Guide Warns AI-Assisted CRM Research Can Hide Uncertainty Behind Plausible Data

A workflow guide published on DEV Community cautions that AI-assisted company research can appear thorough while quietly becoming unreliable when unverified fields are filled with plausible but unsourced values. The guide recommends tracking four elements for every data field: the candidate value, its source, when it was observed, and a status of VERIFIED, NEEDS_CHECK, or NOT_FOUND. It emphasizes that NOT_FOUND should be treated as a legitimate outcome rather than a prompt for the model to guess, and that conflicting values should be held in a review record rather than silently overwritten. The proposed workflow separates retrieval, evaluation, and write-back steps, requiring human approval before any uncertain data is committed to a CRM. The guide also stresses distinguishing between what a system currently returns and what it could theoretically be designed to return, urging researchers to label unverified capabilities rather than treat them as confirmed facts.