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Your factory’s AI maturity

Seven axes, one profile, one weak link. It isolates what blocks AI at your site and gives a concrete plan: this week, this quarter, this year.

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Before launching a projecthow often used

Industrial operations control room — Tool

Seven axes, two minutes. You place your factory and see, on your weakest point, a concrete plan. Nothing is sent, no figure is promised: a decision read, not an audit.

Why the weak link, and not the average

A data-AI chain is only worth its weakest point. A perfect history is useless if production data cannot leave the site network, and the best predictive model dies for want of an owner to keep it alive. An average would mask that breaking point; this diagnostic isolates it and says what to do about it.

The seven axes follow the real order of the field: first the state of the data and its architecture, then risk-based ranking and degradation tracking, then integration, security and governance, and finally skills. This diagnostic does not replace an audit on your actual reports; it tells you where to start.

Definition

The data and AI maturity self-diagnostic assesses whether a plant meets the prerequisites of an industrial artificial-intelligence project, before even talking about an algorithm. It looks at four prerequisites: available and reliable data, a clearly identified use case, the skills to carry the project and a sponsor on the management side. It is not an absolute AI score nor a ranking: it is a status check that shows where to start and what is still missing for a project to have a chance of succeeding.

How to use this tool

Answer honestly on each of the four prerequisites, based on the reality on the ground and not on the stated intention.
Read the result as a profile, not as a grade: one weak prerequisite is enough to undermine the whole project, even if the others are solid.
Identify the weakest link: it is the one to address first, before any investment in a model.
Run the diagnostic again after a few months of work on that link, to check that the missing condition has been lifted.

Worked example

A low score does not condemn the project, it redirects its starting point. If the diagnostic shows scattered data and free-format reports, the first useful project is not a predictive model but a data-cleanup or document-structuring project. Making the raw material reliable first avoids building an AI on data that no one will be able to use.

Frequently asked questions

What does this diagnostic measure?

It places your organisation against the prerequisites of an industrial-AI project: available and reliable data, an identified use case, skills and a sponsor. It does not score an absolute AI level, it shows where the ground is ready and where it is not yet.

Are the answers stored?

No. The calculation runs entirely in your browser: nothing is transmitted or kept. Closing the page erases everything.

Does a low score mean giving up on AI?

On the contrary, it shows where to start. A low score on data signals that a predictive project would be premature and that a first effort on data or on document exploitation will pay off sooner.

Does this diagnostic replace an audit?

No. It gives a first reading in a few minutes, useful to frame a discussion. A thorough audit cross-checks the real data, the systems and the teams on the ground.

The other tools

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