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By Benson Michael
Artificial intelligence has moved from the margins to the centre of policy discussion across Africa. Governments are publishing national strategies, announcing partnerships, and convening forums to show that they are paying attention to a fast-moving global shift.
The activity is real. What is less certain is whether it is translating into capability. That uncertainty sits at the centre of a recent widely circulated BusinessDay article, “What Africa Gets Wrong About AI—and What It Could Still Get Right” by Vincent Okonkwo, a technology governance specialist and AI business advisor whose work focuses on how new technologies interact with institutions and markets in developing economies.
Okonkwo’s does not dispute AI’s importance, nor does it question the need for, or place of, AI ambition. Its concern is narrower and more demanding: whether Africa’s current approach is framed in a way that can produce leverage rather than posture. At the centre of his argument is a simple observation.
Many AI strategies emphasise participation, ethics, and alignment with global norms, but give less attention to infrastructure, institutional capacity, cost, and execution. The documents are ambitious in tone but often unclear about how progress will be measured or delivered.
One of the key contributions of the article is Okonkwo’s separation of three issues that are often treated as the same thing: AI development, AI deployment, and AI adoption. He notes that much of what is presented as AI strategy in Africa is mainly about adoption. The focus is on using existing tools and platforms built elsewhere, rather than on building capacity or shaping how those tools are designed and applied.
This distinction matters because each area raises different challenges. Development requires skills, data, and sustained investment. Deployment depends on whether institutions can integrate new systems into existing operations. Adoption depends on access, affordability, and trust. When strategies blur these differences, priorities become unclear and expectations rise faster than capacity.
For businesses, the impact is immediate. When policy signals are broad and institutions are weak, risk becomes difficult to assess. Compliance is uncertain. Firms respond by keeping plans flexible and commitments short-term. In such an environment, policy does little to encourage serious investment.
Okonkwo also points to a longer-term risk. When countries focus on participation without building capacity, control over systems and standards tends to sit elsewhere. Governments may speak about sovereignty, but decisions about design and deployment are often made outside their reach. This outcome, he argues, reflects not a lack of ambition, but poor strategic focus.
The article does not call for disengagement from global cooperation. Okonkwo’s position is measured. Alignment can be useful, but only when it is backed by institutions that can act. Strategy must reflect what can realistically be built and sustained, not just what sounds right on paper.
As African countries continue to roll out AI strategies, this argument deserves attention. It does not call for delay. It calls for seriousness.
