About

Professor Melissa Valentine is an Associate Professor of Management Science and Engineering at Stanford University and a Senior Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI). Her research focuses on how emerging technologies, including artificial intelligence and algorithms, are transforming work and organizations. Her studies examine topics such as flash teams, AI-powered organizations, algorithmic management, and the development of new algorithmic capabilities in organizations. As a Senior Fellow at Stanford HAI, she advances interdisciplinary research on the intersection of AI and organizational design. Her scholarship has earned numerous accolades, including the NSF CAREER Award and multiple best paper awards from leading management and HCI conferences. Her work has been published in premier journals such as Organization Science, Management Science, and Administrative Science Quarterly, and is frequently featured in outlets like The New York Times, The Wall Street Journal, Harvard Business Review, and Wired.

Research topics

• Computer Science
• Political Science
• Knowledge management
• Sociology
• Psychology
• Public relations
• Law
• Computer Security
• Engineering
• Business
• Nursing
• Medical education
• Medicine
• Communication
• Management
• Cognitive psychology
• Social psychology
• Data science

Selected publications

• Flash Teams

  The MIT Press eBooks · 2025-10-07 · 2 citations

  book1st authorCorresponding

  A dramatic new future of work in which managers assemble exactly the expertise they need—within minutes. Gone are the days of static organizational charts and staffing based on the manager’s rolodex and intuition. Now you can recruit any expertise you need from a global online network within minutes: an on-demand, on-the-spot expert at the exact moment that you need their help. You can right-size their involvement, too; some of those experts give a second opinion or a moment of brainstorming, whereas others join as full-fledged team members for a sustained collaborative effort. This is the future promised by flash teams, a model that The New York Times has already praised for its “revolutionary potential”: a world where experts are available anytime and everywhere, where remote work has become a norm, and where AI is in the loop to guide team decisions. In Flash Teams, award-winning management scholar Melissa Valentine and computer scientist Michael Bernstein chart the opportunities of flash teams and navigate the challenges that teams and managers will face. They distill lessons from their own work assembling and managing flash teams on demand that every manager can learn from so they can successfully use flash teams in their own organizations. Drawing on original research and industry examples, this book will help readers to: Industries are already being transformed by this new approach to teaming. Flash Teams arms leaders, managers, and entrepreneurs with the tools they need to accomplish their goals with confidence, speed, and agility.

  PublisherDOI

• Virtually Even: Status Equalizing in Distributed Organizations

  Organization Science · 2025-07-23 · 2 citations

  article

  In distributed organizations, perceived status differences between workers are ubiquitous and harmful. Yet research suggests that once they are formed, status beliefs in organizations become entrenched in hierarchies and are hard to dismantle. In an inductive qualitative study, we observed how established status differences between remote and in-person workers in distributed organizations dissolved during the initial stages of the COVID-19 pandemic when everyone began working remotely. We use these data to theorize a novel status-equalizing process through which remote workers came to see themselves on an “equal playing field” with their in-person peers. We theorize how this status equalizing occurred through workers’ changing their “in-person default” use of technology—that is, their new behavior challenged embedded cultural practices that had treated the in-person workplace experience as the standard, normal, and valued perspective, implicitly guiding how employees used technology. Workers adopted new and more inclusive technology practices—including the use of asynchronous communication, greater codification of work, and virtual socializing—which resulted in remote workers perceiving new and more equal communication standards, access to information, and opportunity for social connection. As a result, these workers reported feeling less negatively stereotyped and treated more fairly in their virtual interactions with colleagues, fostering feelings of inclusion and deepening relationships across the previously established status divide. At a time when many organizations are grappling with the challenges of distributed, remote, and hybrid work, our research illuminates how inclusive technology practices can help nullify entrenched status imbalances.

  PublisherDOI

• When an AI “Agentforce” enters the workforce: generative AI, employment relations, and the changing social contract

  Journal of Organization Design · 2025-10-01 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Minds and Machines: Expertise in an Age of Intelligent Machines

