The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork

Published Online:https://doi.org/10.1287/orsc.2025.20702

References

  • Agrawal A, Gans J, Goldfarb A (2018) Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, Boston).Google Scholar
  • Alchian AA, Demsetz H (1972) Production, information costs, and economic organization. Amer. Econom. Rev. 62(5):777–795.Google Scholar
  • Ancona DG, Caldwell DF (1992) Bridging the boundary: External activity and performance in organizational teams. Admin. Sci. Quart. 37(4):634–665.CrossrefGoogle Scholar
  • Anthony C, Bechky BA, Fayard AL (2023) “Collaborating” with AI: Taking a system view to explore the future of work. Organ. Sci. 34(5):1672–1694.LinkGoogle Scholar
  • Argote L (1999) Organizational Learning: Creating, Retaining and Transferring Knowledge (Kluwer Academic Publishers, Norwell, MA).Google Scholar
  • Argote L, Lee S, Park J (2021) Organizational learning processes and outcomes: Major findings and future research directions. Management Sci. 67(9):5399–5429.LinkGoogle Scholar
  • Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, Faix DJ, et al. (2023) Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Internal Medicine 183(6):589–596.CrossrefGoogle Scholar
  • Ayoubi C, Lane JN, Szajnfarber Z (2026) The two faces of expertise: How skills and experience shape the evaluation of innovation. Working paper, ESSEC Business School, Cergy, France.Google Scholar
  • Ayoubi C, Pezzoni M, Visentin F (2017) At the origins of learning: Absorbing knowledge flows from within the team. J. Econom. Behav. Organ. 134:374–387.CrossrefGoogle Scholar
  • Bailey DE, Leonardi PM, Chong J (2010) Minding the gaps: Understanding technology interdependence and coordination in knowledge work. Organ. Sci. 21(3):713–730.LinkGoogle Scholar
  • Balasubramanian N, Ye Y, Xu M (2022) Substituting human decision-making with machine learning: Implications for organizational learning. Acad. Management Rev. 47(3):448–465.CrossrefGoogle Scholar
  • Beane M (2019) Shadow learning: Building robotic surgical skill when approved means fail. Admin. Sci. Quart. 64(1):87–123.CrossrefGoogle Scholar
  • Beane M, Anthony C (2024) Inverted apprenticeship: How senior occupational members develop practical expertise and preserve their position when new technologies arrive. Organ. Sci. 35(2):405–431.LinkGoogle Scholar
  • Beaudry A, Pinsonneault A (2010) The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. MIS Quart. 34(4):689–710.CrossrefGoogle Scholar
  • Becker GS, Murphy KM (1992) The division of labor, coordination costs, and knowledge. Quart. J. Econom. 107(4):1137–1160.CrossrefGoogle Scholar
  • Bernerth JB, Beus JM, Helmuth CA, Boyd TL (2023) The more the merrier or too many cooks spoil the pot? A meta‐analytic examination of team size and team effectiveness. J. Organ. Behav. 44(8):1230–1262.CrossrefGoogle Scholar
  • Bick A, Blandin A, Deming DJ (2026) The rapid adoption of generative AI. Management Sci., ePub ahead of print January 20, https://doi.org/10.1287/mnsc.2025.02523.LinkGoogle Scholar
  • Boudreau KJ, Lacetera N, Lakhani KR (2011) Incentives and problem uncertainty in innovation contests: An empirical analysis. Management Sci. 57(5):843–863.LinkGoogle Scholar
  • Boudreau KJ, Guinan EC, Lakhani KR, Riedl C (2016) Looking across and looking beyond the knowledge frontier: Intellectual distance, novelty, and resource allocation in science. Management Sci. 62(10):2765–2783.LinkGoogle Scholar
  • Boussioux L, Lane JN, Zhang M, Jacimovic V, Lakhani KR (2025) The crowdless future? How generative AI is shaping the future of human crowdsourcing. Organ. Sci. 35(5):1589–1607.LinkGoogle Scholar
  • Brown SL, Eisenhardt KM (1995) Product development: Past research, present findings, and future directions. Acad. Management Rev. 20(2):343–378.CrossrefGoogle Scholar
  • Brynjolfsson E, Li D, Raymond LR (2025) Generative AI at work. Quart. J. Econom. 140(2):889–942.CrossrefGoogle Scholar
  • Brynjolfsson E, Mitchell T, Rock D (2018) What can machines learn and what does it mean for occupations and the economy? AEA Papers Proc. 108:43–47.CrossrefGoogle Scholar
