Can AI Do Strategy? A Dialogue and Debate

References

  • Abbott A (1988) The System of Professions: An Essay on the Division of Expert Labor (University of Chicago Press, Chicago).CrossrefGoogle Scholar
  • Acemoglu D (2025) The simple macroeconomics of AI. Econom. Policy 40(121):13–58.CrossrefGoogle Scholar
  • Agrawal A, Gans J, Goldfarb A (2022) Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, Boston).Google Scholar
  • Allport G (1961) Pattern and Growth in Personality (Holt, Rinehart and Winston, New York).Google Scholar
  • Argyle LP, Busby EC, Fulda N, Gubler JR, Rytting C, Wingate D (2023) Out of one, many: Using language models to simulate human samples. Political Anal. 31(3):337–351.CrossrefGoogle Scholar
  • Aristidou A, Jena R, Topol EJ (2022) Bridging the chasm between AI and clinical implementation. Lancet 399(10325):620.CrossrefGoogle Scholar
  • Barney J, Reeves M (2024) AI won’t give you a new sustainable advantage: But using it may amplify the ones you already have. Harvard Bus. Rev. (September–October 2024), https://hbr.org/2024/09/ai-wont-give-you-a-new-sustainable-advantage.Google Scholar
  • Barrett L, Bajaj VS, Kingan RJ (2025) Can LLMs find a needle in a haystack? A look at anomaly detection language modeling. Christodoulopoulos C, Chakraborty T, Rose C, Peng V, eds. Findings Assoc. Comput. Linguistics: EMNLP 2025 (Association for Computational Linguistics, Stroudsburg, PA), 6428–6435.CrossrefGoogle Scholar
  • Bonelli M (2025) Data-driven investors. Rev. Financial Stud., ePub ahead of print October 13, https://doi.org/10.1093/rfs/hhaf078.Google 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
  • Burgelman RA (1983) A process model of internal corporate venturing in the diversified major firm. Admin. Sci. Quart. 28(2):223–244.CrossrefGoogle Scholar
  • Burgelman RA (1991) Intraorganizational ecology of strategy making and organizational adaptation. Organ. Sci. 2(3):239–262.LinkGoogle Scholar
  • Csaszar FA (2025) Unbounding rationality: Why AI is a fundamental issue for strategy. Preprint, submitted September 8, https://doi.org/10.2139/ssrn.5454634.Google Scholar
  • Csaszar FA, Laureiro-Martínez D (2018) Individual and organizational antecedents of strategic foresight: A representational approach. Strategy Sci. 3(3):513–532.LinkGoogle Scholar
  • Csaszar FA, Rhee L (2025) The power and limits of distributed representations in strategic decision-making. Strategy Sci. Forthcoming.LinkGoogle Scholar
  • Csaszar FA, Steinberger T (2022) Organizations as artificial intelligences: The use of artificial intelligence analogies in organization theory. Acad. Management Ann. 16(1):1–37.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
  • Cui Z, Li N, Zhou H (2025) A large-scale replication of scenario-based experiments in psychology and management using large language models. Nature Comput. Sci. 5(8):627–634.CrossrefGoogle Scholar
  • Cyert RM, March JG (1963)ABehavioral Theory of the Firm (Prentice-Hall, Englewood Cliffs, NJ).Google Scholar
  • De Freitas J, Nave G, Puntoni S (2025) Ideation with generative AI—In consumer research and beyond. J. Consumer Res. 52(1):18–31.CrossrefGoogle Scholar
  • Dell’Acqua F, McFowland E III, Mollick E, Lifshitz H, Kellogg KC, Rajendran S, Krayer L, Candelon F, Lakhani KR (2026) Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Organ. Sci. Forthcoming.LinkGoogle Scholar
  • Dell’Acqua F, Ayoubi C, Lifshitz H, Sadun R, Mollick E, Mollick L, Han Y, et al. (2025) The cybernetic teammate: A field experiment on generative AI and teamwork. Harvard Business School Working Paper No. 25–043, Harvard Business School, Boston.Google Scholar
