Course Syllabus

Course Description

Generative AI is widely described as the next productivity frontier. This course tests that claim from a business standpoint: where GenAI creates value, where it does not, who captures it, and what it does to organizations, jobs and public policy.

The course runs in four modules — Foundations, Applications, Operations, Consequences — set out under The structure of the course below. Risk and governance are treated throughout: algorithmic bias, intellectual-property infringement, accelerating cyberattacks, outputs nobody can explain, and uneven impact on the workforce, together with the dilemmas these create around truth and authenticity.

You will work in an industry group across three connected assignments: a domain analysis, an individual transformation roadmap, and a group pitch to a CEO.

Prerequisites: general computer skills. A university-level course in business economics or microeconomics is strongly encouraged.

Learning Objectives

Knowledge and insight

• Explain the core concepts of generative AI, including its risks for privacy and security.
• Explain why GenAI carries economic significance across industry and public administration.

Abilities

• Analyze how GenAI transforms business strategy using Rogers’ five-domain framework.
• Design a digital transformation roadmap using Rogers’ five-step framework.
• Analyze the dilemmas GenAI creates: the dissolution of truth, fake authenticity, rising complexity.
• Assess GenAI’s consequences for the workforce, the digital divide and public policy.

Skills

• Build and defend a case for AI transformation in a specific industry or company.
• Present and critically assess theoretical arguments and empirical evidence from the course.

Faculty

Taught by Stefano Vincenti, MBA, M.Sc. in Economics & Finance, lecturer at the IT University of Copenhagen, cofounder of AI startups as both CEO and CTO, partner at TryZone, and an active consultant and trainer in generative AI (aitrainer.dk). He teaches all classes. The course is housed in the DIS International Business department. Guest lecturers may join the two field studies and selected classes.

The structure of the course

Module	Classes	What you do
Foundations	1–8	Core concepts, the model landscape, business use cases, responsible-AI vocabulary, data quality, privacy and security, the key dilemmas, and problem-centric versus technology-driven thinking.
Applications	9–13	One Rogers domain per class — Customers, Competition, Data, Innovation, Value — applied to your group’s industry. Feeds Assignment 1.
Operations	14–18	Rogers’ five roadmap steps: shared vision, strategic priorities, validating ventures, managing growth at scale, growing tech, talent and culture. Feeds Assignment 2.
Consequences	19–22	Economic consequences and the labor market (19); public policy and ethics (20); the group pitch (21); course retrospective (22).

How the course is taught

• Short input, then work. Most sessions pair a short lecture with group work, a case discussion or a hands-on AI exercise.
• You are expected to talk. Come having read, and bring two or three things that struck you. The quality of the discussion is the course.
• Using AI in class is required, not optional. You will use Copilot, ChatGPT, Claude and others for research and exercises. Bring a laptop.
• Feedback is continuous. You get structured feedback on the domain analysis before the roadmap, and on the roadmap before the pitch. Each assignment is built to improve on the last.
• Field studies count. Two field days are part of the course, not extras, and participation in them is assessed.

Readings, assignments and field studies may change during the semester. Changes are announced on Canvas.

Grading

Assignment	Weight	Due
Participation	25%	Throughout
GAI Business Domain Project — group	20%	Tue 20 October
GAI Roadmap Proposal — individual	40%	Tue 17 November
GAI Transformation Pitch — group	15%	Wed 2 December

The assignment arc

Six industry groups — Retail, Manufacturing, Government/Defense, Software, Pharma/MedTech, Finance — run through all three assignments. Each group analyzes how GenAI transforms its industry across the five domains. Each student then builds an individual roadmap for a company inside that industry. Groups reconvene to synthesize those roadmaps into one pitch, presented to a simulated CEO in Class 21.

Every assignment includes a half-page individual reflection on your AI use and your own thinking. Reflections must be written without AI assistance.

Participation, 25%

Three components, weighted equally: attendance, evidence of preparation, and contribution. Attendance at every class is expected; if you miss one, contact me as soon as possible with an explanation. Preparation means having done the reading. Contribution means advancing the discussion or the group’s work, not merely being present.

AI and computer policy

Laptops are permitted for notes, and AI use for research and in-class exercises is required. For the main assignments you may use any and all generative AI tools, and you are asked to document how you used them. The half-page reflections must be written without AI.

