Your AI diagnostic
Nine questions, three minutes. You get your maturity stage, a score and the three workstreams to open first. Nothing is saved until you send the result.
Why run an AI maturity diagnostic
An AI maturity diagnostic answers a simple question before any project starts: where do you really stand, between what your teams already use and what still needs to be built. Many organisations move forward on instinct, driven by a hunch or by outside pressure, without ever writing down what AI actually does inside their walls today. A diagnostic replaces that impression with answers that are written, comparable and dated. It does not replace a decision, it informs one.
The questions in this diagnostic are not about technology but about your organisation: what draws your interest to AI, what your teams actually use, whether you can put a figure on the value, the state of your data, its sensitivity, the training already given, how far an existing project has come and who owns it. Every answer counts toward a maturity stage and toward a score out of eighteen. No question has a right answer: each situation calls for a different next step.
The result comes down to two things that are easy to read: a maturity stage among the five in our method, and recommendations drawn directly from your answers, not from a generic template. You can stop there. If you send us the result, we read your answers and get back to you with what we would do in your place, including telling you it is too early. Nothing is saved before that.
Our method, in five stages
The diagnostic places your organisation on one stage among five. Those five stages form our engagement method, from the first framing to a system that holds without us.
- 1
Awareness
Understand before you buy: set out what AI really does, what it costs and what regulation requires, before a single line of code is written. This stage ends with a position memo, sourced and dated, in your organisation's name.
- 2
Value diagnostic
Among the tasks your teams keep redoing, find the one truly worth building a tool for. We put a figure on a single case using your real data, and write down what we will not do.
- 3
Conceptualisation
Write the scope before the code: the screens, the data, the moments where the machine decides or hands back control. This stage surfaces bad surprises while they still cost nothing.
- 4
Production prototype
An engineer works on your real data and ships in batches, so the people concerned can use it right away. We measure the chosen indicator before and after, with its method.
- 5
Industrialisation
Make the system operable by your teams without depending on one person: accounts and roles, tested backups, cost tracked by usage, and a real, documented handover.
Our seven pillars
Seven principles guide every project we take on, in the order they actually arise: value before the tool, the tool before the model, training before scale.Value before the project
We establish what a project must deliver before writing a line, with an order of magnitude, never a figure we have not measured. If nobody can say why the project is worth doing, we do not start it.AI only where it is needed
A rule, a search or a script often solves the problem, more leanly and more predictably. A model only steps in where a script is not enough.The right model for the right task, no more, no less
When a model is needed, we take the smallest one that does the job properly, chosen on trials with your own data. A large model on a narrow task wastes resources for nothing.Security by design
Whatever commits your organisation, a price, an availability, a commitment, is computed and checked server side, never produced by a model. Your data is never used to train a third-party model.People validate, operate and do better
The tool takes on the repetitive tasks, people keep validation, judgement and the relationship. An agent always introduces itself as an agent and says who to turn to when in doubt.Measure every workflow, every agent, every operation
Every workflow and every agent has its measurement: what it consumes, its cost, its response time. What we do not measure, we do not claim.Train before scaling
A tool rolled out widely to people who do not know what it does creates more errors than it prevents. We train first on good practice, then scale up with the people who learned.
Frequently asked questions
What is AI maturity?
It is where your organisation stands between intention and actual use: what your teams already do with AI, what they can put a figure on, the state of your data and who owns the topic. This diagnostic places you on one stage among five, based on your answers, not on a sector average.How is the score calculated?
The score out of eighteen adds up the answers to the scored questions in your diagnostic: usage, value, data, training, project progress and ownership. The maturity stage mostly follows the most advanced point of your project, then the value already put a figure on. The scoring rules are written into the page's code, with nothing hidden.Is the diagnostic free?
Yes, entirely, and you get your stage, your score and your recommendations without filling in anything beforehand. Nothing is sent or saved unless you choose to pass us the result through the final form.What happens to my answers?
They stay in your browser while you answer and go nowhere without your action. If you send your diagnostic, they join our contact pipeline so we can read it before getting back to you.Where should I start if I am right at the beginning?
At the Awareness stage: set out what AI really does in your organisation, what it costs and what regulation requires, before buying any tool. It is often the fastest stage to cross and the one that prevents the most mistakes later.What is the difference with the value diagnostic in the engagement method?
This online diagnostic places your organisation in the bigger picture and points you to the right stage, in a few minutes and without a meeting. The value diagnostic, the second stage of our method, goes further: it puts a figure on one specific case using your real data, with a named owner.Does the diagnostic take the AI Act into account?
One question covers the sensitivity of your data, a point the European regulation addresses directly. For the full regulatory framework and its deadlines, we keep a separate memo, sourced and revised as the text evolves.Talk about your result
A conversation to go over your answers and decide what comes next, with no commitment.