Theorizing Artificial Intelligence as an Organizational Actor: Insights from a Narrative Review

This narrative review examines how artificial intelligence (AI) is being theorized as an organizational actor within management and organization studies. The paper synthesizes fragmented theoretical perspectives to develop an integrated conceptual framework that identifies the antecedents, mechanisms, and contextual conditions shaping AI organizational actorhood. A systematic search of Web of Science was conducted, yielding 47 peer-reviewed articles published between 2020 and 2026. The selected literature spans management, business, and accounting disciplines. Through thematic analysis guided by institutional theory, agency theory, and sociomateriality, the review critically synthesizes scholars' conceptualizations of AI's organizational actorhood and proposes a testable conceptual framework. Four key themes emerge: (1) AI as an institutional actor subject to and generative of institutional pressures; (2) AI as an economic agent with principal-agent dynamics; (3) AI as a socio-material ensemble co-constituting organizational realities; and (4) AI's evolving autonomy from tool to quasi-autonomous actor. The review reveals that, while AI is increasingly theorized to possess agentic qualities, conceptualizations remain fragmented across theoretical silos. This review contributes a multi-dimensional framework for theorizing AI organizational actorhood that integrates institutional, economic, and socio-material perspectives. It identifies five antecedents, three moderators, three mediators, and four control variables that collectively shape the emergence of AI as an organizational actor. For managers and policymakers, recognizing AI as an organizational actor with emergent agency necessitates rethinking governance mechanisms, accountability structures, and legitimacy strategies. This is the first narrative review to propose a comprehensive, testable conceptual framework for AI organizational actorhood, synthesizing diverse theoretical traditions to advance a coherent research agenda.

Artificial intelligence, organizational actorhood, agency theory, institutional theory, narrative review

1. Alibašić, H. (2025). A multi-paradigm ethical framework for hybrid intelligence in blockchain technology and cryptocurrency systems governance. FinTech, 4(3), 34. https://doi.org/10.3390/fintech4030034 [Google Scholar] [Crossref]

2. Arias-Pérez, J., Chacón-Henao, J., & López-Zapata, E. (2023). Unlocking agility: Trapped in the antagonism between co-innovation in digital platforms, business analytics capability and external pressure for AI adoption? Business Process Management Journal, 29(6), 1791–1809. https://doi.org/10.1108/bpmj-10-2022-0484 [Google Scholar] [Crossref]

3. Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163(120420), 120420. https://doi.org/10.1016/j.techfore.2020.120420 [Google Scholar] [Crossref]

4. Bag, S., Srivastava, G., Gupta, S., Zhang, J. Z., & Kamble, S. (2023). Climate change adaptation capability, business-to-business marketing capability and firm performance: Integrating institutional theory and dynamic capability view. Industrial Marketing Management, 115, 470–483. https://doi.org/10.1016/j.indmarman.2023.11.003 [Google Scholar] [Crossref]

5. Baharom, Z. (2025). Drivers and obstacles: A narrative review of green technology adoption in SMEs. International Journal of Research and Innovation in Social Science, 9(11), 6903–6917. https://doi.org/10.47772/ijriss.2025.91100538 [Google Scholar] [Crossref]

6. Bechky, B. A., & Davis, G. F. (2024). Resisting the algorithmic management of science: Craft and community after generative AI. Administrative Science Quarterly, 70(1), 1–22. https://doi.org/10.1177/00018392241304403 [Google Scholar] [Crossref]

7. Chakraborty, I., & Kumar, N. (2026). Trust as institutional infrastructure: Balancing isomorphism and legitimacy in the governance of GenAI for healthcare. Technological Forecasting and Social Change, 226(124615). https://doi.org/10.1016/j.techfore.2026.124615 [Google Scholar] [Crossref]

8. Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013 [Google Scholar] [Crossref]

9. Chen, Y.-A., & Dong, N. (2025). AI capabilities and export performance: the moderating role of province market development and cultural distance. International Journal of Emerging Markets, 20(12), 4907–4925. https://doi.org/10.1108/ijoem-12-2023-2014 [Google Scholar] [Crossref]

10. Cimino, A., Longo, F., Solina, V., & Veltri, P. (2026). Integrating large language models with industrial simulation for multi-level decision support: an innovation management perspective in Industry 5.0. European Journal of Innovation Management, 29(11), 27–53. https://doi.org/10.1108/ejim-10-2024-1246 [Google Scholar] [Crossref]

11. Das, P., Uditaa, & Hariprasad. (2026). Unlocking the promise of AI in personalized marketing: mapping the hidden barriers to adoption. International Journal of Quality and Service Sciences, 18(1), 122–145. https://doi.org/10.1108/ijqss-08-2025-0193 [Google Scholar] [Crossref]

12. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147. https://doi.org/10.2307/2095101 [Google Scholar] [Crossref]

13. Du, S., & Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961–974. https://doi.org/10.1016/j.jbusres.2020.08.024 [Google Scholar] [Crossref]

14. Dubey, R., Gunasekaran, A., & Papadopoulos, T. (2024). Benchmarking operations and supply chain management practices using Generative AI: Towards a theoretical framework. Transportation Research Part E: Logistics and Transportation Review, 189(103689). https://doi.org/10.1016/j.tre.2024.103689 [Google Scholar] [Crossref]

