World Model Readiness The gap between your talk and your use

Module · The gap between your talk and your use

The Executive's AI Check

You do not have to build AI, but you sign off on it, fund it, and answer for it. That takes a specific kind of judgment: enough hands-on feel to tell a real capability from a pitch, and enough discipline to know which calls a model should never make alone. This module checks the gap between how much you talk about AI and how well you actually understand it, because that gap is where expensive mistakes get approved.

Question 1 of 5 · You use it yourself

Do you use AI tools hands-on yourself, or only talk about them?

You cannot judge what you have never touched. Leaders who have actually used the tools ask sharper questions and get sold fewer fantasies, because they know from their own hands where the capability ends.

Question 2 of 5 · You ask sharp questions

When your teams bring you AI plans, do you ask questions that expose the weak spots?

The questions a leader asks set what the organisation optimises for. If you ask for the upside and the demo, you get theatre; if you ask about failure modes, data, and what happens when it is wrong, you get honesty.

Question 3 of 5 · You resist the hype

Can you tell a real AI capability from a vendor promise?

The market runs on confident claims, and a lot of them do not survive contact with your actual data and workflow. The ability to tell a demo from a deployment is what keeps you from buying the same disappointment your peers just did.

Question 4 of 5 · You know the boundary

Do you have a clear instinct for which decisions AI should never make alone?

Some calls carry consequences a model cannot own: legal, ethical, human, reputational. Knowing where that line sits, and being able to say why, is a core part of the job that no tool will draw for you.

Question 5 of 5 · You learn from failures

When AI fails inside your own organisation, do you dig into why?

The most useful signal you have is your own failures, and it is the one most likely to get buried to protect a narrative. Whether AI mistakes get examined or quietly disappeared decides whether your organisation actually learns or just repeats.

For the statistics · one click each

Three questions for the public picture

These do not affect your score. They feed the anonymised, aggregated statistics; groups under 8 respondents are never shown.

How often do you personally use AI tools for your own work?

Never
Rarely
Weekly
Daily
Prefer not to say

What do most of your AI decisions rest on?

Vendor claims
What peers do
Trusted advisors
Evidence and trials
Mostly gut feel

When an AI initiative underdelivers, what usually happens?

It gets buried
Quietly dropped
Noted informally
Formally reviewed
None yet

Your context

Used to calibrate the report. Company size and sector remain in the anonymized dataset; your email does not.