Enhancing Employee Productivity Through Technology System AI-Based Approaches

Abstract

This research aims to address the research gap regarding the use of AI in enhancing employee productivity. The study focuses on the role of AI in employee engagement and performance evaluation. The research adopts a quantitative approach, comparing various AI-based algorithms such as random forest, artificial neural network, decision tree, and XGBoost. The study proposes an ensemble approach called RanKer, which combines these algorithms to provide performance ratings for employees. The empirical results demonstrate the efficacy of the proposed model in terms of precision, recall, F1-score, and accuracy. Additionally, the research explores the impact of AI on employee engagement, highlighting the potential for real-time monitoring, sentiment analysis, and natural language processing to create a holistic work environment that promotes clarity, skill development, recognition, and wellness. The findings suggest that the combination of AI and employee engagement can lead to increased productivity, improved communication, and a collaborative work environment. This research contributes to the understanding of how AI can be leveraged to enhance employee productivity and provides recommendations for expanding the use of AI in employee engagement practices.