A System Dynamics Approach to AI Risk Governance and Organizational Resilience in SMEs: Modeling the Trade-off Between Innovation and Compliance

This paper is building a system dynamics model to investigate how the small and medium-sized enterprises (SMEs) can regulate artificial intelligence (AI) risk without reducing the innovation value that can be justified when implementing AI. The work contends that the relationship between innovation and compliance is not a linear kind of trade-off, but a dynamical regulatory system with feedback mechanisms whereby experimentation, deployment levels, risk exposure, governance mechanisms, learning routines, and resilience resources interact with one another through time. The paper incorporates the constructs of SMEs into a stock-flow simulation based on the dynamic capabilities theory, organizational resilience research, technology-organization-environment logic, AI risk management standards, and system dynamics. Findings indicate that innovation-first strategy generates rapid initial ability development and generates shadow AI, risks that were not fully resolved, more recovery time, and shorter resilience. Compliance first approach minimizes risk exposure at the cost of experimentation and introduces approval lag. Balanced adaptive governance is the strongest in the long-term outcome where tiered controls, learning loops, human oversight, and reusable compliance routines minimize risk without impacting the innovation throughput. The article adds a dynamic conceptualization of AI risk governance among SMEs and the leverage points to use by managers and policymakers aiming at responsible adoption of AI given the limited resources.