Governing Enterprise AI at Scale: from Model Risk Management to System Level Intelligence Assurance

Abstract

As enterprises transition from single model deployments to complex multi agent AI ecosystems, governance practices that focus narrowly on individual models (e.g., documentation, validation, and monitoring of model risk) prove insufficient. Failures now emerge from system level dynamics context loss across agents, inconsistent reasoning, untraceable decision lineage, and confidence drift under fragmented data estates. This paper advances a system level intelligence assurance framework that elevates governance from policy and process to architecture and telemetry. We outline a reference approach that combines shared memory, validation loops, confidence governance, policy tagging, and auditable evidence chains with operational KPIs such as reasoning consistency, lineage completeness, and redundant compute rate. Implementation patterns, adoption playbooks, and cross industry use cases demonstrate how enterprises can move beyond model risk management to govern the entire AI system ensuring decisions are explainable, compliant, resilient, and continuously improving. The proposed approach complements Quality Engineering by making trust executable and measurable in production.