User Corrections Always Win: The Streaming Outlook Add‑in UI That Turns Human Edits Into Training Signal (Series Part 4)

I knew I’d built the wrong thing the first time I watched a recruiter hesitate over a single field. The UI was “working”—the extractor was returning data—but the moment the company name landed wrong, everything downstream became fragile. Not because the AI was bad, but because the system was implicitly asking a human to trust a blob of JSON. That’s not a workflow. That’s a gamble. This is Part 4 of my series “How to Architect an Enterprise AI System (And Why the Engineer Still Matters)” . In Part 3, I wrote about the six-tier enrichment cascade and why provenance has to be tracked per-field. This post is the next decision in that chain: user corrections always win—and the system learns from them . The key insight (and why the naive approach fails) The naive architecture is seductive: Extract email → 2. Create CRM records → 3. Let users “fix it later.” It fails for a very human reason: corrections made “later” are expensive, inconsistent, and often never happen. Worse, if the AI writes