Licences are not maturity: how to tell whether AI has really arrived in your company

Ask companies where they stand with AI and you usually get an inventory: licences for Copilot or ChatGPT Enterprise, two or three pilots, a policy under review, perhaps an internal chatbot project. It sounds like progress. Yet day to day, little has changed in most departments.

That is not for lack of effort. It is because availability and impact get confused. A licence is an option. A pilot is an experiment. A company is mature only when its normal operations have changed.

The ground rule: assess normal operations

For our assessments we use our own stage model. It is deliberately strict and follows a few simple rules:

• Cumulative. A stage counts only if all stages below it are met. One impressive agent in one department does not make the company stage 4.
• No compensation. Strength in one area does not offset hard gaps in another.
• Missing evidence is not compliance. What is not evidenced counts as not met.
• Real operations, not intentions. Licences, policies, pilots and demos do not count. What happens day to day does.

The stages at a glance

Stage	Name	What holds day to day
0	Manual	People do the work themselves; AI use is not established company-wide.
1	AI-enabled	Every eligible office worker can safely use a company-provided AI client.
2	Standardised	Key roles work with documented, approved AI methods and shared review steps.
3	Integrated	AI results appear automatically in the workflow. The manual parallel process is no longer needed.
4	Lightly accountable	AI takes on bounded tasks up to a defined result and brings people in at set checkpoints.
5	Strongly accountable	AI runs suitable processes end to end in the normal case; people handle genuine exceptions.
6–8	Strategically learning to autonomous	AI improves processes itself, steers against human-set goals, up to the theoretical end point without people.

Most mid-sized companies we talk to realistically sit between stage 0 and 2. That is nothing to be ashamed of. It is a starting point.

The two stumbling blocks we see most

Shadow processes. An AI solution is in place, but the old manual work continues alongside it because nobody fully trusts the result, or because information would be lost if it failed. As long as the shadow process exists, integration is not complete. You pay twice.

Manual residual effort. The AI delivers, but someone has to start it, paste data in, format the result, forward it, document it afterwards. Each step is small. Together they eat a large share of the benefit, and they are exactly where the next stage begins.

Why the next stage matters more than the target stage

The most common mistake in AI strategies is aiming straight for stage 4 or 5: “We want agents.” Without clean access (stage 1), shared ways of working (stage 2) and integrated data flows (stage 3), these projects fail on fundamentals, not on technology.

So in every assessment we ask: which criterion of the immediate next stage is missing in which specific process? That gap is the real object of cost and benefit. It yields a prioritised list of initiatives whose effort and impact derive from the specific process, not from blanket assumptions.

An honest note on the model

The stage model is our own and deliberately designed as a testable draft. Its architecture and definitions are explicit; the percentage thresholds in the criteria are working hypotheses that we calibrate against real assessments. It is not a market standard, and we do not sell it as one.

Three questions for your next leadership meeting

1. Can every eligible office worker safely use an approved AI tool today, or only the enthusiasts?
2. Is there a documented AI way of working with a review step for your three most important roles?
3. Where does a shadow process still run alongside an AI solution, and what does it cost?

If you cannot answer one of them with confidence, you know where your next stage begins.