  Academy of Management Proceedings · 2024-07-09

  article

  Intelligent machines are transforming the nature of knowledge, skills, and expertise, challenging many of our assumptions about work and organizing. Researchers have long emphasized the impact of emerging technologies on reshaping interactions within organizations and occupational communities. From paper mill operators with software systems (Zuboff, 1988), radiologists with computerized CT scanners (Barley, 1986), librarians with internet search (Nelson & Irwin, 2014), and NASA scientists with open-source innovation (Lifshitz- Assaf, 2018) scholars have found that the introduction of digital technologies can occasion changes to occupational identities and trouble the boundaries of domain knowledge within and between organizations. However, our understanding of expertise in the era of machine learning, algorithms, and AI is still nascent. Unlike previous digital technologies, intelligent machine applications can handle complex decision-making tasks and analysis of large amounts of structured and unstructured data, disintermediating the tasks of managers and workers (Kellogg et al., 2020; Murray et al., 2021; Faraj et al., 2018). As such, recent calls for research emphasize the need for more theorizing on expertise and more empirical studies on how workers, occupational communities, and organizations can adapt to and cultivate the skills needed in this new world of work (Heimstädt et al., 2023; Nicolini et al., 2022). Therefore, this symposium provides new perspectives and insights at the nexus of intelligent machines and the evolving nature of knowledge, skills, and expertise. It will consist of two conceptual and three empirical papers that grapple with differing forms of intelligent technologies and their impacts. In concert, these presentations foreground and question the assumptions and heuristics that scholars of work, management, and organizing have traditionally held preceding the proliferation of intelligent machines. This symposium is designed to encourage discussion and integrate diverse theoretical and methodological approaches to the evolving landscape of work and technology. How Autographic Affiliations Shape Patterns Of Technology Use Author: Callen Anthony; New York U. Ethical Expertise in the Era of Fair Algorithms in Organizations Author: Sarah Lebovitz; U. of Virginia Author: Emmanouil Gkeredakis; IESE Business School Monsters of Our Own Creation: AI, Occupational Cannibalization, and the Future of Work Author: Kevin Woojin Lee; U. of British Columbia Integrated Organizational Training in the Age of Artificial Intelligence Author: Hatim A. Rahman; Northwestern Kellogg School of Management Characteristics as a Complement to Process: Theorizing Skill in an Age of Intelligent Machines Author: Matt Beane; U. of California, Santa Barbara

  PublisherDOI

• Design Choices: Examining the Interplay of Organizational Structure and Digital Technologies

  Academy of Management Proceedings · 2024-07-09

  article

  This panel symposium aims to revisit our assumptions about organizational structure in light of the ever-increasing digitalization of the world of work and the ways in which design choices underpinning digital technologies inform organizing. Although scholars have continuously updated theories of organizational structure, which first emerged in the context of manufacturing technologies, technological advances have made ordinary what was once exceptional. Digital work technologies enable more fluid, dynamic, or scalable structures; in some work settings, these technologies are synonymous with the organization itself. Thus, it is imperative to examine whether and how interface design heuristics and decisions—imbued with specific values, beliefs, and ideologies—affect organizational structure. Given that the examination of design principles—and actual design—of digital technologies is largely the purview of neighboring fields, the symposium brings together experts in Organization Theory, Technology, and Work with scholars in Design Thinking and Human-Computer Interaction to build connections and generate insights into the impact of digital technologies on the structure of work and organizing.

  PublisherDOI

• The Algorithm and the Org Chart: How Algorithms Can Conflict with Organizational Structures

  Proceedings of the ACM on Human-Computer Interaction · 2024-11-07 · 3 citations

  articleOpen access1st authorCorresponding

  Algorithms are introducing changes to individuals? jobs, but do algorithms also lead to changes in the structures of organizations themselves? Organizational structures, as often formalized into organization (org) charts, are meant to facilitate coordinated decision-making. Yet our 10-month ethnographic study of a large online retail company reveals why the organizational structures that facilitate effective decision-making by humans may be in tension with the organizational structures that facilitate effective decision-making using algorithms. Our findings show that the human decision-makers needed small, divided-up sets of decisions, and they had previously accomplished this through how they structured individuals' roles and teams in the org chart. In contrast, when data scientists developed a new algorithm and first deployed it within organizational structures meant to support human decision-making, they realized that these small divided-up decision spaces were arbitrarily constraining the algorithm's search space. When not constrained in this manner, the algorithm could identify and recommend better solutions, but those optimal solutions did not always align with the structure of roles and teams in the org chart. This study suggests that as algorithms are integrated into the workplace, organization designs may begin to more explicitly reflect the contours of those algorithms' behaviors.

  PublisherOA PDFDOI

• Constructing a Classification Scheme - and its Consequences: A Field Study of Learning to Label Data for Computer Vision in a Hospital Intensive Care Unit

  Proceedings of the ACM on Human-Computer Interaction · 2024-11-07

  articleOpen access1st authorCorresponding

  Research on data annotation for artificial intelligence (AI) has demonstrated that biases, power, and culture impact the ways that annotators apply labels to data and subsequently affect downstream AI systems. However, annotators can only apply labels that are available to them in the annotation classification scheme. Drawing on a 3-year ethnographic study of an R&D collaboration between medical and AI researchers, we argue that the construction of the classification schema itself -- decisions about what kinds of data can and cannot be collected, what activities can and cannot be detected in the data, what the possible annotation classes ought to be, and the rules by which an item ought to be classified into each class -- dramatically shape the annotation process, and through it, the AI. We draw on Bowker and Star's [9] classification theory to detail how the creation of a training data codebook for a computer vision algorithm in hospital intensive care units (ICUs) evolved from its original, clinically-driven goal of classifying complex clinical activities into a narrower goal of identifying physical objects and simpler activities in the ICU. This work reinforces how trade-offs and decisions made long before annotators begin labeling data are highly consequential to the resulting AI system.

