Instructional goals
The course aims to provide students with a comprehensive understanding of how data and artificial intelligence (AI) are transforming organizations. Students will learn how data-driven strategies and AI technologies impact organizational processes, decision-making, structures, and competitive positioning.
Prerequisites
None
Intended learning outcomes
By the end of the course, students will be able to:
Understand the strategic role of data and AI within organizations.
Critically analyze how AI technologies affect business models and organizational behavior.
Design and assess data and AI initiatives aligned with organizational goals.
Identify ethical, legal, and societal implications of AI adoption in organizations.
Apply concepts through real-world case studies and propose innovative AI-driven organizational solutions.
Course Contents
This course explores the transformative role of data and artificial intelligence (AI) within organizations. It provides a comprehensive introduction to the strategic value of data, the fundamentals of AI technologies, and the ways in which organizations can leverage these tools to drive innovation, efficiency, and competitive advantage. Students will examine both the opportunities and challenges associated with AI adoption, including ethical, legal, and operational considerations. Through a combination of theoretical frameworks, real-world case studies, and practical exercises, the course aims to equip students with the critical skills needed to design, evaluate, and implement data and AI strategies in diverse organizational contexts.
Reference Books
The course does not adopt a single textbook. Instead, a combination of different materials will be used to provide a comprehensive and up-to-date overview of the field. These include:
Slides prepared by the instructor, covering the core concepts discussed in class;
Selected readings from scientific papers, offering deeper insights into specific topics and recent developments;
Case studies and real-world examples, used to illustrate practical applications and organizational challenges related to data and AI;
Web-based resources and industry reports, providing current perspectives on trends, technologies, and best practices.
All materials will be either distributed during class or made available through the course’s online platform. Additional readings may be suggested to students interested in exploring specific topics in greater depth.
Teaching Methods
1) Lectures with interactive discussions
2) Case study analysis
3) Group projects and in-class exercises
4) Guest speakers from industry
Assessment Method
Student performance will be primarily assessed through a final written exam (70%) and a group project (30%).
The final exam will evaluate each student’s individual understanding of the course content, combining multiple-choice questions with open-ended essays to test both factual knowledge and critical thinking.
The group project will require students to design an AI integration plan for a real or hypothetical organization, fostering collaboration, strategic analysis, and practical application of the theories discussed in class.
Although it will not directly affect the grade, active participation in class discussions, workshops, and activities will be closely monitored and taken into consideration when evaluating borderline cases or awarding honors. Active engagement is strongly encouraged as it enriches the learning experience for all.
Thesis assignment criteria
Students interested in writing their thesis in connection with the "Data, AI and Organizations" course are welcome to apply.
Theses will be assigned based on the following criteria:
Academic Performance: Priority will be given to students who have demonstrated a solid understanding of the course material, particularly through strong exam and project results.
Research Proposal: Students must submit a brief proposal (1-2 pages) outlining their intended topic, research questions, and methodology. Originality, feasibility, and relevance to course themes (data strategies, AI implementation, organizational transformation) will be key factors in the evaluation.
Interest and Motivation: Demonstrated interest in data, AI, and organizational topics, either through coursework, participation, or extracurricular activities, will be considered positively.
Supervisor Availability: The number of available thesis slots may be limited; therefore, assignments will also depend on the instructor’s supervision capacity.
Preference will be given to students proposing empirical or case-study-based theses, but theoretical research projects will also be considered.
Students are encouraged to contact the instructor well in advance to discuss potential topics and to align expectations.
Week 1
Introduction to Data, AI, and Organizations:
Definitions, historical context, and current trends.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 2
The Strategic Value of Data:
Data as a resource, data-driven decision making.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 3
Fundamentals of Artificial Intelligence,
Key concepts in AI: machine learning, natural language processing, computer vision.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 4
AI Technologies in Business: AI applications across industries and functional areas.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 5
Data Governance and Management.
Policies, privacy, data quality, and stewardship.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 6
Organizational Change and AI Adoption. Managing transformation, organizational readiness, change management.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 7
AI in Decision-Making Processes. Augmented intelligence, automation of managerial tasks.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 8
AI in Decision-Making Processes.
Augmented intelligence, automation of managerial tasks.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 9
Designing AI Strategies.
Building AI roadmaps, linking AI to business strategy.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 10
Ethical and Legal Aspects of AI.
Bias, fairness, transparency, regulation.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 11
Human-AI Collaboration.
Interaction between humans and intelligent systems, augmentation vs automation.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.
Week 12
Risks and Challenges in AI Implementation
Technological risks, operational risks, cybersecurity issues.
Teaching Materials: Slides prepared by the instructor, along with additional readings, will be shared during class sessions or uploaded to the online learning platform.