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 synthesizes evidence on AI applications across clinical trial operations and proposes a site-level governance model for responsible implementation in healthcare organizations. 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 trial lifecycle stage, evidence maturity, autonomy level, trial impact and governance implications using a transparent 0–8 evidence-maturity score. Results: The strongest evidence concerns patient–trial matching and eligibility assessment, which nonetheless reached only moderate maturity because the systems were evaluated 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 site-level readiness and deployment-decision model linking evidence maturity, AI autonomy, trial impact and site implementation 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 validation, human accountability, auditability, lifecycle monitoring and alignment with Good Clinical Practice.