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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM50650
Volume:
9 |
6 |
Jun 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The extensive research project engages in a stringent exploration of the drastic impact of Artificial Intelligence (AI) on Human Resource Management (HRM), especially its ability to define the overall organizational performance. Covering the critical timeframe between 2020 and 2024, the research synthesizes quantitative indicators collected in a carefully chosen sample of fortune 500 corporations. The overall goal will be to uncover practical insights related to such key HR parameters as an improvement in employee retention rates, an increase in recruiting efficiency, and tangible cost Savings that can be directly linked to the introduction of AI-powered HR operations. In order to do so, this research adopts a multifaceted analysis technique in which the Return on Investment (ROI) metrics, key performances indicators, and overall cost-benefit analysis are examined with great diligence. It is this intense scrutiny that will be used to outline the positive and negative consequence of the use of AI in HRM in a wide range of industries. Moreover, the background knowledge to this analysis is established on the basis of the well-developed literature review, which insightfully incorporates the insights obtained based on the high-level academic journals and industry authoritative reports. In addition to this theoretical foundation, the project aimed at including extensive case studies, including five examples of successful AI implementations and three cases of applications that experienced critical issues. These real-life situations play a significant role in highlighting the practical complexities, significant milestones, and inherent challenges in merging AI to the human resources functionality
KEYWORDS
Publication Date
6/15/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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