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Assessing the Effects of Artificial Intelligence in Revolutionizing Human Resource Management: A Systematic Review
0
Zitationen
3
Autoren
2025
Jahr
Abstract
This research study will examine the intermittent impacts of AI on traditional human resource management (HRM) across key sectors. The primary objective is to perform a systematic literature review and analysis concerning the contemporary presence of AI in HRM, incorporating studies published from 2020 to 2024. The research is conducted to delineate the advantages and disadvantages of AI adoption in HRM, essential for formulating evidence based recommendations for its optimal implementation. This study is methodologically informed by PRISMA (Preferred Reporting Items for Systematic Reviews and Mata-Analyses) and utilises premier academic resources, including Google Scholar, Scopus, and Science Direct. The results indicate that AI can significantly enhance HRM functions, primarily by streamlining the recruitment process, ensuring objectivity in performance evaluations, and formulating individualised employee development plans. There are serious ethical and practical problems with the research, though, such as the possibility of searching, data privacy, and employer monitoring, as well as the growing digital divide. The study also finds that there aren't enough real-world examples in the literature to back up many of the claimed benefits of AI in HRM. Practically, the study offers useful data that can help organisations employ AI and mitigate its risks. It backs the necessity of strong laws and moral behaviour to encourage the responsible application of AI. This paper synthesises existing literature, contributing to the growing discourse on AI and HRM that can help researchers, policymakers, and HR practitioners create effective and responsible AI integration policies. The disagreement over the long term benefits and drawbacks of AI also serves as a reminder of the unanswered questions regarding the long term effects of this rapidly evolving field, which calls for additional empirical research.
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