15. Fang, C., Wilkenfeld, J. N., Navick, N., & Gibbs, J. L. (2023). “AI am here to represent you”: Understanding how institutional logics shape attitudes toward intelligent technologies in legal work. Management Communication Quarterly, 37(4), 941–970. https://doi.org/10.1177/08933189231158282 [Google Scholar] [Crossref]

16. Humberd, B. K., & Latham, S. F. (2026). When AI becomes an agent of the firm: Examining the evolution of AI in organizations through an agency theory lens. The Journal of Management Studies, 63(2), 668–694. https://doi.org/10.1111/joms.13274 [Google Scholar] [Crossref]

17. Islam, M. A., Rahman, M., Dal Mas, F., Haque, S. E., & Hani, U. (2026). Navigating institutional and capability barriers in agentic artificial intelligence adoption: evidence from small and medium enterprises in Bangladesh. VINE Journal of Information and Knowledge Management Systems, 1–25. https://doi.org/10.1108/vjikms-11-2025-0504 [Google Scholar] [Crossref]

18. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405x(76)90026-x [Google Scholar] [Crossref]

19. Leavitt, K., Schabram, K., Hariharan, P., & Barnes, C. M. (2021). Ghost in the machine: On organizational theory in the age of machine learning. Academy of Management Review, 46(4), 750–777. https://doi.org/10.5465/amr.2019.0247 [Google Scholar] [Crossref]

20. Liyanage, T., Gunasekara, I., Sipnara, S., Givindi, R., & Ranathunga, S. (2025). Braving digital retail frontier through artificial intelligence: Rhetoric, reality, institutionalization. International Journal of Retail & Distribution Management, 53(6), 485–499. https://doi.org/10.1108/ijrdm-05-2024-0216 [Google Scholar] [Crossref]

21. Omrani, N., Rejeb, N., Maalaoui, A., Dabić, M., & Kraus, S. (2024). Drivers of Digital Transformation in SMEs. IEEE Transactions on Engineering Management, 71, 5030–5043. https://doi.org/10.1109/tem.2022.3215727 [Google Scholar] [Crossref]

22. Orlikowski, W. J. (2007). Sociomaterial practices: Exploring technology at work. Organization Studies, 28, 1435–1448. [Google Scholar] [Crossref]

23. Phillips, N. (2026). Fading into insignificance: Why mainstream OT scholars “missed the boat” on intelligent technologies and what we can do about it. Strategic Organization, 24(2), 237–255. https://doi.org/10.1177/14761270261423363 [Google Scholar] [Crossref]

24. Rana, N. P., Pillai, R., Sivathanu, B., & Malik, N. (2024). Assessing the nexus of Generative AI adoption, ethical considerations and organizational performance. Technovation, 135(103064). https://doi.org/10.1016/j.technovation.2024.103064 [Google Scholar] [Crossref]

25. Reis, J. F., & Pinheiro Junior, L. P. (2025). Institutional theory (IT) and diffusion of innovation (DOI): A theoretical approach on artificial intelligence (AI). Brazilian Administration Review, 22(4). https://doi.org/10.1590/1807-7692bar2025250060 [Google Scholar] [Crossref]

26. Rudko, I., Bashirpour Bonab, A., Fedele, M., & Formisano, A. V. (2025). New institutional theory and AI: Toward rethinking of artificial intelligence in organizations. Journal of Management History, 31(2), 261–284. https://doi.org/10.1108/jmh-09-2023-0097 [Google Scholar] [Crossref]

27. Sarfraz, M., Khawaja, K. F., & Waheed, Z. (2026). Business process innovation through digital strategy: unveiling the adoption of big data analytics and the evolution of digital culture. Business Process Management Journal, 32(1), 290–315. https://doi.org/10.1108/bpmj-06-2023-0457 [Google Scholar] [Crossref]

28. Schilke, O., & Reimann, M. (2025). The transparency dilemma: How AI disclosure erodes trust. Organizational Behavior and Human Decision Processes, 188(104405). https://doi.org/10.1016/j.obhdp.2025.104405 [Google Scholar] [Crossref]

29. Sposato, M., & Dittmar, E. C. (2026). Beyond resource constraints: how Ibero-American SMEs leverage AI for competitive advantage through strategic capability development. Journal of Strategy and Management, 1–20. https://doi.org/10.1108/jsma-08-2025-0303 [Google Scholar] [Crossref]

30. Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), 404–428. [Google Scholar] [Crossref]

31. Van Rijmenam, M., & Logue, D. (2021). Revising the ‘science of the organisation’: theorising AI agency and actorhood. Innovation (North Sydney, N.S.W.), 23(1), 127–144. https://doi.org/10.1080/14479338.2020.1816833 [Google Scholar] [Crossref]

32. Zhang, L., Shao, Z., Chen, B., & Benitez, J. (2024). Unraveling Generative AI Adoption in Enterprise Digital Platforms: The Effect of Institutional Pressures and the Moderating Role of Internal and External Environments. IEEE Transactions on Engineering Management, 72, 1–15. https://doi.org/10.1109/tem.2024.3513773 [Google Scholar] [Crossref]

33. Zhang, Q., Liu, Z., & Yang, S. (2025). Enhancing construction workers’ health and safety: Mechanisms for implementing Construction 4.0 technologies in construction organizations. Engineering, Construction and Architectural Management, 32(13), 68–103. https://doi.org/10.1108/ECAM-11-2024-1517 [Google Scholar] [Crossref]

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