Enhance The Precision of Performance Appraisals by Ai Driven Analytics

The primary aim associated with this dissertation is to ascertain the degree to which Analytics powered by artificial intelligence can improve the accuracy, objectivity, and fairness of performance appraisals. It will also assess how AI-driven analytics can reduce biases and subjective judgments in traditional performance appraisals, resulting in more equitable evaluations. Methodology Design /approach: This study employs a quantitative research methodology and Exploratory research type, utilizing secondary data set obtained from the website with the sample size of 741. Data analysis: To anticipate employee performance and analyze the relationship between influencing variables, data analysis uses machine learning algorithms and inferential statistics like correlation coefficient and linear regression. Implications: This research highlights the critical significance of remuneration increases in shaping employee productivity, positing that organizations ought to incorporate monetary incentives within their performance management frameworks. Furthermore, artificial intelligence-powered human resource analytics possess the potential to improve the precision and equity of performance assessments, thereby providing a data-centric methodology for the enhancement of talent management strategies.