  • Brynjolfsson E, Rock D, Syverson C (2019) Artificial intelligence and the modern productivity paradox. Agrawal A, Gans J, Goldfarb A, eds. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 23–57.CrossrefGoogle Scholar
  • Brynjolfsson E, Rock D, Syverson C (2021) The productivity J-curve: How intangibles complement general purpose technologies. Amer. Econom. J.: Macroeconomics 1(13):333–372.CrossrefGoogle Scholar
  • Callon M (1984) Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St Brieuc Bay. Law J, ed. Power, Action and Belief: A New Sociology of Knowledge? (Routledge, Boston), 196–223.CrossrefGoogle Scholar
  • Cattani G, Ferriani S, Lanza A (2017) Deconstructing the outsider puzzle: The legitimation journey of novelty. Organ. Sci. 28(6):965–992.LinkGoogle Scholar
  • Choudhary V, Marchetti A, Shrestha YR, Puranam P (2025) Human-AI ensembles: When can they work? J. Management 51(2):536–569.CrossrefGoogle Scholar
  • Cohen SG, Bailey DE (1997) What makes teams work: Group effectiveness research from the shop floor to the executive suite. J. Management 23(3):239–290.CrossrefGoogle Scholar
  • Csaszar FA (2012) Organizational structure as a determinant of performance: Evidence from mutual funds. Strategic Management J. 33(6):611–632.CrossrefGoogle Scholar
  • Csaszar FA, Ketkar H, Kim H (2024) Artificial intelligence and strategic decision-making: Evidence from entrepreneurs and investors. Strategy Sci. 9(4):322–345.LinkGoogle Scholar
  • Dahan E, Mendelson H (2001) An extreme-value model of concept testing. Management Sci. 47(1):102–116.LinkGoogle Scholar
  • De Freitas JD, Uguralp AK, Uguralp Z, Puntoni S (2024) AI companions reduce loneliness. Preprint, submitted July 26, https://doi.org/10.2139/ssrn.4893097.Google Scholar
  • Dell’Acqua F (2022) Falling asleep at the wheel: Human/AI collaboration in a field experiment on HR recruiters. Working paper, Harvard Business School, Boston.Google Scholar
  • Dell’Acqua F, Kogut B, Perkowski P (2025) Super Mario meets AI: Experimental effects of automation and skills on team performance and coordination. Rev. Econom. Statist. 107(4):951–966.CrossrefGoogle Scholar
  • Dell’Acqua F, McFowland E, Mollick ER, Lifshitz-Assaf H, Kellogg K, Rajendran S, Krayer L, Candelon F, Lakhani KR (2023) Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Working Paper 24-013, Technology and Operations Management Unit, Harvard Business School, Boston.Google Scholar
  • Deming DJ (2017) The growing importance of social skills in the labor market. Quart. J. Econom. 132(4):1593–1640.CrossrefGoogle Scholar
  • Deutsch M (1949) A theory of co-operation and competition. Human Relations 2(2):129–152.CrossrefGoogle Scholar
  • DiBenigno J, Kellogg KC (2014) Beyond occupational differences: The importance of cross-cutting demographics and dyadic toolkits for collaboration in a US hospital. Admin. Sci. Quart. 59(3):375–408.CrossrefGoogle Scholar
  • Doshi AR, Hauser OP (2024) Generative AI enhances individual creativity but reduces the collective diversity of novel content. Sci. Adv. 10(28):eadn5290.CrossrefGoogle Scholar
  • Doshi AR, Bell JJ, Mirzayev E, Vanneste BS (2025) Generative artificial intelligence and evaluating strategic decisions. Strategic Management J. 46(3):583–610.CrossrefGoogle Scholar
  • Dougherty D (1992) Interpretive barriers to successful product innovation in large firms. Organ. Sci. 3(2):179–202.LinkGoogle Scholar
  • Eloundou T, Manning S, Mishkin P, Rock D (2024) GPTs are GPTs: An early look at the labor market impact potential of large language models. Science 384(6702):1306–1308.CrossrefGoogle Scholar
  • Faraj S, Sproull L (2000) Coordinating expertise in software development teams. Management Sci. 46(12):1554–1568.LinkGoogle Scholar
  • Faraj S, Pachidi S, Sayegh K (2018) Working and organizing in the age of the learning algorithm. Inform. Organ. 28(1):62–70.CrossrefGoogle Scholar
  • Farrell H, Gopnik A, Shalizi C, Evans J (2025) Large AI models are cultural and social technologies. Science 387(6739):1153–1156.CrossrefGoogle Scholar
