NBER WORKING PAPER SERIES THE CYBERNETIC TEAMMATE: A FIELD EXPERIMENT ON GENERATIVE AI RESHAPING TEAMWORK AND EXPERTISE Fabrizio Dell'Acqua Charles Ayoubi Hila Lifshitz Raffaella Sadun Ethan Mollick Lilach Mollick Yi Han Jeff Goldman Hari Nair Stewart Taub Karim Lakhani Working Paper 33641 http://www.nber.org/papers/w33641 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 April 2025 We thank Ramona Pop for her critical help managing the experiment. We thank Andrea Dorbu, Bandy Chin, Corey Gelb-Bicknell, Hadi Abbas, Michael Menietti, Sarah Stegall-Rodriguez and Vishnu Kulkarni for very helpful support and research assistance, and Brent Hecht for thoughtful comments. We used Claude and ChatGPT for light copyediting. All errors are our own. Funding for this research was provided in part by Harvard Business School. Procter & Gamble provided financial support to the Digital Data Design Institute (D^3) through gifts to Harvard Business School during the period 2023-2025. Karim Lakhani received compensation as a consultant for Procter & Gamble from the period 2021-2022.The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2025 by Fabrizio Dell'Acqua, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stewart Taub, and Karim Lakhani. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise Fabrizio Dell'Acqua, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stewart Taub, and Karim Lakhani NBER Working Paper No. 33641 April 2025 JEL No. M15, M2, O3, O31, O33 ABSTRACT We examine how artificial intelligence transforms the core pillars of collaboration—performance, expertise sharing, and social engagement—through a pre-registered field experiment with 776 professionals at Procter & Gamble, a global consumer packaged goods company. Working on real product innovation challenges, professionals were randomly assigned to work either with or without AI, and either individually or with another professional in new product development teams. Our findings reveal that AI significantly enhances performance: individuals with AI matched the performance of teams without AI, demonstrating that AI can effectively replicate certain benefits of human collaboration. Moreover, AI breaks down functional silos. Without AI, R&D professionals tended to suggest more technical solutions, while Commercial professionals leaned towards commercially-oriented proposals. Professionals using AI produced balanced solutions, regardless of their professional background. Finally, AI’s language-based interface prompted more positive self-reported emotional responses among participants, suggesting it can fulfill part of the social and motivational role traditionally offered by human teammates. Our results suggest that AI adoption at scale in knowledge work reshapes not only performance but also how expertise and social connectivity manifest within teams, compelling organizations to rethink the very structure of collaborative work. Fabrizio Dell'Acqua Harvard Business School fdellacqua@hbs.edu Charles Ayoubi ESSEC Business School charlesayoubi13@gmail.com Hila Lifshitz Digital Data Design Institute at Harvard hila.lifshitz-assaf@wbs.ac.uk Raffaella Sadun Harvard University Harvard Business School and NBER rsadun@hbs.edu Ethan Mollick The Wharton School, University of Pennsylvania emollick@wharton.upenn.edu Lilach Mollick The Wharton School, University of Pennsylvania lmollick@gmail.com Yi Han Procter & Gamble han.y.7@pg.com Jeff Goldman Procter & Gamble goldman.js@pg.com Hari Nair Procter & Gamble nair.h.1@pg.com Stewart Taub Procter & Gamble taub.sl@pg.com Karim Lakhani Harvard University klakhani@hbs.edu A randomized controlled trials registry entry is available at https://www.socialscienceregistry.org/trials/13603 1 Introduction Teamwork is the cornerstone of modern organizations. Whether designing a new product, solving strategic challenges, or orchestrating large-scale innovation, human collaboration has traditionally been central to achieving higher-quality results than individuals working alone. There are three fundamental pillars upon which the justification for teamwork relies. The first is performance: teamwork is more effective than individual work and allows for more complex problems to be tackled (Ancona and Caldwell, 1992; Lindbeck and Snower, 2000; Wuchty et al., 2007; Deming, 2017; Weidmann and Deming, 2020). The second is expertise sharing and knowledge complementarities: teamwork allows people with different expertise to come together and work on the same problem in an effective way (Kogut and Zander, 1992; Argote, 1999; Nickerson and Zenger, 2004). Finally, human sociality: people enjoy connecting with other people, which increases their motivation to work (Deutsch, 