Research

Mining organizations can become high reliability organizations by transitioning from traditional Safety-I to Safety-II approaches—focusing on enhancing system resilience, learning from success, and empowering frontline decision-making—with generative AI playing a critical role in accelerating learning, detecting weak signals, and supporting adaptive capacity.

Generative AI assistants can now review thousands of pages of evidence in seconds—a game-changer for incident investigators who must deliver timely findings while operations remain suspended. Most existing framings lean on the control-centric paradigm: locate missing or failed defences, highlight individual non-compliances, recommend engineering fixes. In contrast, Human-and-Organisational Performance (HOP) / Safety II thinking argues that incidents emerge through normal adaptations, trade-offs, and information-flow breakdowns.

Safety digital twins, when combined with AI and generative AI, enable high-risk industries like mining to shift from reactive to proactive safety management by providing real-time hazard detection, predictive insights, and immersive training, while supporting high-reliability practices—though success depends on overcoming challenges like data quality, cost, and change management.