The AI problem has moved.

Companies are no longer asking whether they should use AI. Increasingly, they’re asking why the tools they already bought still aren’t changing how people work.

The pattern is familiar: a pilot succeeds, the technology works, and six months later adoption is still low.

Because a successful pilot answers one question:

Can the tool do the job?

Adoption answers a much harder one:

Will a busy team actually change its habits and use it?

That requires workflow integration, ownership, authority and time — not just another proof of concept.

The real AI readiness question is no longer whether the technology is ready.

It’s whether the organisation is ready to change how work gets done.

Most companies talk about data maturity as if it were a compliance score.

I think that misses the point.

Data maturity is really about how far data can reach into a decision.

Can it tell you:

What happened?
Why it happened?
What will happen next?
What should we do about it?

Those are four very different capabilities.

And many mid-sized maisons, even ones with beautiful dashboards and monthly reporting, still operate almost entirely at level one.

Because describing the past is not the same as understanding the cause.

Understanding the cause is not the same as predicting what comes next.

And predicting what comes next is not the same as recommending the decision.

The most valuable transition is often not from “no data” to “more data.”

It is from descriptive reporting to diagnostic and predictive decision-making.

So the better question is not:

How mature is our data?

It is:

For the last important commercial decision, could we explain why the number moved — or only that it moved?

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