Course Syllabus

Business Potential of Generative Artificial Intelligence (GAI)

 

Semester & Location:	Spring 2025 - DIS Copenhagen
Type & Credits:	Elective Course - 3 credits
Major Disciplines:	Digitalization, Globalization and Business Economics
Prerequisite(s):	General computer skills and course followed in business economics or microeconomics at university level.
Faculty Members:	Kristian Sørensen (current students please use the Canvas Inbox)
Time & Place:	Mondays & Fridays TBA Classroom: TBA

Course Description

The rise of Generative Artificial Intelligence (GAI) has a significant economic potential across all sectors of industry and public administration.  It is considered as the next productivity and work frontier for increased growth potential and increased labor productivity across the economy, engendering added value creation in key areas of business, presumably predicting an extreme exponential expansion of the production function in many sectors. Banking, high tech, and life sciences are among the industries predicted to witness the biggest impact as a percentage of their revenues from generative AI. Recent advances in training of foundation models already point at far broader applications of GAI in software development, media industries, marketing and sales, designs, and R&D.

In this course, ways to cope with the use of Generative AI in the context of rewiring business plans and understand the wider impacts on work activities and significant challenges for businesses and society will be explored in four topics:

• A common language for understanding Generative AI and AI implications for privacy and security.
• The transformation of business functions by Generative AI implying a revolutionizing of internal knowledge management systems.
• The rewiring of business plans by Generative AI is made in selected business case studies.
• The assessment of ways to cope with the consequences of using Generative AI focus on the wider organization of work and its significant challenges for businesses and society.

The spread of GAI poses a variety of risks for privacy and security such as algorithmic bias, infringement on intellectual property (IP), accelerating cyberattacks, manipulated or unintended malicious outputs, difficulties in explaining how any given answer is produced, and disproportionately inequal impact on the workforce. This has created some key dilemmas in using AI not least by dissolution of truth, by fake generation and fake authenticity, and by allowing for even higher complexity

Exercises on Gen IA will be deployed in industries and across functions through selected projects. The transformation of business functions by Generative AI is illustrated by business case studies of organizations like Amazon, Maersk, Danske Bank, Google, LEGO Group, and Novo. The chosen business case studies will focus on the need to rewiring business enterprises by revolutionizing internal knowledge management systems. More sophisticated business models will cover areas like strengthening customer and product management, developing digital talent everywhere, introducing product and platform operating models, applying distributed engineering excellence, embedding data and analytics across the organization, and scaling the AI transformation through a cross-cultural change management.

In a wider perspective, ways to cope with the consequences of using Generative AI are analyzed for the organization of work and its significant challenges for businesses and society. The new capabilities of generative AI, combined with previous technologies and integrated into corporate operations around the world, could accelerate the potential for technical automation of individual activities and the adoption of technologies that augment the capabilities of the workforce. At the same time, the acceleration in the pace of work force transformation in different paces of adoption could exacerbate the digital divide and cross-country income and wealth disparity. This also underlines the need for skills/education/training in using AI, as it will constitute the very big dividing line between who will benefit from AI and who risks falling behind significantly.

Generative AI is a disruptive technology and a step change in the evolution of artificial intelligence with large geopolitical risks for businesses and society. Companies and business leaders should move quickly to capture the potential value at stake while managing the risks that generative AI presents, heeding the insight gained from the economic impact of technology change in creative destruction. Policy makers must adopt new public policies for human-centric AI development and workforce planning. Workers, consumers, and citizens need to define a voice in the decisions that will shape the deployment and integration of generative AI into the fabric of their lives.

Learning Objectives

At the end of this course you should be able to have:

Gained Knowledge and Insights:
• Possess knowledge and ability in applying relevant concepts of Generative Artificial Intelligence GAI), including a variety of risks for privacy and security.

• Understand why Generative Artificial Intelligence (GAI) has a significant economic potential across all sectors of industry and public administration.
  • Possess insight in the transformation of business functions and in using Generative Artificial Intelligence (GAI)

Developed Abilities:
• Be able to analyze and assess Generative Artificial Intelligence (GAI) business case studies focusing on the need to rewire internal knowledge management systems.

• Be able to analyze key dilemmas in using AI not least by dissolution of truth, by fake generation and fake authenticity, and by allowing for higher complexity.
• Be able to elucidate consequences of using Generative AI for the organization of work and its significant challenges for businesses and society
  
  Acquired Skills:
  • Have the skills to independently present, critically assess, and analyze theoretical contexts and fundamental concepts as scrutinized in the course
  • Have the skills to account for business cases in Generative Artificial Intelligence (GAI)
• Have the skills to convey the options for managing change in an organization using Generative Artificial Intelligence (GAI))

Faculty

Kristian Sørensen

Cand. polit. (Economic and Social Science, University of Copenhagen). Former Director at United Nations Development Program (UNDP) in NY. Founder and head of Dialogue Development, carrying out consultancy services for mostly EU. External Adjunct Professor in International Business at Copenhagen Business School (CBS). Author of university textbooks, including “International Change Management”. With DIS since 2011.

Michael Hedegaard

2012. Sc. Economics (Cand. Oecon., University of Aarhus). Worked at Export Credit Agency of Denmark, the Industrialization Fund for Developing Countries and West Africa Growth Fund. Founder of international business consulting company and bio-tech company in Kenya. Assistant Professor at the Danish Technical University (DTU) and external lecturer at Copenhagen Business School. Co-author of university textbook “Strategic Investment and Finance”. With DIS since 2012.

Guest lecturers

Guest lecturers from DTU and DIS faculty will primarily be participating in the technical foundation and machine learning issues in classes 1 to 7, see structure of the course below.  Practical details about preparation are indicated in the course calendar.

