Why this object hangs off that object

Determine for each role, system and context which combination of instruction, guidance, practice or training is appropriate.

Hangs off: Article 4: AI literacy

Editorially reviewed | ai-literacy

Map roles against the AI systems they use and record per combination what a person must be able to judge: what the system does, where it fails, who it is applied to, and when to intervene or escalate.

Hangs off: Article 4: AI literacy

Editorially reviewed | ai-literacy

Attach the literacy measure to two fixed moments in existing processes: the rollout of a new AI tool and the onboarding of anyone gaining access to an existing tool.

Hangs off: Article 4: AI literacy

Editorially reviewed | ai-literacy

The regulation entered into force on 1 August 2024, after which the obligations followed in phases.

Hangs off: Article 4: AI literacy, Article 5: prohibited practices

Placed against the official source | timeline

Since 2 February 2025 the Article 5 prohibition and the Article 4 AI literacy duty apply.

Hangs off: Article 4: AI literacy, Article 5: prohibited practices

Placed against the official source | ai-literacy, timeline

The Commission draws the line between software that does and does not fall under the regulation.

Hangs off: Annex III: high-risk AI, Article 4: AI literacy

Placed against the official source | scope

Since 27 July 2026 the organisational duty supports the development of AI literacy without guaranteeing an individual level.

Hangs off: Article 4: AI literacy

Placed against the official source | ai-literacy

Periodically reconcile the list of accounts and licences with access to AI systems against the participation and instruction register, and clear the gap list with an owner and a deadline.

Hangs off: Article 4: AI literacy

Editorially reviewed | ai-literacy

Check when systems, roles or risks change whether the selected measures remain appropriate.

Hangs off: Article 4: AI literacy

Editorially reviewed | ai-literacy, control

Versioned record of roles, context, measures, participation or instruction and review moments.

Hangs off: Article 4: AI literacy

Editorially reviewed | ai-literacy, evidence

Internal register showing who received which instruction, working session, training or guidance, for which system, on which date and on what basis, including new joiners, contractors and external staff.

Hangs off: Article 4: AI literacy

Editorially reviewed | ai-literacy

The recorded matrix of roles against AI systems, with the context of use, affected persons, risk and selected measure per combination, dated and with an owner per row.

Hangs off: Article 4: AI literacy

Editorially reviewed | ai-literacy

An organisation secures the entrances to its buildings with facial recognition and uses that biometric access control to register visitors as well. When the system returns no match, a security officer reviews the camera images and decides personally whether someone may enter. The question is whose measures have to reach that officer: those of the supplier of the model, those of the department that deploys the system, or both.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A police force uses AI to search large volumes of investigation files and surface connections a detective would otherwise miss. The outputs feed into the choice of which suspect is pursued further and end up in documents that enter the criminal process. The question is whether one and the same basic instruction is enough for the analyst operating the model and for the detective who acts on its output.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A newsroom uses generative AI to prepare summaries, headlines and imagery, after which an editor finishes the piece and the desk decides to publish. Part of that work sits with freelancers, and an outside agency produces marketing content with the same tools. The question is whether your AI literacy measures must reach those freelancers and that agency, or only the people on the payroll.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

Asimov AI is a micro organisation of at most fifteen people that supplies AI services for legislative work to government institutions and companies. With every new contract it holds one or more induction calls with the team leads and officials who will use the platform, explaining how the platform and the underlying models work and how hallucinations arise in this domain and can be mitigated.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

Booking.com built a three-part training for its legal and public affairs teams: first basic terminology and the difference between classic machine learning and language models, then how AI works inside the company, then the regulatory landscape and where it meets the law they already practise. The material was also released as a video and podcast series with subtitles and written handouts.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A customer service department runs a database query to find all customers who purchased a specific product last month, and calculates the average from a satisfaction survey in a standard spreadsheet. Every step follows predefined instructions.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

Dedalus Healthcare, which among other things supplies AI that predicts complications for hospital patients, trains its legal staff, data protection officer, compliance function and quality and regulatory affairs department on the technical side. Developers and engineers conversely receive training focused on the legal and compliance aspects of the AI Act. The executive committee received its own session tailored to its role.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A hospital uses an older diagnostic support expert system in which physicians encoded knowledge, facts and rules. Based on the symptoms a doctor enters, the system independently draws a conclusion about possible conditions.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

