AI readiness scorecard
Fourteen questions on whether you’re ready to build AI.
Most AI work fails on things you could have checked first — a problem nobody can state without saying “AI”, data that isn’t there yet, a payoff nobody sized. This scores the five that matter and names the one most likely to stall you.
Saved in this browser as you go, so a refresh doesn’t lose your place.
Your score
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This scores how ready you are in general. It doesn’t score your individual ideas — for that, every one has to go through the full rubric on its own. That’s the Reality Check.
Sending it opens your own mail app with the answers already filled in, and I’ll write back with where I’d start. If nothing opens, your browser has no mail app set — use Copy result below instead.
Need to show someone else? Copy it, or print this page to PDF.
Who this is for
You have AI ideas and no shared way to judge them.
They arrived from a board meeting, a vendor pitch, a competitor’s website and somebody’s offsite. Everyone has a view on which ones matter, and the views don’t agree. Before you can argue about which idea to build, you need to know whether you are in a position to build any of them well.
What gets scored
Five things, and none of them are about the technology.
Every question belongs to one of these. The score you get is the average, and the area that comes out lowest is the one most likely to stall you.
What the AI is for
Whether the problem can be described without the word AI, and whether a rule or a query would do it better. Most of what fails, fails here — the questions that catch it.
Data, and knowing it works
Data readiness in the literal sense — whether it exists today rather than could be collected — and whether anyone has decided how you would tell a good output from a bad one against a baseline.
The payoff
Whether the value has been sized in hours or euros against something you already measure. Unsized work is the first thing cut when budgets tighten — see which numbers hold up.
Risk and sign-off
What happens when the model is wrong, and whether anyone has checked where this sits under the EU AI Act. A low score here means AI governance comes first, never don’t build — how that ran in practice.
Who runs it after launch
Whether a named person owns it, and whether the way people work is expected to change. This is where pilots die between demo and production — models drift, and unowned ones quietly stop being used.
What you get
A number, five bars, and the one area to fix first.
A score out of 100
With a band that says plainly what it means — from high theatre risk through to ready. Most roadmaps land in the middle before anyone has interrogated them.
A breakdown by area
All five scored separately, so a strong average doesn’t hide one area that will stop the work.
Your weakest area, named
With a plain explanation of what typically goes wrong there and what it costs when nobody catches it early.
Answers that don’t apply to you are excluded from the score rather than marked zero — a twelve-person company isn’t penalised for having no legal department.
What happens to your answers
Saved on your device, and not sent to me.
- Your answers are stored in this browser — so a refresh doesn’t lose your place, and a finished score is still there if you come back. They stay until you clear your browser data or press Start again.
- The answers themselves never leave your device. There is no account, no server storing them, and nothing arrives in my inbox when you finish.
- If you accepted analytics, one event is recorded when you finish: the score, which area came out weakest, and the team-size band you picked — never the individual answers. If you rejected analytics, or your browser sends a Global Privacy Control signal, nothing is recorded at all — the cookie page has the detail.
- Nothing is emailed unless you click the button that does it, and that opens your own mail client with the text visible so you can see exactly what you would be sending.
Who built this
The scoring comes out of work that was done, not a framework I read about.
Questions
What this scores, and what it doesn’t.
What is an AI readiness scorecard?
A short structured assessment of whether a team is in a position to build AI successfully — not whether a particular idea is good. It scores five things: whether the problem is defined without reaching for the word AI, whether the data exists today, whether the payoff has been sized, whether the consequences of a wrong answer are understood, and whether anyone will own the thing after launch.
How long does the AI readiness scorecard take?
About four minutes. Fourteen questions, one screen at a time, and you get your score immediately without giving an email address.
Does it score my specific AI idea?
No. It scores how ready you are in general — whether the problems are defined, the data exists, the payoff is sized, the risk is understood and someone will own the result. Scoring an individual use case takes the full rubric, one idea at a time.
Do I have to give an email address to see my score?
No, and there is no email step at any point. The score and the per-area breakdown appear straight away and are yours to copy or print. If you want a view on what to do about the result, you can send it to me and I will write back — but that is a choice, not a gate.
What counts as a good score?
Most teams land in the middle band before anyone has interrogated their roadmap, so a mixed result is the common case rather than a bad one. A high score usually means the thinking is done and what is needed is delivery rather than diagnosis. The number matters less than which of the five areas came out weakest.
Is this the same as an AI maturity assessment?
No. Maturity models score an organisation against a ladder of capability — tooling, talent, governance, platform. This scores whether a specific piece of work is ready to start. A company can be mature and still propose something with no data behind it, and a five-person startup can be ready on every question that matters.
What does a low score mean?
Usually that the thinking hasn't been done yet, not that the idea is bad. Most roadmaps score in the middle band before anyone has interrogated them. A low score on risk in particular means governance comes first — it never means don't build.
What happens next
A score tells you where you stand. It doesn’t tell you what to build.
The Reality Check scores every idea on your roadmap one at a time and hands back three lists — what is real, what is theatre, and what is blocked until something changes. One week, fixed price, and I have no stake in which way any of it goes.