Abstract
This research investigates the transformative effect of artificial intelligence tools in optimizing human resource management practices through consolidating implementation trends and performance effects. The study adopts a systematic literature review method based on Web of Science, Scopus, and Google Scholar databases, examining peer-reviewed articles from 2019 to 2024 to create an innovative typology system of artificial intelligence (AI) tools in human resource (HR) contexts. The research categorizes AI technologies under four broad categories: machine learning-based tools, natural language processing applications, predictive analytics platforms, and automated decision-making systems, and discusses their applications in talent acquisition, performance management, and employee development activities. Empirical research illustrates substantial optimization outcomes, with organizations achieving 40-75% efficiency improvements in recruitment activities, 38% reduction in time-to-hire metrics, and up to 60% cost savings in artificial intelligence (AI) activities. Machine learning software demonstrates 68% recruitment efficiency improvements and 78% screening accuracy, with automated decision systems demonstrating 75% improvement in efficiency and 82% screening accuracy compared to 45% traditional baseline performance. The findings demonstrate significant strategic implications for human resource (HR) practitioners through the ability to move from administrative to data-driven strategic roles based on predictive analytics and improved decision-making capabilities. The research provides theoretical frameworks for the categorization of artificial intelligence (AI) tools and provides empirical confirmation of systematic implementation approaches while identifying the critical success factors like comprehensive change management processes, efficient data governance arrangements, and strategic organizational alignment for long-term competitive advantages.
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