RAIL: An Automatic Classifier of the Artificial Intelligence Readiness Level
A paper proposes RAIL, a panel-of-experts classifier that operationalizes the Unified AI Readiness Level (AIRL), a nine-level ordinal scale for assessing AI technology maturity from natural-language descriptions.
The paper unifies three existing AI readiness frameworks into the Unified AI Readiness Level (AIRL), a nine-level ordinal scale with dimensional caps and assignment disciplines. It then introduces RAIL (Readiness Assessment via Independent LLM-experts), a classifier using one evidence agent and six independent expert agents to automatically determine readiness levels from text.
RAIL uses a panel-of-experts LLM architecture with an evidence agent and six independent expert agents, suggesting a multi-agent approach to reduce bias and improve assessment reliability. The AIRL scale incorporates dimensional caps covering specification, data existence, data quality, data legality, expert knowledge, and algorithmic maturity, enabling decidable readiness levels from natural-language descriptions alone.
Automatic AI readiness classification could streamline investment due diligence, project management, and policy monitoring by providing a standardized, scalable maturity assessment without requiring access to internal process artifacts.
RAIL offers a tool for investors, project managers, and policymakers to assess AI technology maturity quickly and consistently, potentially reducing evaluation costs and improving decision-making.
Next observable signals include peer review or replication of RAIL's performance, adoption by organizations for AI project evaluation, and potential integration into AI governance or investment workflows.