AI expertise

AI literacy refers to the totality of skills, knowledge and understanding required to use artificial intelligence systems competently, categorise how they work and realistically assess the associated opportunities, risks and potential damage. It is therefore not just a question of being able to operate an AI tool, but of understanding what it does, where its limits lie and what consequences its use has for people, data and decisions. This definition is closely aligned with the legal definition in the EU AI Act, which enshrines AI expertise as a binding requirement across Europe for the first time.

For companies, AI competence is therefore no longer a „nice-to-have“ of further training, but a basic organisational requirement: anyone who develops, purchases or uses AI systems in their day-to-day work must ensure that the people involved know what they are dealing with. It is precisely this combination of individual ability and organisational obligation that makes the term so relevant.

The term has rapidly gained in importance because generative AI — language models, image generators, assistance systems — has gone from being a specialised tool to an everyday instrument in almost every department in just a few years. With this spread comes greater responsibility: a tool that delivers convincingly formulated but potentially incorrect results is only as good as the judgement of the person operating it. AI expertise is precisely this judgement — systematically developed instead of left to chance.

Legal anchor: Article 4 of the EU AI Act

The central legal point of reference is Article 4 of the AI Regulation (Regulation (EU) 2024/1689, or EU AI Act for short). This provision obliges both providers and operators of AI systems to take „best endeavours measures to ensure that their staff and other persons involved in the operation and use of AI systems on their behalf have a sufficient level of AI competence“. The decisive factors here are their technical knowledge, experience, training and the specific context of use.

A few key points you should know about Article 4:

• Applicable from 2 February 2025. The AI competence obligation is one of the first provisions of the EU AI Act to come into force — well before most of the other obligations in the Regulation.
• Risk class independent. The requirement applies to any AI system that a company offers or operates — not just high-risk systems. Even those who „only“ use a standard chatbot in customer service fall under Article 4.
• No prescribed training format The EU Commission has clarified that there is no prescribed training format and no general certification obligation. Companies choose the appropriate measures themselves — from internal workshops and e-learning to familiarisation with the specific use case.
No separate fine The regulation does not provide for a separate fine for a pure violation of Article 4. The risk is indirect: if there is a lack of demonstrable AI expertise, this can have an aggravating effect in the event of other infringements, liability issues or official audits.Documentation recommended Even if no specific format is prescribed, it is advisable to document the measures taken in a comprehensible manner — who was trained when and on what content.

The exact wording and the official text can be found in the consolidated version of the Regulation and at artificialintelligenceact.eu/article/4; the text of Regulation (EU) 2024/1689 is legally binding in EUR-Lex.

The three levels of AI competence

AI expertise is not a single field of knowledge, but is made up of several levels that build on each other. In practice, a division into three areas has proven itself, which together result in a sustainable understanding.

1. basic technical understanding

At this level, the question is: How does an AI system work at its core and what can it realistically achieve? This includes a basic understanding of the fact that a large language model does not „understand“ texts, but instead generates statistically probable word sequences. This leads directly to the most important phenomenon that every user should be aware of: hallucinations — i.e. plausible-sounding but factually incorrect or fictitious answers. Anyone who doesn't know this accepts AI outputs without checking them and produces errors.

A basic technical understanding also includes prompting: the ability to give a system clear, context-rich instructions and to control the quality of the answer by asking better questions. It also includes a feel for what a particular model is and isn't suitable for— an image generator is not a fact-finding tool, a chatbot is not reliable legal advice.

2. legal and organisational framework

The second level concerns the rules within which AI may be used in the company. These primarily include:

• Data protection: Which data may be entered into which system? Personal data, business secrets or customer data do not belong in a public AI tool without being checked.
Approvals and responsibility: Who decides whether an AI-supported output is used? The responsibility for a result remains with the human, not the model.Compliance and documentation: Which internal guidelines apply and how is the use made traceable?

This level translates abstract requirements — for example from the GDPR and EU AI Act — into concrete guidelines for day-to-day work. It ensures that technical possibilities do not lead to legal or ethical pitfalls.

3. depth of application in the specific use case

The third level is the most specific: knowing how to use AI sensibly and responsibly in your own area of responsibility. A person in customer service needs a different skills profile than someone in software development or marketing. Here, a basic technical understanding and legal framework are combined with the expertise of the respective role — for example, the question of when an AI-generated customer response must be checked before being sent or how an AI suggestion should be handled in the code review.

