What should MBAs actually know about AI in marketing? Is it the psychology of AI, how consumers perceive and trust automated systems? Or something more foundational: how AI systems are built, so that students can identify good use cases, spot bad ones, and understand why a system might fail?
This course takes the second approach. It is organized around a framework grounded in how these systems are actually engineered: what does the system perceive, how is that information represented, what model operates on it, under what constraints, and what behavior results? Not to make MBAs into engineers, but to give them a basis for evaluating AI in business settings without taking it on faith.
The goal is not to build AI systems. It is to develop the judgment to assess whether a proposed use case will actually work, and to diagnose why it might not. The cases, simulations, and group project are all oriented around the same question: not "what can AI do?" but "will this work here, and how would I know?"
View SyllabusSlides are posted approximately one week after each class session.