Tepper · Undergraduate
70-445 Artificial Intelligence for Business Leaders
Course I built
Where AI creates value in a business, where it does not, and how to explain the difference to the people paying for it.
An undergraduate course on how AI is changing organizations and the decisions managers make. The first part covers how AI works, from expert systems to machine learning, neural networks, and large language models. The second part puts students to work with AI agents on business problems. The third looks at AI in marketing, finance, people analytics, operations, and strategy, and at the ethics, economics, and regulation that decide whether adoption lasts. The semester project has three tracks. Teams can act as an AI investment committee for a real public company, build and red-team an AI agent, or try to earn $100 with a business that uses AI.
| Transactions a day | 100,000 |
|---|---|
| Fraud rate | 0.5% |
| Transactions the model flags | 1,490 |
| Flags that are real fraud | 495 |
| Cost of a review / a missed fraud | $12 / $400 |
At a 0.5% fraud rate, buying pays. Sell the same model to a client with 0.01% fraud and the decision flips.
Topics
- What AI is, and why now
- A short history of AI
- Rules, search, and expert systems
- The five tribes of machine learning
- Machine learning fundamentals
- Neural networks and deep learning
- How large language models work
- Agentic AI and AI workflows, with a lab building a multi-agent customer support system
- Software development with AI assistance
- AI in marketing, finance, people analytics, and operations
- Bias and fairness
- The economics of AI, and AI strategy
- Governance, risk, and regulation
How the course works
The agents lab sits at the center of the course. Students build a customer support team of AI agents for a fictional outdoor retailer, a manager and three specialists, then test it to find where it fails.
The history of AI is taught through its predictions and its failures, with a lot of Pittsburgh in it: Newell and Simon's Logic Theorist, the Navlab van, XCON. In the third class a coding agent built puzzle solvers live while students placed their bets. A solver a student wrote by hand in 2017 scored 34 of 96 on Raven's Matrices. By the end of class the language model solver had about 86, and it claimed 99% confidence on every answer. The finished version, on the Coding with AI Projects page, scores 93.
Students learn to judge where AI creates value and to defend a recommendation to executives. Much of the work is written that way: response memos, a briefing on a current AI topic, and in-class exercises like the fraud vendor above.
Final projects
The first final projects are due in December 2026, in three tracks: an AI investment committee for a real public company, building and breaking an AI agent, and trying to make $100 with an AI-enabled business. Revenue is not the grade. A team that earns nothing and can explain exactly why will outscore a team that earns $200 without insight.
How the course developed
I started designing this course in February 2026 and taught it for the first time on August 25, 2026. It is open to undergraduates with no prerequisites. About forty students enrolled in the first section, and several had already used AI at work during a summer internship.
My first plan built the opening weeks around Pedro Domingos' The Master Algorithm and included a block on AI hardware and compute. Before the term began I dropped the hardware block, added a session on rules, search, and expert systems, and moved agentic AI and software development with AI assistance up to weeks five and six, so students work with agents before the course turns to AI in each business function.
Offerings
- Fall 2026
There is no required textbook. Enrolled students find the materials on Canvas.