What AI Actually Delivers in Quality Management Systems, Beyond Vendor Hype

A quality engineer who spent 18 months piloting AI tools within an electronic Quality Management System (eQMS) has shared practical findings on where the technology genuinely helps and where vendor claims fall short. AI proved useful for semantic search, document summarization, triage scoring, drafting assistance, and auto-linking records — all in a human-supervised capacity. However, bold marketing promises such as 'autonomous CAPA' and 'predictive compliance' were found to be overstated, as AI cannot own regulatory responsibility or replace human judgment on root-cause investigations. The engineer also flagged that large language models can hallucinate plausible but incorrect information, making unchecked AI outputs unacceptable in regulated quality processes. A practical checklist was outlined to help teams evaluate AI features against audit-trail, validation, and regulatory requirements under frameworks like ISO 13485 and 21 CFR Part 820.