Fastweb operates more than ninety AI systems and formally appoints an AI-SPOC in every department, a trained point of contact for AI questions from that team. These people receive separate instruction on prohibited practices and high-risk systems and are allowed to run their department's AI risk assessment themselves.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A meteorological institute runs physics based weather models and uses machine learning to approximate complex atmospheric processes such as cloud microphysics and turbulence. The estimated values are then fed into the established physics model, which produces the actual forecast.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

An asset manager builds a simple baseline model that predicts future prices by always taking the historical average, in order to test whether a more advanced model genuinely adds value. A weather service does the same by predicting tomorrow's temperature using last week's average.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

Generali offers all staff basic courses on what AI is and how the group uses it, intermediate modules for people who use AI systems daily, and advanced sessions with external experts for those who build or maintain them. On top of that it runs internal role academies for data scientists, actuaries and accountants, set up with universities and research institutes.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

Gjensidige Forsikring gives all employees a mandatory e-learning as a baseline and builds role-based depth on top: analysts get model risk and data governance, claims handlers get training on the systems they operate themselves. Where relevant, AI literacy is checked during recruitment and training on AI systems is part of onboarding.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

INECO, a large Spanish engineering firm working for public authorities on rail, airports and digitalisation, ran over twenty AI trainings with more than four hundred participants in 2024 and set up an AI Master Classroom on the intranet with short modules, audio and subtitles. After making staff aware of data leakage risk, the organisation limited the use of public language models and moved to a secured chatbot solution.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

An insurer deploys a trained model that ranks claims by complexity. After deployment the model learns nothing new; the supplier retrains only periodically in a controlled release, so its behaviour is entirely stable between releases.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

In 2024 Mural ran a mandatory AI training for all staff with a 93 percent completion rate, and additionally organised a live interactive session for the AI team specifically on prohibited practices and high-risk categories. For that team the legal function built a visual mind map on a digital whiteboard, with templates and direct references to the provisions.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A grid operator uses a machine learning model to approximate parameters inside a classical optimisation calculation built on linear and logistic regression. The model changes nothing about the decision rules themselves, it only makes a long established calculation method faster and cheaper.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A satellite operator allocates power and bandwidth across transponders using a machine learning model that predicts network traffic, because classical optimisation struggles with demand that varies sharply by region and by moment. Performance is comparable to established methods in the field.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A software company releases a chess program that assesses board positions using a minimax algorithm with heuristic evaluation functions. The program has never learned from game data; it applies pre-programmed rules and search strategies to find a strong move.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

Smals, which supplies IT to Belgian public administrations, describes the knowledge each role needs: all staff know capabilities, limits and internal guidelines, AI ambassadors spot and prioritise use cases, AI experts know governance and technique, and legal staff and the data protection officer get separate deep dives. Before every new AI project there is also a session for all stakeholders on the capabilities, limits, risks and governance of that specific system.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

An organisation adopts an email filter that was trained during its building phase on a set of messages humans labelled as spam or not spam. Once in use, the filter independently assesses new incoming email and classifies it based on the patterns it learned.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A service desk shows customers an expected resolution time calculated as the mean from historical tickets. A retail chain uses a trivial predictor to estimate how many units of a product it will sell each day, as a starting point for purchasing planning.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A commercial team uses reporting software that applies statistical methods to calculate total sales, average sales per region and trends over time, and displays them in charts. The dashboard makes no suggestion about how to improve sales or which products to promote.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

Article 4 prescribes no form. The Commission confirms that no certificate is required, no obligation to measure knowledge exists, no training is mandatory and no governance structure is prescribed.

Hangs off: Article 4: AI literacy

Placed against the official source | guidance

The duty to take measures also covers contractors, service providers and sometimes clients. Supervision lies not with the AI Office but with national market surveillance authorities, enforcing since 2 August 2026.

Hangs off: Article 4: AI literacy

Placed against the official source | guidance

Providers and deployers take measures that support the development of AI literacy.

Placed against the official source | ai-literacy

Public route for structuring measures by role and context.

Hangs off: Article 4: AI literacy

Editorially reviewed | ai-literacy, template