AI Readiness Quick Check — MVP Mockup (Demo)

What this is about

The question "Are we ready for AI?" is fuzzy. This quick check translates it into a sorted view: across five dimensions, it shows which foundation already carries and which one is holding up your next step. At the end you get an overall reading, a heatmap per dimension, your levers, and a roadmap teaser — instantly, no waiting.

Purpose

A snapshot of where you stand, not a report card. A first, honest view of where the next sensible step lies.

Duration

About 10–15 minutes. 30 statements, each rated on a scale of 1 to 5.

Who it's for

A leader or team lead answering on behalf of their own team.

What this test deliberately can't do — and that's on purpose. It measures how you yourself see your team, not how it objectively is. The gap between stated and lived maturity, the why behind the gaps, and what a change would really cost in your day-to-day — only the guided conversation uncovers that. The quick check is the honest first stage, not the substitute. More on this at the end.

How to answer

You rate each statement on a scale of 1 (not true at all) to 5 (fully true). If you can't assess where you stand, that's a 1 — not knowing about yourselves is itself a maturity signal.

1Not true at all / don't know

2Mostly not true

3Somewhat true

4Mostly true

5Fully true / established

Statements marked reversed are deliberately worded the other way around: there, high agreement means low maturity. They're converted automatically in the results (6 − value) — no mental gymnastics needed, just answer honestly.

Foundation

The question behind it: Is there even ground for AI use to stand on? (Technical & process prerequisites)

My team has a safe, approved environment where we're allowed to use AI tools without getting individual sign-off every time.

Our most important recurring workflows are documented well enough that a new colleague could follow them without asking around.

We have at least one workflow where AI is already in regular — not just occasional — use today.

So far, AI use here happens more by chance — when someone tries something out privately — not systematically.reversed

The tools we'd need are technically within reach (access, licence and budget are sorted).

We'd know who to turn to if an AI tool needed to be set up or unlocked.

Data

The question behind it: Does AI even find reliable material here? (Availability, quality, governance, access paths)

The data we need for our work is findable — not scattered across people's heads, email inboxes and individual drives.

We could say how good or bad the quality of our most important data is (how current, how complete, duplicates).

It's clearly settled who may access which data and who is responsible for it.

If I needed a specific piece of information, I'd first have to ask several people before I had it together.reversed

Our data is mostly digital and machine-readable — not on paper, in screenshots, or in PDFs without structure.

We know which of our data is especially sensitive, and we treat it accordingly.

People

The question behind it: Can people do it, do they want to — and do they trust themselves to? (Skills, stance, self-efficacy)

Most people on my team could name a specific task where AI already makes their own work easier.

Our attitude toward AI is neither blanket rejection nor blanket hype, but a sober "depends what for".

There's room and time here to try new things without a failed attempt being punished.

On my team, people worry more about being replaced by AI than feeling relieved by it.reversed

I personally feel confident enough to judge when an AI result is usable and when it isn't.

We talk openly about what AI is changing for us, instead of everyone quietly drawing their own conclusions.

Governance

The question behind it: Are there guardrails, or are we driving without? (Rules, responsibility, ethical guardrails)

It's clear what is and isn't allowed when working with AI here (which data may go in, which may not).

There's a named person or role responsible for the responsible use of AI.

We've engaged with the transparency and labelling obligations of the EU AI Act that take effect in August 2026.

When someone here uses AI, they watch out for data protection on their own and don't carelessly enter sensitive content.

Everyone here uses AI as they see fit — we don't have shared rules for it yet.reversed

It's clear who a problem or borderline case with AI gets escalated to.

Value Creation & Use Cases

The question behind it: Do we know where AI truly creates value — and where people remain irreplaceable? (Condensed operational facets)

We could name two or three specific workflows where using AI would noticeably pay off.

We deliberately distinguish between tasks where AI should support us and those that must stay in human hands (judgment, relationships, responsibility).

For the workflows we'd automate, the desired outcome can be described clearly enough that you'd recognise success.

We check AI results before we use them further, instead of taking them over unchecked.

AI gets used here mainly where it happens to stand out or is being asked for — not where the benefit would be greatest.reversed

We'd have a rough idea of what benefit would stand against the effort of introducing it.

Validation pass 2026-06-26: statement 5.5 is now a true reversed item — agreement ("AI wherever it happens to stand out") signals low value maturity. With that, dimension 5 also carries the acquiescence control required by the Diagnostics HQ house standard.

Not all statements answered yet — in the demo, missing ones count as "don't know" (1).

Your results

Without JavaScript, no calculated results are shown here. The questionnaire above stays fully readable. Enable JavaScript to see your overall traffic light, the heatmap per dimension, your levers, and the roadmap teaser. The methodology below applies either way.

Overall reading

This is a snapshot, not a report card. The overall score is deliberately secondary — the heatmap per dimension is the real message, not a single number.

Heatmap across the five dimensions

Weakest first — so your next lever is immediately visible.

What already carries

Your levers — the critical gaps in plain language

Roadmap teaser

Deliberately a teaser, not a plan — three rough stages drawn from your profile. The actual plan takes shape in the guided conversation.

What this self-assessment deliberately can't do

These limits aren't a weakness to optimise away — they're what makes the test honest:

Self-assessment isn't an outside view. The test measures how your team sees itself, not how it is. The gap between stated and lived maturity only surfaces in the guided conversation.
It says what, not why and not what it costs. The report shows which step would come next. Why that matters right now and what it costs in your day-to-day — only the conversation answers that.
No benchmark, no employee assessment. The quick check is a reflection anchor, never an evaluation tool for individual people.

The next step: consultant-guided depth

This check shows your profile. What it can't do: talk the gaps through with your team, mirror the self-assessment against lived practice, and weigh what the next move really costs in your context. That's exactly where the guided diagnostic comes in.

Request a conversation To the guided in-depth diagnostic

Demo buttons without function — the bridge CTA models the lead-magnet mechanics.