Artificial Intelligence for Business Decision

Objectif

Course Description

This course explores how recent advancements in artificial intelligence are reshaping business strategy, operations, and decision-making. Focusing on real-world applications, it examines technologies such as machine learning, large language models, placing them within the broader context of technological adoption and social transformation. Drawing on recent research, the course enables students to assess how AI generates value across various sectors. It is designed for students with a solid grounding in statistical learning and a keen interest in the intersection of emerging technologies and business dynamics.

Learning Objectives

By the end of this course, students will be able to:

1. Analyze developments in AI and assess their strategic implications for business.

2. Identify and evaluate relevant AI applications across different industries.

3. Communicate findings effectively through project work and oral presentation

Assessment

Assessment will consist of presentations during the course and one final group assignment. The final project will involve a detailed analysis of an AI application in a specific business context, integrating technical and strategic perspectives, and students will be expected to support their analysis with data either from publicly available sources or through web-scraping.

Plan

Week 1: Foundations of Machine Learning and AI in Business Week 1 introduces the fundamentals of artificial intelligence and machine learning, covering supervised, unsupervised, and reinforcement learning, while distinguishing AI from traditional business analytics. The session also draws historical parallels with previous technological shifts— such as automation and digitization—to contextualize AI’s transformative impact on industries and society.

Week 2: LLM in Decision-Making Week 2 examines how machine learning supports business decision-making, comparing traditional machine learning, deep learning, and LLMs across key criteria such as data requirements, feature engineering, model complexity, performance, interpretability, and hardware needs. The session highlights trade-offs between scalability, trustworthiness, and strategic fit, and introduces students to foundation models, LLM costs, and the emerging practice of prompt engineering.

Week 3: AI and Consulting Week 3 explores how AI is reshaping consulting practices and strategic decision-making by enhancing analysis, recommendations, and client engagement. The session examines its impact on knowledge worker productivity and addresses organizational and ethical challenges involved in adopting AI in professional services.

Week 4: AI and Corporate Finance This session explores how AI technologies are reshaping financial services, with a focus on corporate finance. Topics include investment strategies, firm valuation, and fraud detection. Students will evaluate both the opportunities and the risks of applying AI in high-stakes, datadriven environments.

Week 5: AI and Industry Transformation This week explores how AI is reshaping traditional industries such as healthcare, manufacturing, and logistics, enabling new business models, operational efficiencies, and workforce reconfiguration. The discussion will also address barriers to AI adoption, such as data limitations, regulation, and cultural resistance.

Week 6: Future Outlook In the final session, students will explore the long-term implications of AI for work, innovation, and policy. Topics include future labor markets, new types of tasks, regulation, and the balance between automation and augmentation.