Artificial intelligence has now made its way into areas where corporate life rarely looks glamorous: in email drafts, presentations, contract summaries, customer service, research, and reporting.
AI doesn't become relevant only when a company launches a major transformation program. It's often already there beforehand—quiet, practical, and browser-based. And that's exactly why it needs structure.
After all, using AI in a business is not just a technical issue. It involves responsibility, data, roles, transparency, control, and expertise. In short: governance.
The EU AI Act is often discussed in terms of high-risk systems, bans, or fines. That’s understandable, but for many companies, that’s not their first operational point of contact.
Article 4—AI Competence—is often much more relevant.
The point here is not to simply award employees a certificate and then send them out into the wild world of generative systems. The point is for people who use or operate AI systems to understand what they’re doing.
These aren't academic questions. These are everyday questions.
Description: Human-Computer Interaction
A good AI training course doesn't just explain what a prompt is; it also identifies the risks that may arise in a specific company.
Marketing may involve labeling, brand voice, and copyright issues.
In sales, this involves customer data, confidentiality, and the logic behind quotes.
In HR, it's all about applicant data, fairness, and documented decisions.
In Legal and Compliance, the focus is on reliability, traceability, and clear boundaries for automated support.
A standardized introductory course can be useful. But it does not replace applying what you’ve learned to your own daily work.
AI proficiency truly develops only when employees recognize their specific situations.
When it comes to AI, many companies start by asking about tools. Which system can we use? Which license is safe? Which platform is best?
These questions are important. But they don't stand alone.
Anyone who wants to use AI responsibly also needs answers to organizational questions: Who keeps the AI inventory up to date? Who assesses risks? Who documents decisions? Who oversees policies? Who trains new employees? Who responds when an AI system produces unexpected or critical results?
Without such responsibilities, AI use quickly becomes informal. And informal use is rarely auditable.
The actual step involves translating knowledge into structure.
These include:
That sounds rather matter-of-fact. But that is precisely where its value lies. Good governance does not diminish the benefits of AI; it makes them more resilient.
Against this backdrop, the book *AI Compliance – A Guide to Implementing Art. 4 of the EU AI Act* by Alexander Deicke and Hannes Deuerling comes at just the right time. It is designed as an accessible introduction to AI and AI governance and covers, among other topics, the technological fundamentals, the EU AI Act, the Data Governance Act, and the Data Act, ISO/IEC 42001, IDW PS 861, and the role of an AI officer.
It is precisely this broad perspective that is important. After all, AI compliance does not stem from a single law, a single standard, or a single training course. It arises from the interplay of an understanding of technology, legal classification, and practical corporate governance.
Or to put it more simply: You don't have to solve everything at once. But you should understand how the topics are connected.
AI literacy is not a peripheral issue in the AI Act. It is a useful starting point for the responsible use of AI.
Companies should not view Article 4 as a burdensome obligation, but rather as an opportunity to bring order to a trend that has long since begun in many places.
Those who empower employees, organize tools, document processes, and clarify responsibilities do more than just ensure compliance. They build trust in their own use of AI.
K11 helps companies establish practical AI compliance—from AI training and governance structures to documentation in accordance with the EU AI Act.