I get asked some version of this question often enough that it’s worth writing down properly: how do you actually know if your organization is ready for AI, versus just excited about it? I don’t think this is something an outside audit hands you an answer to — it’s something worth sitting with yourself first, honestly, before bringing in anyone else’s framework. Here are five questions I’d genuinely ask myself if I were in a leadership seat wrestling with this right now.
1. Can I describe our AI strategy without the word “pilot”?
Plenty of organizations have run pilots. Far fewer can describe where AI is actually meant to move the business in concrete terms, two years out. If the honest answer is a list of experiments rather than a direction, that’s useful information, not a failure — it just means the next step is defining direction, not running another pilot.
2. Do I know who owns AI risk decisions, by name?
Not a committee. A person. If you can’t answer this quickly, it’s worth noticing that the absence of an answer is itself the answer — governance that lives in a document but not in a name tends not to hold up when a real decision needs making fast.
3. Would our data survive contact with a serious AI use case?
This is less about volume and more about whether the data that would feed a meaningful use case is clean, accessible, and trusted enough that people wouldn’t immediately second-guess the output. Most organizations I’ve seen overestimate this one.
4. When something goes wrong with an AI output, do we know what happens next?
Not in theory — in practice. Is there an actual process, or would the first real incident be the moment the process gets invented under pressure? The organizations most exposed here aren’t the ones without a policy; they’re the ones who’ve never actually rehearsed what the policy means when it matters.
5. Am I measuring adoption, or am I measuring whether it’s working?
License counts and login numbers are easy to report and tell you almost nothing about outcomes. Worth being honest with yourself about which one your organization is actually tracking.
None of this is meant as a scored assessment, and I’d be skeptical of anyone who claims to reduce something this contextual to a single number. It’s meant as a personal starting point — the kind of reflection worth doing before the first big AI investment decision, not after. If it’s useful, I built a short, private reflection tool along these same lines — not a graded audit of your organization, just a structured way to think through where you personally stand. You can try it on the AI Readiness Reflection page.