30754 - ARTIFICIAL INTELLIGENCE IN STRATEGIC MANAGEMENT
Department of Management and Technology
Course taught in English
Go to class group/s: 31
BAI (6 credits - I sem. - OP | SECS-P/07) - BEMACS (6 credits - I sem. - OP | SECS-P/07) - BESS-CLES (6 credits - I sem. - OP | SECS-P/07) - BGL (6 credits - I sem. - OP | SECS-P/07) - BIEF (6 credits - I sem. - OP | SECS-P/07) - BIEM (6 credits - I sem. - OP | SECS-P/07) - BIG (6 credits - I sem. - OP | SECS-P/07) - CLEACC (6 credits - I sem. - OP | SECS-P/07) - CLEAM (6 credits - I sem. - OP | SECS-P/07) - WBB (6 credits - I sem. - OP | SECS-P/07)
Course Director:
ALFONSO GAMBARDELLA
ALFONSO GAMBARDELLA
Mission & Content Summary
MISSION
The goal of this course is to teach students to make decisions under uncertainty and use AI tools to support thee decisions. The course is open to students with technical and non-technical backgrounds. The content will be differentiated depending on the background of the student to bring everyone up to speed with the use of generative AI for strategic decision making in firms and organizations more generally. Students with technical background may be asked to code tools and analyze data. Students with less advanced technical background may be asked to assess strategic management decisions and design the underlying logic so that the underlying process of these decisions can be coded. Ultimately, the goal of the course is to teach all students to learn the microfoundations of strategic decisions and how AI tools can support these decisions. The course will make extensive use of available AI-agents and tools from the ION Management Science Lab.
CONTENT SUMMARY
- Strategic decision making: a framework
- Use of AI tools
- Practical applications of generative AI in strategic management
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- understand the microfoundations of decision making under uncertainty
- use generative AI tools to make these decisions
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- make strategic decisions under uncertainty
- use generative AI tools to support these decisions
Teaching methods
- Lectures
- Practical Exercises
- Collaborative Works / Assignments
DETAILS
Lectures: classical frontal lectures
Practical exercises: practical decisions using AI tools, data analyses from the tools
Collaborative work: group decisions using AI tools, group data analyses
Assessment methods
| Continuous assessment | Partial exams | General exam | |
|---|---|---|---|
|
x |
ATTENDING STUDENTS
The exam consists in a standard format of questions weighted in such a way that the students can totalize up to 31 points. For attending students some of the questions can ask to discuss projects carried out in class during the course.
NOT ATTENDING STUDENTS
The exam consists in a standard format of questions weighted in such a way that the students can totalize up to 31 points. For non-attending students there will be of course no option to discuss projects carried out in class during the course.
Teaching materials
ATTENDING STUDENTS
Class notes
NOT ATTENDING STUDENTS
Class notes. All the relevant material will be available in blackboard.
Last change 25/05/2026 15:27