AI as Core Infrastructure in Radiology: Moving Beyond Pilots to Operational Excellence

Artificial intelligence (AI) adoption in radiology has accelerated, but operational maturity has not kept pace. Many organisations still deploy AI as isolated point solutions, leading to fragmented workflows, duplicated integration work, inconsistent governance, unclear accountability, and limited ability to assess value over time. For radiologist-facing clinical AI, these problems are less about model performance alone and more about how AI is introduced, integrated, and managed in practice. This commentary argues that radiology should treat AI not as a collection of disconnected products, but as a managed operational capability supported by shared governance, integration, measurement, and continuous oversight. A practical six-part framework is proposed: clinical intent, workflow orchestration, integration architecture, governance and safety, measurement and analytics, and continuous improvement. A 90-day roadmap is also presented to help imaging leaders move from scattered pilots to repeatable operational deployment.