Data readiness is how prepared a dataset is to be used reliably by AI models or agents, across usability, integrity, context, consistency, reproducibility, and traceability. Data can exist in volume and still not be ready: it may be restricted by compliance, missing the context AI needs, imbalanced, or impossible to trace when results change.
Assessing data readiness for AI before a project starts shows the specific gaps blocking model or agent use, so teams fix the data state instead of discovering the problem weeks into cleaning.