  • Furman J, Seamans R (2019) AI and the economy. Innovation Policy Econom. 19(1):161–191.CrossrefGoogle Scholar
  • Garud R (1997) On the distinction between know-how, know-why, and know-what. Adv. Strategic Management 14:81–101. Google Scholar
  • Girotra K, Terwiesch C, Ulrich KT (2010) Idea generation and the quality of the best idea. Management Sci. 56(4):591–605.LinkGoogle Scholar
  • Glikson E, Woolley AW (2020) Human trust in artificial intelligence: Review of empirical research. Acad. Management Ann. 14(2):627–660.CrossrefGoogle Scholar
  • Hambrick D, D’Aveni R (1992) Top team deterioration as part of downward spiral of large corporate bankruptcies. Management Sci. 38(10):1445–1466.LinkGoogle Scholar
  • Hoffmann M, Boysel S, Nagle F, Peng S, Xu K (2024) Generative AI and the nature of work. CESifo working paper, Center for Economic Studies, Ludwig-Maximilians-Universität München, Munich, Germany.Google Scholar
  • Hutchins E (1991) Organizing work by adaptation. Organ. Sci. 2(1):14–39.LinkGoogle Scholar
  • Hutchins E (1995) Cognition in the Wild (MIT Press, Cambridge, MA).CrossrefGoogle Scholar
  • Iansiti M, Lakhani KR (2020) Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World (Harvard Business Review Press, Boston).Google Scholar
  • Jacobides MG, Brusoni S, Candelon F (2021) The evolutionary dynamics of the artificial intelligence ecosystem. Strategy Sci. 6(4):412–435.LinkGoogle Scholar
  • Jeppesen LB, Lakhani KR (2010) Marginality and problem-solving effectiveness in broadcast search. Organ. Sci. 21(5):1016–1033.LinkGoogle Scholar
  • Johnson DW, Johnson RT (2005) New developments in social interdependence theory. Genetic Soc. General Psych. Monographs 131(4):285–358.CrossrefGoogle Scholar
  • Jonassen Z, He VF, von Krogh G (2026) Good lessons despite bad feelings: How boundary-spanning teams learn from collaboration failure. Organ. Sci. 37(1):17–47.LinkGoogle Scholar
  • Jones BF (2009) The burden of knowledge and the “death of the Renaissance man”: Is innovation getting harder? Rev. Econom. Stud. 76(1):283–317.CrossrefGoogle Scholar
  • Kacperczyk A, Younkin P (2017) The paradox of breadth: The tension between experience and legitimacy in the transition to entrepreneurship. Admin. Sci. Quart. 62(4):731–764.CrossrefGoogle Scholar
  • Kelley HH (1973) The processes of causal attribution. Amer. Psych. 28(2):107–128.CrossrefGoogle Scholar
  • Kellogg KC, Orlikowski WJ, Yates J (2006) Life in the trading zone: Structuring coordination across boundaries in postbureaucratic organizations. Organ. Sci. 17(1):22–44.LinkGoogle Scholar
  • Kellogg KC, Valentine MA, Christin A (2020) Algorithms at work: The new contested terrain of control. Acad. Management Ann. 14(1):366–410.CrossrefGoogle Scholar
  • Kogut B, Zander U (1992) Knowledge of the firm, combinative capabilities, and the replication of technology. Organ. Sci. 3(3):383–397.LinkGoogle Scholar
  • Kozlowski SWJ, Bell BS (2013) Work groups and teams in organizations: Review update. Schmitt N, Highhouse S, eds. Handbook of Psychology, Vol. 12: Industrial and Organizational Psychology, 2nd ed. (Wiley, Hoboken, NJ), 412–469.Google Scholar
  • Lane JN (2023) The subjective expected utility approach and a framework for defining project risk in terms of novelty and feasibility–A response to Franzoni and Stephan (2023), “uncertainty and risk-taking in science.” Res. Policy 52(3):104707.CrossrefGoogle Scholar
  • Lane JN, Boussioux L, Ayoubi C, Hao Chen Y, Lin C, Spens R, Wagh P, Wang PH (2026) The narrative AI advantage? A field experiment on AI-augmented evaluations of early-stage innovations. Working Paper No. 25-001, Harvard Business School, Boston.Google Scholar
  • Latané B, Williams K, Harkins S (1979) Many hands make light the work: The causes and consequences of social loafing. J. Personality Soc. Psych. 37(6):822–832.CrossrefGoogle Scholar
  • Latour B (1987) Science in Action: How to Follow Scientists and Engineers Through Society (Harvard University Press, Cambridge, MA).Google Scholar
  • Latour B (2007) Reassembling the Social: An Introduction to Actor-Network-Theory (Oxford University Press, Oxford, UK).Google Scholar