  • DiMaggio PJ, Powell WW (1983) The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. Amer. Sociol. Rev. 48(2):147–160.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
  • Eisenhardt KM, Kahwajy JL, Bourgeois LJ (1997) Conflict and strategic choice: How top management teams disagree. California Management Rev. 39(2):42–62.CrossrefGoogle Scholar
  • Elhage N, Hume T, Olsson C, Schiefer N, Henighan T, Kravec S, Hatfield-Dodds Z, et al. (2022) Toy models of superposition. Preprint, submitted September 21, https://arxiv.org/abs/2209.10652.Google Scholar
  • Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, Hanken H, et al. (2020) Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics 10(6):430.CrossrefGoogle Scholar
  • Felin T, Holweg M (2024) Theory is all you need: AI, human cognition, and causal reasoning. Strategy Sci. 9(4):346–371.LinkGoogle Scholar
  • Felin T, Singell M (2026) Technology: Theory-based experimentation and combinatorial salience. Eur. Econom. Rev. 181:105186.CrossrefGoogle Scholar
  • Felin T, Zenger T (2017) The theory-based view: Economic actors as theorists. Strategy Sci. 2(4):258–271.LinkGoogle Scholar
  • Felin T, Sako M, Hullman J (2025) Artificial intelligence and actor-specific decisions. Preprint, submitted June 3, https://doi.org/10.2139/ssrn.5279401.Google Scholar
  • Friis S, Riley JW (2025) Performance or principle: Resistance to artificial intelligence in the US labor market. Harvard Business School Working Paper No. 26–017, Harvard Business School, Boston.CrossrefGoogle Scholar
  • Fu HY, Shrivastava A, Moore J, West P, Tan C, Holtzman A (2025) AbsenceBench: Language models can’t tell what’s missing. Preprint, submitted June 13, https://arxiv.org/abs/2506.11440.Google Scholar
  • Gruver N, Finzi M, Qiu S, Wilson AG (2023) Large language models are zero-shot time series forecasters. Adv. Neural Inform. Processing Systems 36:19622–19635.Google Scholar
  • Gius L (2025) Disagreement predicts startups success: Evidence from venture competitions. Strategy Sci. 10(2):93–108.LinkGoogle Scholar
  • Göttlich D, Loibner D, Jiang G, Voth H-J (2025) History LLMs. Technical report. https://github.com/DGoettlich/history-llms.Google Scholar
  • Handa K, Tamkin A, McCain M, Huang S, Durmus E, Heck S, Mueller J, et al. (2025) Which economic tasks are performed with AI? Evidence from millions of Claude conversations. Preprint, submitted February 11, https://doi.org/10.48550/arXiv.2503.04761.Google Scholar
  • Hansen AL, Horton JJ, Kazinnik S, Puzzello D, Zarifhonarvar A (2024) Simulating the survey of professional forecasters. Preprint, submitted February 10, https://doi.org/10.2139/ssrn.5066286.Google Scholar
  • Hambrick DC (1994) Top management groups: A conceptual integration and reconsideration of the “team” label. Res. Organ. Behav. 16:171–213.Google Scholar
  • Hao Q, Xu F, Li Y, Evans J (2026) Artificial intelligence tools expand scientists’ impact but contract science’s focus. Nature 649(8099):1237–1243.CrossrefGoogle Scholar
  • Heshmati M, Csaszar FA (2024) Learning strategic representations: Exploring the effects of taking a strategy course. Organ. Sci. 35(2):453–473.LinkGoogle Scholar
  • Hewitt L, Ashokkumar A, Ghezae I, Willer R (2024) Predicting results of social science experiments using large language models (August 8), https://samim.io/dl/Predicting%20results%20of%20social%20science%20experiments%20using%20large%20language%20models.pdf.Google Scholar
  • Hodges A (1983) Alan Turing: The Enigma (Simon & Schuster, New York).Google Scholar
  • Hsieh C-P, Sun S, Kriman S, Acharya S, Rekesh D, Jia F, Zhang Y, Ginsburg B (2024) RULER: What’s the real context size of your long-context language models? Preprint, submitted December 5, https://arxiv.org/abs/2404.06654.Google Scholar
