Course Syllabus

Course Description

The rise of Generative Artificial Intelligence (GAI) has significant economic potential across all sectors of industry and public administration. It is considered the next productivity frontier for increased growth potential and labor productivity, engendering added value creation in key areas of business. 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 foundation models already point to far broader applications in software development, media, marketing and sales, design, and R&D.

In this course, students explore the business potential of Generative AI through four connected modules:

• Foundations: building a common language for understanding GAI — key concepts, the model landscape, business use cases, responsible AI vocabulary, and implications for privacy and security.
• Applications: analyzing how GAI transforms business strategy using Rogers' five-domain framework (Customers, Competition, Data, Innovation, Value) through industry case studies and group projects.
• Operations: building a digital transformation roadmap using Rogers' five-step framework — from shared vision and strategic priorities through experimentation, governance, and growing tech, talent, and culture.
• Consequences: assessing the wider impact of GAI on workforce transformation, the digital divide, wealth disparity, and public policy for human-centric AI development.

The spread of GAI poses a variety of risks: algorithmic bias, infringement on intellectual property, accelerating cyberattacks, manipulated or unintended outputs, difficulties in explaining how answers are produced, and disproportionate impact on the workforce. This has created key dilemmas — the dissolution of truth, fake generation and fake authenticity, and ever-higher complexity. These risks and dilemmas are examined throughout the course, not only in a dedicated module.

Students work in industry groups across three connected assignments: a domain analysis, an individual transformation roadmap, and a group pitch to a CEO. The course concludes with student presentations and a structured retrospective.

Note: General computer skills and course followed in business economics or microeconomics at university level is strongly encouraged.

Learning Objectives

At the end of this course you should achieve:

Gained Knowledge and Insights:

• Possess knowledge and ability in applying relevant concepts of Generative Artificial Intelligence (GAI), including assessing a variety of risks for privacy and security.
• Understand why GAI has significant economic potential across all sectors of industry and public administration.
• Possess insight into the transformation of business functions through GAI, including frameworks for strategic analysis and operational planning.

Developed Abilities:

• Be able to analyze and assess how Generative AI transforms business strategy using Rogers' five-domain framework.
• Be able to design a digital transformation roadmap using Rogers' five-step framework, from shared vision through governance and capabilities.
• Be able to analyze key dilemmas in using Generative AI — the dissolution of truth, fake generation and authenticity, and increasing complexity.
• Be able to assess the consequences of using Generative AI for workforce transformation, the digital divide, and public policy.

• I and in using GAI.

Acquired Skills:

• Have the skills to independently present, critically assess, and analyze theoretical contexts and fundamental concepts as covered in the course.
• Have the skills to build and defend a case for AI transformation in a specific industry or company.
• Have the skills to convey the options for managing change in an organization using Generative Artificial Intelligence (GAI).

Faculty and Guest Lecturers

This course is housed at the DIS International Business (IB) department. It has been taught by Lars Malmqvist, Ph.D., MBA in 2025 and in Spring 2026 (Foundations module, Classes 1–8). Lars is a former startup CTO, a partner in a major management consultancy, and an active researcher in generative AI.

From Class 9 onwards, Stefano Vincenti, MBA, M.Sc. in Economics & Finance, and IT University lecturer, is taking over. Stefano is also a cofounder of AI startups, both as CEO and CTO, a partner at TryZone, and an active consultant and trainer in generative AI (https://www.aitrainer.dk/en/about).

Guest lecturers can be invited in the two field days and in selected classes. Practical details about preparation are indicated in the course calendar. 

Readings  

Texts for classes 1–8

"Generative Artificial Intelligence: A Historical Perspective" by Ran He, Jie Cao, and Tieniu Tan. National Science Review 2025 (Vol. 12, No. 5, article nwaf050). Oxford Academic sciengine.com

(Optional) "The Illustrated Transformer" by Jay Alammar. jalammar.github.io 2018; updated 2025 (web article). jalammar.github.io

"The state of AI: How organizations are rewiring to capture value" by Alex Singla, Alexander Sukharevsky, Lareina Yee, Michael Chui, and Bryce Hall. McKinsey & Company 2025, 26 pages. McKinsey & Company

"How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025" by Sarah Wang, Shangda Xu, Justin Kahl, and Tugce Erten. Andreessen Horowitz (a16z) 2025 (web article). Andreessen Horowitz

"Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1)" by National Institute of Standards and Technology. NIST 2024, 64 pages. Reference document — students should familiarize themselves with the framework structure and key risk categories. NIST Publications

"How to Find the Right Business Use Cases for Generative AI" by Beth Stackpole. MIT Sloan – Ideas Made to Matter 9 June 2025 (web article). MIT Sloan

Texts for classes 9–13 (Applications: Five-Domain Framework)

Rogers, D. L. (2016). The Digital Transformation Playbook: Rethink Your Business for the Digital Age. Columbia University Press. Selected chapters (Ch. 1–6) covering the five strategic domains: Customers, Competition, Data, Innovation, and Value. Core analytical framework for the group project and classes 9–13.

