Nigeria has an unusual AI problem: its people appear to be learning faster than its companies are changing. Business A.M. reported on August 24 that Nigeria ranked sixth for workforce AI literacy among 25 leading outsourcing destinations, yet only 19th for enterprise AI adoption. The gap between the two scores was 32 points.
That gap should change how business leaders think about adoption. The obvious response is to buy more tools or send more employees to training. But if workers already have substantial AI literacy, the bottleneck increasingly sits inside the organisation. Individual skill has to survive contact with real workflows, real data, real exceptions, and coworkers who did not build the original solution.
That is why Nigerian companies should add a transfer test before they scale an AI workflow.
A transfer test asks a simple question: can a second qualified employee who did not create the workflow operate it, verify consequential outputs, handle realistic exceptions, and recover when something goes wrong? If the answer is no, the company has created a talented user, not an organisational capability.
The distinction matters because AI adoption can look more mature than it really is. A strong employee may build a useful process for drafting proposals, analyzing customer feedback, forecasting demand, preparing management reports, or screening routine requests. The workflow may save hours while that employee is present. Yet the apparent gain can disappear when the employee goes on leave, changes roles, leaves the company, or simply faces an exception that the original prompt or automation never anticipated.
For 30 days, companies should track five things. First, record the intended business outcome, such as faster quote turnaround, fewer unresolved customer requests, shorter reporting cycles, or higher conversion from qualified leads. Second, record the human verification and correction time that the workflow creates. Third, classify exceptions that require judgment beyond the normal process. Fourth, record how often the original builder has to step in. Fifth, hand the workflow to another qualified employee and measure whether the result remains stable.
This approach fits the direction Nigeria’s technology institutions are already taking. NITDA recently described its own digital transformation as a process of redesigning workflows before automating them, and said its technology deployment is driven by business value rather than trend adoption. The agency also reported that staff across departments are using AI to improve workflows after mandatory training. The lesson for private companies is that training works best when it sits inside process redesign and measurable operating goals.
The transfer test also exposes a hidden form of AI debt: undocumented dependence on one person’s judgment. A workflow may include prompts, connectors, spreadsheet logic, model settings, workarounds, customer context, and informal rules that exist only in the builder’s head. That dependence stays invisible until another employee tries to run the process. The handoff reveals what needs documentation, what requires better controls, and where human expertise still carries the system.
A company should not expect the second operator to reproduce every keystroke. The goal is to reproduce the business result safely. If a different employee can use the workflow, explain why its consequential outputs deserve trust, recognise when to stop, and recover from common failures, then the company has evidence that the capability belongs to the organisation rather than to one enthusiastic adopter.
This changes the role of AI champions, too. Their job should extend beyond demonstrating clever use cases. They should make those use cases teachable, inspectable, and transferable. A champion who saves ten hours a week is useful. A champion who enables five colleagues to produce the same quality of result without constant rescue creates far more durable value.
Nigeria’s workforce AI literacy is an advantage. But literacy alone does not close the enterprise adoption gap. Businesses capture the value when individual know-how becomes repeatable work that other people can operate, verify, and improve.
Before approving the next round of AI spending, Nigerian executives should ask for evidence from the transfer test. If the workflow cannot survive a change of operator, it is not ready to scale.
- business a.m. commits to publishing a diversity of views, opinions and comments. It, therefore, welcomes your reaction to this and any of our articles via email: comment@businessamlive.com
When life doesn’t pause for promotion