Thesis
Digital Transformation of Occupational Safety and Health Management Systems: Integrating Artificial Intelligence, Predictive Risk Assessment, and Safety Leadership for Sustainable Organizational Performance
by Ronnie Jaculbe Morta
Project Overview
The thesis examines how digital transformation can improve Occupational Safety and Health Management Systems (OSHMS) by integrating artificial intelligence, predictive risk assessment, digital technologies, and safety leadership. The study focuses on the transition from traditional, reactive approaches to occupational safety toward proactive and predictive safety management, where organizations can identify hazards earlier, assess risks more effectively, monitor workplace conditions in real time, and support evidence-based decision-making. The research is grounded in several theoretical perspectives, including Sociotechnical Systems Theory, High Reliability Organization Theory, Safety Culture Theory, Resilience Engineering Theory, and Transformational Leadership Theory, while ISO 45001:2018 provides an important management-system framework. The thesis discusses the application of technologies such as artificial intelligence, machine learning, Internet of Things devices, wearable technologies, computer vision, predictive analytics, digital twins, cloud-based systems, and real-time monitoring tools within occupational safety management. The study involved 250 respondents comprising occupational safety and health professionals, managers, supervisors, and employees, and examined their perceptions of digital transformation, AI integration, predictive risk assessment, safety leadership, and sustainable organizational performance.
The findings indicate that digital technologies can improve access to safety information, incident reporting and investigation, workplace monitoring, compliance documentation, and real-time safety decision-making. Artificial intelligence and predictive analytics were found to have potential in identifying unsafe conditions, analyzing historical safety data, predicting hazards, prioritizing preventive actions, and improving the accuracy of risk assessments. At the same time, the study identifies several challenges to digital transformation, including limited organizational resources, inadequate digital competencies, employee resistance to technological change, data privacy and cybersecurity concerns, and the need for continuous training.
The research also highlights the importance of safety leadership in encouraging technology adoption, strengthening safety culture, and ensuring that digital tools are effectively integrated into organizational practices. The thesis concludes that digital transformation, artificial intelligence, predictive risk assessment, and strong safety leadership can collectively strengthen OSHMS and contribute to sustainable organizational performance, but technology alone cannot guarantee improved safety outcomes. Effective implementation requires organizational readiness, employee participation, leadership commitment, digital competency, ethical governance, and alignment with established occupational safety management principles. As its major contribution, the thesis proposes an Integrated Digital Occupational Safety and Health Management Framework that combines AI-based technologies, predictive risk assessment, real-time monitoring, safety leadership, and continual improvement principles aligned with ISO 45001. The study recommends that organizations develop clear digital OSH strategies, invest in appropriate digital safety technologies, strengthen employee training and engagement, establish appropriate cybersecurity and data governance measures, and use artificial intelligence as a supporting tool alongside professional safety judgment.