Insegnamento a.a. 2026-2027

The course explores the evolution of Information Systems in the AI era, focusing on how artificial intelligence, data, and digital technologies are reshaping organizations, managerial practices, and business models. The course combines strategic, organizational, technological, and managerial perspectives, linking foundational concepts in Information Systems with contemporary developments in AI-enabled transformation.

The course is structured around the following macro-topics:

• Evolution of Information Systems and AI-enabled Organizations
  Introduction to the evolution from traditional Information Systems to AI-driven socio-technical systems; digital transformation and the changing role of information, automation, and intelligence in organizations.
• Economics of AI, Data, and Digital Platforms
  AI and IT investments; data as a strategic asset; data governance; data-driven organizations; digital platforms and the economic implications of AI adoption.
• Enterprise Systems and Intelligent Business Platforms
  Transformation of enterprise applications and operational systems in the AI era, including ERP systems, CRM platforms, HR technologies, and intelligent process integration.
• AI Applications for Management and Decision-Making
  Managerial uses of machine learning and generative AI; AI-supported decision processes; customer intelligence; organizational experimentation and AI use cases across business functions.
• Digital Architectures and Technological Foundations
  Core technological infrastructures supporting AI-enabled organizations, including cloud computing, APIs, microservices, platform architectures, and MLOps concepts.
• AI, Organizations, and Governance
  Algorithmic organizations; governance of IT and AI systems; emerging managerial and organizational roles; ethical, organizational, and societal implications of AI adoption.
• Implementation, Change, and Service Design
  Approaches to implementing Information Systems and AI solutions; feasibility analysis; organizational change management; service design principles; user-centricity and digital service innovation.
• Agile Innovation and Experimentation
  Agile approaches to digital innovation, product thinking, experimentation, and iterative development in AI-enabled organizational contexts.

The course also includes applied discussions, company cases, and company visits aimed at connecting conceptual frameworks with real-world organizational practices and contemporary AI adoption challenges.

At the end of the course, students will be able to:

• Explain the evolution of Information Systems in the context of artificial intelligence and digital transformation;
• Describe the role of data, algorithms, and digital infrastructures in contemporary organizations;
• Identify the main technological components underlying AI-enabled systems, including cloud architectures, APIs, platforms, and enterprise systems;
• Discuss the managerial and organizational implications of AI adoption across business functions;
• Illustrate the economic and strategic relevance of data-driven business models and digital platforms;
• Recognize the opportunities and limitations of machine learning and generative AI in managerial decision-making;
• Analyze the governance, ethical, and societal implications associated with AI-enabled organizations;
• Explain the principles of digital innovation, agile experimentation, and service design in organizational contexts;
• Interpret organizational challenges related to the implementation and management of Information Systems and AI solutions;
• Connect conceptual frameworks and technological developments to contemporary business cases and organizational practices.

At the end of the course, students will be able to:

• Analyze how organizations use Information Systems and artificial intelligence to support business processes, decision-making, and innovation;
• Evaluate the organizational and managerial implications of AI adoption in different business contexts;
• Apply conceptual frameworks to interpret digital transformation initiatives and AI-enabled business models;
• Assess the opportunities, risks, and limitations associated with data-driven and AI-supported organizational practices;
• Interpret the role of technological infrastructures, enterprise systems, and digital platforms in organizational settings;
• Discuss business cases involving the implementation of Information Systems and AI solutions;
• Identify organizational challenges related to governance, ethics, and change management in AI-enabled environments;
• Compare alternative approaches to digital innovation, agile experimentation, and service design;
• Collaborate in group activities and discussions focused on contemporary digital and AI-related organizational problems;
• Communicate analyses and recommendations using appropriate managerial and technological terminology;
• Develop critical perspectives on the societal and organizational impact of artificial intelligence and digital technologies.

• Lectures
• Guest speaker's talks (in class or in distance)
• Company visits
• Practical Exercises

The course adopts an integrated teaching approach aimed at combining conceptual understanding with practical application. Topics are presented through a coherent narrative structure that progressively connects Information Systems, artificial intelligence, digital transformation, and organizational change. Each topic is developed by linking theoretical frameworks with concrete business applications, allowing students to understand both the conceptual foundations and their relevance in real organizational contexts.

The teaching methods used in the course include:

• Lectures
  Lectures introduce and discuss the main conceptual frameworks, managerial perspectives, and technological foundations addressed in the course. Topics are presented through an integrated and progressive storyline that connects the different dimensions of Information Systems and AI-enabled organizations, helping students develop a structured understanding of the subject area.
• Guest Speakers’ Talks
  Guest speakers from companies and professional environments contribute to the course by presenting real business cases, organizational experiences, and current challenges related to digital transformation and AI adoption. These sessions provide students with direct exposure to managerial practices, industry perspectives, and contemporary applications of the concepts discussed during lectures.
• Company Visits
  Company visits allow students to observe how digital technologies, Information Systems, and AI solutions are implemented in organizational settings. Through direct interaction with professionals and organizational environments, students are encouraged to connect theoretical concepts with operational practices and organizational realities.
• Practical Exercises and Case Discussions
  Practical exercises, applied activities, and case discussions are used to support the interpretation and application of course concepts. Students analyze organizational scenarios, discuss managerial implications, and reflect on the opportunities and challenges associated with AI-enabled transformation processes. These activities encourage active participation, critical thinking, and collaborative learning.

The combination of these teaching methods is intended to bridge theoretical perspectives and practical understanding, supporting students in developing analytical and managerial competences that can be applied in real-world organizational contexts.

The final grade is based on a final individual written exam combined with continuous assessment during the course.

Continuous assessment includes active participation in class discussions, case study contributions, and in-class activities.

Participation is evaluated based on quality and consistency of engagement, including contributions to discussions on IS and AI-related topics with our guest speakers or during the company visits

The final assignment integrates course concepts and reflects both theoretical understanding and applied insights developed during class interaction.

Continuous assessment may have a moderate impact on the final grade (e.g., as a weighting component).

The final grade is based exclusively on a final individual assignment/project and no in-class participation or continuous assessment is required or considered.

The final assignment evaluates theoretical understanding and applied knowledge of the course content independently of class participation

Class materials provided by the instructor and selected readings

Course materials will be available from EGEA (both digital and printed formats) by the end of September