As business environments in the global context continue to become more uncertain and complex, organizations have been turning to artificial intelligence (AI)-based predictive analytics to help them improve their abilities to manage risk. This paper examines how AI-led predictive analytics and the mediating role of organizational readiness determines the effectiveness of how business risk can be managed, but also looks into how the moderating role of trusting AI systems in the hands of a manager limits that effectiveness. Based on the Technology-Organization-Environment (TOE) model and the Technology Acceptance Model (TAM), the study borrows the quantitative methodology to interpret the data sourcing conducted in 217 managers of diverse industries that face unpredictable markets in terms of strategic risks. The results demonstrate that AI enables much more effective risk identification, assessment, and mitigation processes when it hosts predictive analytics, and the impact depends on the willingness of the organization to use new technologies and the amount of trust that managers put in insights that are generated by AI. The findings can help develop the body of knowledge through the relevance of technological factors, organizational factors, and human factors in achieving the possibilities of AI in business risk management. They provide some practical implications that a manager, AI system developer, and policymaker may pursue in order to promote effective adoption of AI in risk-intensive businesses.