GSB OIT 351 "AI Strategy"

AI and Data Science: Strategy, Management and Entrepreneurship

Stanford Graduate School of Business

Instructors: Kuang Xu & Luis Voloch
Course Assistant: Dylan Daniels


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.

Associate Professor of Operations, Information & Technology
Stanford GSB

Lecturer, Stanford GSB; Entrepreneur & Cofounder of Immunai


Class meets Tuesdays & Fridays 10:00AM-11:45AM.

Class 1: Introduction (5/9)

Class 2: Evaluating Product Ideas: Guidebook for Decision Making (5/12)

Class 3: Evaluating Product Ideas: A Case-Study of AI Imaging (5/16)

Class 4: Managing and Scaling Data Science (5/19)

Class 5: Building Experimentation Capabilities in Your Organization (5/23)

Class 6: The Economics of Data Companies and the Investor's Perspective (5/26)

Class 7: Cross-Functional Data Science Teams (5/30)

Class 8: Artificial Intelligence: Trends, Opportunities, and Challenges (6/2)

Class 9: Student Presentations and Final Observations (6/6)