Skill Deprioritization: Reorganizing in the Age of Generative Artificial Intelligence

References

• Acemoglu D, Wolitzky A (2024) Employment and community: Socioeconomic cooperation and its breakdown. NBER Working Paper No. 32773, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Adan I, Resing J (2015) Queueing systems. Working paper, Department of Mathematics and Computing Science, Eindhoven University of Technology, Eindhoven, Netherlands.Google Scholar
• Aghion P, Bloom N, Lucking B, Sadun R, Van Reenen J (2021) Turbulence, firm decentralization, and growth in bad times. Amer. Econom. J. Appl. Econom. 13(1):133–169.Crossref, Google Scholar
• Angrist JD, Pischke JS (2009) Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Anthony C, Bechky BA, Fayard AL (2023) Collaborating with AI: Taking a system view to explore the future of work. Organ. Sci. 34(1):123–140.Google Scholar
• Autor DH, Levy F, Murnane RJ (2003) The skill content of recent technological change: An empirical exploration. Quart. J. Econom. 118(4):1279–1333.Crossref, Google Scholar
• Babina T, Fedyk A, He A, Hodson J (2024) Artificial intelligence, firm growth, and product innovation. J. Financial Econom. 151:103745.Crossref, Google Scholar
• Bailey DE, Faraj S, Hinds P, von Krogh G, Leonardi PM (2022) We are all theorists of technology now: A relational perspective on emerging technology and organizing. Organ. Sci. 33(5):1123–1140.Google Scholar
• Barley SR (1986) Technology as an occasion for structuring: Evidence from observations of CT scanners and the social order of radiology departments. Admin. Sci. Quart. 31(1):78–108.Crossref, Google Scholar
• Beaudry A, Pinsonneault A (2005) Understanding user responses to information technology: A coping model of user adaptation. MIS Quart. 29(3):493–524.Crossref, Google 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.Google Scholar
• Brynjolfsson E, Mitchell T (2017) What can machine learning do? Workforce implications. Science 358(6370):1530–1534.Crossref, Google Scholar
• Burns T, Stalker GM (1961) Mechanistic and Organic Systems (Tavistock Publications, London).Google Scholar
• Burton RM, Obel B (2004) What is an organizational design? Burton RM, Obel B, eds. Strategic Organizational Diagnosis and Design: The Dynamics of Fit, Information and Organization Design Series, vol. 4 (Springer, Boston), 43–85.Crossref, Google Scholar
• Certo ST, Busenbark JR, Kalm M, LePine JA (2020) Divided we fall: How ratios undermine research in strategic management. Organ. Res. Methods 23(2):211–237.Crossref, Google Scholar
• Chen J, Roth J (2024) Logs with zeros? Some problems and solutions. Quart. J. Econom. 139(2):891–936.Crossref, Google Scholar
• Choi S, Kang H, Kim N, Kim J (2025) How does artificial intelligence improve human decision-making? Evidence from the AI-powered Go program. Strategic Management J. 46(6):1523–1554.Crossref, Google Scholar
• Choudhary V, Marchetti A, Shrestha YR, Puranam P (2025) Human-AI ensembles: When can they work? J. Management 51(2):536–569.Crossref, Google Scholar
• Choudhury P, Starr E, Agarwal R (2020) Machine learning and human capital complementarities: Experimental evidence on bias mitigation. Strategic Management J. 41(9):1381–1411.Crossref, Google Scholar
• Cohen MD, March JG, Olsen JP (1972) A garbage can model of organizational choice. Admin. Sci. Quart. 17(1):1–25.Crossref, Google Scholar
• David PA (1990) The dynamo and the computer: An historical perspective on the modern productivity paradox. Amer. Econom. Rev. 80(2):355–361.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.Crossref, Google Scholar
• Deming D, Kahn LB (2018) Skill requirements across firms and labor markets: Evidence from job postings for professionals. J. Labor Econom. 36(S1):S337–S369.Crossref, Google Scholar
• Demirci O, Hannane J, Zhu X (2025) Who is AI replacing? The impact of generative AI on online freelancing platforms. Management Sci. 70(10):8097–8108.Link, Google Scholar
• Dixon J, Hong B, Wu L (2021) The robot revolution: Managerial and employment consequences for firms. Management Sci. 67(9):5586–5605.Link, Google Scholar
