The Effect of AI (Artificial Intelligence) in Employee Performance Evaluation on Employee Retention in the Information Technology Sector

S. Durairaj and V. Vetrivel ()
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S. Durairaj: Vels Institute of Science, Technology and Advanced Studies, Research Scholar (Ph.D), Department of Business Administration, School of Management Studies
V. Vetrivel: Vels Institute of Science, Technology and Advanced Studies, Assistant Professor and Research Supervisor, Department of Business Administration, School of Management Studies

A chapter in Proceedings of the International Conference on Digital Transformation in Business: Navigating the New Frontiers Beyond Boundaries (DTBNNF 2024), 2024, pp 88-108 from Springer

Abstract: Abstract The purpose of this study abstract is to present a synopsis of the research on the effects of AI on IT performance reviews and staff retention. The function of AI technologies in IT firms’ performance evaluation processes is the primary area of investigation in this research. Artificial intelligence (AI) has seen massive growth in its usage across several sectors, including the information technology industry, in the last several years. This trend has also influenced human resource practices, where AI-powered tools are being used to streamline processes and enhance decision-making. The research methodology includes data collection through surveys or interviews with employees and HR professionals in IT companies. The findings will shed light on how these AI-driven systems impact employees’ perception of their performance and management’s ability to evaluate individual contributions accurately. Based on these findings, recommendations, and suggestions will be provided to help organizations optimize their use of AI tools for performance evaluation while ensuring that it positively affects employee retention rates within the IT sector.

Keywords: Data Quality; Performance Metrics; Feedback Mechanisms; System Integration; Technology Infrastructure; Evaluating Employee Performance; Employee Retention (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-433-4_8

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