What South African business leaders are really asking about AI

Notes from a recent roundtable in Cape Town, and why the hardest questions weren't about technology.

By Pieter de Villiers

I spent a morning recently in a room full of South African operators, people from finance, retail, software and infrastructure, comparing notes on how AI is actually landing inside their businesses. I went in half-expecting a debate about whether AI matters, but realise that debate is over. Everyone in the room had moved past it. What they were wrestling with was harder, and more interesting: not whether, but how, and underneath that, a quieter question about whom to trust and who carries the risk.

Three things stuck with me:

1.        Belief is settled; execution is the contest. Not one leader needed persuading that AI belongs in their business. Several had gone further than I expected — one chief executive described building working tools himself, with no technical background, simply by getting good at asking the right questions. The frontier has quietly moved. The companies that pull ahead won't be the ones that "adopt AI"; they'll be the ones that get it past the pilot and into the daily flow of work without it stalling, mis-firing, or quietly eroding trust along the way.

2.        The two hardest questions turned out to be one question. When the conversation got practical, two needs surfaced again and again. The first: people want a chatbot that knows when it is out of its depth — one that recognises the moment it should hand a customer to a human, with full context, instead of trapping them in a loop. The second, raised with real weight: who is accountable when the AI gets it wrong?

On the surface, one is a product gripe and the other a governance worry. They are the same thing. Both are questions about control — about whether a system knows its own limits and can be held to account when it reaches them. We have spent two years optimising AI to sound more fluent and answer more questions. The market is now asking for something less glamorous and far more valuable: AI that knows when to stop, hands off cleanly, and can show its work afterwards. Fluency was the easy part. Judgement and accountability are the parts that earn a place inside a regulated business.

3.        Adoption is a trust problem before it is a technology problem. The most sobering input came from the infrastructure end of the room, where AI-driven efficiency has collided head-on with organised labour. The lesson generalises well beyond any single company: when automation is framed as a way to cut people, it provokes exactly the resistance that kills the rollout — demands for transparency, for a seat at the table, for a guarantee that no one is managed or dismissed by an algorithm alone. That instinct is not anti-progress. It is a reasonable response to being on the wrong side of a black box. The organisations getting this right are flipping the frame: AI that gives people back time, keeps a human in control of consequential decisions, and is transparent about how it reaches them. Same technology, opposite trajectory. The entire difference lies in whether trust is designed in or bolted on at the end.

There were two themes running underneath it all. The first was skills. AI fluency has become a genuine hiring gate. I heard of a strong CFO candidate passed over because they couldn't demonstrate it, and there was broad agreement that universities can't move fast enough to close the gap, which is pushing companies to build their own internal training. That is a structural shift worth naming: the responsibility for building AI capability is migrating out of the education system and into the firm.

The second was a quiet plea for leadership. More than one person said, in effect, that they have the tools but want guidance; someone to show what good actually looks like. For all the noise in this market, there is a real shortage of grounded, honest, practitioner leadership. That is both an opening and a responsibility for those of us who have been building in this space for a while.

If I had to compress the morning into a single line, it would be this: the winning AI won't be the most capable in the abstract; it will be the most trustworthy in action. The system that knows when to involve a person, that can be audited after the fact, and that is introduced as a way to lift people rather than remove them, that is the one that gets adopted, scaled, and defended inside an organisation.

We tend to talk about AI as a race for capability. The room I sat in was telling a different story. The capability is largely here. The contest now is over trust. Who designs for it, who earns it, and who is willing to be accountable when the machine reaches the edge of what it should decide on its own.

That is a harder problem than building a cleverer model. It is also the one that matters.