Published August 7, 2026 | Version v1
Journal article Open

Artificial intelligence and managerial decision-making: a systematic literature review of its implications for strategic management in organizations

Authors/Creators

  • 1. ROR icon Universidad Autónoma de Nuevo León
  • 2. Universidad La Salle

Description

Artificial intelligence (AI) has been incorporated into business administration to support managerial decision-making; however, recent literature agrees that its strategic value lies not in full automation, but in augmenting human judgment, which introduces governance and responsibility challenges. The objective of this study is to systematically analyze how recent literature addresses the use of AI in managerial decision-making, integrating the constructs of decision augmentation, organizational AI governance, and managerial risk and responsibility. Methodologically, it is a systematic literature review applied to articles indexed in Scopus and Web of Science. The results show a predominance of decision-augmentation and procedural governance approaches, as well as concerns about responsibility and human-AI decision reliance. As a contribution, the study offers an integrated managerial perspective to guide responsible organizational practices regarding AI.

Abstract (Spanish)

La inteligencia artificial (IA) se ha incorporado a la administración de empresas como soporte a la toma de decisiones gerenciales; no obstante, la literatura reciente coincide en que su valor estratégico no reside en la automatización plena, sino en el aumento del juicio humano, lo que introduce desafíos de gobernanza y responsabilidad. El objetivo de este estudio es analizar sistemáticamente cómo la literatura reciente aborda el uso de la IA en la toma de decisiones gerenciales, integrando los constructos de toma de decisiones gerenciales aumentada, gobernanza organizacional de la IA y riesgo y responsabilidad gerencial. Metodológicamente, se trata de una Revisión Sistemática de Literatura aplicada a artículos indexados en Scopus y Web of Science. Los resultados evidencian un predominio de enfoques de aumento decisional y gobernanza procedimental, así como preocupaciones por la rendición de cuentas y la dependencia humano–IA. Como aporte, el estudio ofrece una lectura gerencial integrada para orientar prácticas organizacionales responsables de IA.

