Who should attend
Executives, product owners, change agents, and members of transformation teams who want to systematically drive AI value creation across the organization.
Prerequisites
No special technical knowledge is required. An openness to new technologies and the willingness to learn AI as a business skill are sufficient.
Course Objectives
- Understand AI fundamentals and key terminology (AI vs. ML vs. GenAI, LLMs, RAG, agents, digital twins)
- Understand the opportunities for deploying AI in real business workflows and assess the concrete ROI
- Learn practical fundamentals such as prompt engineering and the use of various AI applications for everyday work support in a hands-on manner
- Implement responsible and secure AI use in daily work and apply regulatory frameworks such as the EU AI Act and GDPR
- Build a personal AI productivity toolbox
- Identify, evaluate, and prioritize high-value AI use cases
- Understand the AI product lifecycle from idea to scale
- Analyze organizational AI readiness across 6 dimensions
- Shape leadership in the AI era and develop new team structures
- Create and pitch an AI transformation strategy including a roadmap
Course Content
Day 1: AI Fundamentals & Business Understanding
- Welcome & Mindset Shift: Why AI literacy is the new core competency
- Reverse Future Walk: How AI is changing work (without replacing people)
- AI explained clearly: AI vs. ML vs. GenAI, LLMs, RAG, agents, digital twins
- What AI can do – and what it cannot
- Exercise: Why prompt engineering is crucial
- AI in real business workflows: Use cases, ROI, AI vs. RPA
- Responsible & secure AI: Data privacy, IP, hallucinations, bias, EU AI Act
- Breakout: Where AI can support your own daily work
Day 2: Personal AI Productivity
- Practical AI for daily work: Emails, meetings, analyses, presentations
- Role-based prompting patterns and thinking in "AI Tasks"
- Breakout: Teams use AI to solve their own work challenges
- Storytelling & communication with AI: Presentations, pitches, storylines
- Building a personal AI productivity toolbox (prompt/skill library, workflow setup)
Day 3: From Ideas to Impactful AI Use Cases
- AI use case strategy: What makes a good use case (value, feasibility, risk)
- Use case checklist: Business value, data quality, regulation & ethics
- AI product lifecycle: From idea → PoC → MVP → product → scaling
- Methods deep dive: AI Innovation Sprint, AI Use Case Canvas
- Breakout: Teams develop real AI use cases for their organization
Day 4: Scale AI & organizational AI readiness
- Organizational AI readiness: The 6 AI readiness dimensions, AI operating model
- Defining roles, governance, and responsibilities
- AI transformation: Good practices & lessons learned
- Breakout: AI Readiness Quick Check + designing the transformation roadmap
- Future of Work with AI: Modern leadership skills, new hybrid human-AI team structures
- Final Capstone: Elevator pitch AI transformation strategy