EU AI Act: Are Companies Ready for the Staff Training Obligations?

The European Union's AI Act is moving towards the point where its rules will shape how companies build and use artificial intelligence. In an article published on Dev.to, Franck Parienti of the training firm BusinessDigital.fr writes that the regulation's main effects will be felt from 2026, and that for many organisations the hardest part will not be technical documentation but the competence of their teams.

A framework built on four risk levels

The act aims to make AI systems safe, transparent, traceable, non-discriminatory and environmentally responsible. Its central mechanism is risk-based classification: systems fall into four categories — unacceptable risk, high risk, limited risk and minimal risk. The strictest obligations apply to high-risk systems, both when they are placed on the market and while they are in operation.

That classification is what draws employees into the compliance picture. A company must show not only technical conformity, but that the people building and using its systems understand the risks involved.

What the training obligation covers

According to the article, staff preparation breaks down into three layers. General awareness means understanding what AI is, what it can do and where it fails. Specific training is aimed at teams directly involved in developing or operating higher-risk systems and goes deeper into the AI Act's requirements. Skills maintenance recognises that the technology changes quickly, so training cannot be a one-off session.

Why acting before the deadline matters

Parienti frames early preparation as a strategic advantage rather than a cost: companies that train their teams now will reach compliance more easily and will adopt AI tools later without disruption. Waiting until the obligations apply is described as a major strategic error.

Among the risks of being unprepared, the article lists financial penalties and possible bans on selling certain AI products or services, bias and discrimination caused by systems that were poorly understood, loss of trust among users and partners, and slower innovation.

From principles to practical skills

The article points to two concrete training tracks. One covers the ethical use of large language models: writing precise prompts, spotting and correcting bias in generated text, knowing which company and customer data must never be shared with these tools, and verifying the facts a model produces. The other covers automation platforms such as n8n: connecting CRM systems, databases, communication tools and AI APIs, embedding AI agents into business workflows, and monitoring the reliability of what has been built.

The author stresses that preparing for the AI Act is a marathon rather than a sprint, and notes that the text addresses general stakes only: for precise legal obligations, official documents and specialist legal advice remain the reference.