A STUDY ON EMPLOYEE PERFORMANCE TRACKING IN AN ORGANIZATION USING ARTIFICIAL INTELLIGENCE

Abstract

Artificial Intelligence (AI) is reshaping contemporary performance management by shifting employee evaluation from episodic, judgment-heavy appraisals to continuous, data-enabled assessment. This revised study examines how AI-based employee performance tracking influences performance quality, productivity, employee perception, trust, and implementation outcomes in organisational settings. The article integrates a structured review of recent scholarship and a quantitative survey of 150 respondents drawn from information technology, manufacturing, and service organisations. A five-point Likert-scale instrument was used to capture perceptions of AI usage, productivity enhancement, transparency, trust, and employee performance. The analytical framework includes descriptive statistics, reliability analysis, correlation analysis, paired-samples t-test, ANOVA, and regression analysis. The results indicate that AI-supported performance tracking is perceived as more effective than traditional appraisal mechanisms and that AI usage is positively associated with employee performance and productivity. Sectoral and experience-based differences are also observed, while transparency emerges as an important predictor of trust in AI-enabled HR systems. The findings support a balanced implementation model in which AI improves performance management efficiency while human oversight safeguards fairness, explainability, and employee acceptance.