A historical perspective on white-collar work

The phenomenon of administrative and clerical work as a significant economic force emerged with the Industrial Revolution and the rise of corporate bureaucracy in the late 19th and early 20th centuries—roughly 150-200 years ago. This represents a tiny fraction of human history – white-collar employment has always been a minority pursuit, and that minority is about to get smaller.

Globally, white-collar workers represent roughly 20-30% of the workforce in developed economies. This could drop to less than 20% as AI absorbs administrative, analytical, and creative tasks aligns with current automation forecasts. The McKinsey Global Institute estimates that by 2030, up to 30% of current work hours in developed economies could be automated, with administrative and legal roles facing the highest exposure.

The demographic safety valve

Case study: Japan’s working-age population (15-64) has already fallen 15% from its 1995 peak of 87 million to 73.7 million in 2024 and is projected to drop below 60 million by 2040—creating a labour shortage of approximately 11 million workers. South Korea faces similar pressures, with one of the world’s lowest fertility rates (0.72 children per woman) and a rapidly aging population.

This demographic contraction could create a natural buffer. As white-collar roles decline, the simultaneous shrinkage of the workforce may absorb much of the displacement—provided these economies can manage the transition without severe social disruption. 

Immigration remains the wildcard; Japan would need to accept 500,000 foreign workers annually to stabilize its workforce, yet 2025 has seen a sharp anti-immigration turn that threatens this calculus.

Africa’s different trajectory

The Scale of Informality: The informal economy dominates African economic life to a degree that is often underappreciated in global policy discussions. According to UNDP research, the informal sector accounts for 50-80% of GDP in sub-Saharan Africa and employs between 75% and 95.7% of the non-agricultural workforce in countries like Mozambique, Haiti, Sudan, and Sierra Leone. This isn’t a marginal sector; it is the economy for most Africans.

White-Collar Reality Check: Research on occupational structures in Africa indicates that white-collar workers (clerks, administrative staff) constitute approximately 23.3% of the workforce—compared to 25.8% in other low-income countries. However, this figure includes a vast spectrum of employment quality. When we narrow to “high-skilled professionals”—the tier most vulnerable to AI disruption—the figure drops to just 5.9% of the workforce, less than half the rate in comparable developing regions.

This distinction matters. The African workforce isn’t composed of 23% knowledge workers at risk of displacement; it’s composed of perhaps 6% who might face AI competition, and 17% in clerical or administrative roles that AI could either eliminate or augment, depending on policy choices.

The productivity bridge

Will AI bridge the productivity gap between large corporates and informal micro-enterprises?

South African SMEs illustrate both the opportunity and the challenge. While over 60% of South African workers report using generative AI personally, most SMEs had not integrated AI into operations as of early 2024. The barriers are real: inadequate digital infrastructure, financial constraints, skills gaps, and resistance to change. Yet the potential is transformative.

Consider the mechanics: A vendor in Lagos, a tailor in Nairobi, or a farmer in Ghana currently operates without the specialised functions that large corporations take for granted. They cannot afford dedicated procurement officers, financial analysts, marketing departments, or customer service teams. AI changes this equation. 

For the cost of a smartphone and data connection, these entrepreneurs can access:

• Procurement optimization through AI-powered market analysis and supplier identification
• Financial management via automated bookkeeping, cash-flow forecasting, and credit scoring
• Digital marketing through generative content creation and targeted customer acquisition
• Customer service via multilingual chatbots operating 24/7

Global evidence suggests these aren’t theoretical benefits. SMEs adopting AI report 32% improvements in operational efficiency and 40% reductions in manual tasks. A family-run business using AI image generation saw ad click-through rates increase 22% and orders rise 21%—without hiring additional staff.

The infrastructure imperative

As of late 2024, only 29% of small businesses in emerging markets had successfully deployed any AI. The challenges are structural: unreliable electricity, limited connectivity, and the absence of contextually relevant training programs.

High-end computational resources remain “largely out of reach for the typical small-scale SME entrepreneur”. The risk is a “computational divide” where AI benefits aggregate at the top while informal enterprises remain on the periphery.

Reframing the AI Narrative

With serious policy action, Africa can “mute” the negative impacts of AI disruption by leveraging it for productivity gains in the informal sector. But it requires a pivot from current approaches.

The obsession with teaching coding skills misses the point. As one African AI strategist notes, “leaders don’t need to write Python; they need to understand the unit economics of AI”—

how to ensure algorithms don’t bake in credit bias, how to evaluate AI tools critically, how to manage AI-driven operations. This “cognitive literacy” or “managerial AI agency” is the real skills gap.

Conclusion

The global AI transition will indeed be a “recalibration,” but its impact will be distributed unevenly. Developed economies face the painful arithmetic of shrinking workforces meeting shrinking job categories. Africa faces a different equation: a growing workforce, minimal exposure to the white-collar roles most at risk, and an informal economy hungry for productivity tools.

The opportunity is not to preserve white-collar employment, but to democratize whitecollar capabilities. If African policymakers can bridge the infrastructure gap and prioritise managerial AI literacy over technical coding skills, the continent’s informal entrepreneurs may leapfrog directly to AI-augmented productivity—accessing for pennies the specialised functions that once required expensive human expertise.

This isn’t technological determinism. It requires active intervention: expanding digital infrastructure, creating accessible AI tools for low-resource environments, and building the cognitive literacy to deploy them effectively. But the raw material is there. Africa’s informal economy isn’t a problem to be solved; it’s a platform for a different kind of AI transition—one that augments human capability rather than replacing it.

The future of work in Africa may not be about finding jobs in the formal sector. It may be about using AI to make the informal sector work better.

About author: Dotun Adeoye is Co-Founder of AI in Nigeria.