Predictive Risk Intelligence and Early-Warning Systems for Saudi Giga-Projects: Strengthening Business Continuity and Adaptive Crisis Response

Saudi Arabia’s giga-project programme concentrates exceptional capital, technological novelty, inter-organisational dependence and delivery pressure within projects that must remain operationally credible while their scope and environment continue to evolve. Conventional risk registers are necessary but insufficient in such settings because they largely describe known exposures at discrete review points. This review develops a predictive risk intelligence perspective in which heterogeneous project data are continuously converted into weak signals, forecast risk states and decision triggers that support business continuity before disruption becomes crisis. Thirty peer-reviewed studies published between 2020 and 2025 were synthesised across construction risk analytics, digital twins, real-time monitoring, megaproject crisis precursors, supply-chain resilience and organisational resilience. The synthesis shows that effective early warning depends less on a single algorithm than on a socio-technical architecture connecting reliable data, explainable models, threshold governance, escalation authority and pre-agreed continuity responses. Machine learning can improve prediction of delay, systemic risk and cyber exposure, while digital twins and sensor systems can provide near-real-time situational awareness. However, fragmented data ownership, model drift, weak interpretability and organisational reluctance to act on uncomfortable signals can neutralise technical capability. A five-layer Predictive Risk Intelligence and Adaptive Continuity framework is therefore proposed for Saudi giga-projects. It links sensing, risk inference, warning governance, continuity activation and organisational learning. The paper argues that predictive intelligence should be treated as a governance capability rather than a dashboard feature, with performance judged by decision lead time, false-warning control, response effectiveness and recovery adaptability.