Digitalization and Artificial Intelligence in Occupational Safety and Environmental Management

Abstract

     The rapid advancement of digital technologies and artificial intelligence (AI) has transformed the landscape of occupational safety and environmental management (HSE). Traditionally, safety management systems have relied on reactive and paper-based approaches that limit data accessibility and hinder proactive decision-making. However, the integration of digitalization tools—such as Internet of Things (IOT), cloud computing, big data analytics, and AI—has enabled real-time monitoring, predictive analysis, and intelligent risk management. This paradigm shift marks the beginning of a new era of “Smart HSE Management,” where digital ecosystems integrate safety, health, and environmental data to enhance sustainability and organizational resilience.

This paper explores the emerging role of AI-driven digitalization in improving occupational safety and environmental performance across industrial sectors. It discusses how digital twins, machine learning algorithms, and sensor-based analytics enable predictive hazard detection and automated environmental monitoring. Furthermore, the paper reviews the ethical, technical, and regulatory challenges associated with AI implementation—such as data privacy, algorithmic transparency, and cybersecurity risks—that may affect the reliability of digital safety systems.

Through an analysis of recent European and international case studies, the paper demonstrates how AI applications—ranging from predictive maintenance and exposure monitoring to environmental risk modeling—contribute to accident prevention, pollution reduction, and regulatory compliance. The findings suggest that AI-supported HSE systems not only improve risk anticipation and emergency response but also align industrial operations with the goals of Industry 5.0 and sustainable development.

In conclusion, the study highlights that digitalization and AI offer transformative potential for HSE management by integrating human expertise with intelligent automation. However, it emphasizes that successful adoption requires a balanced framework that combines technological innovation with ethical governance, workforce training, and international standardization.