Know in advance whether your data, systems, and people are ready for AI

Someone on the management team has seen a demo and wants to do something with AI too. A vendor promises it can be done in a month. But nobody knows exactly where the data lives, whether it is correct, or who may access it. Six months later, a pilot runs on one laptop and a report sits in a drawer. That is rarely a technology problem. It comes down to what was not ready before anyone started building.

Why most AI pilots stall before they start

The model is the easy part. The difficulty lies around it. The order history sits in an ERP that the vendor will not export from. The client data is spread over three mailboxes and a spreadsheet. Nobody can say whether feeding that data to a model is allowed, so someone does it quietly, or nobody does it. And the employees who will work with the result hear about it only when it is finished.

The AI readiness assessment maps exactly that, before a euro goes into building. It is not meant to tell you that you are not ready. It makes sure you are ready when you begin. What you fix now takes a few weeks. What you discover halfway through a project can cost the project.

What we review: data, systems, security, and people

We look at four areas. Data: where it is, how complete it is, who owns it, and how far back the history goes. A model that must forecast next month's sales needs a few years of figures that were not recorded differently every quarter. Systems: can we reach that data without manual work, through an API or an integration, and is there a server where it may run. Security and rules: what the GDPR allows for personal data, and whether your use case falls under the EU AI Act. A chatbot that answers opening hours is treated differently from a system that assesses job applicants. People: who will work with it, who decides, and whether anyone feels responsible for it.

For each area, we give a score from 1 to 5, with the reason in plain language. A 3 for data means, for example: the figures are there, but the product codes changed twice and that must be straightened out first. A 2 for people means there is enthusiasm, but nobody yet who will take it over and run it.

An honest report in 2 weeks

We start with a free intake. We hear what you want to achieve, and that often raises the first question: is AI the right tool for this, or is an ordinary integration or report enough? Then we talk to the people who work with the systems every day, an hour per person, and get read access to the data. We look ourselves and do not only ask. A data export says more than a presentation about it.

After 2 weeks, we present the result. It is not a thick report but a few pages you can show your management: this is what is there, this can start now, this must come first, and this is better left alone. If the conclusion is that your organization is not ready yet, we tell you right away. That is not bad news. It is the reason you did the assessment.

And then: build what makes sense

The assessment ends with a list in order. First the things you can fix yourself, such as naming an owner for the client data or requesting an export from your vendor. Then the things where you need help. If you also want to know exactly where the hours leak away in your processes, we combine the assessment with the AI process scan. When the foundation is in place, the work continues as AI implementation: one specific process, a prototype in 10 business days, on a server inside the EU.

The report is yours, as is the data we looked at. We store none of it, and you withdraw the access you gave us after the assessment. You are free to continue with another provider. If it later grows into something you must answer for, the assessment connects to AI governance and audits, with the same four areas as the starting point.