In many companies, artificial intelligence has evolved from a topic of the future to a tool of the trade. Employees use generative AI to conduct research, create texts, analyze information, or get assistance with daily tasks. At the same time, executives decide where AI should be deployed, which risks are acceptable, and which rules apply within the organization.
This brings a new skill to the forefront: AI literacy.
But what does that mean, exactly? Does every employee need to understand how an AI model works from a technical standpoint? Do managers need to be able to generate prompts themselves? And what knowledge do companies need to use artificial intelligence responsibly?
With the adoption of the EU AI Act, at the very latest, this issue has also become relevant from an organizational perspective. Article 4 requires providers and operators of AI systems to take measures to promote the AI competence of individuals who work with these systems on their behalf. In doing so, they must take into account, among other things, existing knowledge, experience, training, and the specific context of use. However, the AI Act does not prescribe a specific, uniform level of competence.
For companies, this means one thing above all else: AI expertise must be a good fit for the job.
What does "AI literacy" actually mean?
AI proficiency is often equated with the ability to write a good prompt or to use a specific AI tool.
That doesn't go far enough.
Anyone who uses artificial intelligence in their professional life should do more than just know how to operate a system. It is equally important to understand how to make meaningful use of its results and to recognize its limitations.
AI proficiency therefore encompasses several skills:
- Understanding the Basic Principles of AI
- Assessing the Opportunities and Limitations of AI Results
- Carefully Review the Results
- Identifying Risks and Potential Errors
- Handle sensitive information with care
- know when human judgment is required
- Be familiar with internal rules for the use of AI
- Taking Responsibility for How We Use AI
The European Commission is taking a similar approach. When implementing measures to promote AI literacy, companies should, among other things, consider which AI systems are being used, what prior knowledge the individuals involved possess, and what risks arise in the specific context of use.
AI literacy is therefore not a single skill. It is the prerequisite for using artificial intelligence thoughtfully rather than merely for convenience.
Why AI Competence Is Becoming a Corporate Priority Right Now
Just a few years ago, the use of AI in many companies was limited to specialists or individual projects.
Generative AI has changed this situation.
Today, employees without technical expertise can create content, summarize information, interpret data, or develop ideas in a matter of seconds. As a result, the number of people working directly with AI is growing.
At the same time, the quality of the results is not automatically guaranteed.
AI can express itself convincingly and still be wrong. It can overlook important connections, generate irrelevant information, or adopt problematic assumptions. Added to this are concerns about data protection, transparency, fairness, and the use of corporate information.
This makes AI expertise a prerequisite for safe and productive use.
So the crucial question is no longer:
"Do our employees use AI?"
Rather:
"Can you determine when and how to use AI effectively?"
What the EU AI Act Requires in Terms of AI Competence
Article 4 of the EU AI Act has given this issue added significance.
The requirement to promote AI literacy has been in effect since February 2025. The corresponding monitoring and enforcement regulations have also been in effect since August 2026. Following the changes made in 2026, the requirement remains in place; however, no specific or uniform level of literacy is mandated for individuals.
This is an important distinction for companies.
The AI Act does not state in general terms:
"Every employee must complete the same AI training."
Instead, the measures should be tailored to the actual situation. For example, the following should be taken into account:
- technical background knowledge
- past experience
- Education and Training
- the AI systems used
- the specific context of use
- Possible effects on other people
This means that the regulatory approach also follows a role- and risk-based logic.
An employee who occasionally uses AI to assist with writing needs different skills than someone who procures or approves an AI system, or who uses its results to make far-reaching decisions.
What Employees Should Know About AI
For many employees, the first step is learning how to use AI effectively in their daily work.
No in-depth technical knowledge is required. What matters is a solid basic understanding.
AI results are suggestions, not facts
A key component of AI literacy is the ability to critically evaluate results.
Generative AI can formulate a response in a very convincing way. However, the quality of the language says nothing about whether the information it contains is actually correct.
Employees should therefore be aware that:
- Which results need to be reviewed?
- What sources can be used for verification?
- When is additional expertise necessary?
- Which decision should not be based solely on an AI output?
Precisely because AI is easy to use, this ability to make sound judgments becomes even more important.
Employees must know what data they are allowed to enter
The ability to handle information is also a fundamental AI skill.
Not every piece of information that would be helpful for a task should automatically be entered into an AI system.
Employees therefore need clear guidance on what company information they are permitted to process and what internal policies apply.
However, a general statement such as “Do not enter any sensitive data” is often not sufficient.
People must be able to recognize, within their own work context, when information is, for example, confidential, personal, or otherwise protected.
AI literacy, therefore, combines the ability to operate a system with a conscious approach to the information being used.
Good prompts are part of it, but they aren't everything
Prompt engineering is an important part of working with generative AI.
Those who clearly define a task, provide context, and describe requirements in an understandable way usually achieve better results.
But a good prompt alone does not guarantee competent use of AI.
Even a perfectly worded query can lead to an incorrect or inappropriate response. That is why employees must be able to evaluate the result as well as the input.
Professional use of AI therefore consists of several steps:
Understand the task, formulate it clearly, check the results, and use them responsibly.
Prompting is part of it, but it does not cover all the necessary skills.
What Leaders Also Need to Understand
For executives, the perspective is shifting.
You don't need to be able to use every AI tool better than your employees. Rather, your job is to create the right conditions for their use.
The first step is to understand where AI is actually being used within your own organization.
Only then can questions like these be answered meaningfully:
- Which tasks are suitable for AI support?
- Which decisions should not be automated without oversight?
