About

Lynn Wu is an Associate Professor at the Wharton School of the University of Pennsylvania, where she teaches MBA, undergraduate, and doctoral courses on the transformative impact of emerging technologies on business and society. Her research focuses on the intersection of artificial intelligence, analytics, and innovation, exploring how these technologies reshape business strategy, productivity, and workforce dynamics. Her work spans three core areas: AI, Labor, and Innovation, Digital Platforms, and Antitrust and Policy. She examines the effects of AI on firm-level innovation, labor outcomes, and productivity across both large enterprises and startups, and investigates how enterprise social media and AI-driven platforms influence employee performance, career trajectories, and entrepreneurial success. Additionally, she studies the antitrust implications of emerging technologies and advises various agencies such as the U.S. Department of Justice. Her research has been published in leading journals across economics, management, and computer science, earning numerous accolades including Early Career Awards from INFORMS and AIS, as well as best paper awards from prominent conferences. Lynn holds undergraduate degrees in Finance and Computer Science, a master’s degree in Computer Science, and a Ph.D. in Management Science, all from MIT. Prior to her academic career, she worked as a software engineer and research scientist at the MIT AI Lab and IBM. She has collaborated with leading technology companies, advised government organizations, and consulted for startups, combining academic rigor with industry expertise to uncover insights at the frontier of technology and business.

Research topics

• Computer Science
• Business
• Artificial Intelligence
• Sociology
• Political Science
• Finance
• Economics
• Knowledge management
• Marketing
• Management
• Computer Security
• Medicine
• Engineering
• Microeconomics
• Psychology
• Bioinformatics
• Financial system
• Data science
• Risk analysis (engineering)
• Accounting
• Labour economics
• Engineering management
• Geography
• Process management

Selected publications

• Offline Privacy Concerns in Mental Health Care Delivery Through Telehealth

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Artificial Intelligence, CEO Turnover, and Exploration Orientation in Firm Innovation

  Information Systems Research · 2025-11-21 · 1 citations

  articleSenior author

  Leadership transitions often create uncertainty in corporate innovation. Our study shows that artificial intelligence (AI) plays a crucial role in helping firms navigate these transitions. Using data on patents, job postings, and CEO turnover across U.S. public firms, we find that organizations with stronger AI investment are more successful in pursuing explorative innovation after CEO turnover. AI enables this shift by helping leaders overcome two common barriers: managerial myopia, the tendency to rely on familiar past practices, and information overload, which can overwhelm new executives. Firms that leverage AI more effectively reallocate resources to research and development, elevate innovation as a strategic priority, and launch new initiatives. These effects are especially pronounced in manufacturing industries and when CEOs have science, technology, engineering, and mathematics backgrounds. For practice, our findings highlight AI as a strategic capability that helps firms sustain organizational creativity during leadership change. For policy, they suggest that promoting AI adoption could strengthen innovation dynamism and competitiveness in rapidly changing environments.

  PublisherDOI

• Artificial Intelligence, CEO Turnover, and Exploration Orientation in Firm Innovation

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Innovation and Regulation of Emerging Technologies from a Global Perspective

  Academy of Management Proceedings · 2025-07-01

  article

  This symposium explores global challenges of innovation and regulation in the field of artificial intelligence (AI) and other emerging technologies. As a scholarly and global community, we need to focus on how innovation and regulatory approaches influence, and are influenced by, advancements in AI. The differential speeds in innovation, available venture capital and regulatory guardrails create potential for both competition and collaboration. Scholars from regions including the EU, US, China, and other parts of the world will share diverse perspectives, addressing opportunities and challenges in fostering innovation while navigating regulatory complexities. The session brings together researchers whose work bridges academic theory and practical insights. Its goal is to advance theoretical understanding and provide actionable ideas and research opportunities for academics and practitioners in the rapidly evolving AI landscape.

  PublisherDOI

• Impact of Engagement Allocation Across Social Platform Modalities on E-Commerce Performance

  SSRN Electronic Journal · 2025-01-01 · 7 citations

  preprintOpen accessSenior author
  PublisherDOI

• Artificial Intelligence, Lean Startup Method, and Product Innovations

  Management Science · 2025-08-26 · 6 citations

  articleSenior author

  Although artificial intelligence (AI) has the potential to drive significant business innovation, many firms struggle to realize its benefits. We investigate why some firms succeed in using AI for innovation, whereas others fail, focusing on the organizational support necessary for leveraging AI in both novel and incremental innovation. Specifically, we examine how the lean startup method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping—focused on developing minimum viable products—and controlled experimentation—focused on rigorous testing such as A/B testing. We find that LSM complements discovery-oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using A/B testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high-quality products in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct because different AI capabilities require distinct organizational processes to achieve optimal outcomes. This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection. Funding: Financial support from the Mack Institute for Innovation Management is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03905 .

  PublisherDOI

• Impact of Multi-Platform Social Media Strategy on Sales in E-Commerce

  ArXiv.org · 2025-03-12 · 1 citations

  preprintOpen accessSenior author

  Over the past several decades, major social media platforms have become crucial channels for e-commerce retailers to connect with consumers, maintain engagement, and promote their offerings. While some retailers focus their efforts on a few key platforms, others choose a more diversified approach by spreading their efforts across multiple sites. Which strategy proves more effective and why? Drawing on a longitudinal dataset on e-commerce social media metrics and performance indicators, we find that, all else being equal, companies with a more diversified social media strategy outperform those focusing on fewer platforms, increasing total web sales by 2 to 5 percent. The key mechanism driving this finding appears to be the complementary effect of overlapping impressions across platforms. When followers are present on multiple platforms, repeated exposure to consistent messaging reinforces brand awareness and enhances purchase intent. Our findings highlight important managerial implications for diversifying social media efforts to reach potential customers more efficiently and ultimately boost sales.

  PublisherOA PDFDOI

• Artificial Intelligence, Lean Startup Method, and Product Innovations

  ArXiv.org · 2025-06-19

  preprintOpen accessSenior author

  Although AI has the potential to drive significant business innovation, many firms struggle to realize its benefits. We examine how the Lean Startup Method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping focused on developing minimum viable products, and controlled experimentation, focused on rigorous testing such as AB testing. We find that LSM complements discovery oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using AB testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high quality product in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct, as different AI capabilities require distinct organizational processes to achieve optimal outcomes.

  PublisherOA PDFDOI

• Artificial Intelligence, Lean Startup Method, and Product Innovations

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• Innovation Strategy after IPO: How AI Analytics Spurs Innovation after IPO

  SSRN Electronic Journal · 2024-01-01 · 5 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Lorin M. Hitt

  University of Pennsylvania

  17 shared

• Erik Brynjolfsson

  National Bureau of Economic Research

  15 shared

• Bowen Lou

  8 shared

• Sinan Aral

  Massachusetts Institute of Technology

  8 shared

• Aykut Firat

  7 shared

• Fujie Jin

  Indiana University Bloomington

  7 shared

• Stuart Madnick

  Massachusetts Institute of Technology

  7 shared

• Daniele Lain

  ETH Zurich

  6 shared

Labs

• Operations, Information and Decisions DepartmentPI

Awards & honors

• Early Career Awards from INFORMS and AIS
• Best Paper Award from Information Systems Research
• Best Paper Award from ICIS
• Best Paper Award from CHITA
• Best Paper Award from Kauffman