• Donaldson L, Joffe G (2014) Fit—The key to organizational design. J. Organ. Design 3(3):38–45.Crossref, Google Scholar
• Felten EW, Raj M, Seamans R (2021) Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management J. 42(12):2195–2217.Crossref, Google Scholar
• Felten EW, Raj M, Seamans R (2023) Occupational heterogeneity in exposure to generative AI. Preprint, submitted April 19, http://dx.doi.org/10.2139/ssrn.4414065.Google Scholar
• Foss NJ, Klein PG (2022) Why Managers Matter: The Perils of the Bossless Company (Hachette, London).Google Scholar
• Foss NJ, Lyngsie J, Zahra SA (2013) The role of external knowledge sources and organizational design in the process of opportunity exploitation. Strategic Management J. 34(12):1453–1471.Crossref, Google Scholar
• Gaba V, Greve HR (2019) Safe or profitable? The pursuit of conflicting goals. Organ. Sci. 30(4):647–667. Link, Google Scholar
• Garicano L (2000) Hierarchies and the organization of knowledge in production. J. Political Econom. 108(5):874–904.Crossref, Google Scholar
• Garicano L, Rossi-Hansberg E (2006) Organization and inequality in a knowledge economy. Quart. J. Econom. 121(4):1383–1435.Crossref, Google Scholar
• Glynn PW, Greve HR, Rao H (2020) Relining the garbage can of organizational decision-making: Modeling the arrival of problems and solutions as queues. Indust. Corporate Change 29(1):125–142.Crossref, Google Scholar
• Hui X, Reshef O, Zhou L (2024) The short-term effects of generative artificial intelligence on employment: Evidence from an online labor market. Organ. Sci. 35(6):1977–1989.Link, Google Scholar
• Jaimovich N, Siu HE (2012) The trend is the cycle: Job polarization and jobless recoveries. NBER Working Paper No. 18334, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Joseph J, Ocasio W (2012) Architecture, attention, and adaptation in the multibusiness firm: General Electric from 1951 to 2001. Strategic Management J. 33(6):633–660.Crossref, Google Scholar
• Joseph J, Sengul M (2025) Organization design: Current insights and future research directions. J. Management 51(1):249–308.Crossref, Google Scholar
• Kellogg KC, Valentine MA, Christin A (2020) Algorithms at work: The new contested terrain of control. Acad. Management Ann. 14(1):366–410.Google Scholar
• Kellogg KC, Lifshitz-Assaf H, Randazzo S, Mollick ER, Dell’Acqua F, McFowland E III, Candelon F, Lakhani KR (2024) Don’t expect juniors to teach senior professionals to use generative AI: Emerging technology risks and novice AI risk mitigation tactics. Harvard Business School Technology & Operations Management Unit Working Paper No. 24-074, Harvard Business School, Boston.Google Scholar
• Keren M, Levhari D (1979) The optimum span of control in a pure hierarchy. Management Sci. 25(11):1162–1172.Link, Google Scholar
• Keren M, Levhari D (1989) Decentralization, aggregation, control loss and costs in a hierarchical model of the firm. J. Econom. Behav. Organ. 11(2):213–236.Crossref, Google Scholar
• Koçak Ö, Puranam P, Yegin A (2023) Decoding cultural conflicts. Frontiers Psych. 14:1166023.Crossref, Google Scholar
• Law KK, Shen M (2025) How does artificial intelligence shape audit firms? Management Sci. 71(5):3641–3666.Link, Google Scholar
• Lawrence PR, Lorsch JW (1967) Differentiation and integration in complex organizations. Admin. Sci. Quart. 12(1):1–47.Crossref, 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. Organ. Sci. 33(1):122–139.Link, Google Scholar
• Lee MY, Edmondson AC (2017) Self-managing organizations: Exploring the limits of less-hierarchical organizing. Res. Organ. Behav. 37:35–58.Crossref, Google Scholar
• Lieberman MB, Lee GK, Folta TB (2017) Entry, exit, and the potential for resource redeployment. Strategic Management J. 38(3):526–544.Crossref, Google Scholar
• March JG, Simon HA (1958) Organizations (John Wiley & Sons, New York).Google Scholar
• Martin JA, Eisenhardt KM (2010) Rewiring: Cross-business-unit collaborations in multibusiness organizations. Acad. Management J. 53(2):265–301.Crossref, Google Scholar
