The Impact of AI-embedded Technologies in Improving the HRM Practices

Xinyang Zhang and Ziqi Zhou
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Xinyang Zhang: Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do, 16890, Korea.
Ziqi Zhou: Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do, 16890, Korea.

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Abstract: The rapid evolution of artificial intelligence creates a major impact on the traditional HRM practices by offering different innovative solutions in various domains, including performance evaluation, engagement, and recruitment and onboarding. This study aims to explore the integration of AI technology in HRM by identifying the impact of operational strategic decision-making, efficiency, and employee performance. Evaluating different types of secondary data from various academic databases, it is found that AI can offer opportunities in HRM with the help of training, personalisation, talent acquisition and predictive analytics. Despite the benefits, AI can also create some challenges during the integration process, such as high implementation cost, employee resistance, and ethical and legal issues. For the methodology, the researcher has chosen the secondary data collection method to get better results. The AI holds a huge potential for revolutionising the HR functions with the help of strategic planning and a clear ethical framework. This research has a limitation of using only secondary method not primary method. As per the future direction, this research can offer a detailed idea about the applications of AI and challenges faced in using AI. Therefore, the study concludes that the gradual AI implementation with the pilot testing process and training of the workforce to use AI can offer the best results to the organisation in terms of HRM and performance management.

Date: 2025-06-17
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Published in Journal of Global Economics, Management and Business Research, 2025, 17 (2), pp.105-116. ⟨10.56557/jgembr/2025/v17i29428⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05118153

DOI: 10.56557/jgembr/2025/v17i29428

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