The Influence of AI on Human Capital Management in Ibero-America from 2021 to 2023
DOI:
https://doi.org/10.18687/LEIRD2024.1.1.500Keywords:
Artificial Intelligence, Human Capital, Ibero-AmericaAbstract
In the competitive business environment, talent management is crucial for organizational success. This study examines the evolution of knowledge regarding the influence of Artificial Intelligence (AI) on Human Capital Management in Ibero-America between 2021 and 2023. It investigates the integration of AI technologies into talent management practices, focusing on optimizing recruitment, development, and retention processes through strategies such as AI-driven recruitment. The general objective of the study is to determine how AI impacts Human Capital Management. Specific objectives include analyzing its influence on Payroll Management and employee performance control in the region. The methodology is based on a systematic review of academic articles from sources such as Scopus, Scielo, and Redalyc. The results highlight significant benefits in productivity, payroll control, and labor performance efficiency, emphasizing the transformation that AI is generating in this area. Furthermore, the need to strengthen collaborative research in human resources in Ibero-America is underscored, promoting development and innovation in the region and facilitating a strategic adoption of AI in talent management. In conclusion, AI is enhancing performance and payroll management in Ibero-American organizations, optimizing resource allocation and promoting sustainable growth among employees.Downloads
Published
2024-12-12
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Articles
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Copyright (c) 2024 LEIRD
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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How to Cite
Gamarra Gutiérrez, A., Larios Soldevilla, O., Huaman Morillo, M., Loarte Alvarado, H., Montoya Lucas, N. E., Rodriguez Cortez, A., & Urdanegui Sibina, R. (2024). The Influence of AI on Human Capital Management in Ibero-America from 2021 to 2023. LACCEI, 2(11). https://doi.org/10.18687/LEIRD2024.1.1.500