Your Data Is Not Ready for AI, and That’s Fine
Data readiness is not a binary gate. Start with the sources and quality level required by one valuable decision.
“Our data is not ready” can become a reason to delay AI indefinitely. Enterprise data will never be uniformly clean, documented and current, nor does every use case require it to be.
Readiness is specific to a use case. A drafting tool may work with incomplete history; an automated eligibility decision cannot. Define the required sources, acceptable freshness, known gaps and consequence of a wrong answer.
Work from decisions backwards
Identify the smallest authoritative evidence set for a valuable task. Give those sources owners. Measure missing fields, contradictions and stale records that affect the outcome. Fix what changes performance rather than launching an abstract enterprise cleanup.
Make uncertainty visible in the product. Cite sources, distinguish missing evidence from negative evidence and refuse when the decision boundary is crossed.
Repeated retrieval failures and user corrections can show where data investment matters. Use that evidence to build a quality backlog tied to decisions, not a generic campaign to cleanse everything. The goal is data good enough for a declared purpose, with limitations the system and its users can understand.