Syllabus
Seminar Title
Generative Ai For Business
Overview
In the months before this course begins, generative AI stopped being something you chat with and became something that does work. The frontier models released this September, OpenAI's GPT-6 Astra and Anthropic's Claude Opus 5.5 among them, can operate a computer, carry out long multi-step tasks and deliver finished documents, spreadsheets and code. Google has introduced personal agents that keep running in the cloud, and Apple has opened Siri to third-party models. At the same time, the rules have become concrete. The EU AI Act's transparency obligations for AI-generated content apply from August 2026, and Italy has adopted its own national AI law.
For companies, the question has changed from "Should we use it?" to "Why isn't it paying off yet?" Adoption is close to universal, but McKinsey's State of AI 2026 finds that only 37% of organizations can attribute any earnings impact to AI, and just 6% qualify as high performers. The difference is rarely the model. It lies in how work, data, accountability and people are organized around the model.
This course is built for that gap. Over six sessions you will learn how the technology works, in enough depth to challenge a vendor but without writing a line of code. You will follow the money behind the infrastructure race, practice the craft of instructing models and designing agents, and learn to govern AI responsibly. Every session combines solid frameworks with cases from the past few months, live demonstrations and hands-on work with the tools companies are deploying right now. No technical background is required, only curiosity and a willingness to engage seriously.
The goal is simple. By the end of the course you should be able to walk into a job interview, a board meeting or a client engagement and discuss generative AI with clarity and judgement: where it creates value, where it fails, what it costs and who remains responsible.
Four questions run through the whole course
- How is GenAI disrupting traditional business models, and which new ones is it creating?
- What competitive advantages can it unlock, and for whom, now that the most powerful models are available to everyone?
- How can leaders move from individual experimentation to AI embedded in processes, agents and operating models?
- Which ethical, regulatory, security and organizational risks must be addressed from day one?
Programme
The 18-hour course is organized in six three-hour lectures. The sequence moves from understanding the technology, to reading the market, to applying GenAI in daily work, to redesigning organizations around agents, and finally to governing it responsibly. Each lecture combines concepts, recent cases and practical activities. Examples and cases are refreshed every edition to reflect the latest developments in the field.
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Lecture 1 - Foundations: How Generative AI Actually Works Almost everyone uses AI, but few people understand it well enough to lead with it. We trace the path from Turing to today's generative models, see why data is the raw material of every AI system, and open the box: machine learning, neural networks, Transformers and large language models. No code is involved. The aim is to understand what a model does when it "answers", and why a confident output can be wrong. Key topics
Didactic goal Build a precise mental model of what generative AI is, and what it is not. |
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Lecture 2 - Models, Platforms and the Infrastructure Race Frontier AI is an industrial business built on energy, chips, data centers and cloud. We look at the deals between hyperscalers and AI labs, and at how they are being rewritten. We then map the model landscape as it stands this autumn: US frontier labs, open-weight challengers from China and Europe's push for sovereign AI. Finally, we learn to choose models by use case rather than by brand. Key topics
Didactic goal Evaluate AI platforms and vendors critically, and understand the economics behind prices and lock-in. |
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Lecture 3 - Strategy, Markets and AI at Work Follow the money. Using Big Tech's latest quarterly results as a live case, we ask whether AI spending is turning into revenue. We also look at the strategic moves reshaping the industry. We then turn to the workplace: how employees are already using AI, what the data says about jobs, and why most companies are not yet seeing an impact on earnings. We close with a focus on Italy. Key topics
Didactic goal Read the AI market like an analyst, and connect it to decisions on strategy, investment and talent. |
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Lecture 4 - GenAI as a Business Capability and Consulting Tool This lecture is about turning technology into value. We map business problems to AI solutions and build a credible value case, starting from the recurring patterns behind most enterprise use cases. In a hands-on workshop we practice prompt and context engineering on realistic business tasks. We also look at consulting as a preview of how white-collar work is changing, and at the rise of low-code and "vibe coding". Key topics
Didactic goal Translate GenAI capabilities into concrete use cases, and get reliable, professional-grade output. |
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Lecture 5 · Building AI-Ready and Agentic Organizations An LLM generates text. An agent does work. We explain what an agent is, how it is built and where it fits in an organization: from personal assistants to agents that run end-to-end processes. We then face the question every client asks: who is responsible when an agent makes a mistake? In a hands-on workshop, teams design and test a working agent for a real business process. Key topics
Didactic goal Move from using tools to designing operating models: where agents fit, how they are governed and who is accountable. |
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Lecture 6 - Ethics, Governance and the Future of Generative AI Capability without trust does not scale. We examine the main risks (hallucinations, bias, privacy and security), and look at how content provenance can prove what is real. We cover the EU AI Act as amended in 2026 and Italy's national AI law. The lecture includes a guest speaker session with an industry expert on the ethical dilemmas of Big Tech, and closes with a course recap. Key topics
Didactic goal Govern generative AI responsibly, and anticipate the next wave rather than react to it. |
Language
English
Teaching Methods
The course is designed to be practiced as well as listened to. Each method has a specific role:
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- In-person lectures to introduce foundational concepts, frameworks and emerging insights. Every session opens with "This week in AI", a short briefing on the developments that matter for business.
- Case discussions on how companies across sectors are adopting GenAI, and on where and why they struggle.
- Live demonstrations of GenAI tools and platforms: frontier assistants, productivity copilots and agent-building environments, shown working in real time.
- Hands-on activities and workshops to practice prompt and context engineering and to build and test a working AI agent.
- Guest speakers from industry to share practical experience and strategic perspectives.
- Team-based exercises and presentations to synthesize learnings and propose GenAI strategies for real business problems.
Calendar
| Day | Typology | Starting at | to | Room |
| Monday November 2, 2026 | Lecture | 11:00 | 14:00 | P8 |
| Friday November 6, 2026 | Lecture | 11:00 | 14:00 | S2 |
| Monday November 9, 2026 | Lecture | 11:00 | 14:00 | P8 |
| Friday November 13, 2026 | Lecture | 11:00 | 14:00 | P11 |
| Monday November 16, 2026 | Lecture | 11:00 | 14:00 | P8 |
| Friday November 20, 2026 | Lecture | 11:00 | 14:00 | S12 |
Exams
Exams Timetable
| A.Y. 2026-2027 Winter Session I Call Exam Modality: Written November 27, 2026 at: 11:00 in Room ... |