South African companies are moving quickly from experimenting with artificial intelligence to letting systems plan, recommend and act across multi-step workflows. South African Business Matters recently highlighted research showing that African executives are especially likely to view AI agents as colleagues rather than tools, and that they expect agents to take on a growing share of current work. That confidence can be an advantage. It can also hide the small failures that usually arrive before a costly one.
Most organisations already record major incidents: a data breach, a fraudulent payment, a regulatory violation or a customer complaint that reaches senior management. They are less likely to capture the near misses that reveal how an AI-supported workflow is actually behaving. A near miss might be an invoice routed to the wrong supplier but caught before payment, a customer response that sounded authoritative but relied on stale information, or a recruitment shortlist that omitted a strong candidate until a manager intervened.
These events often disappear because the employee corrects them quietly. The immediate problem is solved, but the organisation loses the evidence needed to improve the system. As agents gain more autonomy, that habit becomes dangerous. The correction made by one alert employee today may become an unreviewed action across hundreds of transactions tomorrow.
South African firms do not need another heavy governance structure to address this gap. They need a simple AI near-miss register attached to the workflows where agents are being tested. South African Business Matters has rightly emphasised practical, low-friction use cases that reduce friction in work already being done. The same practical standard should apply to oversight.
For each near miss, the register should capture six facts: what the agent was asked to do; what went wrong or nearly went wrong; how the problem was detected; who corrected it; whether a customer, employee or supplier could have been harmed; and what change will prevent a recurrence. The entry should take minutes, not hours. Its purpose is learning, not blame.
That final point matters. Employees will not report near misses if doing so makes them look careless, disloyal or resistant to innovation. A company can publish an impressive AI policy and still learn very little if staff quietly conceal errors. South African Business Matters has also argued that a human-centric internal culture and psychological safety are part of organisational resilience. In AI adoption, they are also part of risk detection.
Managers should therefore distinguish between reckless behaviour and responsible disclosure. Using prohibited data or bypassing a clear approval rule requires accountability. Reporting that an approved system produced an unsafe recommendation deserves recognition. The employee who catches a subtle failure is not slowing adoption. That employee is providing operational intelligence that the vendor dashboard cannot supply.
A useful register also changes how leaders measure return on investment. Time saved is important, but it is incomplete. If an agent cuts processing time by 30 per cent while doubling the time experienced staff spend checking exceptions, the apparent gain may not be real. Leaders should compare speed with correction work, reversals, customer complaints, override rates and the number of repeated near misses. They should also ask whether the same people keep catching problems, because that may signal that essential judgment has not been built into the workflow.
The register should feed a short monthly review involving the workflow owner, an experienced frontline user and the relevant risk, legal, HR or technology leader. The group should look for patterns rather than isolated mistakes. Are failures concentrated around outdated data? Does the agent struggle with South African terminology, local customer contexts or unusual supplier arrangements? Are employees unclear about when to stop the system and escalate? A pattern of minor corrections can reveal a design flaw long before an audit or public complaint does.
This approach is especially important for smaller and mid-market businesses. They may not have dedicated AI governance teams, yet they are often adopting embedded agentic features through software they already use. A lightweight register gives them evidence without requiring a new bureaucracy. It also supports the straightforward goal described in practical guidance on AI governance without complexity: systems that work, remain safe and can be explained when something goes wrong.
The organisations that learn fastest will not be those that pretend their AI systems make no mistakes. They will be those that make small failures visible while the cost of correction is still low. South African businesses are well placed to move quickly with agentic AI. A near-miss register will help ensure that speed produces capability rather than hidden fragility.
