Why One-Time Approval is No Longer Enough for AI Systems

Abstract

The advent of artificial intelligence (AI) systems that learn, evolve, and interact with their environment after initial deployment requires a shift from traditional "one-off" approval models of governance. This article discusses problems with one-time approvals and recommends a life-cycle approach to oversight. It explains how changes in data, operating environments, and interactions can result in model drift, new risks, and a loss of performance over time. Drawing on recent advances in AI monitoring and governance approaches, the article offers a pragmatic view that combines technical monitoring, human oversight, and organisational accountability. It presents key governance factors such as types of risk, monitoring approaches, and decision-making processes, and considers the challenges of implementation for large and small to medium-sized enterprises. The key point is that approval should be the first step, not the last, in AI governance, allowing organisations to sustain trust, compliance, and performance over the lifetime of AI systems.