Assessing the maturity of artificial intelligence technologies is essential for investment decisions, project management, and policy monitoring. However, the available readiness frameworks are heterogeneous and difficult to apply automatically. To address this issue, researchers proposed RAIL (Readiness Assessment via Independent LLM-experts), a panel-of-experts classifier that operationalizes the Unified AI Readiness Level (AIRL) scale. AIRL is a nine-level ordinal scale built on an environmental evidence ladder and complemented by dimensional caps (covering specification, data existence, data quality, data legality, expert knowledge, and algorithmic maturity) together with a generality-anchoring rule and explicit assignment disciplines. RAIL consists of one evidence agent and six independent dimension agents, each a large language model with a narrowly scoped mandate, delivering verdicts that a deterministic minimum rule aggregates and a chief expert reviews under asymmetric authority. The method was tested in the analysis of several research works showing consistency and avoiding overestimation from monolithic LLM classifiers. Blogger's Review: RAIL is a significant advancement in the field of AI readiness assessment, enabling the automation of the evaluation process and reducing subjectivity and human error. In the future, RAIL is expected to be widely applied in the evaluation and management of AI projects, promoting the healthy development of AI technologies.