AI-Driven Incident Investigations for Safer Industrial Systems: Integrating AI, Systems Thinking, and Predictive Tools for Modern Safety Leadership

Heavy industrial sectors such as mining, oil and gas, construction, and large-scale manufacturing operate at the confluence of complexity, scale, and inherent hazard. These industries are characterized by high energy systems, interdependent processes, and dynamic risk profiles where a single failure can have catastrophic consequences for people, assets, the environment, and corporate reputation. In such contexts, incident investigation must serve not as an isolated technical exercise, but as a critical pillar of enterprise risk management, operational resilience, and strategic governance.
This book, AI-Driven Incident Investigations for Safer Industrial Systems: Integrating AI, Systems Thinking, and Predictive Tools for Modern Safety Leadership, presents a comprehensive framework for transforming incident investigation into a data-driven, technology-enabled function that supports predictive risk management and continuous organizational learning. It is designed for executives, directors, senior safety professionals, and technical leaders responsible for shaping the safety, reliability, and sustainability of high-hazard operations.
This high-level guide bridges traditional safety science with modern technologies to transform how organizations investigate, analyze, and prevent incidents. Designed for executives, engineers, and safety leaders in high-risk industries, the book explores how AI, machine learning, digital twins, and smart systems enhance root cause analysis, hazard control, and risk intelligence.
With practical frameworks, real-world applications, and system-based models like STAMP, Bowtie, and AcciMap, this book equips readers to move from reactive reporting to predictive, data-driven safety excellence.