Risk management in global supply chains using predictive artificial intelligence

We live in an age of globalization where we constantly face threats bought by geopolitical vulnerability as well as pandemics, climate change and cyber threats. Traditional risk management methods only respond after the obstruction has taken place and the business continuity has been broken. This study helps us examine how predictive AI can transform global supply chain risk management from a reactive approach into a prescriptive and adaptive system. Our target is to rely on secondary data of academic studies, industry publications, OECD models that delve into AI strengthening of warning systems, data-driven decision-making, and simulation of scenarios. The findings highlight that machine learning algorithms are capable of anticipating obstructions by analyzing real time logistics, environmental data, trade, by which it strengthens responses and business resilience. In addition, AI-driven data ecosystems foster the connection between the public and the private sector by enhancing the level of transparency and increasing the speed of the recovery process. By initiating these technologies, organizations can be better at predicting potential risks, allocating resources more efficiently, and sustaining operations even during the global crisis. The paper concludes by stating that predictive AI is not only an application of technology but a business approach that enables firms to establish better, smarter, and more resilient supply chains. In the uncertain world, there is a possibility of AI becoming the means of redefining the global risk management to offer a stable, adaptable, and sustainable future of businesses throughout the world.