ask who it serves, what it displaces, and whether the terms of its adoption actually fit our social and economic reality.
That question matters because Africa’s development challenge is not the same as that of the countries currently setting the pace of the global AI race. Much of the momentum behind AI adoption in North America, Europe, and parts of East Asia is being driven by ageing populations, high labour costs, and tightening labour markets. In those places, automation is often sold as a practical response to worker shortages. Africa, by contrast, is young, expanding, and under immense pressure to create livelihoods at scale. Nigeria alone had an estimated population of 237.5 million in 2025, with 41 per cent aged 0–14, 56 per cent aged 15–64, and only 3 per cent aged 65 and above. This is not a country defined by labour scarcity. It is a country defined by a vast and rising youth population looking for entry points into productive life.
This difference is not cosmetic. It is foundational. If a society has too few workers, aggressive automation can look like a relief mechanism. If a society has millions of young people trying to enter the economy each year, the same automation can narrow opportunities before institutions are strong enough to create alternatives. What is efficient in one context can be destabilising in another.
This is why Africa must be careful not to import an AI future designed to solve somebody else’s problem. The current global AI economy is already highly concentrated. UN Trade and Development estimates that the AI market could reach 4.8 trillion dollars by 2033, but it also warns that the gains are being cornered by a narrow set of actors. Just 100 firms, mostly in the United States and China, account for 40 per cent of global corporate research and development spending, while leading tech companies each command market values of around 3 trillion dollars, rivaling the GDP of the entire African continent. AI is therefore not arriving in a neutral marketplace of equal participants. It is arriving through deeply unequal systems of ownership, infrastructure, capital, and control.
That should concern African governments and African companies. If adoption is mindless, what appears to be innovation can quickly become dependence: dependence on foreign models, foreign cloud infrastructure, foreign pricing systems, foreign product roadmaps, and foreign rules about how data is used and value is captured. We may celebrate speed while surrendering agency.
The case for caution becomes even stronger when one looks honestly at how African economies actually function. According to the International Labour Organization, nearly 83 per cent of employment in Africa and 85 per cent in sub-Saharan Africa is informal. That means the overwhelming majority of work on the continent is not taking place in highly digitised formal firms alone. It is happening in trading, repair, transport, agriculture, hospitality, construction, market logistics, informal manufacturing, neighbourhood enterprise, and countless human-facing services where trust, proximity, adaptability, and lived experience are central to value creation. In economies like these, the first question should not be how to replace people. It should be how to raise the productivity, income, resilience, and market reach of the people already doing the work that keeps the economy alive. That is a very different strategic orientation from the one implied by a lot of AI evangelism.
Even the global evidence on jobs is more nuanced than the hype suggests. The ILO’s 2025 study on generative AI found that while one in four workers globally is in an occupation with some exposure to generative AI, only 3.3 per cent of global employment falls into the highest exposure category. More importantly, the study concludes that job transformation is the most likely effect, not full automation, because most occupations still consist of tasks requiring human input. Clerical work remains the most exposed, while exposure is generally lower in low-income countries than in high-income ones. This should reduce panic, but it should also force discipline. If transformation is more likely than outright replacement, then the real policy question is not whether AI is coming. It is who gets upgraded, who gets shut out, and who carries the burden of transition. In Africa, where educational inequality, low digital access, and regional exclusion are already entrenched, that burden will not fall evenly.
There is another risk, quieter but deeply corrosive: the aspirational distortion that often shapes how young people imagine success. For too long, our societies have subtly taught young people to respect the suit more than the skill, the office more than the workshop, the banker more than the mechanic, and the polished digital career more than the work that visibly carries daily life. Yet in many communities, it is those mechanics, fabricators, traders, artisans, growers, transporters, builders, and repair workers who solve immediate problems, create circulation in the local economy, and often earn more sustainably than supposedly prestigious white-collar workers. When AI hype enters this environment, it can deepen that distortion. It can make millions of young Africans feel that meaningful work exists only in prompt engineering, machine learning, automation, or remote white-collar systems, while the sectors that actually absorb labour at scale are treated as second-class or backward. That is not just bad economics. It is bad social conditioning. A society that teaches its youth to despise the labour that sustains it is weakening its own productive foundations.
There is also a macroeconomic danger hidden beneath the corporate language of efficiency. When companies use AI primarily to cut headcount, they often treat wage savings as an uncomplicated gain. But workers are not only costs on a spreadsheet. They are customers, tenants, transport users, borrowers, parents, and spenders in the wider economy. A business that saves money by shrinking payroll may simultaneously be shrinking the disposable income of the very market from which it hopes to make future profits.
The World Bank’s recent framing is useful here. It argues that countries can only benefit meaningfully from digital and AI transformation when the foundations are in place: connectivity, computing, data, and capabilities. As of 2025, more than 2 billion people worldwide remained offline; internet use in low-income countries stood at just 23 per cent, compared with over 90 per cent in high-income ones; and nearly 800 million people still lacked official identification. The Bank also warns that nearly 800 million people in low- and middle-income countries risk being without work over the next decade, making investment in skills and workforce readiness essential. In such settings, a copy-and-paste AI strategy can widen exclusion faster than it creates opportunity.
It is important to state that none of this is an argument against AI. Africa should not sit outside this transition. It should not romanticise technological delay. But neither should it confuse speed with wisdom. The African Union’s Continental AI Strategy gets the tone right when it insists on an Africa-centric, development-focused approach and frames AI as something that should create high-value jobs, preserve and advance African culture, and be governed in ethical, responsible, and equitable ways.
That is the standard African policymakers and business leaders should adopt. Use AI where it augments teachers, health workers, farmers, traders, artisans, and small businesses. Use it where it reduces friction in logistics, payments, records, and public services. Use it to strengthen access, lower transaction costs, expand local language interfaces, and improve the productivity of the informal and small-enterprise sectors that already carry most of the continent’s labour burden. Build local capability, local governance, local datasets, and eventually local infrastructure. Do not make the mistake of treating displacement as sophistication.
African companies, especially, need a more disciplined framework for decision-making. Before adopting AI at scale, they should ask whether a tool expands value creation or merely cuts labour costs; whether it actually fits their customer base and operating environment; what reskilling plan accompanies its rollout; how much of the value chain remains foreign-controlled; and whether the adoption strengthens or weakens youth inclusion – especially for those in marginalised context. In a continent this young, that last question is not a footnote. It is the heart of the matter.
This is why at Jobberman, we believe Africa’s young people are not a surplus population to be managed around. They are our greatest asset. Any AI policy or business strategy that treats them as expendable, delayable, or collateral damage is not modern. It is shortsighted. The task before us is not to stop technological progress, but to shape it intelligently. We must build policies and programmes that expand the agency of young people, not narrow it; that equip them not only to use AI tools, but to own value, solve local problems, create enterprises, and strengthen the productive fabric of our economies.
If Africa gets this right, AI can become a tool of inclusion, productivity, and institutional renewal. But if we get it wrong, it will deepen dependence, widen inequality, and harden the exclusion of the very generation on which our future depends. Africa’s young people should not become collateral damage in the AI rush. They should be the reason we rethink it.
