About This Course
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 Syllabus Student AI-chatbot Survey Dashboard
Lectures
Slides are posted approximately one week after each class session.
Note: In-class exercises and recordings are only available to the Georgetown community.
Week 1 · March 18
Day 1 — Introduction
What is AI? How do machines learn? Introduction to the course project.
Perception · Representation · Models · Constraints · Rule-Based vs Learning · Supervised, Unsupervised & Reinforcement Learning · LLMs
View Slides →
Study Guide →
Week 2 · March 25
Day 2 — AI Text Generation & Customer Acquisition
Case: HubSpot CLV math & bot design (disclose, voice, interface) · How LLMs work (tokenize, embed, attend, distribute, sample, output) · AI across the acquisition funnel (targeting, creatives, lead qualification)
HubSpot case · LLM architecture · Five acquisition decisions · Three ML types · HubSpot Breeze
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Study Guide →
Week 3 · April 1
Day 3 — AI Targeting, Algorithmic Fairness & Chatbot Infrastructure
Case + Dashboard: Artea (HBS #521-021) · A/B testing, ML targeting policies, algorithmic fairness (independence, separation, sufficiency) · Chatbot tech stacks, token pricing, API keys, LUCID project setup
Artea case · Uplift modeling · Fairness criteria · Token math · API infrastructure · LUCID
Slides 3A →
Slides 3B →
Study Guide 3A →
Study Guide 3B →
Week 4 · April 8
In-class: SEMrush AI Visibility Exam
Week 5 · April 15
Day 5 — AI-Driven Pricing & Privacy
Case + Spreadsheet: PittaRosso (HBS #522-046) · Guest speaker & AI Visibility
Posted after class
Week 6 · April 22
Day 6 — Group Project Presentations
Team presentations · Peer evaluations
Posted after class