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
Summary:
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
Syllabus:
Class meets Tuesdays & Fridays 10:00AM-11:45AM.
Class 1: Introduction (5/9)
Intro to class, DS, course overview
Class 2: Evaluating Product Ideas: Guidebook for Decision Making (5/12)
How do we design Minimum Viable Products for AI- and ML-based products?
Class 3: Evaluating Product Ideas: A Case-Study of AI Imaging (5/16)
How do we decide whether to invest further (post MVP / in general) in a product?
How do we decide on how to expand a product?
Guest Speakers: David Golan (VizAI), Eyal Gura (Zebra).
Class 4: Managing and Scaling Data Science (5/19)
What are considerations for managing and scaling DS teams?
Guest Speaker: Shaquille Vayda (Lux Capital)
Class 5: Building Experimentation Capabilities in Your Organization (5/23)
How can we build technical capabilities in a company to experiment and learn as fast as possible?
Guest Speaker: Su Wang (Lyft)
Class 6: The Economics of Data Companies and the Investor's Perspective (5/26)
How is the economics of DS/ML companies?
How defensible are their core assets?
Guest Speakers: Martin Casado (Andreessen Horowitz), James Currier (nfx)
Class 7: Cross-Functional Data Science Teams (5/30)
What are the possible org structures and collaboration methods to maximize DS cross-functional productivity?
Guest Speaker: Niva Ran (Apple)
Class 8: Artificial Intelligence: Trends, Opportunities, and Challenges (6/2)
With generative AI in mind, what are the specific challenges and opportunities ahead?
Guest Speakers: Guido Imbens (Stanford), Henrique Ponde (OpenAI)
Class 9: Student Presentations and Final Observations (6/6)