  • Lazar M, Lifshitz H, Ayoubi C, Emuna H (2025) Would Archimedes shout “eureka” with algorithms? The hidden hand of algorithmic design in idea generation, the creation of ideation bubbles, and how experts can burst them. Acad. Management J. 68(5):881–906.CrossrefGoogle Scholar
  • Lazer D, Katz N (2003) Building effective intra-organizational networks: The role of teams. Working paper, Northeastern University, Boston.Google Scholar
  • Lebovitz S, Lifshitz-Assaf H, Levina N (2022) To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organ. Sci. 33(1):126–148.LinkGoogle Scholar
  • Leonardi P, Neeley T (2022) The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI (Harvard Business Review Press, Boston).Google Scholar
  • Levina N, Vaast E (2005) The emergence of boundary spanning competence in practice: Implications for implementation and use of information systems. MIS Quart. 29(2):335–363.CrossrefGoogle Scholar
  • Levinthal DA (1997) Adaptation on rugged landscapes. Management Sci. 43(7):934–950.LinkGoogle Scholar
  • Li JZ, Herderich A, Goldenberg A (2024) Skill but not effort drive GPT overperformance over humans in cognitive reframing of negative scenarios. Working paper, Harvard University, Cambridge, MA.Google Scholar
  • Li D, Raymond LR, Bergman P (2026) Hiring as exploration. Rev. Econom. Stud. 93(2):1200–1240.Google Scholar
  • Li H, Zhang R, Lee Y-C, Kraut RE, Mohr DC (2023) Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being. NPJ Digital Medicine 6(1):236.CrossrefGoogle Scholar
  • Lindbeck A, Snower DJ (2000) Multitask learning and the reorganization of work: From Tayloristic to holistic organization. J. Labor Econom. 18(3):353–376.CrossrefGoogle Scholar
  • Malle BF (2006) How the Mind Explains Behavior: Folk Explanations, Meaning, and Social Interaction (MIT Press, Cambridge, MA).Google Scholar
  • March JG (1991) Exploration and exploitation in organizational learning. Organ. Sci. 2(1):71–87.LinkGoogle Scholar
  • March JG, Simon HA (1958) Organizations (John Wiley & Sons, New York).Google Scholar
  • McElheran K, Li JF, Brynjolfsson E, Kroff Z, Dinlersoz E, Foster L, Zolas N (2024) AI adoption in America: Who, what, and where. J. Econom. Management Strategy 33(2):375–415.CrossrefGoogle Scholar
  • Mollick E (2024) Co-Intelligence (Random House, London).Google Scholar
  • Nelson RR, Winter SG (1982) An Evolutionary Theory of Economic Change (Harvard University Press, Cambridge, MA).Google Scholar
  • Nickerson JA, Zenger TR (2004) A knowledge-based theory of the firm—The problem-solving perspective. Organ. Sci. 15(6):617–632.LinkGoogle Scholar
  • Noy S, Zhang W (2023) Experimental evidence on the productivity effects of generative artificial intelligence. Science 381(6654):187–192.CrossrefGoogle Scholar
  • Orlikowski WJ (2002) Knowing in practice: Enacting a collective capability in distributed organizing. Organ. Sci. 13(3):249–273.LinkGoogle Scholar
  • Otis N, Clarke RP, Delecourt S, Holtz D, Koning R (2024) The uneven impact of generative AI on entrepreneurial performance. Preprint, submitted January 17, https://doi.org/10.2139/ssrn.4671369.Google Scholar
  • Page SE (2019) The Diversity Bonus: How Great Teams Pay off in the Knowledge Economy (Princeton University Press, Princeton, NJ).Google Scholar
  • Peng S, Kalliamvakou E, Cihon P, Demirer M (2023) The impact of AI on developer productivity: Evidence from GitHub copilot. Preprint, submitted February 13, https://arxiv.org/abs/2302.06590.Google Scholar
  • Puranam P (2018) The Microstructure of Organizations (Oxford University Press, New York).CrossrefGoogle Scholar
  • Raisch S, Fomina K (2025) Combining human and artificial intelligence: Hybrid problem-solving in organizations. Acad. Management Rev. 50(2):441–464.CrossrefGoogle Scholar
  • Raisch S, Krakowski S (2021) Artificial intelligence and management: The automation–augmentation paradox. Acad. Management Rev. 46(1):192–210.CrossrefGoogle Scholar
  • Raj M, Seamans R (2019) Primer on artificial intelligence and robotics. J. Organ. Design. 8(1):11.CrossrefGoogle Scholar