Only the interaction of all three levels results in what the EU AI Act refers to as a „sufficient level of AI competence“. A purely technical understanding without a legal framework is just as incomplete as pure policy training without practical application knowledge.

Differentiation from related terms

KI competence is often confused with related concepts. The following overview categorises the most important terms.

Distinction between AI skills and related skills concepts Term Focus Relationship to AI competence Data literacy Read, interpret, critically scrutinise and use data for decision-making Important foundation: If you want to evaluate AI outputs, you also need to be able to categorise data. However, AI expertise goes beyond pure data expertise. Digital expertise General confident use of digital tools, devices and media Overarching term: Digital competence is broader. AI skills are a specialised, more sophisticated subset of this. AI skills Understanding of the functionality, opportunities, risks and damage of AI systems in actual use The term defined here — combines technical, legal and application-related knowledge about AI.

In short: digital literacy is the framework, data literacy is an important building block, and AI literacy is the specialised skill that has taken on a meaning of its own due to the rise of generative AI and the EU AI Act.

How companies are building AI expertise

Building AI expertise is less of a one-off training event and more of an ongoing process. In practice, a four-step approach has proven successful:

Inventory. First, clarify where AI is already being used in the company (often more than expected, including „shadow AI“ on your own), which roles come into contact with it and what prior knowledge is available. This results in the actual skills required for each role.

Training on the use case. Instead of generic AI slides, role-specific, practical formats are more effective: What does responsible use of AI mean in concrete terms for sales, development and marketing? Real examples and your own tools beat abstract theory.Measure Expertise that is not tested remains an assumption. Short knowledge queries, case studies or observation in everyday working life show whether the „sufficient level“ has been reached and where it needs to be sharpened.

Documentation Keep a record of who has been trained on which content and when. This is not only sensible governance, but also the pragmatic way to demonstrably fulfil the requirements of Article 4 of the EU AI Act.

Because AI systems develop rapidly, AI competence is not a status that is achieved once and then ticked off. A recurring rhythm makes sense: regular refresher courses, updates for new tools and a clear point of contact for questions from the team.

Real-life example and practical relevance

A concrete point of reference from practice: industry associations and studies show a significant increase in demand for AI training since Article 4 of the EU AI Act came into force in February 2025. The digital association Bitkom, for example, regularly points out in its surveys on AI use in German companies that the spread of generative AI in everyday working life is growing faster than the development of structured expertise in this area — a gap that Article 4 now addresses. A typical use case: A medium-sized company introduces an AI-supported chatbot in customer service. As an operator within the meaning of the EU AI Act, it must ensure that the service employees understand how the chatbot generates responses, when they need to intervene and what data they must not entrust to it — this is precisely AI expertise in action.

This example also illustrates why AI expertise needs to be considered on a role-specific basis: The manager who decides on the introduction of the chatbot primarily needs an understanding of liability, data protection and the selection of a suitable provider. Employees in day-to-day operations, on the other hand, need practical knowledge about borderline cases and escalation channels. Both groups have AI expertise — but to different degrees. A well thought-out competence concept reflects these differences instead of sending all employees through the same generic training programme.

Frequently asked questions about AI skills

What does AI competence mean in simple terms

AI expertise is the ability to use AI systems competently: to understand how they work, what they can do and where their limits lie, and to realistically assess the opportunities and risks of their use. It combines technical, legal and application-related knowledge.

Is AI expertise mandatory for companies?

Yes. Article 4 of the EU AI Act (Regulation (EU) 2024/1689) obliges providers and operators of AI systems to ensure that their staff have a sufficient level of AI competence as of 2 February 2025. The obligation applies regardless of the risk class of the system.

What training format does the EU AI Act prescribe?

None. The EU Commission has clarified that there is no prescribed training format and no general certification obligation. Companies choose suitable measures themselves — it is recommended that they are documented in a comprehensible manner.

Does a company face fines if it lacks AI expertise?The regulation does not provide for an independent fine for a pure violation of Article 4. The risk is indirect: a lack of AI expertise can have an aggravating effect in the event of other infringements, liability issues or official audits.

What is the difference between AI expertise and digital expertise?

Digital competence is the broader generic term for the confident use of digital tools in general. AI competence is a specialised subset that focuses specifically on the understanding and responsible use of AI systems.