Empresas autónomas: toma de decisiones estratégicas impulsada por inteligencia artificial en la administración empresarial

Edwin Gerardo Acuña Acuña

doi:10.64183/pw7nw416

Autores/as

Edwin Gerardo Acuña Acuña Universidad San Marcos Autor/a https://orcid.org/0000-0001-7897-4137

DOI:

https://doi.org/10.64183/pw7nw416

Palabras clave:

Inteligencia Artificial Cognitiva, Empresas Autónomas, Toma de Decisiones Estratégicas, Administración de Empresas, Colaboración Humano-IA

Resumen

Este artículo analiza el crecimiento de las empresas autónomas, donde las tecnologías de Inteligencia Artificial (IA) cognitiva apoyan directamente la toma de decisiones estratégicas, la planificación y la previsión. El estudio analiza cómo la integración de la IA afecta la velocidad de decisión, la rentabilidad y la satisfacción de la dirección mediante una metodología de métodos mixtos que abarca cuatro empresas latinoamericanas de los sectores bancario, logístico, tecnológico y de servicios. Los resultados indican que la satisfacción de la dirección se mantuvo alta, especialmente cuando los sistemas de IA eran visibles e interpretables; la agilidad en la toma de decisiones aumentó hasta un 42 % y el ahorro de costes alcanzó el 18 %. Sin embargo, también se observaron problemas éticos, como la opacidad algorítmica y la sobreautomatización, lo que indica la necesidad de un control más robusto. El artículo sugiere un enfoque de coliderazgo en el que las personas y la IA colaboran para tomar decisiones. El verdadero valor organizacional proviene de la integración de la inteligencia analítica con la responsabilidad ética; no de la automatización por sí sola. Este artículo apoya la creación de marcos estratégicos para la adopción de la IA centrados en el ser humano, enfatizando que, en una era impulsada por los sistemas inteligentes, el liderazgo debe basarse en valores humanos.

Descargas

Los datos de descarga aún no están disponibles.

Referencias

Abdurrahman, A. (2025). Examining the impact of digital transformation on digital product innovation performance in banking industry through the integration of resource-based view and dynamic capabilities. Journal of Strategy & Innovation, 36(1). https://doi.org/10.1016/j.jsinno.2025.200540

Acuña Acuña, E. G. (2023). Fortaleciendo la enseñanza de ingeniería en Educación Superior. Actualización docente en minería de datos, internet de las cosas y metaversos. Codes. https://doi.org/10.15443/codes2044

Acuña Acuña, E. G. (2024). Sustainable digital business management: Challenges and opportunities Proceedings of the 22nd LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2024): “Sustainable Engineering for a Diverse, Equitable, and Inclusive Future at the Service of Education, Research, and Industry for a Society 5.0.”. https://laccei.org/LACCEI2024-CostaRica/full-papers/Contribution_261_final_a.pdf

Acuña Acuña, E. G., Huertas Rosales, Á., & Vásquez Espinoza, S. (2024). Sistemas De Monitoreo Iot Para La Seguridad Laboral En Costa Rica. New Trends in Qualitative Research, 20(4). https://doi.org/10.36367/ntqr.20.4.2024.e1054

Acuña, E. G. A., Ferruzca, A. A., Rojas, J. M. C., Bayona, M. F. G., Soto, J. S. P., & Rojo Rojo, C. N. (2025). Optimization of Urban Mobility with IoT and Big Data: Technology for the Information and Knowledge Society in Industry 5.0. In S. Nesmachnow & L. Hernández Callejo, Smart Cities Cham.

Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289. https://doi.org/10.1016/j.jclepro.2021.125834

Arranz, C. F. A., Arroyabe, M. F., Arranz, N., & de Arroyabe, J. C. F. (2023). Digitalisation dynamics in SMEs: An approach from systems dynamics and artificial intelligence. Technological Forecasting and Social Change, 196. https://doi.org/10.1016/j.techfore.2023.122880

Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2020.102225

Chaturvedi, V. (2025). A futuristic aspect towards modern healthcare system facilitated through artificial intelligence: A comprehensive perspective. In Revolutionizing Medical Systems using Artificial Intelligence (pp. 245-264). https://doi.org/10.1016/b978-0-443-32862-6.00013-4

Dahiya, R., Le, S., & Kroll, M. J. (2025). Big data analytics and firm performance: The effects of human capital and mediating firm capabilities. Journal of Strategy & Innovation, 36(1). https://doi.org/10.1016/j.jsinno.2025.200535

Garay Gallastegui, L. M., & Reier Forradellas, R. (2024). FASECO: A Framework for Advanced Support of E-Commerce and digital transformation in SMEs with natural language processing- enhanced analysis. Journal of Open Innovation: Technology, Market, and Complexity, 10(4). https://doi.org/10.1016/j.joitmc.2024.100412