Workload

Most classes have one required reading, usually between 5 and 20 pages. Where a reading specifies a section or page range, only that portion is required — several citations below look longer than the actual assignment.

Readings

Rogers’ two books are the backbone of this course. Everything else is here because it does something the books cannot: it is current, or it is contested. All required readings are free through Canvas unless noted.

The two core texts

• Rogers, D. L. (2016), The Digital Transformation Playbook, Ch. 1–6 — the five domains. Not distributed by DIS. Read The Five Domains: Companion Guide on Canvas instead, which maps the framework to pages you already have in the Roadmap. Classes 9–13.
• Rogers, D. L. (2023), The Digital Transformation Roadmap — the five roadmap steps. Distributed by DIS. The core text for the Roadmap Proposal. Read one chapter per class: Ch. 3–4 for Class 14, Ch. 5 for Class 15, Ch. 6 for Class 16, Ch. 7 for Class 17, Ch. 1–2 and the Conclusion for Class 18.

Classes 1–8 — Foundations

• Eurostat, Use of AI in enterprises, 2025 data. EU 20% · Denmark 42%, first in the EU. ~10 min. Class 1.
• He, Cao & Tan, “Generative Artificial Intelligence: A Historical Perspective”, National Science Review 12(5), 2025. Foundation-models and challenges sections only, ~6 pp. Classes 1–3.
• Deloitte, “State of AI in the Enterprise 2026”. Key findings and agentic AI section, ~15 pp. Classes 4 and 5.
• Stackpole, “Action Items for AI Decision Makers in 2026”, MIT Sloan. ~4 min. Classes 4 and 8.
• European Commission, AI Act Article 50 transparency obligations — guidelines excerpt. Enforceable since 2 August 2026. Classes 6–7.
• Zirpoli, “Generative Artificial Intelligence and Copyright Law”, Congressional Research Service LSB10922, 7 pp. Class 7.

Reference — consult and cite, do not read cover to cover

• NIST AI 600-1, Generative AI Profile, 2024. Know the structure and the risk categories; you will cite this in the Roadmap Proposal.
• European Commission AI Act Service Desk — Explorer and Compliance Checker. Used in class.

Classes 9–13 — Applications

• McKinsey, “Where AI will create value, and where it won’t”, Apr 2026, ~9 pp. Read this against Rogers, not alongside him. Classes 10 and 13.

Industry sources for the Domain Project are deliberately not prescribed. Finding and assessing them is part of the assignment.

Classes 14–18 — Operations

• Smaje & Levin, “Rewiring for AI: From ambition to advantage”, McKinsey, May 2026, 7 pp. Classes 14 and 16.
• Arbour, Bojinov, Feller & Ni, “Does Your AI Work? Test It Where It Lives”, HBS AI Institute, Jul 2026, ~5 pp. How you would know a pilot actually worked. Class 15.

Class 18 — critical-reading exercise, in place of an additional reading

• MIT NANDA, “The GenAI Divide: State of AI in Business 2025”. Skim for the claim, then read the methodology closely.
• Wharton + GBK, “Accountable Acceleration”, Oct 2025. ROI and measurement section only, ~15 pp of 80.
• Futuriom, “Why We Don’t Believe MIT NANDA’s Weird AI Study”, Aug 2025.

These three cover the same period and reach opposite conclusions about whether companies are getting a return on AI. Work out why, then decide which numbers you would put in front of a CEO.

Classes 19–20 — Consequences

• Jaumotte et al., “Bridging Skill Gaps for the Future”, IMF SDN/2026/001, Jan 2026. Executive summary and Sections I–III, ~20 pp. Class 19.
• Brynjolfsson, Chandar & Chen, “Canaries in the Coal Mine?”, Stanford Digital Economy Lab, Nov 2025, ~15 pp — plus two short counterpoints from the New York Fed, “Do Job Postings Show Early Labor-Market Effects of AI?” and “Remote Work Leaves Younger Workers Sidelined”, ~10 min. Class 19.
• World Bank, World Development Report 2026, Ch. 4, Aug 2026. Main messages and pp. 109–125, ~17 pp. Class 20.

Class 19 deliberately assigns sources that disagree about whether AI is displacing entry-level workers. You are not asked to find the right answer. You are asked to work out what evidence would settle it.

Further materials will be added during the semester, including by using AI.

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.