Inteligencia artificial y toma de decisiones gerenciales: implicaciones para la gestión estratégica en las organizaciones

Autores/as

DOI:

https://doi.org/10.5281/zenodo.21463159

Palabras clave:

inteligencia artificial, toma de decisiones gerenciales, Gobernanza organizacional de IA, toma de decisiones gerenciales aumentada, responsabilidad gerencial

Resumen

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.

Biografía del autor/a

Iván Miguel García López, Universidad La Salle

Investigador en inteligencia artificial, modelos abiertos de lenguaje y gestión estratégica en instituciones de educación superior. Sus líneas de trabajo integran ciencias computacionales, derecho, ética y administración aplicada a entornos educativos.

Jessica Nájera Ochoa, Universidad La Salle

ULSA México. Doctora en Administración, Licenciada en Informática por el IPN y Doctora en Sostenibilidad por la UEMA. Cuenta con 28 años de experiencia en TIC, Administración y Sostenibilidad; Gerencia, Jefatura y Docencia en Posgrado.

Citas

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

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Publicado

07-08-2026

Cómo citar

García López, I. M., & Nájera Ochoa, J. (2026). Inteligencia artificial y toma de decisiones gerenciales: implicaciones para la gestión estratégica en las organizaciones. Universita Ciencia, 14(40), 68–92. https://doi.org/10.5281/zenodo.21463159