AI readiness assessment: score your organisation in ten minutes
Sixteen questions across five dimensions. You get a score per dimension, an overall maturity level and the next steps we would recommend, with the maths shown.
- 16 questions, 5 dimensions
- Runs in your browser, no sign-up
- Answers saved locally
Every answer scores 0 to 3. A dimension score is points earned divided by points available. The overall score is total points out of 48, mapped to four levels: Exploring (0–25), Piloting (26–50), Scaling (51–75) and Leading (76–100). Answer honestly; when two people disagree, take the lower option.
Answer the sixteen questions
Your results update as you go. Nothing leaves your browser unless you choose to email the result.
Not started 0 of 16 answered
- Strategy and sponsorship–
- Data readiness–
- Technology and integration–
- Skills and operating model–
- Risk and governance–
Answer all sixteen questions to unlock tailored next steps.
What each dimension measures, and why it decides outcomes
Strategy and sponsorship
AI projects without an accountable sponsor stall at the pilot. This dimension checks whether someone owns the outcome, the budget and a prioritised list of use cases with baselines.
Data readiness
The most common reason a use case slips. Accessible, permissioned data and a way to build a test set matter more than volume. Our data readiness guide goes deeper.
Technology and integration
AI that cannot reach your systems can only talk. APIs, cloud hygiene and at least one production ML workload are what make integration a week of work rather than a quarter.
Skills and operating model
Who builds, who runs, who uses. Teams that already ship software often can own AI systems after a handover; teams that release quarterly need a different plan, such as a fractional AI CTO.
Risk and governance
Policies, regulatory mapping and review processes. Weak governance does not stop a pilot, but it stops production. See AI governance and EU AI Act readiness.
What each level should do next
| Level | Score | Typical situation | Recommended first move |
|---|---|---|---|
| Exploring | 0–25 | Interest, no owner, data access unclear | One sponsor, one narrow use case, a data-access decision. AI strategy. |
| Piloting | 26–50 | A sponsor and some data; prototypes have not shipped | A measured pilot with evals and a KPI. AI implementation. |
| Scaling | 51–75 | Something in production; each project reinvents the platform | Shared platform, monitoring and governance. LLMOps. |
| Leading | 76–100 | Several systems live, governed and measured | Agents across functions, formal management system. ISO 42001. |
Ready to see where the money goes next? Try the LLM cost calculator or browse the solutions catalogue.
Frequently asked questions
Is the assessment really free and anonymous?
Yes. The questions, scoring and recommendations run entirely in your browser. Your answers are stored only in this browser's local storage so you can come back to them, and nothing is sent to us unless you choose to email the result.
How is the score calculated?
Each of the 16 questions scores 0 to 3. A dimension score is the points earned divided by the points available, as a percentage. The overall score is total points out of 48. Bands: 0–25 Exploring, 26–50 Piloting, 51–75 Scaling, 76–100 Leading. The maths is shown on the page because a readiness score you cannot explain is not useful.
Who should answer the questions?
Ideally two people together: someone who owns the business case and someone who knows the systems and data. Where they disagree, pick the lower score. That is usually the honest one, and it is the gap a readiness workshop would surface anyway.
What happens in a readiness workshop?
Half a day, remote or on site. We go through the five dimensions with your team, pick two or three candidate use cases, check data access and governance constraints, and leave you with a written assessment and a 90-day plan. It is the first step of our AI strategy service and can be booked on its own.
Our score is low. Does that mean we should wait?
No. A low score tells you which foundation to fix first, usually one narrow use case plus a sponsor and a data-access decision. Our AI readiness guide walks through what each level should do next.