  • Randazzo S, Joshi A, Kellogg KC, Lifshitz H, Dell’Acqua F, Lakhani KR (2025a) GenAI as a power persuader: How professionals get persuasion bombed when they attempt to validate LLMs. Working paper, Warwick Business School, Coventry, UK.Google Scholar
  • Randazzo S, Lifshitz-Assaf H, Kellogg K, Dell’Acqua F, Mollick ER, Candelon F, Lakhani KR (2025b) Cyborgs, centaurs and self-automators: The three modes of human-GenAI knowledge work and their implications for skilling and the future of expertise. The Wharton School Research Paper, Harvard Business School Working Paper, (26-036), 26-036.Google Scholar
  • Retelny D, Robaszkiewicz S, To A, Lasecki WS, Patel J, Rahmati N, Doshi T, Valentine M, Bernstein MS (2014) Expert crowdsourcing with flash teams.Proc. 27th Annual ACM Sympos. User Interface Software Tech. (Association for Computing Machinery, New York), 75–85.Google Scholar
  • Riedl C, Weidmann B (2025) Quantifying human-AI synergy. Working paper.Google Scholar
  • Rivkin JW (2000) Imitation of complex strategies. Management Sci. 46(6):824–844.LinkGoogle Scholar
  • Singh J, Fleming L (2010) Lone inventors as sources of breakthroughs: Myth or reality? Management Sci. 56(1):41–56.LinkGoogle Scholar
  • Souitaris V, Peng B, Zerbinati S, Shepherd DA (2023) Specialists, generalists, or both? Founders’ multidimensional breadth of experience and entrepreneurial ventures’ fundraising at IPO. Organ. Sci. 34(2):557–588.LinkGoogle Scholar
  • Stein MK, Newell S, Wagner EL, Galliers RD (2015) Coping with information technology. MIS Quart. 39(2):367–392.CrossrefGoogle Scholar
  • Steiner ID (1972) Group Process and Productivity (Academic Press, New York).Google Scholar
  • Tarafdar M, Cooper CL, Stich JF (2019) The technostress trifecta—Techno eustress, techno distress and design: Theoretical directions and an agenda for research. Inform. Systems J. 29(1):6–42.CrossrefGoogle Scholar
  • Teodoridis F (2018) Understanding team knowledge production: The interrelated roles of technology and expertise. Management Sci. 64(8):3625–3648.LinkGoogle Scholar
  • Terwiesch C, Loch CH (2004) Collaborative prototyping and the pricing of custom-designed products. Management Sci. 50(2):145–158.LinkGoogle Scholar
  • Terwiesch C, Xu Y (2008) Innovation contests, open innovation, and multiagent problem solving. Management Sci. 54(9):1529–1543.LinkGoogle Scholar
  • Valentine M, Bernstein M (2025) Flash Teams: Leading the Future of AI-Enhanced, On-Demand Work (MIT Press, Cambridge, MA).CrossrefGoogle Scholar
  • Valentine MA, Edmondson AC (2015) Team scaffolds: How mesolevel structures enable role-based coordination in temporary groups. Organ. Sci. 26(2):405–422.LinkGoogle Scholar
  • Vuori TO, Huy QN (2016) Distributed attention and shared emotions in the innovation process: How Nokia lost the smartphone battle. Admin. Sci. Quart. 61(1):9–51.CrossrefGoogle Scholar
  • Wang D, Huang D, Shen H, Uzzi B (2026) A large-scale comparison of divergent creativity in humans and large language models. Nature Human Behav. 10:531–540.CrossrefGoogle Scholar
  • Weber RA, Camerer CF (2003) Cultural conflict and merger failure: An experimental approach. Management Sci. 49(4):400–415.LinkGoogle Scholar
  • Weidmann B, Deming DJ (2020) Team players: How social skills improve group performance. NBER Working Paper No. 27071, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Wiener N (1948) Cybernetics: Or Control and Communication in the Animal and the Machine (MIT Press, Cambridge, MA).Google Scholar
  • Wiener N (1950) The Human Use of Human Beings: Cybernetics and Society, 1st ed. (Houghton Mifflin, Boston).Google Scholar
  • Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in production of knowledge. Science 316(5827):1036–1039.CrossrefGoogle Scholar
  • Xiao C, Cai J, Zhao W, Lin B, Zeng G, Zhou J, Zheng Z, Han X, Liu Z, Sun M (2025) Densing law of LLMs. Nature Machine Intelligence 7:1823–1833.CrossrefGoogle Scholar
  • Zander U, Kogut B (1995) Knowledge and the speed of the transfer and imitation of organizational capabilities: An empirical test. Organ. Sci. 6(1):76–92.LinkGoogle Scholar
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