Insegnamento a.a. 2026-2027

- Explain what current AI systems can and cannot do, and evaluate AI tools for specific management tasks

- Use prompt engineering and AI workflows to multiply personal productivity in writing, analysis, and communication

- Apply AI to career strategy, job search, networking, and professional development in a disrupted labor market

- Use AI tools to improve team collaboration, consensus formation, negotiation, and cross-cultural coordination

- Accelerate the entrepreneurial process, from idea generation through hypothesis testing, prototyping, and scaling, using AI at every stage

- Analyze how AI changes organizational structure, decision-making, knowledge management, and competitive strategy

- Deploy AI for market-facing activities: sales, marketing, market research, and customer development

- Critically assess AI’s impact on labor markets, geopolitics, regulation, and the philosophy of management

- Build a portfolio of real-world projects demonstrating applied AI competence to future employers and collaborators

• Explain what current AI systems (large language models, machine learning, and AI agents) can and cannot do at a level sufficient for managerial and strategic decisions.
• Distinguish between the principal AI approaches (supervised and unsupervised learning, reinforcement learning, and LLMs) and between open-source and proprietary model strategies and their competitive implications.
• Estimate unit economics of AI using knowledge on cost structures, data center, token and compute pricing, market concentration, and value-chain disruption.
• Illustrate how AI reshapes management across levels of activity — the individual, teams, startups, established organizations, markets, and the broader economy.
• Identify the organizational, strategic, and competitive consequences of AI adoption for both new ventures and established firms.
• Recognize the regulatory, geopolitical, and ethical context shaping AI use, including the EU AI Act, industrial policy, and the changing future of work.
• Summarize the frameworks used to evaluate AI tools and generative outputs, including human-in-the-loop assessment, A/B testing, and ROI measurement.

• Use prompt engineering and AI workflows to multiply personal productivity in writing, analysis, presentation design, and professional communication.
• Apply AI tools to career strategy (including job search, CV and cover-letter optimization, interview preparation, and professional networking) in a labor market reshaped by automation.
• Deploy AI to support team collaboration, consensus formation, negotiation, and cross-cultural coordination, including the orchestration of multi-agent AI systems.
• Build, design, and validate an AI-native venture, using AI for ideation, customer discovery, prototyping, and rapid experimentation at machine speed while leading agentic teams.
• Build, design, and validate  AI-powered, market-facing pipelines for sales, marketing, pricing, and customer development, and document their process and results.
• Design an AI-augmented strategic and organizational analysis for a real company and generate actionable recommendations.
• Formulate an evidence-based policy proposal addressing how AI can improve a country's economic competitiveness.
• Evaluate AI-generated outputs critically, taking responsibility for their accuracy, bias, and originality (transversal: critical judgment).
• Communicate project outcomes persuasively to expert audiences and provide constructive feedback to peers (transversal: communication and teamwork).

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Individual works / Assignments
• Interaction/Gamification
• Competitions/Hackathons

Session format. Each 90-minute session is divided into two halves:

- Theory (~45 minutes) (lectures + guest speakers): Lecture, case discussion, and conceptual frameworks. The instructor introduces key ideas, connects them to management research, and facilitates discussion.

- Practical Lab (~45 minutes) (practical exercise + interaction/gamification): Students work hands-on with AI tools, i.e., running experiments, building prototypes, conducting analyses, or simulating scenarios. Lab exercises are designed to produce outputs that contribute directly to module projects.

Projects (individual assignments; includes hackathons). Each of the seven modules includes a real-world project. Projects are not hypothetical exercises; they are designed to create tangible value in students’ lives. The grading system (see Section 5 in the Syllabus) rewards ambition: students who achieve extraordinary real-world outcomes receive outsized credit.

AI tool usage. Students are expected and strongly encouraged to use AI tools (ChatGPT, Claude, Cursor, Perplexity, and others) throughout the course — in class, for assignments, and for projects. The ability to work effectively with AI is itself a core learning outcome. AI-generated content must be used thoughtfully and critically; students remain responsible for the quality, accuracy, and originality of their work.

For attending students: Class participation and individual project deliverables.

To be communicated at the beginning of the course.

To be communicated at the beginning of the course.

To be communicated at the beginning of the course.