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2024/2025  KAN-CINTV2401U  The Business Application of Generative AI

English Title
The Business Application of Generative AI

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 120
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Jan Damsgaard - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Strategy
Teaching methods
  • Face-to-face teaching
Last updated on 30-01-2024

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Understand GenAI Technologies: Gain foundational knowledge of generative AI technologies and their evolution.
  • Apply GenAI in Business: Learn to integrate GenAI into various business models and processes.
  • Navigate Ethical and Legal Issues: Grasp the ethical considerations and legal frameworks, including the EU AI Act, affecting GenAI use
  • Assess AI's Business Impact: Analyze the influence of GenAI on labor markets and supply chain management
  • Prepare for Future Trends: Develop insights into future developments and long-term implications of GenAI in the business realm
The Business Application of Generative AI:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 2-3
Size of written product Max. 15 pages
Assignment type Synopsis
Release of assignment Subject chosen by students themselves, see guidelines if any
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The exam assignment consists of a group presentation (max 15 PowerPoint slides). Each group must develop this presentation at the end of the course and it will summarize the class topics over the semester. It is expected that students apply the material from the course on a case (business or organization) using tools, frameworks, and ideas covered in class to explain how generative AI can be applied. Specifically, the presentation is expected to contain the following elements: i) the chosen case, ii) a short description of the business and how generative AI can be applied iii) all relevant material the group has worked on during the interactive workshop sessions incl. prompts, generative AI functionality, governance, etc.

They will upload and submit this presentation. The presentation will be delievered by the whole group at the oral exam, and will provide material for their individual exam assessment. 

Course content, structure and pedagogical approach

This course immerses students in the dynamic realm of Generative Artificial Intelligence (GenAI), delving into its pivotal role in reshaping contemporary business practices. Students will navigate foundational GenAI concepts, including cutting-edge technologies like GPT and DALL-E, critically examining their ethical and societal implications.


The course begins with an exploration of fundamental Generative AI principles, setting the stage for a deeper examination of its transformative potential in modern business. As the course progresses, the focus shifts towards understanding the maturity of AI use in businesses through real-world case studies, providing tangible insights into optimizing operations.


The curriculum further explores the diverse business applications and models within the GenAI framework, showcasing the versatility of these technologies across various industries. A strategic examination of the intersection of GenAI and public companies reveals its impact on corporate strategies and decision-making processes.


The spotlight then turns to supply chain management, where students gain insights into how GenAI transforms dynamics, optimizing efficiency and responsiveness. Examining the impact of GenAI on the labor market, the course addresses the evolving relationship between AI technologies and the workforce, uncovering challenges and opportunities.


Legal, ethical, and regulatory considerations take center stage, delving into the intricacies of the EU AI Act and addressing complex issues such as AI hallucinations and bias. Armed with this academic knowledge, students are prepared to navigate the legal landscape surrounding GenAI.


Finally, the course propels students into the future of GenAI in business, providing a forward-looking academic and practitioner perspective. Armed with a strategic understanding of the intersection between GenAI and business strategy, students are empowered to contribute to and thrive in a business landscape shaped by the dynamic forces of artificial intelligence.


Description of the teaching methods
Lectures by teachers and case presentations from practioners
Feedback during the teaching period
Each lecture starts with a recap of the previous lecture based on students' feedback. Thereafter a lecture on the topic, And a guest presentation of a relevant case.
Student workload
Lecture 24 hours
Workshops 6 hours
Preparation for class 50 hours
Preparation for the exam 50 hours
Working on group project 76 hours
Total 206 hours
Expected literature

van Giffen, Benjamin and Ludwig, Helmuth (2023) "How Siemens Democratized Artificial Intelligence," MIS Quarterly Executive: Vol. 22: Iss. 1, Article 3. Available at: https:/​/​aisel.aisnet.org/​misqe/​vol22/​iss1/​3


Schoormann, T., Strobel, G., Möller, F., Petrik, D., & Zschech, P. (2023). Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature. Communications of the Association for Information Systems, 52, pp-pp. https:/​/​doi.org/​10.17705/​1CAIS.05209


Vainionpää, Fanny; Väyrynen, Karin; Lanamaki, Arto; and Bhandari, Aayush, "A Review of Challenges and Critiques of the European Artificial Intelligence Act (AIA)" (2023). ICIS 2023 Proceedings. 14.


Carl Benedikt Frey and Michael Osborne on how AI benefits lower-skilled workers. The economist. https:/​/​www.economist.com/​by-invitation/​2023/​09/​18/​carl-benedikt-frey-and-michael-osborne-on-how-ai-benefits-lower-skilled-workers


The economic potential of generative AI: The next productivity frontier


What’s the future of generative AI? An early view in 15 charts


What every CEO should know about generative AI


5 steps to put healthcare on the AI fast-track. World Economic Forum. www.weforum.org


EU Artificial intelligence act. https:/​/​www.europarl.europa.eu/​news/​en/​headlines/​society/​20230601STO93804/​eu-ai-act-first-regulation-on-artificial-intelligence


The Danish National Strategy for Artificial Intelligence. https:/​/​en.digst.dk/​strategy/​the-danish-national-strategy-for-artificial-intelligence/​


The National Strategy for Digitalization. https:/​/​en.digst.dk/​strategy/​the-national-strategy-for-digitalisation/​

Last updated on 30-01-2024