Are you an AI enabler, or an AI blocker?
Answer five honest questions. Not how you'd answer them in a board meeting, but how you'd answer if your most capable people were in the room. Nothing is saved or shared.
- 5
- questions
- ~2 min
- to complete
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- data saved
Blocking doesn't always look like blocking
Sometimes it looks like governance. Sometimes it looks like caution. Sometimes it looks like leadership. The gap between what leaders believe about their role in AI adoption and what their teams actually experience is often significant, and quietly costly.
If a proposal to use an AI tool triggers a working group, a policy review, a risk committee and six weeks, you may be blocking without realising it. The tools change fast. A process designed for annual software procurement is the wrong instrument for evaluating something someone could test in an afternoon.
If your people are more confident with AI outside work than inside it, you may have already met what some are calling an AI lobotomy: a capable, AI-fluent person walks through your door and reverts to doing things the slow way because your policies don't permit anything else. That isn't a governance success. It's a productivity and retention risk wearing a governance costume.
And if a poor AI output becomes evidence against AI adoption, that's a problem. Every new capability fails before it flies. The question isn't whether AI makes mistakes, it's whether your culture treats those mistakes as learning or as proof you were right to be cautious.
What enablers actually do
They use the tools themselves. Not performatively, not in a demo, but regularly, as part of how they actually work. A leader who doesn't use AI can't credibly set the pace of adoption for people who do.
They make governance proportionate. Drafting internal communications is a different conversation from deploying an agent with access to customer data. Blockers apply the same scrutiny to everything, so the low-risk, high-value opportunities die in committee alongside the genuinely sensitive ones.
They talk about failure openly. When a tool doesn't deliver, they say so, and they describe what they learned. That gives teams permission to experiment, which is the only condition under which real adoption happens.
They measure outcomes, not activity. Not how many people completed AI training or how many licences were bought, but whether the work is better, faster or more valuable than it was before.
The honest question
AI adoption in most organisations doesn't fail because of the technology. It fails because of the culture around the technology, and culture flows from the top. The most important question a C-suite leader can ask isn't “are we adopting AI?” It's “what does my behaviour tell my organisation about how seriously I actually want this to happen?”
That answer is worth more than any strategy document.