AI Value Creation
Niveau
Second cycle of study, master's level
Learning outcomes of the courses/module
The participants:
• can explain, compare, and apply the fundamental value logics of AI—efficiency, effectiveness, and innovation—to concrete application scenarios
• can analyze the potential and limitations of these value-creation mechanisms and evaluate them across different industries
• can compare AI maturity models, systematically assess an organization’s maturity level, and derive development needs
• can select appropriate use cases for AI and assess their value potential using structured criteria
• can identify improvement potential through AI and derive suitable forms of automation or augmentation
• can explain and assess organizational roles, responsibilities, and change management aspects in the implementation of AI
• can explain, compare, and apply the fundamental value logics of AI—efficiency, effectiveness, and innovation—to concrete application scenarios
• can analyze the potential and limitations of these value-creation mechanisms and evaluate them across different industries
• can compare AI maturity models, systematically assess an organization’s maturity level, and derive development needs
• can select appropriate use cases for AI and assess their value potential using structured criteria
• can identify improvement potential through AI and derive suitable forms of automation or augmentation
• can explain and assess organizational roles, responsibilities, and change management aspects in the implementation of AI
Prerequisites for the course
Not applicable
Course content
- Introduction to AI Value Creation: value logics, efficiency vs. effectiveness vs. innovation
- Maturity models and organizational prerequisites (data strategy, operating models, AI culture)
- Identification and evaluation of use cases
- Process analysis and AI-supported process design (automation and augmentation)
- Economic analysis of AI projects (KPIs, ROI, risk analysis, business case)
- Application areas in marketing, sales, operations, HR, finance, public administration, and research
- Governance, ethics, and legal frameworks (bias, fairness, data privacy, transparency)
- Implementation of AI in organizations (roles, responsibilities, change management considerations)
- Case studies of successful and failed AI projects
- Maturity models and organizational prerequisites (data strategy, operating models, AI culture)
- Identification and evaluation of use cases
- Process analysis and AI-supported process design (automation and augmentation)
- Economic analysis of AI projects (KPIs, ROI, risk analysis, business case)
- Application areas in marketing, sales, operations, HR, finance, public administration, and research
- Governance, ethics, and legal frameworks (bias, fairness, data privacy, transparency)
- Implementation of AI in organizations (roles, responsibilities, change management considerations)
- Case studies of successful and failed AI projects
Recommended specialist literature
PRIMÄRLITERATUR:
- Davenport, T. (2018): The AI Advantage: How to Put the Revolution in Artificial Intelligence to Work
- Kreutzer, R. (2023): Künstliche Intelligenz verstehen. Grundlagen – Use-Cases – unternehmenseigene KI-Journey
- Thomas, R., Zikopoulos, P., Soule, K. (2025): AI Value Creators: Beyond the Generative AI User Mindset
- Barenkamp, M. (2025): Wertschöpfung durch KI. Chancen für Unternehmen und Gesellschaft
- Wodecki, A. (2019): Artificial Intelligence in Value Creation: Improving Competitive Advantage
- Bernhard, E. M. (2025): Unternehmensweite KI-Transformation – Der Leitfaden für C-Level und Aufsichtsrat
- Davenport, T. (2018): The AI Advantage: How to Put the Revolution in Artificial Intelligence to Work
- Kreutzer, R. (2023): Künstliche Intelligenz verstehen. Grundlagen – Use-Cases – unternehmenseigene KI-Journey
- Thomas, R., Zikopoulos, P., Soule, K. (2025): AI Value Creators: Beyond the Generative AI User Mindset
- Barenkamp, M. (2025): Wertschöpfung durch KI. Chancen für Unternehmen und Gesellschaft
- Wodecki, A. (2019): Artificial Intelligence in Value Creation: Improving Competitive Advantage
- Bernhard, E. M. (2025): Unternehmensweite KI-Transformation – Der Leitfaden für C-Level und Aufsichtsrat
Assessment methods and criteria
Portfolio
Language
German
Number of ECTS credits awarded
4
Semester hours per week
Planned teaching and learning method
Lecture, interactive workshop, and discussion of solutions to exercises
Semester/trimester in which the course/module is offered
2