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Potencial de la inteligencia artificial en la gestión empresarial: optimización de toma de decisiones y eficiencia organizacional

Authors: Hernández Beltrán, Martha Patricia; García Torres, Teresa Elizabeth; Casas Miramontes, Mario; González Rivera, Carola; Méndez Locheo, Jenny Guadalupe; Ramírez Llamas, Tania;

Potencial de la inteligencia artificial en la gestión empresarial: optimización de toma de decisiones y eficiencia organizacional

Abstract

El presente artículo aborda cómo las empresas reciben los beneficios de la Inteligencia Artificial (IA), así como también las dificultades que enfrentan de no aprovechar el potencial que tiene la IA para optimizar la toma de decisiones y la mejora de la eficiencia organizacional. La investigación que le da origen se llevó a cabo mediante un enfoque descriptivo, combinando el análisis documental, entrevistas semiestructuradas y encuestas a los empleados de cinco empresas automotrices que implementaron las tecnologías de la IA. Se identificaron los beneficios como la automatización de tareas repetitivas, la optimización de procesos y la capacidad de adaptarse a entornos cambiantes. Sin embargo, también se reconocen desafíos relacionados con la falta de conocimiento sobre su implementación, como aspectos éticos y limitaciones técnicas. El estudio demuestra cómo la Inteligencia Artificial puede transformar la Ingeniería en Gestión Empresarial al hacerla más eficiente, competitiva y adaptable ofreciendo mejores herramientas para impulsar la productividad de las organizaciones. Asimismo, resalta la necesidad de que las empresas y futuros profesionales en esta área dominen estas tecnologías para mantenerse competitivos en un mercado global que está en constante evolución. 

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green