Intelligent monitoring of employee productivity as a driver for improving corporate management efficiency in a digital business environment

Abstract

This article examines the conceptual and practical foundations for implementing intelligent performance monitoring of personnel in the context of digital business transformation. The authors argue that traditional methods of monitoring and tracking working hours are losing their effectiveness in a digitalized environment. A comprehensive model of intelligent controlling (Smart Labor Controlling—SLC) is proposed, based on Big Data, artificial intelligence (AI), and predictive analytics. Statistical data on the impact of digital controlling tools on enterprise KPIs are analyzed, and a matrix for the relocation of human resources and a mathematical model for optimizing personnel costs are developed.

Purpose. The aim of this study is to provide a theoretical foundation, develop a methodological framework, and offer practical recommendations for establishing a system of intelligent labor productivity monitoring (Smart Labor Controlling) as a strategic driver for maximizing enterprise management efficiency in the context of the digital transformation of the business environment.

Methods. The research methods are based on the use of systemic, comparative, and structural-logical analysis, the generalization of scientific approaches, as well as elements of behavioral economics and management theory.

Results. The study found that intelligent performance management is a modern tool for strategic human capital management that integrates digital technologies, HR analytics, and artificial intelligence into the enterprise management system. It has been demonstrated that the transformation of traditional controlling into intelligent controlling facilitates the transition from retrospective analysis to predictive labor productivity management. It was determined that artificial intelligence technologies and Business Intelligence systems have the greatest impact on improving enterprise performance, contributing 35% and 32%, respectively, to the positive effect of implementation.

The feasibility of using a multi-level HR analytics system, which includes Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics, and Cognitive Analytics, is substantiated; this system ensures the timely identification of HR risks, the forecasting of staff performance outcomes, and the formulation of well-founded management decisions.

Conclusions. The study’s findings confirmed that intelligent monitoring of employee productivity is one of the key drivers for improving corporate management efficiency in a digital business environment. Its implementation facilitates the transition from traditional monitoring to an intelligent management decision-support system based on the use of digital technologies and artificial intelligence.

It has been demonstrated that intelligent controlling establishes a new paradigm for human resource management, in which personnel are viewed as a strategic asset of the enterprise, and digital technologies become the foundation for ensuring its sustainable development. Prospects for further research are linked to the development of cognitive HR analytics, digital employee twins, generative artificial intelligence, and adaptive labor productivity management systems in the context of the digital economy.