 

Readings  

 “The AI Advantage - How to put the Artificial Intelligence revolution to work” by Thomas H. Davenport, The MIT Press 2018, 231 pages. The seminal book on AI and enterprise strategy.

“Attention is All You Need” by Ashish Vaswani et al. Google Brain 2018, 12 pages. The idea of Transformer Neural Networks. This is the essential model that Large Language Models are built on.

“Stable Diffusion Models with breakthrough performance in image generation becomes commercially available”.  By Machine Vision & Learning research group (CompVis) @LMU_Muenchen. These are built on Dall-E and Midjourney. A deep learning, text-to-image model released in 2022 based on diffusion techniques.

“ChatGPT (Chat Generative Pre-trained Transformer)”  A chatbot developed by OpenAI and launched on November 30, 2022. Wikipedia, 10 pages

“The economic potential of generative AI: The next productivity frontier” by Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, and Rodney Zemmel. The McKinsey Institute 2023, 60 pages. A good summary of the many research papers and surveys made by the organization.

“Gen-AI: Artificial Intelligence and the Future of Work” by Mauro Cazzaniga, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus J. Panton, Carlo Pizzinelli, Emma Rockall, and Marina M. Tavares, International Monetary Fund 2024, 40 pages SDN/2024/001

“A generative AI reset: Rewiring to turn potential into value in 2024” by Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel. The McKinsey Quarterly 2024, 10 pages. The generative AI payoff may only come when companies do deeper organizational surgery on their business.

All texts, together with case studies and class slides are of course available electronically at the DIS Canvas. But as the course is based on a collective knowledge process further readings and task materials will be built up during the course using ChatGTP4.0 and Copilot PRO.

 

 Approach to teaching and expectations of the students

1.You are expected to actively engage in class by asking questions, making comments, sharing ideas, etc. Learning is a two-way road and the more you talk in class, the more the instructors will learn about how well you understand the material being presented, how to tailor and focus the course material, etc. An integral part of the teaching approach will be based on practical experience with hands-on student assignments with AI on cases/tasks/problems that will enable you to better participate in the learning. Also active participation in the field studies will be taken into account. Throughout the semester, short lectures are combined with student presentations, group work, field and case studies, and in-class activities.

2. Practical application through workshops or simulations will be based on active learning, such as workshops or simulation exercises where students apply GAI tools in real-world scenarios, collaboration with businesses or tech labs to provide hands-on experience, and independent projects undertaken by students they can explore specific aspects of GAI that interest them, fostering self-directed learning.
3. Continuous Feedback on business case study create opportunities for students to reflect critically on their learning process and the content of business cases being taught.
4. Critical reflection on possible road maps for business development will be facilitated through reflective essays or journals on experience with insights and reflections on what AI can do, what the risks are, what the consequences can be.

Assignments, field studies and readings may be subject to change but sharing your reflections will provide an idea of how you will approach the teaching of the course. Contact hours with supervisor and how many hours students are expected to put in for independent work will play out according to mutual agreement.

Grading

The grading is distributed in line with the four points outlined in the teaching approaches above:

Assignment	Percent
Participation	25%
Business projects	25%
Business case study	30%
Road map proposal	20%

 

Your participation grade will be determined by 3 factors: attendance, preparedness for class, and active engagement in lectures and other class activities. You are required to attend each and every class. If you miss a class, you must contact an instructor as soon as possible and provide an explanation. The assigned readings for each lecture should be read prior to the lecture. Here is a suggestion: as you read the assigned readings, write down 2 or 3 things that strike you about the reading, such as some key findings, interesting arguments, questions you have etc. Then review your notes once you arrive in class.

Computer policy: Laptop computers are allowed in class only for note-taking purposes, use of Copilot/Chatgtp research, and class exercises. Any other use will have a negative impact on your final grade. Furthermore, any student violating this policy will not be allowed to continue using their laptop in class for the remainder of the semester.

 

The structure of the course

The structure of the course is organized around four modules:

Foundations (classes 1-7) Get to know the academic field of Generative Artificial Intelligence (GAI) – key concepts, discussions, theory, and methodology:

Generative AI as a technology catalyst to create a more intelligent organization. A brief History of AI. Generative AI, general and specific foundation models, and machine learning. Natural Data Processing NLP and the Importance of Data. Vocabulary for using generative AI responsibly on how computers and people can be combined to foster collective intelligence. AI implications for privacy and security. Key dilemmas of using AI.

Applications (classes 8-11) Exploring revolutionizing of internal knowledge management systems in projects across industries and functions such as strengthening customer and product management, developing digital talent everywhere, introducing product and platform operating models, applying distributed engineering excellence, embedding data and analytics across the organization, and scaling the AI transformation through a cross-cultural change management.

Operations (classes 12-18) Using the knowledge gained in classes 1-11 through own projects, case presentations and feed-back. The rewiring of business functions by Generative AI is illustrated in business case studies of organizations like Amazon, Maersk, Danske Bank, Google, LEGO Group, and Novo.

Consequences (classes 19-22) Assessing ways to cope with the consequences of using Generative AI.  Accelerating the work potential, while risking exacerbating the digital divide and cross-country income and wealth disparity.  Adopting new public policies for human-centric AI development and workforce planning. Discussion on philosophical, operational, and ethical implications of the wider organization of work and its significant challenges for businesses and society.

 

DIS Accommodations Statement 

Your learning experience in this class is important to us.  If you have approved academic accommodations with DIS, please make sure I receive your DIS accommodations letter within two weeks from the start of classes. If you can think of other ways we can support your learning, please don't hesitate to talk to us. If you have any further questions about your academic accommodations, contact Academic Support acadsupp@dis.dk. 

Academic Regulations

Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on:

• Course Enrollment and Grading
• Academic Expectations and Honor Code

 

 

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