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References

  • Abhishek, A., Erickson, L., & Bandopadhyay, T. (2025). Data and AI governance: Promoting equity, ethics, and fairness in large language models (Versión 1). arXiv. https://doi.org/10.48550/ARXIV.2508.03970
  • Abramski, K., Citraro, S., Lombardi, L., Rossetti, G., & Stella, M. (2023). Cognitive Network Science Reveals Bias in GPT-3, GPT-3.5 Turbo, and GPT-4 Mirroring Math Anxiety in High-School Students. Big Data and Cognitive Computing, 7(3), 124. https://doi.org/10.3390/bdcc7030124
  • Abunaser, F. M., Hamd, M. M. M., Bani-Oraba, A. M. N., Hamed, O., Alshiyab, M. Q. M., & Shebani, Z. (2025). Dynamic Capabilities of University Administration and Their Impact on Student Awareness of Artificial Intelligence Tools. Sustainability, 17(15), 7092. https://doi.org/10.3390/su17157092
  • Acosta-Enriquez, B. G., Ballesteros, M. A. A., De Los Angeles Guzman Valle, M., Angaspilco, J. E. M., Blanco-García, L. E., Ventura, G. C., Requejo, J. D. C., & Torre, M. C. (2025). Determinants of AI Use in University Teachers: The Role of Leadership, Teaching Concerns, and Constructivist Pedagogical Beliefs. Human Behavior and Emerging Technologies, 2025(1), 4834893. https://doi.org/10.1155/hbe2/4834893
  • Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in AI: Progress, Challenges, and Future Directions (Versión 3). arXiv. https://doi.org/10.48550/ARXIV.2403.14680
  • Ahmad, M., Alhalaiqa, F., & Subih, M. (2023). Constructing and testing the psychometrics of an instrument to measure the attitudes, benefits, and threats associated with the use of Artificial Intelligence tools in higher education. Journal of Applied Learning and Teaching, 6(2), 114-120. Scopus. https://doi.org/10.37074/jalt.2023.6.2.36
  • Ahmed Ali Linkon, Mujiba Shaima, Md Shohail Uddin Sarker, Badruddowza, Norun Nabi, Md Nasir Uddin Rana, Sandip Kumar Ghosh, Hammed Esa, & Faiaz Rahat Chowdhury. (2024). Advancements and Applications of Generative Artificial Intelligence and Large Language Models on Business Management: A Comprehensive Review. Journal of Computer Science and Technology Studies, 6(1), 225-232. https://doi.org/10.32996/jcsts.2024.6.1.26
  • Akiba, D., & Fraboni, M. C. (2023). AI-Supported Academic Advising: Exploring ChatGPT's Current State and Future Potential toward Student Empowerment. Education Sciences, 13(9). Scopus. https://doi.org/10.3390/educsci13090885
  • Alharthi, S. (2025). Harnessing Knowledge: The Robust Role of Knowledge Management Practices and Business Intelligence Systems in Developing Entrepreneurial Leadership and Organizational Sustainability in SMEs. Sustainability, 17(14), 6264. https://doi.org/10.3390/su17146264
  • Al-Kfairy, M., Mustafa, D. G., Kshetri, N., Insiew, M., & Alfandi, O. (2024). Ethical challenges and solutions of generative AI: An interdisciplinary perspective. Informatics. https://doi.org/10.3390/informatics11030058
  • Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548
  • Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973-989. https://doi.org/10.1177/1461444816676645
  • Bacon, D. R. (2024). Recommendations for the Use of Experimental Designs in Management Education Research. Journal of Management Education, 48(6), 1121-1147. https://doi.org/10.1177/10525629241252802
  • Baothman, F. A. (2021). An Intelligent Big Data Management System Using Haar Algorithm-Based Nao Agent Multisensory Communication. Wireless Communications and Mobile Computing, 2021. Scopus. https://doi.org/10.1155/2021/9977751
  • Batool, A., Zowghi, D., & Bano, M. (2024). Responsible AI Governance: A Systematic Literature Review (Versión 1). arXiv. https://doi.org/10.48550/ARXIV.2401.10896
  • Ben-Michael, E., Greiner, D. J., Huang, M., Imai, K., Jiang, Z., & Shin, S. (2024). Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies (Versión 3). arXiv. https://doi.org/10.48550/ARXIV.2403.12108
  • Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., Pieler, M., Prashanth, U. S., Purohit, S., Reynolds, L., Tow, J., Wang, B., & Weinbach, S. (2022). GPT-NeoX-20B: An Open-Source Autoregressive Language Model. Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models, 95-136. https://doi.org/10.18653/v1/2022.bigscience-1.9
  • Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., … Liang, P. (2021). On the Opportunities and Risks of Foundation Models (Versión 3). arXiv. https://doi.org/10.48550/ARXIV.2108.07258
  • Brajovic, D., Renner, N., Goebels, V. P., Wagner, P., Fresz, B., Biller, M., Klaeb, M., Kutz, J., Neuhuettler, J., & Huber, M. F. (2023). Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development (Versión 1). arXiv. https://doi.org/10.48550/ARXIV.2307.11525
  • Buijsman, S., Carter, S. E., & Bermúdez, J.-P. (2025). Autonomy by Design: Preserving Human Autonomy in AI Decision-Support. Philosophy & Technology, 38(3), 97. https://doi.org/10.1007/s13347-025-00932-2
  • Casey, B., Farhangi, A., & Vogl, R. (2019). Rethinking explainable machines: The gdpr's 'right to explanation' debate and the rise of algorithmic audits in enterprise. Berkeley Technology Law Journal, 34(1), 143. https://doi.org/10.15779/Z38M32N986
  • Chan, C. K. Y., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10(1). Scopus. https://doi.org/10.1186/s40561-023-00284-4
  • Chang, S.-H., Yao, K.-C., Chung, C.-Y., Nien, S.-C., Chen, Y.-T., Ho, W.-S., Lin, T.-C., Shih, F.-C., & Chung, T.-C. (2023). Integration of Artificial Intelligence and Machine Learning Content in Technology and Science Curriculum. International Journal of Engineering Education, 39(6), 1343-1357. Scopus.
