CQ | AI literacy: the new mandatory competence for companies in the era of the AI Act

Artificial intelligence is no longer a topic reserved for technical departments. In more and more organizations, AI is used for drafting documents, data analysis, process automation, decision support, recruitment, marketing, financial services, customer relations, or risk management. That is why the important question is no longer just whether a company uses AI, but whether the people using it understand well enough its limitations, risks, and associated responsibilities.

What AI literacy means

AI literacy no longer just means knowing how to write a prompt. It means understanding what an AI system can and cannot do, when it needs to be checked, what risks it generates, what data should not be entered, and who remains responsible for the decision. In the European Union, the AI Act introduces the obligation for providers and users of AI systems to take measures to ensure a sufficient level of AI literacy for those operating or using these systems.

For companies, this change is important because it shifts the discussion about AI from the area of individual productivity to governance, risk, and compliance. An employee who uses AI without understanding the risks can expose the organization to errors, data loss, incorrect decisions, reputational damage, or regulatory breaches. Consequently, AI literacy must be treated as part of the company’s control infrastructure, not as an optional course on digital tools.

AI literacy can be understood as the set of knowledge, skills, and understanding that enables a person to use artificial intelligence in an informed, responsible, and risk-proportionate way. It is not just about the ability to use an AI application, but about the capacity to understand the context in which AI is used and the possible consequences of its use.

• The first level is functional: employees need to know what AI is, what types of systems are used in the company, and in which processes they appear.
• The second level is critical: people need to understand that AI can produce incorrect, incomplete, biased, or hard-to-explain results.
• The third level is organizational: users need to know what internal rules apply, what data can be used, who validates results, and when human intervention is needed.

A common confusion is between general digital competence and AI literacy. A person may be very good at using digital applications but not understand the specifics of AI systems. Unlike traditional software, which executes relatively clear rules, an AI system can produce probabilistic results, generate plausible but false answers, and function differently depending on the data, context, and instructions provided.

Why the AI Act turns AI competence into an obligation

The AI Act changes the framework in which European organizations must view artificial intelligence. The regulation starts from the idea that AI is not just a neutral technology, but a tool that can have effects on rights, safety, work, access to services, economic decisions, and relationships between organizations and individuals.

In this context, AI literacy becomes a practical obligation because AI systems cannot be effectively governed if the people using them do not understand what they do. A written internal policy is not enough if employees do not know how to identify a risk. A risk model is not enough if managers do not know what questions to ask. A validation procedure is not enough if users automatically accept AI-generated answers.

For companies, the message is clear: it is not enough to implement AI, you must also build the organizational competence needed to use it. This competence must be tailored to the company’s role, the types of AI systems used, the level of risk, and the people affected by these systems.

• For example, a company that uses AI only for drafting marketing texts has a different risk profile from a financial institution that uses automated models for scoring, risk analysis, fraud prevention, or decision support.
• In the first case, risks may relate to accuracy, reputation, copyright, or confidentiality.
• In the second case, risks may include discrimination, incorrect decisions, lack of explainability, financial impact, or compliance breaches.

The AI Act does not turn every employee into a technical specialist, but it does require organizations to ensure that those involved have a sufficient level of understanding for the context in which they operate. This is the essential difference: AI literacy is not universal and identical for everyone, but must be calibrated according to role, responsibility, and risk.

Who needs to be trained in a company

One of the most common mistakes is treating AI literacy as training intended only for IT or data science teams. In reality, AI use is transversal. It can appear in commercial, financial, legal, operational, HR, marketing, audit, compliance, or management departments.

• First, direct users of AI systems need to be trained. These are employees who actually interact with AI tools, enter data, formulate requests, interpret results, or use the output in their work. For them, training must be practical and applied: what data can be entered, what data cannot, how to check a result, how to recognize an error, when to escalate a problem, and what responsibility they have in the process.
• Second, managers need to be trained. They do not necessarily need advanced technical details, but must understand AI well enough to make correct decisions about implementation, budgeting, control, and impact assessment. A manager who does not understand AI’s limitations may overestimate benefits, underestimate risks, or automatically accept unrealistic commercial promises.
• Third, control functions must be involved: compliance, risk management, legal, internal audit, information security, and data protection. For these teams, AI literacy must include elements of governance, traceability, documentation, data protection, responsibility, auditability, and ongoing monitoring.
• Fourth, the board and top management must have a strategic level of AI literacy. It is not realistic for the board to understand every technical detail of AI models, but it is essential to know where these systems are used, what risks they introduce, what controls exist, and how AI use aligns with the company’s strategy and risk appetite.

Depending on the activity, there may also be external people who need to be included in this logic: contractors, suppliers, consultants, or partners who use AI systems on behalf of the organization. AI literacy does not stop at the formal boundary of the employment contract, because risks can arise wherever there is operational use of AI on behalf of the company.

