How can you tell how far your company really is with AI?
Not from a maturity model, but from honest answers to concrete questions from your daily business. Almost every leadership team places its company “in the middle” on AI. That is statistically impossible. The middle is the only position with no burden of proof. How far you really are shows in whether AI runs as a fixed part of a process, whether someone is accountable for the result, and in seven questions you can answer in half an hour.
Why almost everyone is “in the middle”
In no room this year has anyone put their hand up for the bottom third. The middle feels safe: you are not ahead, so you have nothing to prove, and not behind, so there is no pressure to act. The honest answer is often a different one: “I don't know.” That is not a bad starting point. It is the beginning of a real assessment.
The first test: tool, project or operation?
“We use ChatGPT” is not adoption. First ask yourself which stage AI has reached in your company:
- Tool: individuals use AI at their own desk. That is usage.
- Project: a team tests a use case. That is an experiment.
- Operation: AI runs as a fixed part of a process, one person is accountable for the result, and it is measured.
Only the third stage counts for the business. Why many get stuck at stage two is in the answer Why do AI pilots get stuck?
Seven honest questions
Give each question a colour. Red: this is where it hurts. Amber: so-so. Green: we are genuinely good here. Choose each colour deliberately, and answer for yourself, not for the presentation.
- Knowledge: How much of what your best employee knows is written down anywhere outside their head? An AI can only work with what you can give it.
- Speed: From enquiry to quote, how much of it is waiting? Most time is lost between two steps, not in slow work.
- Repetition: What do your people redo almost identically every week? The time saved lies in what is frequent, not in what is spectacular.
- Decisions: Which decision was most recently made without the figures it needed?
- Data: For last year's lost quotes, how quickly would you know why? You usually have the data. The question is whether you can get to it.
- People: Who is already using AI today without you having asked them to? If you think “nobody”, you probably just don't know. The risk is not the use, it is the secrecy.
- Your day: How many hours of your week could someone else handle if they knew what you know?
What your answers mean
Do not count points. Look at where the red clusters:
| Pattern | Red on | First step |
|---|---|---|
| Knowledge depends on individuals | Questions 1 and 7 | Make knowledge visible before automating anything |
| Time disappears in waiting | Questions 2 and 3 | Look at the process, not the tools |
| Decisions without a basis | Questions 4 and 5 | Make the data accessible first |
Question 6 belongs to no pattern, it concerns all of them: if AI is being used secretly in the company, that is a leadership issue, not a matter for a ban.
Why questions say more than a maturity model
A maturity model tells you which stage you are at. Seven honest questions show where things get stuck day to day, and therefore where to start. Your shortlist grows out of the clearest pattern:
No AI project without a shortlist: collect 10 to 20 ideas, sort them by effort, value and risk, pick three.
How to do this step by step is in the answer How do you choose your first AI use cases?
Frequently asked questions
Who should answer the questions?
Ideally each person in the leadership team on their own first. Then you compare. The most interesting question is not how much red there is, but where you answer differently from your colleagues.
What if almost everything is red for us?
Then you know more than most of those who see themselves in the middle. Start with the clearest pattern and choose your first three use cases for it.
Officially nobody uses AI in our company. Is that good?
Most likely it is just unknown. Ask openly, without blame. People who are already experimenting often have the best ideas for the first use cases.
Do we need an external analysis for this?
Honest answers are enough to start. If you also want to see where an AI-native competitor would strike, you can play it through with the AI Challenger Check.
More answers
- Where does AI deliver measurable results in sales first, in mid-sized companies?
- How do you choose your first AI use cases?
- Why do AI pilots get stuck, and how does AI get into daily business?
- Where does AI pay off in marketing for mid-sized companies, and where not?
- Who should own AI adoption: IT, the business units or executive management?
About the authorSelina Gaertner, AI for Leadership, Sales and Marketing. TÜV Rheinland certified AI Strategy Consultant, more than ten years in sales and marketing in medical technology, co-author of the Amazon bestseller “Fokus Mensch”.
Your next step: the AI Challenger Check
Tomorrow a well-funded competitor enters your market, built around AI from day one. In the AI Challenger Check an AI plays that challenger against you: where you are exposed, its plan for the first 90 days and which AI team puts you ahead. Free, about 15 minutes.