Making AI Observability a Production Requirement

AI systems can degrade while infrastructure metrics remain healthy, making conventional monitoring insufficient for production AI. The article defines AI observability around model performance, data and concept drift, explainability, bias detection, and incident response. It recommends establishing business-linked metrics, automated thresholds, audit trails, and response procedures from deployment rather than after failures emerge. Technology leaders should treat observability as part of AI architecture and operating governance, particularly where model decisions affect regulated or high-consequence processes.