Is your organisation ready for AI?

Your score comes only from your own answers and the rules below. It is not a benchmark against other companies, and we have no data on how others score.

1. Every question adds points to one dimension. Single choice earns the listed points (0 to 4). Each ticked option of a multiple-choice question earns 1 point. A scale answer earns its number minus 1.
2. A dimension score is the points you earned divided by the points available in that dimension, as a percentage.
3. The overall score is all points earned divided by all points available. Every dimension has the same number of questions, so each counts equally.
4. Levels follow the overall score: Starting 0 to 29, Developing 30 to 54, Established 55 to 79, Leading 80 to 100.
5. Recommendations come from the rules listed per check. The weakest dimensions rank first, and the top three are shown.

Points per question

Each answer shows the points it earns on the dimension of the question.

1. 01 / Data foundation Where does the data for your most promising AI use case live? Up to 4 points

   • Scattered over e-mail, spreadsheets and people's heads 0 pts
   • In a few business systems that are hard to combine 1 pt
   • In a shared database or warehouse we can query 3 pts
   • In a governed platform with named data owners 4 pts

2. 02 / Data foundation How good is that data? Up to 4 points

   Scale 1 to 5 earns the answer minus 1 (0 to 4 points).

3. 03 / Use case clarity How concrete is the process you want to improve? Up to 4 points

   • We have no use case yet 0 pts
   • We have general ideas, such as 'use AI for reporting' 1 pt
   • We named one process but never measured it 2 pts
   • We named one process and measured volume, time and error rate 4 pts

4. 04 / Use case clarity Which of these describe that process? Up to 4 points

   • It runs hundreds of times a month or more 1 pt
   • The input is text, documents or speech 1 pt
   • The steps are mostly the same each time 1 pt
   • A person can check the result quickly 1 pt
   • None of these 0 pts

5. 05 / Ownership and skills Who would own the result after launch? Up to 4 points

   • Nobody has been named 0 pts
   • IT would work it out 1 pt
   • A business owner is named but has little time 3 pts
   • A business owner and a technical owner are named, with time set aside 4 pts

6. 06 / Ownership and skills How much experience does your team have running data or AI solutions in production? Up to 4 points

   Scale 1 to 5 earns the answer minus 1 (0 to 4 points).

7. 07 / Risk and rules Could the data for this use case be sent to an external AI service? Up to 4 points

   • We do not know 0 pts
   • No, and we have no alternative arranged 1 pt
   • Yes, but only data without personal information 3 pts
   • There is a written rule and approved tooling 4 pts

8. 08 / Risk and rules Which of these are in place for AI use? Up to 4 points

   • A list of approved AI tools 1 pt
   • Data classified by sensitivity 1 pt
   • A named contact for privacy questions 1 pt
   • A written rule for using external AI services 1 pt
   • None of these 0 pts

9. 09 / Pilot readiness What budget and time exist for a first pilot? Up to 4 points

   • None 0 pts
   • It is possible, but nothing is planned 1 pt
   • A small budget and a likely window 3 pts
   • Budget approved and a decision date set 4 pts

10. 10 / Pilot readiness How would you decide that a pilot worked? Up to 4 points

    • We have not thought about it 0 pts
    • By gut feeling 1 pt
    • By one agreed measure, such as hours saved 3 pts
    • By an agreed measure and a stop rule if it is not met 4 pts

Recommendation rules

• Data foundation scores below 50 → recommend: Make one data source usable first
• Use case clarity scores below 50 → recommend: Choose one process and measure it
• Ownership and skills scores below 50 → recommend: Name a business owner and a technical owner
• Risk and rules scores below 50 → recommend: Write down the rules before the first model
• Pilot readiness scores below 50 → recommend: Scope a pilot with one budget and one measure
• You answered 7: "No, and we have no alternative arranged" → recommend: Plan for AI that runs where your data may stay
• Overall score is 70 or higher → recommend: Add evaluation and shared building blocks before project two

Levels

• Starting (0 to 29): AI projects would run into basics first. That is a normal place to start, and a short scan usually finds the cheapest first step.
• Developing (30 to 54): Some foundations exist. One well-chosen use case with a clear owner is realistic, and a few gaps should be closed first.
• Established (55 to 79): The basics are in place. A pilot on one concrete process is a sound next step, with the remaining gaps handled inside it.
• Leading (80 to 100): You have what a first or second AI project needs. The next question is how to run several safely, with evaluation and shared building blocks.