Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality

Published Online:https://doi.org/10.1287/orsc.2025.21838

References

  • Acemoglu D, Autor D (2011) Skills, tasks and technologies: Implications for employment and earnings. Card D, Ashenfelter O, eds. Handbook of Labor Economics, vol. 4 (Elsevier, Amsterdam), 1043–1171.Google Scholar
  • Agrawal A, Gans J, Goldfarb A (2018) Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Press, Boston).Google Scholar
  • Ali R, Tang OY, Connolly ID, Fridley JS, Shin JH, Zadnik Sullivan PL, Cielo D, et al. (2023) Performance of ChatGPT, GPT-4, and Google Bard on a neurosurgery oral boards preparation question bank. Neurosurgery 93(5):1090–1098.CrossrefGoogle Scholar
  • Allen R, Choudhury P (2022) Algorithm-augmented work and domain experience: The countervailing forces of ability and aversion. Organ. Sci. 33(1):149–169.LinkGoogle Scholar
  • Anthony C, Bechky BA, Fayard A-L (2023) “Collaborating” with AI: Taking a system view to explore the future of work. Organ. Sci. 34(5):1672–1694.LinkGoogle Scholar
  • Aral S, Brynjolfsson E, Van Alstyne M (2012) Information, technology, and information worker productivity. Inform. Systems Res. 23(3 Part 2):849–867.LinkGoogle Scholar
  • Athey S, Bryan K, Gans J (2020) The allocation of decision authority to human and artificial intelligence. AEA Papers Proc. 110(1):80–84.CrossrefGoogle Scholar
  • Bailey DE, Faraj S, Hinds PJ, Leonardi PM, von Krogh G (2022) We are all theorists of technology now: A relational perspective on emerging technology and organizing. Organ. Sci. 33(1):1–18.LinkGoogle Scholar
  • Barrett M, Oborn E, Orlikowski WJ, Yates J (2012) Reconfiguring boundary relations: Robotic innovations in pharmacy work. Organ. Sci. 23(5):1448–1466.LinkGoogle Scholar
  • Beane M (2019) Shadow learning: Building robotic surgical skill when approved means fail. Admin. Sci. Quart. 64(1):87–123.CrossrefGoogle Scholar
  • Beane MI, Leonardi PM (2025) Pace layering as a metaphor for organizing in the age of intelligent technologies: Considering the future of work by theorizing the future of organizing. J. Management Stud. 62(5):2025–2052.CrossrefGoogle Scholar
  • Berg JM, Raj M, Seamans R (2023) Capturing value from artificial intelligence. Acad. Management Discoveries 9(4):424–428.CrossrefGoogle Scholar
  • Blandin A, Bick 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
  • Boiko DA, MacKnight R, Kline B, Gomes G (2023) Autonomous chemical research with large language models. Nature 624(7992):570–578.CrossrefGoogle Scholar
  • Boussioux L, Lane JN, Zhang M, Jacimovic V, Lakhani KR (2024) The crowdless future? Generative AI and creative problem-solving. Organ. Sci. 35(5):1589–1607.LinkGoogle Scholar
  • Brynjolfsson E, Jin W, McElheran K (2021) The power of prediction: Predictive analytics, workplace complements, and business performance. Bus. Econom. 56(4):217–239.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(1):43–47.CrossrefGoogle Scholar
  • Buçinca Z, Malaya MB, Gajos KZ (2021) To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proc. ACM Human-Comput. Interaction 5(CSCW1):188.CrossrefGoogle Scholar
  • Çalli E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K (2021) Deep learning for chest X-ray analysis: A survey. Medical Image Anal. 73(2):102125.CrossrefGoogle Scholar
  • Choi JH, Schwarcz D (2024) AI assistance in legal analysis: An empirical study. J. Legal Ed. 73(2):384–420.Google Scholar
  • Choudhary V, Marchetti A, Shrestha YR, Puranam P (2023) Human-AI ensembles: When can they work? J. Management 51(2):536–569.Google Scholar
