Definition
AI-ready data is data demonstrably fit for a named AI use. Readiness can include accurate values and labels, suitable structure, representative coverage, documented collection, provenance, legal rights, privacy controls, freshness, accessible formats, and separation between training, validation, evaluation, and production feedback.
The definition is intentionally conditional. A dataset ready for search may be unsuitable for model training. Data licensed for internal analysis may not be licensed for fine-tuning. A clean historical sample may fail a forecast when the population changes. A useful readiness statement names the model task, users and affected population, permitted use, quality thresholds, known gaps, and owner.
Readiness is an evidence claim
NOAA's emerging AI-data guidance treats readiness as more than machine readability. That is the right posture: a parquet file with no lineage or rights can be technically convenient and operationally unusable. Readiness should be reviewed again when the purpose, model, policy, or population changes.
Distinguish it from nearby terms
Data quality measures fitness against requirements. Data governance assigns authority and controls across the lifecycle. AI-ready data combines those concerns for a specific AI use. Training data teaches a model; evaluation data must remain sufficiently independent to measure it.
Check your understanding
A customer-support archive is clean and searchable but contains private data and no record of consent for model training. Is it AI-ready? Perhaps for authorized retrieval, not for training. Readiness changes with purpose and rights.