Course 2026-2027 a.y.

The course is organized into five blocks:

Block 1: Foundations. The entrepreneurial mindset and the AI moment; opportunity recognition; AI strengths and limitations, including a map of the AI toolkit for entrepreneurs.
Block 2: From idea to business. Customer discovery and validation; AI Lab 1 on synthetic personas and interview design; value propositions and business models; AI business models and AI product design; AI Lab 2 on qualitative analysis; the entrepreneurial strategy compass.
Block 3: Building and funding: Funding AI ventures and the European AI funding landscape; the AI-augmented founding team; guest speaker (a European AI founder).
Block 4: Growing and governing. Scaling and go-to-market; ethics, bias, the EU AI Act, and regulation as strategy.
Block 5: Synthesis. Demo day across two sessions, with peer feedback and course synthesis.

• Understand the core stages of creating an entrepreneurial venture, from opportunity recognition to value proposition design, business model development, funding, and scaling, and the key frameworks that inform decisions at each stage.
• Explain how AI enables new ventures, reshapes the different stages of creating an entrepreneurial venture, and where it faces fundamental limitations.
• Recognise the distinctive economics of AI companies and the implications for business model and product design.
• Understand the main sources of capital available to AI ventures.
• Understand the main provisions of the EU AI Act and GDPR relevant to entrepreneurial ventures.
• Recognise the ethical, privacy, and human-impact implications of AI use at each stage of the entrepreneurial journey.

•    Apply a data-driven approach to entrepreneurial decisions: formulate hypotheses, design experiments, interpret evidence.
•    Explain what AI is and evaluate where it enables new ventures versus where it faces fundamental limitations.
•    Use AI tools for persona simulation, interview design, qualitative analysis, and prototyping, and critically assess AI-generated outputs.
•    Navigate the EU AI Act and GDPR, and articulate how regulatory compliance can serve as competitive advantage.
•    Develop, test, and pitch a team venture concept grounded in real customer evidence and rigorous experimentation.
•    Identify the ethical, privacy, and human-impact implications of AI use at each stage of the entrepreneurial journey.

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Collaborative Works / Assignments

Case study discussion. Most sessions combine the lecture with a structured discussion of a venture case, addressing concrete entrepreneurial decisions.

Practical Exercises. Two dedicated AI labs (synthetic personas and interview design; qualitative analysis) give students direct experience with AI tools, paired with critical evaluation of AI-generated outputs. 
 

Guest speaker. One session features a European AI founder who shares the practitioner perspective on the topics covered in the course.

Collaborative work / team venture project. Teams develop, test, and refine a venture concept throughout the course, applying the frameworks and AI tools introduced in each session. The project ends in a Demo Day (Sessions 15–16) where teams pitch their ventures and provide peer feedback.

Attending students will be assessed through in-class work, a venture project and a written exam.

Venture project. Students develop and pitch a venture project that runs across the course. This method verifies that students can apply the course's frameworks and AI tools in practice and contributes to assessing their skills and abilities, including teamwork and communication.

In-class work. Students are assessed on the quality of their participation in case discussions and labs and on their ability, in the individual lab critiques, to identify non-obvious failure modes in AI-generated output. This verifies that students can engage critically with the material and exercise sound judgment about AI's possibilities and limits.

Written exam. The exam covers the main topics of the course. It verifies that students have acquired the expected knowledge and understanding and can reason critically about how AI applies across the entrepreneurial journey.

Non-attending students will be assessed through a written exam.

Written exam. The exam covers the main topics of the course. It verifies that students have acquired the expected knowledge and understanding and can reason critically about how AI applies across the entrepreneurial journey.

A list of readings will be provided at the beginning of the course.

All teaching materials are equally accessible to not attending students. The list of readings, provided at the beginning of the course, is available to all students, and the corresponding readings can be accessed through the University library or the course platform. Slides and any additional materials used in class are made available to all students through the University's online learning platform (Blackboard).