- What risks arise for customers, employees, or other affected parties?
- What rules do teams need?
- Who is responsible for individual AI applications?
- What skills do employees need to develop to do this?
Managers therefore need a different kind of AI expertise than mere users.
Not only do you have to evaluate results, but you also have to shape the organizational framework.
Leadership also means setting limits for AI
The use of AI in companies often evolves faster than the corresponding policies.
A new tool can be made available in just a few minutes. However, making a sound organizational decision about what it can be used for takes more time.
Managers should therefore ensure clarity.
For example, employees need to know which AI applications have been approved, what tasks they may be used for, and when additional oversight is necessary.
In the absence of such guidelines, two extremes often arise.
Either employees experiment without sufficient guidance, or AI is restricted so severely due to uncertainty that its useful applications go untapped.
AI expertise at the executive level helps create a responsible framework that balances these extremes.
Different roles require different AI skills
That is exactly why a company should not view AI literacy as a one-time training initiative for everyone.
The European Commission explicitly identifies prior knowledge, experience, training, and the operational context as factors that should be taken into account in initiatives aimed at developing AI literacy.
In practice, this can result in varying skill requirements.
Employees
Above all, you need a basic understanding, the ability to apply the concepts confidently, and the ability to critically evaluate results.
Executives
You must also be able to assess opportunities, risks, and organizational implications, and define responsibilities.
Project Managers
You need to know how to plan, implement, and monitor AI applications effectively.
Compliance and Governance Officers
For them, regulatory requirements, responsibilities, risks, and documentation are a greater focus.
Service Manager
You need to understand how AI can become an integral part of a service over the long term and what oversight and control are required during operation.
Software Tester
Their skill needs are more focused on how generative AI is transforming development, quality assurance, and testing.
The goal, therefore, is not to turn everyone into an AI expert.
The goal is for everyone to have the skills necessary to fulfill their responsibilities.
AI proficiency begins with one's own context of use
Companies do not need to develop a comprehensive training program right away to achieve this.
A good first step is to take stock of the situation.
Where is AI already being used? Who is using it? For what tasks? And what impact might the results have?
Building on this, the competency requirements can be defined in much greater detail.
Some helpful questions include, for example:
- What AI systems do our employees use?
- What tasks can it be used to complete?
- What kinds of errors could occur in this process?
- Who verifies the results?
- What data is processed?
- What internal rules are already known?
- Which roles require in-depth knowledge?
- Where is there still a lack of direction?
This transforms the abstract goal of “We need more AI expertise” into a concrete training objective.
A single training session alone isn't enough in the long run
AI systems are evolving rapidly. At the same time, companies are changing how they use them, their processes, and their internal policies.
That is why AI literacy should not be viewed as knowledge acquired once and for all.
What is a supportive tool today may be more deeply integrated into a process tomorrow. New features can open up additional possibilities, but they can also raise new questions.
Companies should therefore make sure to review their knowledge regularly and update it as needed.
The European Commission now also provides practical examples from various organizations to support these efforts. These range from e-learning and in-person training to more comprehensive learning and exchange formats.
This shows that there isn't just one prescribed way.
It is crucial that measures be tailored to the actual use of AI and the people involved.
AI expertise is more than just compliance
The EU AI Act is raising awareness of AI literacy. Nevertheless, companies should not view AI literacy solely as a regulatory requirement.
The greatest benefit is in day-to-day work.
Employees who are better able to assess AI can use the technology in a more targeted manner. They are better able to recognize when results are helpful and when caution is needed. Managers can evaluate potential applications more thoroughly and provide teams with clearer guidance.
As a result, AI proficiency has a direct impact on:
- Quality
- Productivity
- Safety
- Trust
- Sense of Responsibility
- Innovation Capacity
An organization with skilled users does not, therefore, have to use AI any less.
She can use AI in a more conscious and targeted way.
Questions Companies Should Be Answering Right Now
If you want to assess how well your organization is positioned in terms of AI expertise, you can start by asking a few simple questions:
- Do we know which AI systems are actually being used in the company?
- Do employees generally understand what AI can and cannot do?
- Can they critically evaluate AI results?
- Do you know what information you're allowed to use?
- Are you familiar with internal policies and points of contact?
- Do executives have sufficient knowledge to effectively manage the use of AI?
- Do we differentiate competency requirements by role and usage context?
- Can we clearly demonstrate how we promote AI literacy?
- Do we update our knowledge when systems or areas of application change?
If several of these questions remain unanswered, the next step isn't necessarily a comprehensive AI program.
Good AI expertise often starts with a solid foundation and a realistic assessment of the current situation.
Conclusion: AI literacy means being able to assess AI correctly
Artificial intelligence is becoming a standard tool for an increasing number of people. As a result, the ability to accurately assess its capabilities and limitations is becoming more important.
Employees do not need to understand the technical inner workings of every AI model. However, they should know how to use AI effectively, critically evaluate results, handle information responsibly, and recognize when human judgment is necessary.
In addition, managers need the ability to evaluate areas of responsibility, clarify roles, and establish appropriate conditions.
The EU AI Act underscores this need for action. However, it does not require companies to follow a one-size-fits-all training program. The key factors are prior knowledge, role, context of use, and the risks associated with the AI systems being used.
This means that AI expertise is not just another item on a compliance checklist.
It is a prerequisite for people to be able to use artificial intelligence safely, effectively, and responsibly within a company.
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Would you like to know which AI training programs are best suited for different tasks and responsibilities? Then be sure to read the previous post:
"AI Training for Businesses: Which Training Program Is Right for Which Role?"
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