A4Q
AI Foundation
In the "A4Q AI Foundation" seminar, you will gain a comprehensive understanding of how generative AI can be used responsibly and effectively in accordance with regulatory requirements. You will acquire basic AI skills in accordance with the EU AI Act.
Certification according to the international A4Q standard.
This seminar is aimed at specialists and managers, project managers and employees from specialist departments who want to acquire a sound understanding of artificial intelligence in a corporate context - without any prior technical knowledge.
You will gain a solid theoretical and practical understanding of key AI concepts and the legal framework. The focus is on the requirements of the EU AI Act, the importance of the GDPR when using AI, transparency and labeling obligations as well as ethical issues and possible risks (e.g. discrimination, bias, black box systems)
The aim is to enable you to responsibly and confidently classify AI systems in your professional environment - be it in the procurement, development, application or evaluation of AI solutions.
Key data
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Duration: 2 days
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Language: German, English
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Training formats: Live online seminar , classroom seminar
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Also available as anin-house seminar
Request: Offer for
an in-house seminar
More about this topic:
- Target group
- Learning objectives
- Seminar contents
- Seminar documents
- Learning material
- Examination
Target group
The seminar is aimed at software developers, project and IT managers, product owners, business analysts and consultants who use or evaluate AI systems responsibly.
Seminar contents
Chapter 1 - The basics of artificial intelligence
- What is AI? Differentiation from rule-based software
- Important terms: Machine learning, deep learning, neural networks
- Brief history and practical examples
- Overview: NLP, computer vision, generative AI
Chapter 2 - Data & training methods
- Data life cycle: collection, preparation, labeling, training, monitoring
- Learning paradigms: supervised, unsupervised, reinforcing
- Data quality, bias, overfitting and evaluation metrics
Chapter 3 - EU AI Act (overview)
- Objective, scope and key definitions
- Risk categories: prohibited / high / limited / minimal
- Obligations for providers, operators and users
- Practical examples and implementation schedule
Chapter 4 - Transparency & labeling
- Transparency requirements and traceability
- Labelling obligations (e.g. chatbots, generated content)
- Documentation: model cards, logging, reproducibility
Chapter 5 - Data protection & GDPR in the AI context
- Basic principles: Lawfulness, purpose limitation, data minimization
- Handling personal data, pseudonymization/anonymization
- DPIA (data protection impact assessment) and obligations to provide evidence
Chapter 6 - Human oversight & governance
- Forms of supervision: human-in/ on/ out-of-the-loop
- Roles, responsibilities and approval workflows
- Monitoring, audit, incident management
Chapter 7 - Risks & ethics
- Types of bias and discrimination risks
- Security risks (e.g. adversarial attacks) and reputational risks
- Measures for fairness, testing strategies and continuous monitoring
Chapter 8 - Deployment, business benefits & evaluation
- Typical use cases: Automation, assistance, insights
- KPIs, success criteria and ROI considerations
- Criteria for selecting tools and providers
Learning objectives
After the seminar, participants will be able to
- categorize the historical development and economic relevance of artificial intelligence.
- explain central AI technologies (e.g. machine learning, neural networks, deep learning, NLP, computer vision) and name typical fields of application.
- describe suitable training methods and learning paradigms for specific business tasks.
- explain the data life cycle in AI projects (collection, preparation, training, monitoring) and assess data quality.
- recognize data bias and discrimination risks and apply simple measures for fairness.
- use AI-supported analyses and automation to gain operational insights.
- Apply criteria for evaluating AI tools and providers (technical, legal, business)
- Recognize requirements from the EU AI Act and data protection (e.g. GDPR principles) and name initial implementation steps.
- Describe governance elements and forms of human oversight and propose simple control measures.
- Formulate simple, targeted prompts for generative AI and improve them iteratively (practically usable, optional).
Seminar documents
Participants receive comprehensive accompanying material for the seminar, consisting of
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Digital seminar documents with the complete course content: including exercises, explanations of terms, review questions and sample exam tasks
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Practical hands-on exercises, provided via a password-protected GitHub repository for independent in-depth study
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Additional digital support material for follow-up, e.g. example prompts, checklists and transparency samples for implementation in the company
Get certified
After the seminar, you can take the A4Q AI Foundation certification exam. This internationally recognized certificate documents your knowledge of the responsible use of artificial intelligence in a corporate context.
- Exam form: Online - you determine the time and place yourself
- Type of exam: Multiple-choice exam with 40 questions
- Duration: 60 minutes (plus 15 minutes additional time for non-native speakers)
- Language: German or English
- Prerequisites: No formal requirements
- Pass mark: 65% (at least 26 correct answers)
- Price: 150 € plus VAT
Learning material
his seminar is based on the A4Q AI Foundation Syllabus and provides the important learning and examination materials.
- Syllabus: The current version of the A4Q AI Foundation Syllabus forms the basis of the seminar.
- Glossary: Technical terms used are defined in the A4Q glossary and in common standards.
- Sample exam: A practice exam serves as preparation for the certification exam.
- Additional material: Seminar materials, slides, exercises and case studies are provided during the course.