  • Hwang AH-C (2022) Too late to be creative? AI-empowered tools in creative processes. Barbosa S, Lampe C, Appert C, Shamma DA, eds. CHI EA'22: CHI Conf. Human Factors Comput. Systems Extended Abstracts (Association for Computing Machinery, New York), 1–9.Google Scholar
  • Johnson G, Melin L, Whittington R (2003) Micro strategy and strategizing: Towards an activity-based view. J. Management Stud. 40(1):3–22.CrossrefGoogle Scholar
  • Kahneman D (2018) Comment on “artificial intelligence and behavioral economics.” The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 608–610.Google Scholar
  • Kahneman D, Sibony O, Sunstein CR (2021) Noise: A Flaw in Human Judgment (Little, Brown & Co., New York).Google Scholar
  • Kambhampati S (2024) Can large language models reason and plan? Ann. New York Acad. Sci. 1534(1):15–18.CrossrefGoogle Scholar
  • Kiciman E, Ness R, Sharma A, Tan C (2023) Causal reasoning and large language models: Opening a new frontier for causality. Preprint, submitted April 28, https://doi.org/10.48550/arXiv.2305.00050.Google Scholar
  • Kim J, Lai S, Scherrer N, Aguera y Arcas B, Evans J (2026) Reasoning models generate societies of thought. Preprint, submitted January 15, https://arxiv.org/abs/2601.10825.Google Scholar
  • Knuth DE (1974) Computer programming as an art. Comm. ACM 17(12):667–673.CrossrefGoogle Scholar
  • Lai S, Kim J, Kunievsky N, Potter Y, Evans J (2025) Biased AI improves human decision-making but reduces trust. Preprint, submitted August 12, https://arxiv.org/abs/2508.09297.Google Scholar
  • Lakhani KR (2025) AI needs clinical trials: Harvard’s findings on democratization. [Video]. TEDxBoston https://youtu.be/Jb9b8j0-RIQ.Google Scholar
  • Lanham T, Chen A, Radhakrishnan A, Steiner B, Denison C, Hernandez D, Perez E (2023) Measuring faithfulness in chain-of thought reasoning. Preprint, submitted July 17, https://doi.org/10.48550/arXiv.2307.13702.Google Scholar
  • Lewis M, Mitchell M (2024) Evaluating the robustness of analogical reasoning in large language models. Preprint, submitted November 21, https://arxiv.org/abs/2411.14215.Google Scholar
  • Li W, Wang X, Yuan S, Xu R, Chen J, Dong Q, Xiao Y, Yang D (2025) Curse of knowledge: When complex evaluation context benefits yet biases LLM judges. Preprint, submitted September 3, https://arxiv.org/abs/2509.03419.Google Scholar
  • Liu NF, Lin K, Hewitt J, Paranjape A, Bevilacqua M, Petroni F, Liang P (2023) Lost in the middle: How language models use long contexts. Preprint, submitted July 6, https://arxiv.org/abs/2307.03172.Google Scholar
  • Madsen A, Chandar S, Reddy S (2024) Are self-explanations from large language models faithful?Findings Assoc. Comput. Linguistics ACL 2024(ACL, Stroudsburg, PA), 295–337.Google Scholar
  • Mancoridis M, Weeks B, Vafa K, Mullainathan S (2025) Potemkin understanding in large language models. Preprint, submitted June 26, https://arxiv.org/abs/2506.21521.Google Scholar
  • March JG (1991) Exploration and exploitation in organizational learning. Organ. Sci. 2(1):71–87.LinkGoogle Scholar
  • Meehl PE (1954) Clinical Versus Statistical Prediction (University of Minnesota Press, Minneapolis).Google Scholar
  • Meincke L, Girotra K, Nave G, Terwiesch C, Ulrich KT (2024) Using large language models for idea generation in innovation. Research paper, The Wharton School of the University of Pennsylvania, Philadelphia.Google Scholar
  • Mirzadeh I, Alizadeh K, Shahrokhi H, Tuzel O, Bengio S, Farajtabar M (2024) GSM-Symbolic: Understanding the limitations of mathematical reasoning in large language models. Preprint, submitted October 7, https://arxiv.org/abs/2410.05229.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