Ji, L., & Huang, X. (2022). Analysis of social governance in energy-oriented cities based on artificial intelligence. Energy Reports, 8, 11151- 11160. https://doi.org/10.1016/j.egyr.2022.08.206

Kar, A. K., Choudhary, S. K., & Singh, V. K. (2022). How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production, 376. https://doi.org/10.1016/j.jclepro.2022.134120

Kuppuchamy, S. K., Srinivasan, S., Dhandapani, G., Nagaraj, S., Celin, J. A., & Subramanian, M. (2025). Journey of computational intelligence in sustainable computing and optimization techniques: An introduction. In Computational Intelligence in Sustainable Computing and Optimization (pp. 1-51). https://doi.org/10.1016/b978-0-443-23724-9.00001-3

Li, L. T., Haley, L. C., Boyd, A. K., & Bernstam, E. V. (2023). Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. J Biomed Inform, 147, 104531. https://doi.org/10.1016/j.jbi.2023.104531

Miracle, D. B., & Thoma, D. J. (2024). Autonomous research and development of structural materials – An introduction and vision. Current Opinion in Solid State and Materials Science, 33. https://doi.org/10.1016/j.cossms.2024.101188

Mishra, R., Kr Singh, R., Daim, T. U., Fosso Wamba, S., & Song, M. (2024). Integrated usage of artificial intelligence, blockchain and the internet of things in logistics for decarbonization through paradox lens. Transportation Research Part E: Logistics and Transportation Review, 189. https://doi.org/10.1016/j.tre.2024.103684

Nawaz, N., Arunachalam, H., Pathi, B. K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1). https://doi.org/10.1016/j.jjimei.2023.100208

Shang, Y., Jiang, J., Zhang, R., Zhang, Y., Liu, P., & Yu, L. (2025). The mechanism of human-machine collaboration driving sustainable business models: A single case study from the electric vehicle industry. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2025.145152

Sumrit, D. (2024). Enhancing readiness degree for Industrial Internet of Things adoption in manufacturing enterprises: An integrated Pythagorean fuzzy approach. Heliyon, 10(20), e39007. https://doi.org/10.1016/j.heliyon.2024.e39007

Sun, Z., Che, S., & Wang, J. (2024). Deconstruct artificial intelligence’s productivity impact: A new technological insight. Technology in Society, 79. https://doi.org/10.1016/j.techsoc.2024.102752

Uctu, R., Tuluce, N. S. H., & Aykac, M. (2024). Creative destruction and artificial intelligence: The transformation of industries during the sixth wave. Journal of Economy and Technology, 2, 296-309. https://doi.org/10.1016/j.ject.2024.09.004

Valle-Cruz, D., Fernandez-Cortez, V., & Gil-Garcia, J. R. (2022). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Quarterly, 39(2). https://doi.org/10.1016/j.giq.2021.101644

Verbeke, A., Oh, C. H., & Jain, R. (2025). What is the future of regional multinational enterprises? International Business Review. https://doi.org/10.1016/j.ibusrev.2025.102442

Wang, S., & Zhang, H. (2025). Generative artificial intelligence and internationalization green innovation: Roles of supply chain innovations and AI regulation for SMEs. Technology in Society, 82. https://doi.org/10.1016/j.techsoc.2025.102898

Wang, X., Shi, X., Chen, J., Guo, X., & Li, D. (2024). Exploring optimal pathways for enterprise procurement management systems based on fast neural modeling and semantic segmentation. Heliyon, 10(7), e26474. https://doi.org/10.1016/j.heliyon.2024.e26474

Wu, L., Sun, L., Chang, Q., Zhang, D., & Qi, P. (2022). How do digitalization capabilities enable open innovation in manufacturing enterprises? A multiple case study based on resource integration perspective. Technological Forecasting and Social Change, 184. https://doi.org/10.1016/j.techfore.2022.122019

Ye, X., Yan, Y., Li, J., & Jiang, B. (2024). Privacy and personal data risk governance for generative artificial intelligence: A Chinese perspective. Telecommunications Policy, 48(10). https://doi.org/10.1016/j.telpol.2024.102851

Zamlynskyi, V., Kalinichenko, S., Kniazkova, V., Skrypnyk, N., & Avriata, A. (2025). Healthcare management using artificial intelligence. In Revolutionizing Medical Systems using Artificial Intelligence (pp. 1-24). https://doi.org/10.1016/b978-0-443-32862-6.00001-8

Zhang, C., Zhu, W., Dai, J., Wu, Y., & Chen, X. (2023). Ethical impact of artificial intelligence in managerial accounting. International Journal of Accounting Information Systems, 49. https://doi.org/10.1016/j.accinf.2023.100619

Zhong, Q., Zhang, Q., & Yang, J. (2025). Can artificial intelligence empower energy enterprises to cope with climate policy uncertainty? Energy Economics, 141. https://doi.org/10.1016/j.eneco.2024.108088