  PublisherOA PDFDOI

• “We Have to Equip Them to Sell It”: How External Activists Help Internal Agents Implement Change

  Academy of Management Proceedings · 2024-07-09

  articleSenior author

  To enact social movement goals and create social change, external activists often wish to bring about change within target organizations. Yet, research demonstrates that organizational change is difficult. Even if there are internal advocates who are motivated to enact the desired social change, these internal change agents often face barriers to mobilizing support. In this paper, we ask: How do external activists enable internal change agents in target organizations to implement desired social changes? Using a 15-month ethnography of an organization attempting to spread caregiver support benefits by influencing target organizations through insider allies, this paper develops theory about social movements and organizational change. Specifically, we identify practices through which external activists help internal agents overcome positional barriers to implementing change. Our findings demonstrate how the external activists strategically developed catalyzing resources, which we define as resources deployed by an external actor to facilitate internal agents’ implementation of organizational change. Further, the external activists equipped internal change agents to mobilize these catalyzing resources to overcome their limited power and authority in implementing change – including selling the change to executives and employees, and measuring impact to sustain devoted resources. We thus illustrate how the barriers faced by internal agents created opportunities for external activists to help those agents and in doing so, strategically influence change implementation.

  PublisherDOI

• Legitimating Illegitimate Practices: How Data Analysts Compromised Their Standards to Promote Quantification

  Organization Science · 2023 · 12 citations

  Senior authorCorresponding
  • Sociology
  • Political Science
  • Computer Science

  Prior studies that examine how new expertise becomes integrated into organizations have shown that different occupations work to legitimate their new expertise to develop credibility and deference from other organizational groups. In this study, we similarly examine the work that an expert occupation did to legitimate their expertise; however, in this case, they were legitimating practices that they actually considered illegitimate. We report findings from our 20-month ethnography of data analysts at a financial technology company to explain this process. We show that the company had structured data analytics in ways similar to Bechky’s idea of a captive occupation: They were dependent on their collaborators’ cooperation to demonstrate the value of data analytics and accomplish their work. The data analysts constantly encountered or were asked to provide what they deemed to be illegitimate data analysis practices such as hacking, peeking, and poor experimental design. In response, they sometimes resisted but more often reconciled themselves to the requests. Notably, they also explicitly lowered their stated standards and then worked to legitimate those now illegitimate versions of their expert practices through standardization, technology platforms, and evangelizing. Our findings articulate the relationship between captive occupations and conditions wherein experts work to legitimate what they consider illegitimate practices.

  PublisherDOI

• The Humanness of Work in an Era of Artificial Intelligence

  Academy of Management Proceedings · 2023-07-24

  article

  With the increasing development and adoption of artificial intelligence (AI) technologies, the nature of work across today’s organizations has been fundamentally changing. One central area of concern has revolved around what the rationalization of work through AI technologies might portend for work’s humanness: a conceptually vague, if evocative and deeply meaningful, dimension of our sense of distinctiveness as a species. To explore how AI will impact the “humanness of work,” and given that so much of the existing discourse on AI has been “largely speculative”, we gathered a set of empirical studies that have gone out into this new world of work and observed what has been happening across today’s workplaces. The insights illustrate that on one hand, AI can threaten work’s humanness, as embodied in workers’ expertise, the “soulness” of their emotive expressive work, and the “human touch” that they add to how organizations select their human capital. On the other hand, automation and algorithms have the potential to enrich work’s humanness. Through discussing these findings as part of the symposium, we aim to derive both theoretical and practical insights on how AI can be leveraged to put human needs at the center and contribute to creating both a sustainable and effective work environment. Helping Experts Test Their Theories: How Algorithms Both Rely on and Threaten Occupational Expert Author: Melissa Valentine; Stanford U. Author: Rebecca Hinds; Stanford U. Augmenting or Automating? Breathing Life into the Uncertain Promise of AI Author: Kevin Woojin Lee; U. of British Columbia In Pursuit of Data: The Unanticipated Consequences of Feeding AI Tools Author: Elmira Van Den Broek; Stockholm School of Economics Author: Natalia Levina; New York U. Solve and Be Seen: How Workers in Deskilled Jobs Advance within and Organization Author: Matt Beane; U. of California, Santa Barbara Author: Dan Sholler; U. of California, Santa Barbara Author: Danielle Elaine Bovenberg; U. of California, Santa Barbara AI Tools as Teammates: How Algorithmic Perceptions and Emotions Shape Collective Decision-making Author: Zoe Jonassen; NYU Stern School of Business Author: Katherine C. Kellogg; MIT

  PublisherDOI

Recent grants

• CAREER: Computational work design: How networked, intelligent technologies are changing organizational design and worker experience

  NSF · $470k · 2019–2024

Frequent coauthors

• Michael S. Bernstein

  17 shared

• Amy C. Edmondson

  15 shared

• Sara J. Singer

  Center for Innovation

  12 shared

• Jonathan Clark

  Google (United States)

  10 shared

• Nancy M. Kane

  10 shared

• Bradley R. Staats

  7 shared

• Steven M. Asch

  6 shared

• Rebecca Hinds

  Stanford University

  6 shared

Education

• Ph.D., Management Science and Engineering

  Stanford University

  2008

• M.S., Management Science and Engineering

  Stanford University

  2003

• B.S., Operations Research and Industrial Engineering

  University of California, Berkeley

  1999

Awards & honors

• NSF CAREER Award