Certified AI Manager

• Project leaders and program managers in AI or digitalization initiatives
• Executives from IT, data analysis, innovation, or product development
• Consultants and internal change agents supporting AI projects
• Compliance and governance managers bridging technical and regulatory perspectives
• Professionals and executives who want to establish a legally compliant and responsible use of AI

Module 1 - Fundamentals and diversity of AI methods

• Introduction to AI: definition, history, technological foundations
• Learning principles: what does AI learn, what do humans know about it
• Overview of key methods:
  • Machine learning: linear regression, decision trees, nearest neighbour, XGBoost
  • Training types: supervised, unsupervised, reinforcement learning
  • Deep learning: neural networks, overfitting/underfitting, concept/data drift
  • Model evaluation
  • Natural language processing: from sentiment analysis to LLMs
  • Computer vision: segmentation, classification, detection
  • Transfer learning: simple and complex approaches
  • Generative AI: architectures and current developments

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Module 2 - Implementation and evaluation of AI models

• Identifying and preparing data sources
• Annotation steps and use of experts
• Ensuring data quality (representation, statistical distribution)
• Creating training, test, and validation datasets
• Requirements for storage, computing, and bandwidth
• Selecting AI models or providers
• Evaluating external models: validity and transferability to own projects

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Module 3 - Legal and ethical frameworks

• AI development:
  • EU AI Act
  • Data governance
  • Data protection, copyright
• AI usage:
  • User obligations, data subject rights
  • Liability issues, AI liability directive, sanctions
• Ethical challenges:
  • Bias, transparency, data protection
  • Gender, diversity, inclusion
  • AI Act as ethical reference framework
• Case study: analysis and discussion

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Module 4 - Management and operations of AI projects

• Planning AI projects:
  • Identifying use cases
  • Roles and responsibilities in project teams
  • ROI, feasibility, and risk analysis
  • Data management and data governance
• Implementation and operations:
  • User perspective and acceptance
  • Testing and evaluation methods
  • Deployment strategies
  • Monitoring and maintaining AI systems
  • Best practices for sustainable operations

• No technical background is required. Curiosity and openness to new technologies are sufficient.

• Exam format: project presentation and oral knowledge exam
• Language: German or English
• Duration: approx. 60 minutes