  • Cowgill B, Dell’Acqua F, Deng S, Hsu D, Verma N, Chaintreau A (2020) Biased programmers? Or biased data? A field experiment in operationalizing AI ethics. Proc. 21st ACM Conf. Econom. Comput. (EC '20) (Association for Computing Machinery, New York), 679–681.Google Scholar
  • Davies A, Veličković P, Buesing L, Blackwell S, Zheng D, Tomašev N, Tanburn R, et al. (2021) Advancing mathematics by guiding human intuition with AI. Nature 600(7887):70–74.CrossrefGoogle Scholar
  • Dell’Acqua F (2022) Falling asleep at the wheel: Human/AI collaboration in a field experiment on HR recruiters. Working paper, Laboratory for Innovation Science, 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
  • DeStefano T, Kellogg K, Menietti M, Vendraminelli L (2022) Why providing humans with interpretable algorithms may, counterintuitively, lead to lower decision-making performance. MIT Sloan Research Paper No. 6797, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Drucker PF (1959) Landmarks of Tomorrow: A Report on the New Post Modern World (Routledge, Abingdon-on-Thames, UK).Google Scholar
  • Eloundou T, Manning S, Mishkin P, Rock D (2024) GPTs are GPTs: Labor market impact potential of LLMs. Science 384(6702):1306–1308.CrossrefGoogle Scholar
  • Faraj S, Leonardi PM (2022) Strategic organization in the digital age: Rethinking the concept of technology. Strategic Organ. 20(4):771–785.CrossrefGoogle Scholar
  • Felten EW, Raj M, Seamans R (2023) Occupational heterogeneity in exposure to generative AI. Preprint, submitted April 10, https://doi.org/10.2139/ssrn.4414065.Google Scholar
  • Feuerriegel S, Shrestha YR, von Krogh G, Zhang C (2022) Bringing artificial intelligence to business management. Nature Machine Intelligence 4(7):611–613.CrossrefGoogle Scholar
  • Furman J, Seamans R (2019) AI and the economy. Innovation Policy Econom. 19(1):161–191.CrossrefGoogle Scholar
  • Gaessler F, Piezunka H (2023) Training with AI: Evidence from chess computers. Strategic Management J. 44(11):2724–2750.CrossrefGoogle Scholar
  • Geerling W, Mateer GD, Wooten J, Damodaran N (2023) ChatGPT has aced the test of understanding in college economics: Now what? Amer. Economist 68(2):233–245.CrossrefGoogle Scholar
  • Glaeser E, Hillis A, Kim H, Kominers SD, Luca M (2024) Decision authority and the returns to algorithms. Strategic Management J. 45(4):619–648.CrossrefGoogle 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.LinkGoogle Scholar
  • Humlum A, Vestergaard E (2025) The unequal adoption of ChatGPT exacerbates existing inequalities among workers. Proc. Natl. Acad. Sci. USA 122(1):e2414972121.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 Press, Boston).Google Scholar
  • Karpathy A (2024) Commentary on jagged capabilities of large language models. Twitter (July 25, 2024), https://x.com/karpathy/status/1816531576228053133.Google Scholar
  • Karpathy A (2025) 2025 LLM year in review. Accessed February 3, 2026, https://karpathy.bearblog.dev/year-in-review-2025/.Google Scholar
  • Kellogg KC (2022) Local adaptation without work intensification: Experimentalist governance of digital technology for mutually beneficial role reconfiguration in organizations. Organ. Sci. 33(2):571–599.LinkGoogle Scholar
  • Kellogg KC, Myers JE, Gainer L, Singer SJ (2021) Moving violations: Pairing an illegitimate learning hierarchy with trainee status mobility for acquiring new skills when traditional expertise erodes. Organ. Sci. 32(1):181–209.LinkGoogle Scholar
  • Lebovitz S, Levina N, Lifshitz-Assaf H (2021) Is AI ground truth really true? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quart. 45(3):1501–1526.CrossrefGoogle 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
  • Lee P, Bubeck S, Petro J (2023) Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. New England J. Medicine 388(13):1233–1239.CrossrefGoogle Scholar