The difference between tool training and risk understanding

Many organizations confuse AI literacy with training on how to use a tool. A course on how to use a chatbot application, how to write prompts, or how to automatically generate a report can be useful, but it is not enough. It answers the question “how do I use the tool?”, not “what risks arise when I use it?”.

Tool training is focused on efficiency. It teaches employees to save time, automate repetitive tasks, generate content, or speed up analysis. In contrast, AI literacy is focused on responsible use. It teaches employees to understand the system’s limitations, check results, respect confidentiality rules, avoid bias, and maintain human responsibility over decisions.

A mature AI literacy program should answer questions such as: What types of AI do we use? In which processes? With what data? Who validates the output? What do we do if the result is wrong? How do we document usage? How do we avoid entering confidential data into unauthorized systems? What decisions cannot be fully delegated to AI? How do we recognize a biased or unverifiable result?

Companies that limit themselves to prompt engineering training risk creating faster users, but not necessarily more responsible ones. And in a regulatory context, speed without control can become a vulnerability.

What a minimum AI literacy program looks like

A minimum AI literacy program should be simple enough to be implemented quickly, but rigorous enough to produce real effects. There is no need for every company to create a complex internal academy, but a coherent, documented framework adapted to its own risks is necessary.

• The first step is to inventory AI uses. The organization needs to know where artificial intelligence is used: approved tools, applications integrated into existing software, internally developed models, public tools informally used by employees, and AI solutions provided by partners. Without this overview, any training program remains incomplete.
• The second step is audience segmentation. Not all employees need the same level of training. Occasional users need clear basic rules. Advanced users need process-specific training. Managers need decision and governance understanding. Control functions need knowledge about risk, compliance, audit, and documentation.
• The third step is defining the minimum content. This should include concepts about what AI is, how it works in general, what types of risks it generates, what hallucinations, bias, lack of explainability mean, data protection, information security, human responsibility, and use proportional to purpose.
• The fourth step is integrating internal policies. AI literacy should not be disconnected from company rules. Training must clearly explain which tools are approved, what data can be used, which processes require validation, how incidents are reported, and who can approve new uses of AI.
• The fifth step is documentation. Companies should keep records of trainings conducted, audiences trained, materials used, policies communicated, and follow-up actions. Not to create unnecessary bureaucracy, but to be able to demonstrate that AI literacy is treated as a governance process, not as a one-off initiative.
• The sixth step is periodic updating. AI evolves rapidly, and risks change along with the tools. A program done once and forgotten in an internal folder is not enough. AI literacy must be periodically reviewed, especially when the company introduces new systems, changes suppliers, expands automation, or uses AI in processes impacting clients, employees, or financial decisions.

An effective minimum program is not the longest or most sophisticated, but the one that changes behaviors: employees do not enter sensitive data into unapproved tools, managers require validations before important decisions, risk teams can ask the right questions, and the board understands where AI can create value and where it can create exposure.

Why the board and management must be involved

AI literacy cannot be fully delegated to IT. This is perhaps the most important conclusion for companies. If AI is used in business processes, responsibility does not belong only to technical specialists, but to the entire governance structure.

The board and management must be involved because AI affects strategy, risk, organizational culture, and the operating model. A company may decide to use AI for efficiency, but this decision has consequences for how decisions are made, employee responsibilities, customer relations, and internal controls.

• At board level, AI literacy means the ability to ask essential questions: Where do we use AI? Which systems are critical? What risks do they introduce? What data is processed? Who validates results? What controls exist? How do we measure value created? What do we do if the system makes a mistake? Who is accountable?
• At management level, AI literacy means the ability to turn these questions into processes. Managers must decide which uses are permitted, what training is needed, what procedures need updating, what controls are implemented, and how to avoid uncontrolled use of AI tools.

In mature companies, AI literacy should become part of the risk culture. Just as employees are trained in data protection, cybersecurity, or compliance, so should they be trained to understand responsible use of artificial intelligence. The difference is that AI is not an isolated risk. It cuts across processes, departments, and decisions.

Conclusion: AI literacy is governance, not just training

AI literacy marks the transition from AI enthusiasm to responsible use of AI. For companies, the challenge is no longer just to adopt new tools, but to build a framework in which these tools can be used safely, intelligently, and proportionately to the risks.

An organization that treats AI literacy as a simple productivity course will at best get faster employees. An organization that treats it as part of governance will get more attentive employees, more clear-sighted managers, more controlled processes, and better-founded decisions.

CorpQuants can help organizations turn AI literacy into an applied governance and risk program, tailored to their operational, regulatory, and strategic context.

(This material was assisted by an AI tool and reviewed by our team before publishing).