• McEvily B, Soda G, Tortoriello M (2014) More formally: Rediscovering the missing link between formal organization and informal social structure. Acad. Management Ann. 8(1):299–345.Crossref, Google Scholar
• Mintzberg H (1979) The Structuring of Organizations (Pearson, Upper Saddle River, NJ).Google Scholar
• Orlikowski WJ (1992) The duality of technology: Rethinking the concept of technology in organizations. Organ. Sci. 3(3):398–427.Link, Google Scholar
• Puranam P (2018) The Microstructure of Organizations (Oxford University Press, Oxford, UK).Crossref, Google Scholar
• Puranam P (2024) Re-Humanize: How to Build Human-Centric Organizations in the Age of Algorithms (Penguin Random House SEA, Singapore).Google Scholar
• Puranam P, Alexy O, Reitzig M (2014) What’s “new” about new forms of organizing? Acad. Management Rev. 39(2):162–180.Crossref, Google Scholar
• Rambachan A, Roth J (2023) A more credible approach to parallel trends. Rev. Econom. Stud. 90(5):2555–2591.Crossref, Google Scholar
• Raveendran M, Puranam P, Warglien M (2022) Division of labor through self-selection. Organ. Sci. 33(2):810–830.Link, Google Scholar
• Raveendran M, Silvestri L, Gulati R (2020) The role of interdependence in the micro-foundations of organization design: Task, goal, and knowledge interdependence. Acad. Management Ann. 14(2):828–868.Crossref, Google Scholar
• Rerup C (2009) Attentional triangulation: Learning from unexpected rare crises. Organ. Sci. 20(5):876–893.Link, Google Scholar
• Rosenberg N, Trajtenberg M (2004) A general purpose technology at work: The Corliss steam engine in the late 19th century US. J. Econom. Hist. 64(1):61–99.Google Scholar
• Santos Silva JMC, Tenreyro S (2006) The log of gravity. Rev. Econom. Statist. 88(4):641–658.Crossref, Google Scholar
• Shrestha YR, Ben-Menahem SM, von Krogh G (2019) Organizational decision-making structures in the age of artificial intelligence. California Management Rev. 61(4):66–83.Crossref, Google Scholar
• Simon HA (1947) A comment on “The science of public administration”. Public Admin. Rev. 7(3):200–203.Crossref, Google Scholar
• Tandon P, Gupta A, Khanna T (2024) Balancing allocative and dynamic efficiency with redundant R&D allocation. Strategic Management J. 45(1):50–78.Google Scholar
• Thompson JD (1967) Organizations in Action: Social Science Bases of Administrative Theory (McGraw-Hill, New York).Google Scholar
• Tonellato M, Tasselli S, Conaldi G, Lerner J, Lomi A (2024) A microstructural approach to self-organizing: The emergence of attention networks. Organ. Sci. 35(2):496–524.Link, Google Scholar
• Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Quart. 27(3):425–478.Crossref, Google Scholar
• Waardenburg L, Huysman M, Sergeeva AV (2021) In the land of the blind, the one-eyed man is king: Knowledge brokerage in the age of learning algorithms. Organ. Sci. 32(6):1478–1498.Google Scholar
• Wang W, Gao G, Agarwal R (2024) Friend or foe? Teaming between artificial intelligence and workers with variation in experience. Management Sci. 70(9):5753–5775.Abstract, Google Scholar
• Weber M (1978) Economy and Society (University of California Press, Berkeley).Google Scholar
• Wooldridge JM (2023) Simple approaches to nonlinear difference-in-differences with panel data. Econometrics J. 26(3):C31–C66.Crossref, Google Scholar
• Wuebker R, Zenger T, Felin T (2023) The theory-based view: Entrepreneurial microfoundations, resources, and choices. Strategic Management J. 44(12):2922–2949.Crossref, Google Scholar
• Wulff JN, Sajons GB, Pogrebna G, Lonati S, Bastardoz N, Banks GC, Antonakis J (2023) Common methodological mistakes. Leadership Quart. 34(1):101677.Crossref, Google Scholar
• Yilmaz ED, Naumovska I, Aggarwal VA (2023) AI-driven labor substitution: Evidence from Google Translate and ChatGPT. Preprint, submitted April 17, http://dx.doi.org/10.2139/ssrn.4400516.Google Scholar
• Zammuto RF, Griffith TL, Majchrzak A, Dougherty DJ, Faraj S (2007) Information technology and the changing fabric of organization. Organ. Sci. 18(5):749–762.Link, Google Scholar