What Exactly is 'AI Literacy'? Breaking Down the EU AI Act Requirements

While the EU AI Act mandates "sufficient AI literacy" for staff working with AI systems, many organizations struggle to define what this means in practice. This guide breaks down the concrete knowledge and skills your team needs to be compliant.

This is part 2 of our 5-part series on AI Literacy under the EU AI Act. Read part 1 on training requirements, or explore implementation steps, role-specific requirements, and maintaining compliance.


Core Components of AI Literacy


1. Understanding AI Fundamentals

• Basic concepts of how AI systems work 

• Different types of AI (machine learning, neural networks, etc.)

 • Key terminology and concepts 

• Distinction between AI and traditional software


2. Capabilities and Limitations

• What AI can and cannot do

 • Common misconceptions about AI

 • Understanding AI bias and fairness issues

 • Recognition of AI system boundaries


3. Practical Usage Skills

• How to interact with AI systems effectively

 • Input formulation and prompt engineering basics 

• Output interpretation and validation

 • When to trust (and when to question) AI outputs


4. Risk Awareness

• Common AI failure modes 

• Security implications of AI use 

• Privacy considerations 

• Ethical considerations in AI deployment


Required Knowledge Levels

The depth of required AI literacy varies by role and interaction level:


Basic Level (All Staff)

• Recognition of AI systems in their workflow

 • Understanding of basic AI capabilities and limitations 

• Awareness of when to seek expert guidance

• Basic safety and security practices


Intermediate Level (Regular Users)

• Deeper understanding of AI system functionality 

• Ability to optimize AI system inputs

 • Critical evaluation of AI outputs 

• Recognition of potential biases


Advanced Level (Decision Makers)

• Strategic understanding of AI capabilities

• Risk assessment abilities

• Implementation considerations 

• Compliance requirements


Documentation Requirements

Organizations must demonstrate AI literacy through: 

• Training completion records 

• Competency assessments 

• Regular knowledge updates 

• Role-specific certification

Learn more about documentation in our implementation guide.


Common Misconceptions About AI Literacy


Myth 1: Technical Expertise Required

Reality: AI literacy focuses on practical understanding, not technical expertise.


Myth 2: One-Size-Fits-All Training

Reality: Requirements vary by role and AI interaction level. See our guide on role-specific requirements.


Myth 3: One-Time Training is Sufficient

Reality: AI literacy requires ongoing updates and maintenance. Learn more about maintaining compliance.


Measuring AI Literacy

Organizations should assess: 

• Understanding of core concepts 

• Practical application skills

• Risk awareness

 • Role-specific competencies

Next Steps

  1. Assess your team's current AI literacy levels
  2. Identify knowledge gaps
  3. Plan role-appropriate training
  4. Implement assessment methods
  5. Document progress and compliance


Ready to build your team's AI literacy?

 Our certified AI literacy course covers all these components in a structured, EU AI Act-compliant format.