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Free · About twelve minutes · Instant result

Find out whether your people can work with data — and with AI.

Twenty-five statements across two tracks, scored separately. The gap between how your people handle data and how they handle AI is usually the finding. You get your score and a prioritized list of what to fix first, on this page, immediately. Scoring happens in your browser — nothing leaves it unless you ask for the report by email at the end.

25Statements
2Tracks scored
~12 minTo complete
NoneEmail required

0 / 25 answered

This assessment needs JavaScript to score itself. The questions below are worth reading either way — they are the five things that decide whether your people can work with data and with AI. If you would rather talk them through, book a strategy call, or see the Data Platform Accelerator, where we verify these against your systems rather than your answers.

Reading Data track

Whether people understand what they are being shown

0 of 5

Can your team read a chart and say what it does not show, as readily as what it does?

Would most people here notice if an axis had been truncated to exaggerate a trend?

Can people tell a correlation from a cause when a dashboard puts two lines side by side?

Do people know the difference between an average and a median, and when the choice matters?

Would someone spot that a large percentage rise is unremarkable because the base is tiny?

Questioning Data track

Whether anyone interrogates it before repeating it

0 of 5

When a number looks surprising, do people check where it came from before passing it on?

Does anyone routinely ask what population a figure covers, and who was left out of it?

Would a team challenge a metric that has quietly changed definition between quarters?

Do people ask how recent the data is, rather than assuming a dashboard is live?

When two reports disagree, do people investigate rather than pick the one they prefer?

Acting Data track

Whether it changes what anyone does

0 of 5

Do the people who own decisions look at the data before deciding, rather than after?

Can a team say what evidence would change their mind about a current course of action?

When a dashboard shows something is failing, does anything actually happen?

Do people separate a metric worth acting on from one that is merely moving?

Is it acceptable here to reverse a decision because the data said so?

Limits AI track

What AI can and cannot be trusted with

0 of 5

Can people explain, in plain terms, why a model can be fluent and wrong at the same time?

Would someone verify an AI-generated figure, citation or quote before it reaches a client?

Do people understand that a confident tone carries no information about accuracy?

Can your team tell a task AI is suited to from one it merely appears to handle?

Would people recognise that a model trained on the past will reproduce the past, including its biases?

Safe use AI track

What leaves the building, and who carries the decision

0 of 5

Do people know what happens to the information they paste into an AI tool?

Is it clear which AI tools are approved here — and which are being used anyway?

Would someone raise it if an AI system produced an output that treated a person unfairly?

Is it understood that whoever ships an AI-assisted decision owns it, not the model?

Do people disclose when AI materially shaped work they are presenting as their own?

Answer all three to continue.

Data Products · Literacy Report

AI & Data Literacy Assessment

Prepared —
Reference —
Scored in your browser
0 of 50 —

Your result

Data track

0%

Reading, questioning and acting on the numbers you already have

—

points apart

AI track

0%

What AI can be trusted with, what leaves the building, who carries the decision

0%Overall literacy across the five areas
0Areas a team can rely on without a specialist
0Areas flagged as a gap — 4 of 10 or below

Literacy by area

Each bar is your score out of 10. The marker sits at 7, the level where a team can be relied on without a specialist in the room. Hover or focus a row to see which of its five statements pulled it down.

What is holding up

    Where the risk sits

      What to fix first

      Ordered by where you scored lowest, with ties broken toward the more foundational layer. Fixing these in another order usually means doing the work twice.

      View the scores as a table
      Your score for each of the five literacy areas, out of 10.
      DimensionScoreOfStanding
      Total036—
      Talk through your result Close the gap

      Prefer to keep a copy? — it is a PDF, not a mailing list.

      The two-week version

      The version that is not self-reported.

      Knowing the gap does not close it, and literacy is the one dimension that never improves on its own — nobody wakes up more able to spot a confident wrong answer. The Executive AI Workshop is built for exactly this: a working session that moves the weaker track, using your own decisions and your own data rather than generic examples.

      01 Baseline by team

      The same five areas, run across your actual teams rather than one respondent, so you can see where the gap is widest.

      02 Gap register

      Where a build actually breaks, named specifically enough that someone can put a number against fixing it.

      03 Order-of-magnitude cost view

      What each path costs to stand up and to run, sized at your volumes — enough to know whether the number is five figures or seven.

      04 Prioritized next steps

      One sequence with the reasoning attached, so the order can be argued with rather than taken on faith.

      Two weeks, fixed fee. It is designed as a low-risk way in, and the output is yours to use whether or not you take the work further with us. When a decision needs more than that, there is a longer engagement above it.

      See the Executive AI Workshop
      Being straight about it

      What this is, and what it is not.

      It is a structured self-assessment

      Eighteen questions drawn from the reasons builds actually fail — thin data foundations, no review path, no evaluation harness, unmodeled cost, no adoption, unexamined exposure. Answering honestly is the whole method.

      It is not a diagnosis

      It scores what you tell it. It cannot see your architecture, read your contracts or interview your people, and it will not catch a problem you do not know you have.

      Nothing is collected unless you ask

      Scoring happens in your browser. Your individual answers are never transmitted. The one exception is deliberate: if you ask for the report by email at the end, your address and the six dimension scores are sent to us so we can send it. Skip that and nothing leaves your machine.