Artificial Intelligence in Business Continuity Management and the Resilience of Production Systems to Safety Disruptions

Abstract This article investigates the transformative role of artificial intelligence in business continuity management and the resilience of production systems exposed to safety disruptions. The study conceptualizes AI not merely as an optimization technology but as a structural component of organizational security architectures embedded within resilience engineering and continuity governance. A mixed-method, model-driven research design was employed, integrating quantitative system modelling, simulation-based scenario analysis, and qualitative organizational assessment across high-risk manufacturing environments. The findings demonstrate that AI-enabled continuity systems significantly enhance early disruption detection, reduce cascading failure propagation, and accelerate recovery dynamics compared to traditional continuity frameworks. Predictive analytics and adaptive recovery coordination substantially increase system shock absorption capacity, shorten mean time to recovery, and improve procedural compliance. At the same time, the results reveal that the effectiveness of AI-driven continuity governance is contingent upon data integrity, cybersecurity robustness, and human – AI collaboration quality. The study advances continuity management and resilience engineering theory by reconceptualizing resilience as an emergent, algorithmically governed system property rather than a static infrastructural attribute. From a practical perspective, the results provide evidence-based guidance for organizations seeking to design intelligent, self-regulating safety architectures capable of sustaining operational continuity under complex, multi-dimensional safety disruptions.