Using natural and artificial intelligence in the talent management system

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Abstract

The article describes the nature of the use of natural and artificial intelligence in the talent management system in the Moscow Region. The extent of using talent management technology, the tools, the ratio of demand for natural and artificial intelligence in talent management, and the level of employee confidence in man (robot) in management are analyzed. It is revealed and substantiated that personnel management services and the management of companies do not effectively use natural intelligence in the framework of the talent management system, relying on chance and a typical approach. Ambiguity in the assessment of artificial intelligence by both employees and company management was revealed. Increased use and fears of the negative impact of artificial intelligence become opposing factors in talent management. It is proposed to quickly implement advanced technologies based on artificial intelligence, with no harm to the human and his potential. It is also proposed to constantly keep in focus the risks of intercepting initiatives in the management of artificial intelligence and the crowding out of a person from the labor market. The main advantage of the article is an integrated approach to the study of the pattern of using natural and artificial intelligence in the talent management system. The authors considered the issue of identifying the ratio of artificial and natural intelligence in talent management for the first time. The results can serve as a basis for further research in the system of human resource management, as well as good support for making management decisions in the implementation of artificial intelligence in business processes and organization management.

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APA

Vinichenko, M. V., Rybakova, M. V., Сhulanova, O. L., Kuznetsova, I. V., Makushkin, S. A., & Lobacheva, A. S. (2019). Using natural and artificial intelligence in the talent management system. International Journal of Recent Technology and Engineering, 8(3), 7417–7423. https://doi.org/10.35940/ijrte.C6152.098319

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