Authors: Nivaan Varma
Abstract: As global business environments become increasingly volatile, traditional enterprise risk management frameworks struggle to keep pace with high-velocity, interconnected disruptions. This review article investigates the integration of artificial intelligence into risk management lifecycles to enhance business resilience. We examine how machine learning, natural language processing, and predictive analytics transform the stages of risk identification, assessment, and mitigation from reactive to proactive processes. The study highlights the role of AI in critical domains such as cybersecurity, supply chain elasticity, and financial stability, while also addressing the theoretical shift toward the anticipate-absorb-recover-adapt cycle of resilience. Furthermore, the article explores the significant challenges associated with AI adoption, including model opacity, data bias, and the urgent need for explainable AI and human-in-the-loop governance. By synthesizing current research with emerging trends like generative AI and quantum-resistant modeling, we provide a strategic roadmap for organizations aiming to build antifragile systems. This study concludes that the synergy between human strategic judgment and machine intelligence is the fundamental requirement for maintaining long-term survivability in the digital age.
DOI: http://doi.org/10.5281/zenodo.18221560
