Content
“AI and Business” provides TUM students with the opportunity of gaining essential knowledge on
applying AI in business scenarios, while offering students the chance to extend on this knowledge
by building hands-on AI solutions for real-world business use-cases. The seminar is structured
in two separate blocks. The first one is AI for Business - Essentials, and the second one is AI for
Business - Advanced. AI for Business - Essentials provides students with the essential theoretical
knowledge on understanding AI and how it can be enhanced for business purposes. AI for
Business – Advanced is designed as hands-on module, throughout which students collaborate in
interdisciplinary teams (á 3-4 members) with partnering companies to solve real-world business
problems with AI. The Advanced module cumulates in a hackathon-themed afternoon during which
teams will present their solutions; with best projects having the chance of being awarded.
Learning outcomes
The mix of theoretical foundations and practical implementation contexts of “AI and Business” is
intended to enhance student’s overall AI literacy, as well as their understanding on the capabilities
and limits of AI in real-world scenarios. As a consequence, the course will provide students with
the necessary understanding and soft skills needed to successfully implement AI solutions in
business.
Students will learn:
How to ‘think’ AI for Business in order to come up with practical and effective solutions for AI
implementations in solving real-world problem settings.
How AI is already implemented in business scenarios today
How to identify pitfalls, problems, as well as cost and sustainability concerns when implementing
AI
How to work effectively and on basis of scientific principles towards solving real-world problems
Critical thinking, reflection, and application of concepts and scientific findings to concrete
challenges.
Presentation and Public Speaking skills, including how to prepare contents and pitches with
excellence
Examination
Part 1 (50% of total grade):
1x 15 mins Presentation about a specific AI Application in Industry per Participant (30% of total
grade)
Blog entry of 400 – 800 words per Student documenting the open challenges for a specific AI
Application. Publication either on TUM CSO website or via internal Notion page, dependent of
student privacy preferences. (20% of total grade)
Part 2 (50% of total grade):
1x Project Presentation and Prototype Showcase during Project week in January (max 15 min./per
Participant) (50% of total grade)
Prerequisites
Basic knowledge in AI and Business Management; those who have already successfully
completed the module MGT001407 cannot take this module again
Teaching & learning methods
In this course, a combination of lectures, practical exercises, seminars, and project work is
employed to achieve the intended learning outcomes. Lectures provide foundational knowledge
and advanced concepts in AI and its applications in business, ensuring students have a solid
theoretical base. Practical exercises allow students to apply theoretical knowledge in simulated
real-world scenarios, enhancing their problem-solving skills and understanding of various
perspectives.
Seminars involve in-depth discussions, reflections, and presentations. These methods encourage
critical thinking, self-assessment, and peer learning. Students engage in reflective sessions to
internalize the material and develop their critical thinking skills, while interactive discussions foster
a deeper understanding of complex topics through collaborative learning.
Project work is a crucial component where students conduct independent research on specific
topics related to AI in business. This includes preparing educational content, presenting their
findings, and receiving feedback. The project work is designed to develop students' research
skills, enhance their ability to create didactic materials, and improve their presentation and
public speaking abilities. The iterative process of receiving and incorporating feedback ensures
continuous improvement and deeper comprehension of the subject matter.
Media & reading list
Online resources, slides, academic papers
Iansiti, M. (2020). Competing in the Age of AI: Strategy and Leadership When Algorithms and
Networks Run the World
Brynjolfsson, E. & McAfee, A. (2017). The Business of Artificial Intelligence. Harvard Business
Review. https://hbr.org/2017/07/the-business-of-artificial-intelligence
Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American
psychologist, 34(7), 571.
Fleck, L., Rounding, N., & Özgül, P. (2022). Artificial Intelligence in Hiring: Friend or Foe?. ROA.
•Goldfarb & Gans (2018). Prediction Machines: The Simple Economics of Artificial Intelligence.
Ingram Publisher Services. ISBN 978-1633695672.
Nestor et al. (2023). “The AI Index 2023 Annual Report,” AI Index Steering Committee, Institute for
Human-Centered AI, Stanford University, Stanford, CA, April 2023.Chugunova, Marina.
Sele, Daniela (2022). We and It: An Interdisciplinary Review of the Experimental Evidence on How
Humans Interact with Machines, Journal of Behavioral and Experimental Economics 99.
Suleyman, Mustafa. 2023. The Coming Wave: Technology, Power, and the Twenty-first Century's
Greatest Dilemma. Crown Publishing. New York. ISBN: 978-0593593950.
• Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433–460. http://
www.jstor.org/stable/2251299
Helmus, T. C. (2022). Artificial Intelligence, Deepfakes, and Disinformation: A Primer. RAND
Corporation. http://www.jstor.org/stable/resrep42027
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