A striking data point from a McKinsey report published earlier this month has prompted one of Nigeria’s most prominent policy voices to issue what amounts to an unsolicited briefing note to African governments: the window for strategic positioning on artificial intelligence is not years away. It is closing now.
Writing on his Facebook page, Osita Chidoka, former Nigerian Aviation Minister, drew on McKinsey’s “The AI Assembly Line: Strategic Imperatives for CEOs“ to construct an argument that should be circulating in every African finance ministry, planning commission, and presidential advisory council.
The core exhibit is China’s electric vehicle sector.
Chinese automakers have collapsed the development cycle for a new vehicle to just 24 months, deploying AI across research, design, and software integration to lower barriers so dramatically that more than 50 new electric vehicle brands have entered the Chinese market in five years alone.
The result is not just industrial efficiency, it is market conquest.
Chinese brands have captured more than 50 percent of their home market for the first time, while non-Chinese joint ventures have watched their share fall from 64 percent in 2020 to approximately 43 percent today.
“That is what happens when a country decides to industrialise intelligence,” Chidoka wrote. “The question for Africa is whether we read those numbers as news from a distant country or as a prompt.”
The African Parallel is Not Theoretical
Chidoka’s argument does not rest on abstract technological ambition. It is grounded in sectors African economies already possess: agriculture, food processing, environmental management, urban administration, and public service delivery.
The illustration he offers is precise. A maize farmer in Benue State, Nigeria, equipped with AI tools capable of predicting rainfall, detecting leaf disease from a smartphone photograph, timing harvest windows, and benchmarking crop prices against the regional market, is not simply a better-equipped smallholder.
He becomes, in Chidoka’s framing, “a node in a system.” Multiplied across ten million farmers, that transformation constitutes the beginning of a continental economic shift.
The policy implication is direct. Africa does not need to build its own electric vehicle sector to absorb the lesson from Shenzhen. It needs to apply the same logic, AI-accelerated compression of inefficiency, to the sectors it already leads and the populations it already serves.
The Scaffolding Requirement: A Four-Point Infrastructure Doctrine
Chidoka is careful not to let the argument tip into wishful thinking, and this is where his post transitions from commentary into something closer to a policy specification.
He identifies four non-negotiable infrastructure conditions without which AI deployment in African contexts produces no returns:
Reliable electricity. Broadband that reaches the village. Data centres on African soil. And a regulatory posture that is, in his precise formulation, “light enough to invite capital and firm enough to protect citizens.”
Each of these is a policy decision, not a market outcome. Electricity reliability is a function of generation investment and grid governance.
Rural broadband is a function of universal service frameworks and spectrum policy. Sovereign data infrastructure requires deliberate industrial policy, not passive reliance on hyperscaler cloud presence.
And regulatory calibration, the balance between enabling innovation and protecting citizens from algorithmic harm, data exploitation, and market concentration, is arguably the most consequential and least-addressed dimension of African AI governance today.
“Without that scaffolding, AI is a poster on a wall,” Chidoka writes. It is a line that deserves to be quoted in policy documents across the continent.
The Labour Question: A Historical Precedent African Policymakers Must Internalise
One of the most politically sensitive dimensions of the AI transition, its displacement of human labour, is addressed head-on.
“No human can outrun a tractor. No farmer can out-harvest a combine harvester. Yet we did not abolish farmers when those machines arrived. We taught them to drive.”
The framing is historically accurate and politically important. Africa experienced its own version of mechanised agriculture’s arrival, and the lesson was not the abolition of farming as a livelihood, it was the transformation of what farming requires. The same logic applies to AI’s arrival as what Chidoka calls “the tractors of cognitive work.”
The policy directive embedded in this framing is clear: African governments should not be designing AI policy primarily around protection from displacement.
They should be designing education systems, vocational frameworks, and labour market institutions around capability acquisition. The worker who learns to operate the machine is not displaced by it. The worker who does not is.
The Education Imperative and the Two-Track Problem
Chidoka discloses his own institutional response to this challenge, the Mekaria Institute of Technology and Administration (MITA), designed for the 18-to-20-year-old entering the labour market after secondary school, offering diploma programmes reinforced by global certifications, practical project work, and university pathways, accessible both online and in-person.
But he is explicit about the limits of this intervention and, by implication, the limits of any single institutional response.
The young person MITA is built for, he notes, is “the one with a phone and a path.” Her cousin in a village three hours away may have neither. “Reaching her is the longer task, and one we have to do in parallel, not after.”
This distinction contains a policy challenge that African governments have historically managed poorly: the tendency to build for the already-connected while deferring the harder and more expensive work of reaching the unconnected.
In the AI transition, that deferral is not a phased approach, it is the compounding of a structural disadvantage.
Every year that rural, offline, infrastructure-poor populations are excluded from AI-augmented economic activity is a year in which the gap between them and connected populations widens irreversibly.
The two-track requirement, serving the reachable now while investing in the infrastructure to reach the unreachable in parallel, is a fiscal and governance challenge that no private institution can resolve. It is a job for governments, multilateral institutions, and the development finance architecture that exists precisely for this purpose.
The Policy Advisory in Plain Language
Read in aggregate, Chidoka’s post constitutes a five-point advisory to African governments that deserves formal circulation:
One, treat AI as an industrial policy question, not a technology enthusiasm question. China’s EV lesson is not about cars.
It is about what happens when a government decides to systematically lower the cost of doing complex things at scale.
African governments should be asking which sectors they want to transform at speed, and building the enabling environment accordingly.
Two, invest in the four infrastructure prerequisites without which AI deployment produces no development returns: stable electricity, rural broadband, sovereign data infrastructure, and calibrated regulation.
Three, reorient education and skills policy around capability acquisition, not credential distribution. The diploma that confers no practical skill is not a safety net, it is a false assurance that leaves young people underprepared for a labour market being restructured around them.
Four, address the connectivity divide in parallel with the skills agenda, not sequentially. The unreached population cannot wait for a later phase.
Five, act with urgency calibrated to the pace of change, not the pace of African policy cycles. The development cycle for a vehicle is now 24 months in China. The development cycle for an African AI policy framework should not be longer.
“Africa does not have the luxury of waiting to see how this settles,” Chidoka concludes. “The tractor is already in the field.”
*Osita Chidoka served as Nigeria’s Minister of Aviation and previously as the Corps Marshal of the Federal Road Safety Corps (FRSC). He is the founder of the Mekaria Institute of Technology and Administration (MITA).
