FORECASTING PERFORMANCE INDICATORS WITH THE HELP OF ARTIFICIAL INTELLIGENCE: TSUE PRACTICE (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
This article presents an AI-driven approach for predicting employee performance within the KPIup digital platform, designed to enhance staff evaluation processes in higher education institutions. Multidimensional HR data were analyzed and predictive models were developed using Gradient Boosting, Random Forest, and regression algorithms to forecast future employee productivity. Data preprocessing, normalization, and feature engineering significantly improved prediction accuracy. The results demonstrate that integrating AI analytics into the KPIup platform increases the speed and reliability of managerial decision-making, reduces subjectivity in performance evaluation, and enables full automation of HR processes.
Keywords: KPI; artificial intelligence; employee performance; prediction model; KPIup platform; business analytics; Gradient Boosting; HR technologies