Articles & Insights

“AI governance” can make this problem sound as though it belongs in a policy document. An organisation might approve the model, assign an owner and document acceptable use, while leaving the agent considerable freedom to decide how to complete its work.

PwC’s 2026 Global CEO Survey asked 4,454 chief executives about their businesses. Of those, 56% said they had yet to see either revenue or cost benefits from AI.

Before counting the salary saving, consider who will define the work, connect the systems, enforce permissions, review exceptions, monitor performance and take responsibility when something goes wrong. Consider who will build and maintain the software that allows the model’s output to become an action.

Someone convenes a group, internal ops, or customer experience, or “the AI taskforce,” and asks a version of one question: where could AI help us? That’s the wrong question. It invites the room to search for places where a capability might apply, rather than for the thing that’s actually constraining the result they want to change.

A private equity owner planning to exit a SaaS business in two or three years faces a genuine dilemma. Is a bird in the hand worth two in the bush?

Boards under pressure reach for AI in two postures: as a shield against something they think is coming for them, or as a lever they think will pull the stock up. Both postures skip a step. Before you can point AI at a threat, you have to know precisely what the threat is and precisely where your business is exposed to it.

Leadership teams still commonly treat AI adoption as a technology programme. The logic seems sound: AI is technology, technology belongs to IT, AI systems run on software, and software is built by engineers. So naturally, AI adoption gets handed to IT and staffed mostly with developers. That logic mixes up two different things: building the machinery and doing the organizational work that makes the machinery useful.

Boards want the optics. CEOs want the announcement. Neither wants the unglamorous work that has to happen first. So they skip it, sign the cheque, and act surprised when the wheels come off six months later.

Every day on LinkedIn, another post surfaces with the same tired revelation: "ChatGPT made up citations!" or "AI hallucinated facts!" These posts, dressed up as serious analysis, are really just intellectual laziness disguised as skepticism.

Don’t be fooled into checking the wrong boxes: Do we have enough data? Is our tech stack modern? Have we hired data scientists? Can we run pilots? Those questions matter eventually. But there's a faster way to know if you're ready. It takes about 10 minutes: compare your gross and net margins to your industry peers.

Every seismic shift in business history begins with denial. Railroads dismissed automobiles. Publishers shrugged at the internet. Film studios laughed at streaming. Each time, incumbents convinced themselves that “business as usual” would hold. Each time, they were wrong. And each time, value shifted to those who saw the change for what it was.

AI can strengthen trust, or it can undermine it. The deciding factor is segmentation. Understand which customers chase the cutting edge, which value predictable outcomes, and which want proof before they act. Trust is built differently in each segment.

What’s the Difference Between Asking a Human to Complete a Task and Asking AI? On the surface, not much. You say “build me an app,” and both human and AI will deliver code. You say “write me some market copy,” and both will produce words on a page.

Fraud used to be limited by human effort. You could spot the broken English in the phishing email. You could hear the hesitation in the voice. You could flag the dodgy PDF with the pixelated invoice. But now?

leadership in a complex business isn’t linear. It’s not even rational in the strictest sense. It’s a real-time collision of context, competing priorities, and partial information. Decisions don’t arrive in order, they come in waves. Many are interdependent.

AI improves productivity by automating repetitive tasks, freeing up time, and unlocking capacity. It’s true, on the surface. But that story is only half the plot, and if you stop there, you’ll miss where the real value is hiding.

AI isn’t magic. It’s a powerful set of tools that extend well-run processes and ruthlessly expose broken ones. Lead with strategy, pick the business lever that matters most, and deploy AI where it can create visible, measurable value.

A few years ago, executives worried that they’d missed the mobile-app boom. Then it was cloud migration FOMO. Now the scramble is for AI—and the potential applications are so vast that most leadership teams are paralyzed by choice.

When it comes to innovation, artificial intelligence (AI) is often heralded as the holy grail, a game-changer capable of transforming industries overnight. The excitement is understandable. AI has the potential to revolutionize how we work, solve problems, and deliver value. However, there’s a concerning trend emerging: businesses leading their innovation efforts with AI rather than using AI to enable innovation.

While some businesses are quick to adopt AI solutions, others may believe that if they're not actively using AI, they don't need to worry about it. However, even if you have no formal plans to integrate AI into your operations, having a generative AI policy is essential. Here's why.