Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework

Background/Objectives: Artificial intelligence (AI) is increasingly introduced into clinical trial operations, but operational usefulness does not imply regulatory or site-level readiness. This review maps AI applications across trial operations and proposes an author-developed, unvalidated site-level readiness framework and preliminary deployment-decision aid. Methods: We conducted a structured narrative review with evidence mapping of peer-reviewed literature, regulatory documents and contextual sources (2020–2026). AI use cases were mapped by lifecycle stage, technical-validity reporting, evidence maturity, autonomy, trial impact and governance implications. Maturity was assessed with an author-developed 0–8 score intended for transparent mapping, not risk-of-bias grading. Results: The comparatively strongest evidence concerns patient–trial matching and eligibility assessment, which nonetheless reached only moderate maturity, being evaluated mainly retrospectively or in simulated screening rather than inside a live trial; no use case reached the highest band. Other applications remain less mature or context-dependent. Recurrent risks include hallucination, automation bias, weak local validation, limited auditability, model drift and unclear accountability. We propose a preliminary framework linking evidence maturity, technical validity, AI autonomy, trial impact and site capacity. Conclusions: AI readiness in clinical trial operations should be assessed at the level of the AI-enabled workflow rather than the model alone. Safe adoption requires context-specific technical and operational validation, human accountability, auditability, lifecycle monitoring and alignment with Good Clinical Practice.