1949; Kozlowski and Bell, 2013; Johnson and Johnson, 2005). Despite significant research on how teamwork and collaborations function, we know remarkably little about how these core pillars hold up when an emerging technology enters the equation: artificial intelligence (AI). The integration of AI into knowledge work poses a foundational challenge: while AI, particularly Generative AI (GenAI), has demonstrated the capacity to enhance individual creativity, productivity, and decision-making (Noy and Zhang, 2023; Dell’Acqua et al., 2023b; Brynjolfsson et al., 2025; Peng et al., 2023), its ramifications for teambased collaboration remain largely unexplored. Prior work has treated AI primarily as a tool, like a spreadsheet or calculator, that can be used to enhance performance. But a unique aspect of Large Language Models, the most common form of GenAI, is that they are trained on human language and often act more like a person than a machine (Mollick, 2024). This leads to a key question: can GenAI fill the role of humans in teamwork? We examine this by moving past considering AI as a mere tool, but instead ask whether it can provide some of the same benefits of human teamwork, namely collective performance, expertise sharing, and social connection. To address these questions, we designed a large-scale field experiment exploring three main dimensions. 1) Does GenAI provide the performance gains traditionally attributed to teamwork? 2) Does GenAI enable a broadening of expertise even when employees lack certain specialized knowledge and skills? Finally, 3) Can GenAI offer the kind of social engagement that we typically associate with human collaboration? Put simply, to what extent can AI be treated as a "cybernetic teammate," rather than as yet another software tool? 3 Our research addresses these questions through a unique field experiment and organizational upskilling program involving 776 experienced professionals at Procter & Gamble (P&G), a global consumer packaged goods company. Participants engaged in their company’s standardized new product development process, randomly assigned to one of four conditions, in a 2x2 experimental design: (1) an individual working without GenAI, (2) a team of two humans without GenAI, (3) individuals with GenAI, and (4) a team of two humans plus GenAI. All teams comprised one Commercial professional and one R&D professional, ensuring authentic cross-functional collaboration that reflects real-world organizational structures. Each individual or team was assigned to develop a new solution to address a real business need for their business unit, ensuring they could leverage their domain expertise on the business needs they regularly target in their work. Within this framework, we focus on three main outcomes that map onto the pillars of teamwork. First, we examine performance: Can AI help people produce high-quality work at scale, potentially with less time invested or more thorough exploration of solutions? Second, we look at expertise: Does AI enable participants to breach typical functional boundaries—for instance, allowing R&D professionals to produce commercially viable ideas or commercial professionals to propose technically sound solutions? Third, we measure human sociality. While this can take many forms, we operationalize it as the emotional dimensions of the collaborative experience. Specifically, we ask: To what extent does AI actually affect emotional experiences—such as excitement, engagement, or frustration—that traditionally emerge from human-to-human interaction? Our findings show that AI replicates many of the benefits of human collaboration, acting as a “cybernetic teammate.”1 Individuals with AI produce solutions at a quality level comparable to two-person teams, indicating that AI can indeed stand in for certain collaborative functions. Digging deeper, the adoption of AI also broadens the user’s reach in areas outside their core expertise. Workers without deep product development experience, for example, can leverage AI’s suggestions to bridge gaps in knowledge or domain understanding, effectively replicating the knowledge integration typically achieved through human collaboration. This has the potential to diminish functional boundaries, democratizing expertise within teams and organizations. 1 The term draws from Norbert Wiener’s foundational work on cybernetics, which describes feedback-regulated systems that dynamically adjust their behavior in response to environmental inputs. Rather than simply automating tasks, such systems modify their functioning through iterative feedback loops, a property that makes them capable of participating in collaborative processes (Wiener, 1948, 1950). 4