AI compliance as ROI: Dynamic value preservation in the age of algorithmic governance

The rapid adoption of artificial intelligence across financial services, digital platforms, healthcare, and other sectors has prompted increased regulatory attention to algorithmic decision-making, transparency, data governance, and risk management. Existing discussions of AI compliance frequently characterize governance obligations as costs imposed on firms through regulation, enforcement, and litigation risk. While this perspective captures an important dimension of compliance, it does not fully account for the broader economic effects of governance failures in AI-intensive environments.
This paper develops a framework for evaluating AI compliance as a mechanism of enterprise value preservation rather than solely a means of avoiding legal liability. Drawing upon principles of risk management and corporate governance, it introduces the Dynamic Value Preservation (DVP) framework, which conceptualizes AI governance as a tool for reducing volatility across interconnected legal, operational, financial, and reputational risk domains. The framework posits that governance structures capable of identifying, monitoring, and mitigating AI-related risks contribute to organizational stability and long-term value retention under conditions of regulatory uncertainty.
To examine this proposition, the paper conducts a comparative case study of JPMorgan Chase & Co. and Meta Platforms, Inc., representing contrasting approaches to AI governance. JPMorgan’s governance structure is characterized by the integration of compliance functions within enterprise risk management and model oversight processes, whereas Meta’s approach reflects a greater reliance on post-deployment governance adaptation. Applying the DVP framework to these firms illustrates how differences in governance architecture may affect regulatory exposure, operational resilience, reputational risk, and value preservation outcomes.
The analysis suggests that AI compliance performs functions that extend beyond traditional legal risk mitigation. As regulatory requirements continue to expand and AI systems become more deeply embedded in commercial activity, governance capabilities may increasingly influence firm resilience, transaction efficiency, and competitive positioning. The paper concludes that AI governance should be evaluated as a component of enterprise risk strategy and long-term value preservation rather than solely as a regulatory compliance obligation.