Inteligencia artificial en la empresa contemporánea: implicaciones filosóficas, éticas y organizacionales
Artificial intelligence in business: organizational transformation, ethical risks, and governance in Latin AmericaBarra lateral del artículo
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Introducción: La incorporación de la inteligencia artificial en las empresas transforma la estructura organizacional, la toma de decisiones y las condiciones del trabajo. Objetivo: Analizar las implicaciones organizacionales, éticas y laborales de la incorporación de la inteligencia artificial en las empresas. Método: El estudio desarrolló una revisión narrativa sustentada en 54 fuentes, de las cuales se seleccionó un corpus analítico de 26 artículos publicados entre 2022 y 2026 para la síntesis de resultados, mediante búsquedas en Scopus, Web of Science, Google Scholar, SciELO, Redalyc y Dialnet. Resultados: La inteligencia artificial opera como factor que determina la racionalidad gerencial, redistribuye el poder hacia quienes controlan los datos y traslada parte del juicio profesional hacia modelos opacos, reproduce sesgos discriminatorios, intensifica la vigilancia del trabajo y genera costos psicosociales. Conclusión: La adopción responsable exige marcos de gobernanza organizacional con supervisión humana, transparencia algorítmica y políticas de formación acordes con las condiciones desiguales de la región.
Introduction: The integration of artificial intelligence into business organizations is transforming organizational structures, decision-making processes, and working conditions. Objective: To analyze the organizational, ethical, and labor implications of integrating artificial intelligence into business organizations. Method: The study conducted a narrative review supported by 54 sources, from which an analytical corpus of 26 articles published between 2022 and 2026 was selected for the synthesis of results, through searches in Scopus, Web of Science, Google Scholar, SciELO, Redalyc, and Dialnet. Results: Artificial intelligence functions as a factor shaping managerial rationality, redistributes power toward those who control data, and shifts part of professional judgment toward opaque models; it also reproduces discriminatory biases, intensifies workplace surveillance, and generates psychosocial costs. Conclusion: Responsible adoption requires organizational governance frameworks that incorporate human oversight, algorithmic transparency, and training policies aligned with the region's unequal conditions.
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