Why AI transcripts need an acceptance pipeline before anyone relies on them

A guide argues that automatic speech recognition can look convincing in demos while still failing in production on overlapping speech, noise, domain terms, names, and numbers. It recommends asking what evidence a transcript must provide before people or systems depend on it, rather than asking how accurate a model is in general. The author frames local processing versus managed APIs as a choice driven by the actual data flow, security posture, and operational capacity, not a generic privacy-versus-convenience trade-off. A comparison table lists the evidence each option requires, including data-flow diagrams, runbooks, regression suites, and deletion proof. The core principle is keeping audio, transcript, speaker structure, uncertainty, corrections, and downstream actions linked in one traceable record.