Impacto de la inteligencia artificial generativa en la empleabilidad y reconversión laboral: un análisis sectorial
Impact of generative artificial intelligence on employability and workforce retraining: a sectoral análisisContenido principal del artículo
La inteligencia artificial generativa (IAG) transforma aceleradamente el mercado laboral contemporáneo. El estudio se propuso como objetivo analizar el impacto de la IAG en la empleabilidad y reconversión laboral mediante un análisis sectorial comparativo. La metodología es de enfoque mixto concurrente, tipo descriptivo-analítico, diseño transversal comparativo. Contó con una población de 450 profesionales de cinco sectores (tecnología, educación, salud, finanzas y servicios creativos) de Argentina, Chile, Colombia, México y Perú. Se utilizó un Cuestionario de Exposición a IA Generativa (α=0.91), Escala de Empleabilidad Percibida (α=0.88) e Inventario de Competencias para la Era IA (α=0.89); entrevistas semiestructuradas y grupos focales. Los resultados indican que el 58.3% de puestos presenta obsolescencia de habilidades; la competencia en supervisión crítica de IA (β=0.42) y mentalidad de aprendizaje continuo (β=0.37) predicen empleabilidad (R²=0.67). En conclusión, la reconversión exitosa requiere competencias complementarias a la IA y ecosistemas institucionales de apoyo diferenciados por sector.
Generative artificial intelligence (GIA) is rapidly transforming the contemporary labor market. The study aims to analyze the impact of IAG on employment and labor reconversion through a comparative sectoral analysis. The methodology has a mixed concurrent approach, descriptive-analytical type, comparative transversal design. It counts on a population of 450 professionals from five sectors (technology, education, health, finance and creative services) from Argentina, Chile, Colombia, Mexico and Peru. A Generative AI Exposure Cuestionary (α=0.91), Perceived Employment Scale (α=0.88) and Skills Inventory were used for Era IA (α=0.89); semi-structured interviews and focus groups. The results indicate that 58.3% of people present skills obsolescence; competence in critical AI supervision (β=0.42) and continuous learning mindset (β=0.37) predict employment (R²=0.67). In conclusion, successful conversion requires complementary AI skills and institutional support ecosystems differentiated by sector.
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