(Optional) "The Gen AI Playbook for Organizations" by Marco Iansiti and Karim R. Lakhani. Harvard Business Review, November 2025. A practical deployment framework for organizing AI initiatives around cost of errors and type of knowledge. Harvard Business Review

Texts for classes 14–18 (Operations: Five-Step Roadmap)

Rogers, D. L. (2023). The Digital Transformation Roadmap: Rebuild Your Organization for Continuous Change. Columbia Business School Publishing, New York. Book handed out by DIS. Core framework for the individual Roadmap assignment.

"The Effects of Generative AI on Productivity, Innovation and Entrepreneurship" by OECD, June 2025, 59 pp. A general overview of the effects of generative AI as we know them today. Good baseline knowledge for the Roadmap discussions. OECD

"The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI" by MIT Sloan Management Review and Boston Consulting Group, November 2025. Based on a global survey of 2,102 respondents across 21 industries. Addresses four strategic tensions in scaling AI — directly relevant to the Roadmap steps on governance, experimentation, and capabilities. MIT Sloan Management Review

Texts for classes 19–20 (Consequences)

"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. IMF

"Bridging Skill Gaps for the Future: New Jobs Creation in the AI Age" by Florence Jaumotte, Jaden Kim, David Koll, Elmer Li, Longji Li, Giovanni Melina, Alina Song, and Marina Mendes Tavares. International Monetary Fund, January 2026, SDN/2026/001. The official sequel to the 2024 note — shifts from what AI does to jobs to how to prepare workers. IMF

(Optional) "Four Futures for Jobs in the New Economy: AI and Talent in 2030" by World Economic Forum, January 2026. Scenario-based analysis of how AI and talent trends could reshape jobs by 2030. World Economic Forum

These texts, apart from the available Roadmap and Playbook books, are together with additional case studies and class slides available electronically at the DIS Canvas. As the course is based on a collective knowledge process, further readings and task materials will be built up during the course, also using AI.

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%
GAI Business projects	20%
GAI Roadmap proposal	40%
GAI Transformation Pitch	15%

Assignment Progression

The three main assignments form a connected arc. Students work in six industry groups (Retail, Manufacturing, Government/Defense, Software, Pharma/MedTech, Finance) throughout the semester:

Assignment 1 — GAI Business Domain Project (group, 20%): Each group analyzes how Generative AI transforms their assigned industry using Rogers' five-domain framework. Due Friday, March 13.

Assignment 2 — GAI Roadmap Proposal (individual, 40%): Each student individually builds a digital transformation roadmap for a company or domain within their group's industry, using Rogers' five-step Roadmap framework. Due Friday, April 17.

Assignment 3 — GAI Transformation Pitch (group, 15%): Groups reconvene to synthesize their individual roadmaps into a single strategic pitch to a CEO, advocating for enhanced AI integration. Presented in Classes 21–22.

A half page individual reflection on both AI usage and personal reflections is required with each assignment and must be written without AI assistance.

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 for note-taking purposes, also use of Copilot/ChatGTP/Claude etc. for research, and class exercises is required. For assignments you are allowed to use any and all generative AI tools for the main assignment. However, the half page individual reflection pieces attached to the main assignment must be done without any AI assistance.

 

The structure of the course

The course is organized around four modules:

Foundations (classes 1-8) 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. The model landscape, companies and players. Key business use cases for generative AI. 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 9–13) Applying Rogers' five-domain framework to analyze how Generative AI transforms business strategy. Each class maps one domain — Customers, Competition, Data, Innovation, Value — through current case studies, frameworks, and group exercises tied directly to the GAI Business Domain Project (due Friday, March 13).

Operations (Classes 14–18) Building a GenAI transformation roadmap using Rogers' five-step framework — from shared vision and strategic priorities through experimentation, governance, and growing tech, talent, and culture. Student groups shift from domain analysis to operational planning, progressing through structured feedback cycles and industry case studies to produce the GAI Roadmap Proposal (due Friday, April 17).

Consequences, Student Presentations & Wrap up (Classes 19–22)  Assessing the consequences of Generative AI for the economy and society: workforce transformation, value creation and growth scenarios, the digital divide, and cross-country income and wealth disparity (Class 19); public policies for human-centric AI development, workforce planning, and the philosophical, operational, and ethical implications for businesses and society (Class 20). The course concludes with group presentations — each industry group pitches their GAI Transformation Roadmap to a simulated CEO audience (Class 21) — and a structured course retrospective (Class 22).

 

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.