Dr. Mahamudu Bawumia speaking as the Keynote Speaker at the London School of Economics and Political Science’s 2026 Africa Summit
Former Vice President, Dr. Mahamudu Bawumia was the Keynote Speaker at the London School of Economics and Political Science’s 2026 Africa Summit.
Speaking on the theme: Artificial Intelligence and Uniting Borders, Dr. Bawumia stated the distinct difference between digitalisation and artificial intelligence (AI), which he described as the latest innovation of the digital age.
Dr. Bawumia pointed out the relationship between AI and digitalisation, stating that without digitalisation in a country, which he described as a foundation, AI cannot successfully take off, thereby calling on African countries that are behind in digitalisation to urgently prioritise it in order to catch up and embrace the new phenomenon, AI.
One important area Dr. Bawumia addressed was the deeply held misconception that AI is negative, as many have held the view that adopting AI means people losing jobs.
Dr. Bawumia, renowned as a champion of digital innovation, who believes in the ability of the tech industry to create millions of jobs for the youth, allayed the fears and misconceptions.
On the contrary, Dr. Bawumia points to how AI, once effectively prioritised and deployed, will be the catalyst for job creation and enhance productivity in many areas, including agriculture, education, and health.
Below is Dr. Bawumia’s presentation on the question of whether or not AI means robots taking over jobs:
Economic and social impacts: jobs, inclusion, and the “stack” reality
Now to the question that matters to every household: what does AI mean for jobs, incomes, and fairness?
We should be clear at the outset: most people do not experience “AI” as a single model sitting in a distant data center. They experience AI as a stack: connectivity, electricity, data, software tools, workplace processes, and the rules that determine whether technology complements their labor or replaces their income.
That “stack reality” is why the jobs debate cannot be reduced to slogans about “robots taking jobs.” It must be grounded in what happens in schools, clinics, farms, SMEs, and public services.
The International Monetary Fund estimates that almost 40% of global employment is exposed to AI-driven change, but exposure differs sharply by income group and job structure. In advanced economies, about 60% of jobs are exposed; exposure is about 40% in emerging market economies and 26% in low-income countries. The IMF’s point is not that “40% of jobs will vanish,” but that AI will reshape tasks and productivity across a large share of work and that countries that are less prepared may miss the productivity gains even if disruption arrives later. The same analysis also flags distributional realities: women and highly educated workers are often more exposed (even as they may benefit more), while older workers can struggle more in transitions, an important warning for labor policy and lifelong learning systems.
So, exposure is not destiny. Outcomes depend on whether workers can transition into roles where AI complements productivity or whether they face displacement without skills, mobility, and protection. This is why the fear-versus-hype framing is unhelpful. The policy challenge is to expand “high-complementarity” pathways for workers and firms, while reducing the costs of transition through training, job matching, and credible social protection. In early 2026, the IMF again emphasized that with nearly 40% of global jobs exposed to AI-driven change, governments need proactive policymaking to prepare workers and ensure gains are broadly shared.
The International Labour Organization adds task-level precision, which is essential for good policy, especially with generative AI. Its analysis finds that clerical work is the only broad occupational group that is highly exposed, about 24% of clerical tasks are highly exposed to generative AI, and another 58% have medium exposure, while most other occupational groups have far smaller shares of highly exposed tasks. That is why the ILO concludes that the dominant effect is more likely augmentation than full automation: AI automating some tasks inside jobs while leaving time for other duties. This matters for fairness because clerical work is a major pathway into formal employment in many countries, and it is often gendered; therefore, training, job redesign, and workplace standards must be built into the transition.
For Africa, the “stack reality” matters even more because our labor markets do not mirror the standard rich-country debate. A UN analysis reports that, on average, about 83% of African workers were in informal employment in 2024, and that women, young people, and rural populations are most likely to be informal. Many livelihoods will therefore not be disrupted mainly through direct automation of office tasks; they will also be shaped through other channels: pricing and visibility on digital platforms, access to credit and working capital, the efficiency of logistics, and the ability of small firms to comply with new digital requirements in value chains. In other words, the AI transition for Africa is as much about market structure and inclusion as it is about “machines replacing labor.”
If Africa gets this right, AI can become a productivity engine for agriculture, health, education, climate resilience, and digital trade but only if deployments are built around real workflows and constraints. World Bank emphasizes the “four Cs” for inclusive AI ecosystems: connectivity, compute, context (data), and competency (skills). It also highlights the rise of “small AI”, more affordable applications designed to run on everyday devices like mobile phones, already helping extend AI’s reach in sectors such as agriculture, health, and education. That is a practical lesson for Africa: inclusive outcomes will come not only from frontier models, but from well-designed systems that work on today’s practical infrastructure.
But if we get it wrong, AI will deepen inequality between countries and within countries. The IMF warns that differences in preparedness can widen cross-country income disparities because productivity gains accrue earlier and faster to more AI-ready economies. It also highlights a reshoring risk for developing economies: call centers are a concrete example of service activities that could be replaced by AI-driven solutions, shifting activity and jobs toward more technologically advanced economies. So, Africa’s jobs strategy must be built for transition: large-scale skilling, stronger labor-market information systems, protection against abusive or discriminatory algorithmic management, and deliberate support for SMEs so that AI adoption does not become a “big-firm advantage” that squeezes small producers out of markets.
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