  • Cheng, K., & Wu, H. (2024). Policy framework for the utilization of generative AI. Critical Care, 28(1), 128.
  • Del Pozo, C., Nuno Gomes de Andrade, N., & Rojas Arroyo, D. (2023). Prototipo de políticas públicas sobre transparencia y explicabilidad de sistemas de inteligencia artificial [Informe técnico]. Open Loop. https://openloop.org/reports/2023/10/Prototipo-de-Politicas-Publicas-sobre-Transparencia-y-Explicabilidad-de-Sistemas-de-IA.pdf
  • Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., de Prado, M. L., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation (Versión 2). arXiv. https://doi.org/10.48550/ARXIV.2305.02231
  • Ding, Z., Jiang, S., Xu, X., & Han, Y. (2022). An Internet of Things based scalable framework for disaster data management. Journal of Safety Science and Resilience, 3(2), 136-152. Scopus. https://doi.org/10.1016/j.jnlssr.2021.10.005
  • Duan, W., Liu, Z., Jia, C., Wang, S., Ma, S., & Gao, W. (2023). Differential Weight Quantization for Multi-Model Compression. IEEE Transactions on Multimedia, 25, 6397-6410. https://doi.org/10.1109/TMM.2022.3208530
  • European Union. (2024). Article 9: Risk management system. https://artificialintelligenceact.eu/article/9/
  • for Economic Co-operation, O., & (OECD), D. (2025). AI openness: A primer for policymakers (No. 44; OECD artificial intelligence papers). OECD Publishing. https://doi.org/10.1787/958d292b-en
  • García-López, I. M., Ramírez-Montoya, M. S., & Molina-Espinosa, J. M. (2025). Generative artificial intelligence in education: A systematic analysis of opportunities, challenges, and responses. Interactive Learning Environments, 1-24. https://doi.org/10.1080/10494820.2025.2519133
  • Golovianko, M., Gryshko, S., Terziyan, V., & Tuunanen, T. (2023). Responsible cognitive digital clones as decision-makers:a design science research study. European Journal of Information Systems, 32(5), Article 5. Scopus. https://doi.org/10.1080/0960085X.2022.2073278
  • Grimmelikhuijsen, S., & Meijer, A. (2022). Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response. Perspectives on Public Management and Governance, 5(3), 232-242. https://doi.org/10.1093/ppmgov/gvac008
  • Henderson, L. H., Wersun, A., Wilson, J., Mo-ching Yeung, S., & Zhang, K. (2019). Principles for responsible management education in 2068. Futures, 111, 81-89. Scopus. https://doi.org/10.1016/j.futures.2019.05.005
  • Himabindu, M., V, R., Gupta, M., Rana, A., Chandra, P. K., & Abdulaali, H. S. (2023). Neuro-Symbolic AI: Integrating Symbolic Reasoning with Deep Learning. 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), 1587-1592. https://doi.org/10.1109/UPCON59197.2023.10434380
  • Jiang, H., Zhang, X., Mahari, R., Kessler, D., Ma, E., August, T., Li, I., Pentland, A. «Sandy», Kim, Y., Roy, D., & Kabbara, J. (2024). Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling (Versión 4). arXiv. https://doi.org/10.48550/ARXIV.2402.17019
  • Landers, R. N., & Behrend, T. S. (2023). Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models. American Psychologist, 78(1), 36-49. https://doi.org/10.1037/amp0000972
  • Larsson, S. (2020). On the Governance of Artificial Intelligence through Ethics Guidelines. Asian Journal of Law and Society, 7(3), 437-451. https://doi.org/10.1017/als.2020.19
  • Li, Z., Zhu, H., Lu, Z., Xiao, Z., & Yin, M. (2025). From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered Analysis. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1-18. https://doi.org/10.1145/3706598.3713133
  • Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., Newman, B., Yuan, B., Yan, B., Zhang, C., Cosgrove, C., Manning, C. D., Ré, C., Acosta-Navas, D., Hudson, D. A., … Koreeda, Y. (2022). Holistic Evaluation of Language Models. https://doi.org/10.48550/ARXIV.2211.09110
  • Méndez Carpio, C. R., Arévalo Medranda, S. R., León Segovia, L. S., Parra Guerrero, E. F., & Siguencia Tello, S. P. (2023). Tecnopatías y dependencias: Uso incorrecto de la tecnología. Killkana Social, 7(3), 77-88. https://doi.org/10.26871/killkanasocial.v7i3.1407
  • Mišić, J., Van Est, R., & Kool, L. (2025). Good governance of public sector AI: A combined value framework for good order and a good society. AI and Ethics, 5(5), 4875-4889. https://doi.org/10.1007/s43681-025-00751-3
  • Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). Declaración PRISMA 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), Article 9. https://doi.org/10.1016/j.recesp.2021.06.016
  • Paul, J., Lim, W. M., O'Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR‐4‐SLR). International Journal of Consumer Studies, 45(4). https://doi.org/10.1111/ijcs.12695
  • Pushkarna, M., Zaldivar, A., & Kjartansson, O. (2022). Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI (Versión 1). arXiv. https://doi.org/10.48550/ARXIV.2204.01075
  • Sauer, P. C., & Seuring, S. (2023). How to conduct systematic literature reviews in management research: A guide in 6 steps and 14 decisions. Review of Managerial Science, 17(5), 1899-1933. https://doi.org/10.1007/s11846-023-00668-3
  • Silva, A. A. (2022). Gobernanza, poder y autonomía universitaria en la era de la innovación. Perfiles Educativos, 44(178), Article 178. Scopus. https://doi.org/10.22201/iisue.24486167e.2022.178.60735
  • Wang, Y. (2021). When artificial intelligence meets educational leaders' data-informed decision-making: A cautionary tale. Studies in Educational Evaluation, 69, 100872. https://doi.org/10.1016/j.stueduc.2020.100872
  • Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7