GSB OIT 351 "AI Strategy"

AI and Data Science: Strategy, Management and Entrepreneurship

Stanford Graduate School of Business

Winter 2024

Instructors: Kuang Xu & Luis Voloch

UPDATE: the deadline for non-GSB interest form registration has passed. Thank you all for the interest, but we are no longer taking applications. We will not respond to email inquiries for enrollment at this point. 


How can one best put data science and AI to work in a modern company and manage data science teams effectively? Leaning on the emerging theory and best practices, we will examine companies at various sizes and stages, from seed through IPO, and study real-life cases to understand how companies should leverage data, data science and machine learning, build effective teams, core competencies, and competitive advantages. We will draw similarities and contrasts between regular technology and data-science-heavy companies in terms of management, technical risks, and economics, and more. The students will learn how to reason about the cost and benefits of building up a data science capability within a company, how to best manage teams to maximize performance and innovation, as well as how to evaluate the value creation through data and AI from the perspective of investors. We will have several AI entrepreneurs, executives, and investors participating in discussions.

Prerequisites: Basic fluency in Python, ML and data analysis is highly recommended, though not strictly necessary.  While the class will mainly focus on the managerial and strategic aspects of AI and data science, we expect the students to have basic fluency in mathematics and quantitative reasoning. 

Stanford Explore Courses: Link

Past Iterations: Spring 2023

Associate Professor of Operations, Information & Technology
Stanford GSB

Lecturer, Stanford GSB; Entrepreneur & Cofounder of Immunai


Class meets Tuesdays & Fridays 1:15-2:35PM.

Class 1-2: Introduction and Fundamentals

Class 3-4: Evaluating Product Ideas: Guidebook for Decision Making  

Class 5-6: Evaluating Product Ideas: A Case-Study of AI Imaging 

Class 7-8: Managing and Scaling Data Science  

Class 9-10: Building Experimentation Capabilities in Your Organization

Class 11-12: The Economics of Data Companies and the Investor's Perspective

Class 13-14: Cross-Functional Data Science Teams

Class 15-16: Artificial Intelligence: Trends, Opportunities, and Challenges 

Class 17-18: Course Project Demo and Final Observations