Turn readiness into SMART thresholds
Write SMART data-readiness thresholds that define what is acceptable for a specific AI workflow.
The move: make readiness pass or fail on named evidence. SMART criteria turn "the data is good" into a usable acceptance standard. Specific points to the exact data asset, field, entity, label, or document group. Measurable gives the team a calculation or review method. Achievable avoids fantasy standards that block every pilot. Relevant ties the threshold to model behavior, user trust, compliance, or operational cost. Time-bound gives the standard a freshness window or review date. For AI systems, this matters because readiness is use-case dependent. A field can be acceptable for a weekly planning report and unacceptable for an AI…
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