21123 - AI, PLATFORMS AND DIGITAL ECOSYSTEMS
Department of Management and Technology
Course taught in English
Go to class group/s: 31
ACME (6 credits - II sem. - OP | SECS-P/06) - AFM (6 credits - II sem. - OP | SECS-P/06) - AI (6 credits - II sem. - OP | SECS-P/06) - CLMG (6 credits - II sem. - OP | SECS-P/06) - DSBA (6 credits - II sem. - OP | SECS-P/06) - EMIT (6 credits - II sem. - OP | SECS-P/06) - ESS (6 credits - II sem. - OP | ECON-04/A | SECS-P/06) - FIN (6 credits - II sem. - OP | SECS-P/06) - GIO (6 credits - II sem. - OP | SECS-P/06) - IM (6 credits - II sem. - OP | SECS-P/06) - MM (6 credits - II sem. - OP | SECS-P/06) - PPA (6 credits - II sem. - OP | SECS-P/06) - TS (6 credits - II sem. - OP | SECS-P/06)
Course Director:
NICOLETTA CORROCHER
NICOLETTA CORROCHER
Suggested background knowledge
No formal prerequisites.
Mission & Content Summary
MISSION
The course examines how artificial intelligence is reshaping the economics of digital platforms and the architecture of digital ecosystems. Building on the foundations of platform economics (network effects, two-sided markets, standards, complementarities, modularity), the course investigates how AI technologies, and in particular generative AI and foundation models, are transforming competitive dynamics, business models, and value creation across the platform economy. The analysis adopts a micro-level perspective, looking at the strategies of incumbents and new entrants along the AI value chain (cloud and compute providers, foundation model developers, application and agent builders, data providers), and at the role of data and computational resources as sources of competitive advantage. It also takes a macro-level perspective, considering the implications of AI adoption for productivity, innovation, entrepreneurship, and labour markets, and the policy and regulatory challenges that arise as AI becomes embedded in platform ecosystems (EU AI Act, Digital Markets Act, with comparative attention to approaches in the US, UK, and China).
Students will learn the basic principles of platform economics and the emerging economics of AI, and will apply them to current cases involving AI-enabled platforms, foundation model ecosystems, and digital infrastructures. Traditional lectures will be complemented with in-depth analysis of case studies from different industries, with the active
CONTENT SUMMARY
- AI: basic concepts and the technology landscape
- The AI value chain and new business models
- Platform governance and competition
- Pricing in platforms
- The microeconomic effects of AI: skills, innovation and entrepreneurship
- The macroeconomic effects of AI: productivity, employment and growth
- AI regulation
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Identify the characteristics of AI platforms and value chain
- Understand the economics of platforms (pricing, network effects, multi-sided dynamics) and the economics of AI (data, compute, foundation models, the AI value chain)
- Recognize the implications of AI for skill development, innovation, and entrepreneurship
- Discuss the effects of AI for growth, employment and productivity
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Apply the methodologies and relevant theoretical approaches to discuss the strategies of incumbent and entrant firms in AI-enabled platform markets
- Analyse the main regulatory implications of AI in platforms and digital ecosystems, with comparative attention to EU, US, UK, and China
- Develop and evaluate innovative ideas for new AI-enabled platforms and business models
- Show teamwork abilities and presentation/communication skills
Teaching methods
- Lectures
- Collaborative Works / Assignments
DETAILS
The learning experience of the course is articulated around different teaching methods. Besides traditional frontal lectures, the students have the opportunity to discuss case studies and incidents concerning the development of innovation in AI-related platforms and to work in a team for the development of a final group project. The group project consists of a 15-page report and a 30-minute presentation analysing a firm whose business model is substantially based on, or being reshaped by, AI. The firm does not need to be an AI provider (foundation model labs, AI infrastructure firms): cases involving incumbents or new entrants in any sector (retail, finance, healthcare, logistics, media, manufacturing) that are deploying AI as a core part of their value proposition are equally welcome. Students will use the frameworks developed in class to investigate the technology and data foundations, the competitive landscape, the firm's positioning along the AI value chain, the business model, and the regulatory environment.
Assessment methods
| Continuous assessment | Partial exams | General exam | |
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ATTENDING STUDENTS
The assessment is split into two parts: one written exam at the end of the course on the entire program, and a group project.
The written exam is based on the course readings and slides. It consists of two open-ended questions to be chosen out of four. The exam will last 45 minutes.The exam will typically include a set of statements to discuss, aimed to assess the ability of students to articulate their reasoning and to evaluate the potential effects of AI at the micro-level of companies' strategies and at the more macro-level of sectors and countries.
The group project consists of a 15-page report and a 30-minute presentation analysing a firm whose business model is substantially based on, or being reshaped by, AI. The firm does not need to be an AI provider (foundation model labs, AI infrastructure firms): cases involving incumbents or new entrants in any sector (retail, finance, healthcare, logistics, media, manufacturing) that are deploying AI as a core part of their value proposition are equally welcome. Students will use the frameworks developed in class to investigate the technology and data foundations, the competitive landscape, the firm's positioning along the AI value chain, the business model, and the regulatory environment. Groups may consist of max three students.
The final grade is the weighted sum of the group project (50%) and the written exam (50%).
NOT ATTENDING STUDENTS
For non attending students, the final grade is completely based on a written exam including 3 compulsory open questions, which cover all the topics of the course and aim at assessing the learning outcomes both in terms of the understanding of theoretical approaches and in terms of the capability to analyse different issues in relation to innovation patterns in different service sectors.
To this aim, besides course readings and lecture notes, students have to study a set of additional readings.
Teaching materials
ATTENDING STUDENTS
- Course slides
- A list of papers to be announced in class
NOT ATTENDING STUDENTS
The list of compulsory readings will be announced in class
Last change 20/05/2026 14:24