Every leadership team I talk to is being told to “do something with AI.” Far fewer are asking what that something will require of them once it’s running in production, touching real customer data, and making decisions someone has to answer for.
AI doesn’t change the fundamentals of good governance. It just raises the stakes and shortens the timeline. Here’s the filter I use with clients to separate the projects worth the overhead from the ones that only look good in a slide deck.
Start with the blast radius, not the model
The first question is never “which model?” It’s “what happens when this is wrong?” An AI that drafts internal meeting notes has a small blast radius. An AI that approves transactions, screens applicants, or summarizes medical records has a large one. The size of that radius — not the sophistication of the tool — determines how much governance you need.
Match the controls to the consequences. Everything else is theater.
Three questions before you deploy
- Can you explain a decision? If a customer or a regulator asks why the system did what it did, you need an answer that isn’t “the model decided.”
- Where does the data go? Every prompt is a disclosure. Know what leaves your walls and what the vendor retains.
- Who owns the failure? If no named person owns the outcome, the project isn’t ready — it’s an experiment wearing a deadline.
Governance is what makes speed safe
Teams often treat governance as the brake. In practice it’s the thing that lets you move fast without betting the company on a tool you don’t fully understand. The clients who adopt AI well aren’t the most cautious or the most aggressive — they’re the ones who decided, deliberately, where to be each.
You can have the upside. Just go in knowing which risks you’re accepting, and make sure they’re written down somewhere other than in your head.
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