MG4J8      Half Unit
Managing Artificial Intelligence

This information is for the 2025/26 session.

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Why and how should we manage artificial intelligence (AI) and maintain humanity? The course approaches the current and emerging managerial and strategic matters around big data, AI and robotics, covering the development and implementation of AI technologies from organisational, technical, social, economic and political viewpoints. 

The concepts and frameworks in the course provide an in-depth understanding of the designing and organising logic for AI. Students will engage in research and practice on AI management, understand the promises and perils of data-driven and algorithmic decision-making, analyse the roles of human judgment, and critically assess the implications of big data and AI technologies for individuals, organisations and the society at large.

15 hours of lectures and 13.5 hours of seminars in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

In its Ethics Code, LSE upholds a commitment to intellectual freedom. This means we will protect the freedom of expression of our students and staff and the right to engage in healthy debate in the classroom.

The formative assessments (submitted to receive oral feedback) include:

• An initial proposal (500 words) of the individual essay.
• An outline (1-page A4 document) of the team project proposal.

The first formative assessment allows students to propose a research question relevant to AI management, demonstrate knowledge of the background and related literature, and outline the key structure before substantively developing the essay. The second formative assessment allows students to apply their conceptual understanding of AI management and propose AI strategic plans to address real-world challenges.

• Acemoglu, D. (2021). Redesigning AI. Boston Review/Boston Critic Inc.
• Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Press.
• Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing Artificial Intelligence. MIS Quarterly, 45(3), 1433-1450.
• Brynjolfsson, E., & McAfee, A. (2017). Artificial Intelligence, for Real. Harvard Business Review.
• Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
• Floridi, L. (2023). The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities. Oxford University Press.
• Hosanagar, K. (2020). A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control (Illustrated edition). Penguin Books USA.
• Iansiti, M., & Lakhani, K. R. (2020). Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World (Illustrated edition). Harvard Business Review Press.
• Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation–Augmentation Paradox. Academy of Management Review, 46(1), 192-210.
• Russell, S.J., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach (Fourth Edition), Global Edition. Pearson Education.
• Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media, Inc.
• Zerilli, J. (2021). A Citizen’s Guide to Artificial Intelligence. MIT Press.

Project (40%)

This component of assessment includes an element of group work.

Essay (60%)

Students will present their team projects in the seminars and receive developmental feedback. After the final presentation, each student team will submit a project report.

For detailed assessment information, including all deadlines and timings, please see the relevant course Moodle page. Assessment timings will be available at the start of each term.