Enhance the Precision of Performance Appraisals by AI Driven Analytics

Abstract: The primary aim associated with this paper is to ascertain the degree to which analytics powered by artificial intelligence can improve the accuracy, objectivity, and fairness of performance appraisals. It also assesses how AI-driven analytics can reduce biases and subjective judgments in traditional performance appraisals, resulting in more equitable evaluations. This study employs a quantitative methodology and exploratory research type, utilizing secondary data set obtained from the website with the sample size of 741. To anticipate employee performance and analyse the relationship between influencing variables, data analysis uses machine learning algorithms and inferential statistics like correlation coefficient and linear regression. 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. Keywords: HR Analytics, AI, Job Satisfaction, Performance rating, Machine learning Algorithms, Performance Prediction.