AI started small: a few pilots, some dashboards, a couple of chatbots. But then it spread, quickly. Now every department wants a model, every vendor adds “AI-powered” to their pitch, and every regulator is asking about risk and transparency. Governance suddenly went from a nice idea to a full-time job.
Scaling governance is harder than launching AI. Policies look great on slides, but in practice, ownership blurs and enforcement stalls. Central control slows things down, while local freedom invites risk. Everyone agrees AI should be safe and ethical, but no one agrees on who signs off when something goes wrong, all leading to AIs living as permanent PoCs.
So how do you scale oversight without creating bureaucracy? How do you distribute responsibility between IT, business, and compliance? And what controls actually hold up when AI keeps changing after deployment? Let’s explore how organisations make governance part of daily operations, not an afterthought. A closed conversation for those trying to keep AI credible, compliant, and under control while it spreads across the enterprise.
