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Generative AI: from fundamentals to business applications
Generative AI: from fundamentals to business applications
Course description
Welcome to the course ‘Generative AI: From Fundamentals to Business Applications' offered to you by Bocconi University.
In this course, we will get to grips with key concepts, present you with some real-world applications in business and take a look at how companies can translate an AI-based idea into a real-world functioning system that can be exploited effectively and successfully by internal users and customers.
The main objective of this MOOC is to understand the key functioning and logics behind Generative AI and how it can be effectively applied in business contexts. The course will focus on laying down the “building blocks” of the technology, its emerging market structure, its practical applications in business with special reference to emerging use cases and integration logics, as well as the risks and limits that it underpins. The whole course blends solid technological foundations with a business perspective, suitable for students who want to mix technical understanding with managerial points of attention and implications to form a comprehensive and pragmatic view of Generative AI.
Therefore, this MOOC is suitable both for students interested in becoming aware users of Generative AI technologies and participants interested in pursuing further studies to assume managerial roles at the intersection of business functions and Generative AI.
The course is divided into four modules, guiding participants through a structured learning path to understand AI fundamentals and its business applications.
Each module includes videos, video transcripts, interactive activities, infographics, readings and self-assessment quizzes.
Total workload of the course: 50 hours
This MOOC was produced as part of the Edvance project – Digital Education Hub per la Cultura Digitale Avanzata. The project is funded by the European Union – Next Generation EU, Component 1, Investment 3.4 “Didattica e competenze universitarie avanzate".
Intended Learning Outcomes
By the end of this course, students will be able to:
- Distinguish between traditional AI and Generative AI
- Match appropriate AI tools to specific business challenges
- Recognise risks and ethical problems in AI implementation in business
- Describe Generative text-based, image and video-based models and relative business applications
- List key features and business applications of Agentic AI and synthetic data in Gen AI
- Categorize current and future trends in business-applied AI
Prerequisites
No prerequisites are required to participate in this course.
Activities
Throughout the course, you will find activities to help consolidate your understanding, to practice translating abstract concepts into practical applications in the real-world, and to hone your skills in assessing AI tools and applications with a critical eye and from a managerial perspective. Furthermore, at the end of each week, you will encounter a Reflection Point, designed to let you check that you have indeed learned to:
- Synthesize what you’ve learned by connecting key ideas.
- Apply concepts to real-world scenarios relevant to your field or interests.
- Think critically about AI’s implications from a managerial perspective.
Section outline
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In this Week, we'll explore:
- What Generative AI is and how it differs from traditional AI
- The right tool for the right problem: when to use Generative AI and traditional AI
- A taxonomy of Generative models
- Generative AI in business: general purpose Generative AI
- Generative AI in business: vertical Generative AI
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In this Week, we'll explore:
- Introduction to NLP
- Technical Details of NLP
- Language Architectures
- Overview of LLM providers
- Specializing Models
- Prompt Engineering
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In this Week, we'll explore:
- Introduction to Computer Vision
- Overview of vision models (teaching machines to see) - 1 of 2
- Diffusion models (teaching machines to see) - 2 of 2
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In this Week, we'll explore:
- Generative AI & synthetic data
- Agentic AI: definition, applications and trends
- Economic, social and political outlook of Generative AI
- Generative AI market structure
- What an AGI (Artificial General Intelligence) really is: Narrow AI vs Strong AI
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Video transcripts Folder
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Assessment
Your final grade for the course will be based on the results of your answers to the assessed quizzes. You have an unlimited number of attempts at each quiz, but you must wait 15 minutes before you can try again. You will have successfully completed the course if you score 60% (or higher) in each one of the assessed quizzes. The maximum score possible for each quiz is given at the beginning of the quiz. You can view your score in the quiz on your last attempt or on the 'Grades' page.
Certificate
You can achieve a certificate in the form of an Open Badge for this course, if you reach at least 60% of the total score in each one of the assessed quizzes and fill in the final survey.
Once you have completed the required tasks, you will be able to access ‘Get the Open Badge’ and start issuing the badge. Instructions on how to access the badge will be sent to your e-mail address.
The Badge does not confer any academic credit, grade or degree.
Information about fees and access to materials
The course is delivered in online mode and is available free of charge.
Course faculty
Lorenzo Diaferia
lorenzo.diaferia@sdabocconi.it
Lecturer, SDA Bocconi School of Management
Recent publications
DE ROSSI L., DIAFERIA L.Dall’hype tecnologico alla realtà: l’applicabilità a tre dimensioniEconomia & Management, 2024, no. 4, pp.19-26
DIAFERIA L., DE ROSSI L., SALVIOTTI G., AI Management. Strategie e Approcci in AziendaEgea, Milano, Italia, 2024
ARMENI P., POLAT I., DE ROSSI L., DIAFERIA L., MEREGALLI S., GATTI A., Exploring the potential of digital therapeutics: An assessment of progress and promiseDigital Health, 2024, vol.10
DIAFERIA L., Qui la penna, ChatGPT! L’AI generativa alla prova del testoEconomia & Management, 2023, no. 3, pp.72-77
ARMENI P., POLAT I., DE ROSSI L., DIAFERIA L., MEREGALLI S., GATTI A., Digital Twins in Healthcare: Is It the Beginning of a New Era of Evidence-Based Medicine? A Critical ReviewJournal of Personalized Medicine, 2022, vol.12, no. 8, pp.1255
CENNAMO C., DIAFERIA L., GAUR A., SALVIOTTI G., Assessing Incumbents’ Risk of Digital Platform DisruptionMIS Quarterly Executive, 2022, vol.21, no. 1, pp.55-74
Michele Russo
michele.russo@sdabocconi.it
Fellow, SDA Bocconi School of Management
Recent publications
RUSSO M., PRIX S., GOERGEN J., DE BELLIS E., The 3 Types of Customers Who Buy Smart Products—and How to Market to ThemHarvard Business Review, 4 Novembre, 2025
GABBI G., TONINI D., RUSSO M.A Novel Supervised-Unsupervised Approach for Past-Due PredictionRisk Management Magazine Aifirm, 2024, vol.19, no. 02, pp.4-21
CASELLI S., GABBI G., DE ROSSI L., ABBATEMARCO N., RUSSO M., MORETTI S., For a digital euro that citizens will embrace - Per un euro digitale che piaccia ai cittadini2025, SDA Bocconi Insight, Milano, Italia
TAVA L. V., RUSSO M.IDRO - Negotiation Exercise2023, The Case Centre, Gran Bretagna
Contact details
If you have any enquiries about the course or if you need technical assistance please contact pok@polimi.it. For further information, see FAQ page.