IS 609: AI Strategy: Business Models, Competition, and Markets

Contents
This course equips management students with the tools to design business models, navigate competition, and understand market dynamics for artificial intelligence (AI). It provides conceptual frameworks for strategic decision-making, focusing on the implications of recent breakthroughs in machine learning and generative AI (hereafter simply AI).
In particular, the course expands into:

• AI as a product: How firms can compete through AI as a product, including business models, monetization, and value capture. This analysis is grounded in economic principles of information goods, such as marginal costs, network effects, and switching costs.
• AI adoption in firms and impact on markets: How AI adoption transforms industries, using real-world case studies as a group assignment to explore business applications, automation potential, and adoption barriers. This analysis builds on frameworks for technology diffusion and impact assessment.
• Risks and ethical considerations: The broader implications of AI for business decision-making, including issues of bias, fairness, privacy, and market power.

This is a business-focused course—it does not involve programming. The course provides an introductory understanding of neural networks, reinforcement learning, and generative AI, but the emphasis of the course is on strategy, economics, and business decision-making in the context of AI and technology-driven transformation.

Learning outcomes
After successfully completing the course, students..:

• ..can explain the conceptual foundation of generative AI, including neural networks and reinforcement learning,
• ..can explain the basic economic principles that govern AI as an information good (e.g., production costs, pricing, network effects, switching costs),
• ..can design strategies to use AI as a product, focusing on business models, monetization, and competitive strategy,
• ..can evaluate the impact of AI adoption on businesses through adoption frameworks and industry use cases,
• ..are familiar with the current understanding of the economic implications of AI, including labor replacement, technological change, and AI as a General Purpose Technology,

..can discuss implications of AI externalities, including bias, fairness, privacy, and market power, for businesses and policymakers.

Necessary prerequisites
–

Recommended prerequisites
–

Forms of teaching and learning	Contact hours	Independent study time
Lecture	2 SWS	8 SWS
Case Study Presentation	2 SWS	5 SWS

ECTS credits	6
Graded	yes
Workload	180h
Language	English
Form of assessment	70% of the total grade: written exam, closed book (60 mins) 30% of the total grade: group case study (slides and presentation; 3–5 students)
Restricted admission	yes
Further information	The course is limited to 100 participants. Please register via the student portal

Examiner Performing lecturer

Prof. Dr. Jens Förderer Prof. Dr. Jens Förderer

Frequency of offering	Spring semester & fall semester
Duration of module	1 semester
Range of application	M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf.
Preliminary course work	–
Program-specific Competency Goals	CG 1, CG 3