Content:

The course consists of the following topics and may be extended or adapted based on the audience. The lab is designed for 1-2 days and deliberately alternates between short inputs and longer hands-on blocks in which participants work with AI tools themselves. For in-house formats, tool selection, examples, and use cases can be tailored to the own organisation.

– Introduction to the AI landscape for management:
... - Terms: AI, generative AI, large language models, agentic systems
... - What is practically possible today – and what is not
... - Hype vs. substance: positioning current developments
– Hands-On: First steps with AI assistants:
... - Using chat-based AI tools effectively
... - Prompting basics for management tasks
... - Hands-On: Process own typical work tasks with AI
– AI-assisted workflows in daily work:
... - Research, summarisation, analysis, decision preparation
... - Working with documents, data, and unstructured information
... - Hands-On: Implement an own workflow with AI support
– Understanding and experiencing agentic systems:
... - From chat to agent: what is an agent, what can it do?
... - Agents with tools and data access – examples from practice
... - Hands-On: Solve a multi-step task with an agentic system
– Agentic engineering from a management perspective:
... - How teams today develop and operate software with AI agents
... - Impact on roles, skills, team setup, and delivery capability
... - Hands-On: Walk through a small engineering task with an agentic coding tool
– AI in product and business model:
... - Adding AI features vs. thinking AI-native products
... - Build vs. buy: in-house development, API usage, platforms
... - Use case evaluation: where AI adds real value, where not
– Data, context, and integration:
... - Why context is decisive – retrieval, knowledge bases, connectors
... - Model Context Protocol (MCP) and similar integration approaches
... - Hands-On: Let AI work with own documents or data
– Risks, governance, and compliance:
... - Hallucinations, bias, reproducibility, auditability
... - Data protection, confidentiality, and regulatory topics (e.g. EU AI Act)
... - Secure working: secrets, access, approvals in agentic setups
– Economics: cost, benefit, risk:
... - Cost models (token, API, infrastructure, personnel time)
... - ROI considerations for AI initiatives
... - Realistic expectation management in the own environment
– Adoption in the own organisation:
... - Building AI literacy in team and company
... - Identifying and prioritising pilot use cases
... - From pilot to scaling: platform, guardrails, responsibilities
– Outlook and next steps:
... - Market developments, models, tools – what to watch
... - Hands-On: Sketch a personal roadmap and own next experiments

The focus is on well-established, vendor- and model-independent tools so that what is learned remains transferable. Concepts, workflows, and evaluation criteria can be applied to other tools and platforms.

Disclaimer: The actual course content may vary from the above, depending on the trainer, implementation, duration and constellation of participants.

Whether we call it training, course, workshop or seminar, we want to pick up participants at their point and equip them with the necessary practical knowledge so that they can apply the technology directly after the training and deepen it independently.