Teaching AI-first business and data science approaches at Brown University

This summer, I co-taught ENGN 2120: Business Fundamentals II in Brown University's Master of Science in Innovation Management and Entrepreneurship (PRIME) program at the School of Engineering. Over eight intensive weeks, students learned how organizations work and how AI is changing them, covering corporate functions and the C-suite, governance and regulation, data science, business cases, and agentic workflows.

We built the course around five design principles. The first is that competency cannot be outsourced. AI is a tool for producing work, not a substitute for developing judgment. We also pushed students past the question "how can AI make my job faster?" toward a more entrepreneurial one: "what businesses and strategies are only possible because AI exists?" And we treated governance, data privacy, and use-case rigor as competitive advantages, not compliance checklists.

PRIME students arrive with real operational experience, so the classroom was full of lessons about how AI adoption actually works, or doesn't, inside organizations. Developing this course alongside Gaute Solaas and Keith Cotterill was a real pleasure.