  • Meincke L, Girotra K, Nave G, Terwiesch C, Ulrich KT (2024) Using large language models for idea generation in innovation. Working paper, Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia.Google Scholar
  • Monisha R, Sen S, Davangeri RU, Sri Lakshmi KS, Dey S (2021) An approach toward design and implementation of distributed framework for astronomical big data processing. Udgata SK, Sethi S, Gao XZ, eds. Intelligent Systems, Lecture Notes in Networks and Systems, vol. 431 (Springer, Singapore), 267–275.Google Scholar
  • Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, Rajpurkar P (2023) Foundation models for generalist medical artificial intelligence. Nature 616(7956):259–265.CrossrefGoogle Scholar
  • Noy S, Zhang W (2023) Experimental evidence on the productivity effects of generative artificial intelligence. Science 381(6654):187–192.CrossrefGoogle Scholar
  • OpenAI (2023) GPT-4 technical report. Preprint, submitted March 15, https://arxiv.org/abs/2303.08774.Google Scholar
  • Otis NG, Delecourt S, Cranney K, Koning R (2024a) Global evidence on gender gaps and generative AI. Working Paper No. 25-023, Harvard Business School, Boston.Google Scholar
  • Otis N, Clarke R, Delecourt S, Holtz D, Koning R (2024b) The uneven impact of generative AI on entrepreneurial performance. Preprint, submitted February 27, https://doi.org/10.2139/ssrn.4671369.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
  • Pichai S (2025) Interview with Lex Fridman. Lex Fridman Podcast #471. Transcript. Accessed February 3, 2026, https://lexfridman.com/sundar-pichai-transcript.Google Scholar
  • Raisch S, Krakowski S (2021) Artificial intelligence and management: The automation–augmentation paradox. Acad. Management Rev. 46(1):192–210.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 No. 26-021, Harvard Business School, Boston.Google Scholar
  • Randazzo S, Lifshitz H, Kellogg KC, Dell Acqua F, Mollick E, 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. Working Paper No. 26-036, Harvard Business School, Boston.Google Scholar
  • Reed S, Zolna K, Parisotto E, Gomez Colmenarejo S, Novikov A, Barth-Maron G, Gimenez M, et al. (2022) A generalist agent. Preprint, submitted November 11, https://arxiv.org/abs/2205.06175.Google Scholar
  • Schaeffer R, Miranda B, Koyejo S (2023) Are emergent abilities of large language models a mirage? Adv. Neural Inform. Processing Systems, vol. 36 (Curran Associates Inc., Red Hook, NY), 55565–55581.Google Scholar
  • Sergeeva AV, Faraj S, Huysman M (2020) Losing touch: An embodiment perspective on coordination in robotic surgery. Organ. Sci. 31(5):1248–1271.LinkGoogle Scholar
  • Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, Scales N, et al. (2023) Large language models encode clinical knowledge. Nature 620(7972):172–180.CrossrefGoogle Scholar
  • Skitka LJ, Mosier KL, Burdick M (1999) Does automation bias decision-making? Internat. J. Human-Comput. Stud. 51(5):991–1006.CrossrefGoogle Scholar
  • Vaccaro M, Almaatouq A, Malone TW (2024) When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behav. 8(12):2293–2303.CrossrefGoogle Scholar
  • Vlamis K, Varanasi L, Paradis T (2024) MBB explained: How hard it is to get hired and what it’s like to work for the prestigious strategy consulting firms, McKinsey, Bain, and BCG. Bus. Insider Africa (November 29), https://africa.businessinsider.com/careers/mbb-explained-how-hard-it-is-to-get-hired-and-what-its-like-to-work-for-the/v6gf0lp.Google Scholar
  • Zhou E, Lee D (2024) Generative artificial intelligence, human creativity, and art. PNAS Nexus 3(3):pgae052.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.