  • Nickerson JA, Wuebker R, Zenger T (2017) Problems, theories, and governing the crowd. Strategic Organ. 15(2):275–288.CrossrefGoogle Scholar
  • Olah C, Cammarata N, Schubert L, Goh G, Petrov M, Carter S (2020) Zoom in: An introduction to circuits. Distill 5(3):e00024.001.CrossrefGoogle Scholar
  • Parasuraman R, Riley V (1997) Humans and automation: Use, misuse, disuse, abuse. Human Factors 39(2):230–253.CrossrefGoogle Scholar
  • Rabanser S, Kapoor S, Kirgis P, Liu K, Utpala S, Narayanan A (2026) Towards a science of AI agent reliability. Preprint, submitted February 18, https://doi.org/10.48550/arXiv.2602.16666.Google Scholar
  • Ramachandran P, Zoph B, Le QV (2017) Searching for activation functions. Preprint, submitted October 16, https://doi.org/10.48550/arXiv.1710.05941.Google Scholar
  • Sako M, Felin T (2025) Does AI prediction scale to decision making? Comm. ACM 68(4):18–21.CrossrefGoogle Scholar
  • Shao Y, Zope H, Jiang Y, Pei J, Nguyen D, Brynjolfsson E, Yang D (2025) Future of work with AI agents: Auditing automation and augmentation potential across the US workforce. Preprint, submitted June 6, https://arxiv.org/abs/2506.06576.Google Scholar
  • Shumailov I, Shumaylov Z, Zhao Y, Papernot N, Anderson R, Gal Y (2024) AI models collapse when trained on recursively generated data. Nature 631(8022):755–759.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
  • Sutton RS, Barto AG (2018) Reinforcement Learning: An Introduction, 2nd ed. (MIT Press, Cambridge, MA).Google Scholar
  • Tang X, Zheng Z, Li J, Meng F, Zhu SC, Liang Y, Zhang M (2023) Large language models are in-context semantic reasoners rather than symbolic reasoners. Preprint, submitted May 24, https://doi.org/10.48550/arXiv.2305.14825.Google Scholar
  • Tranchero M, Brennink CF, Murugan A, Nagaraj A (2025) Theorizing with large language models. Preprint, submitted October 8, 2024, https://doi.org/10.2139/ssrn.4978831.Google Scholar
  • Turpin M, Michael J, Perez E, Bowman S (2023) Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting. Adv. Neural Inform. Processing Systems 36:74952–74965.Google Scholar
  • Wang A, Morgenstern J, Dickerson JP (2025) Large language models that replace human participants can harmfully misportray and flatten identity groups. Nature Machine Intelligence 7(3):400–411.CrossrefGoogle Scholar
  • Ward P, Schraagen JM, Gore J, Roth E (2020) The Oxford Handbook of Expertise (Oxford University Press, New York).Google Scholar
  • Whittington R (2003) The work of strategizing and organizing: For a practice perspective. Strategic Organ. 1(1):117–125.CrossrefGoogle Scholar
  • Whittington R, Cailluet L, Yakis-Douglas B (2011) Opening strategy: Evolution of a precarious profession. British J. Management 22(3):531–544.CrossrefGoogle Scholar
  • Wingate D, Burns BL, Barney JB (2025) Why AI will not provide sustainable competitive advantage. MIT Sloan Management Rev. 66(4):9–11.Google Scholar
  • Yerramilli-Rao B, Corwin J, Li Y, Lakhani KR (2025) Strategy in an era of abundant expertise.Harvard Bus. Rev.(March–April 2025), https://hbr.org/2025/03/strategy-in-an-era-of-abundantexpertise.Google Scholar
  • Zhang X, Chowdhury RR, Gupta RK, Shang J (2024) Large language models for time series: A survey. Preprint, submitted February 2, https://arxiv.org/abs/2402.01801.Google Scholar
  • Zhao C, Tan Z, Ma P, Li D, Jiang B, Wang Y, Liu H (2025) Is chain of-thought reasoning of LLMs a mirage? A data distribution lens. Preprint, submitted August 2, https://doi.org/10.48550/arXiv.2508.01191.Google 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.