AI-Driven Risk Management Decision Framework and Value Creation in Chemical Enterprises

Abstract

The chemical industry is experiencing a paradigm shift from traditional passive risk management to AI-driven proactive prevention. However, systematic research on how AI technology translates into risk management capabilities and enterprise value remains limited. Based on the Technology Acceptance Model, Technology-Organization-Environment Framework, and Dynamic Capability Theory, this study constructs a theoretical model of AI-driven risk management and value creation for chemical enterprises. A mixed-methods approach was employed, surveying 328 Chinese chemical enterprises using structural equation modeling and conducting in-depth case analysis of 5 typical enterprises.Results show that AI technology characteristics significantly impact risk management capabilities (β=0.54, p<0.001), which in turn affect value creation (β=0.48, p<0.001). Risk management capabilities partially mediate this relationship, with indirect effects accounting for 53.4% of the total effect. Organizational factors positively moderate the relationship between AI technology and risk management capabilities (β=0.18, p<0.01). Case analysis reveals that comprehensive transformation enterprises achieve 35-45% risk reduction rates with 18-24 month payback periods, significantly outperforming gradual optimization and pilot approaches. Three value creation pathways were identified: efficiency enhancement, capability strengthening, and innovation-driven.This research extends theoretical understanding of AI applications in high-risk industries by developing an integrated technology-capability-value framework and a three-dimensional value evaluation model. Practically, it provides validated implementation pathways and identifies key success factors for chemical enterprises' AI transformation, offering decision-making references for enterprises and policymakers.