Traditional software fails loudly and consistently — the same input breaks the same way every time, so once you fix it, it stays fixed. AI features fail quietly and inconsistently, which means the usual signals a team relies on to know something is "done" — it passed QA, it's been in production for two weeks with no bug reports — actively mislead you. Here's the pattern I keep seeing.
The demo tested the model. Production tests the distribution.
Whoever built the feature tested it against the ten to twenty examples they had in mind while building it, and it performed well, because those examples were exactly the shape of input the model handles best. Production users don't type in that shape. They paste in malformed data, ask questions sideways, use the feature for something adjacent to what it was built for. The failure rate on the distribution you didn't test for is the one that matters, and it's invisible until real usage volume arrives.
"No bug reports" isn't the same as "working"
With deterministic software, silence from users is decent evidence things are fine. With an AI feature, a user who gets a slightly wrong or oddly-worded answer usually doesn't file a ticket — they just quietly lose a bit of trust and use the feature less next time. The absence of complaints looks identical to the absence of problems. The only way to tell them apart is to actually sample outputs against real usage and check them against a standard, rather than waiting for someone to complain.
The regression that doesn't look like a regression
A model or prompt update meant to fix one issue can silently degrade performance on a use case nobody was watching, because there's no compiler error to catch it — the output is still syntactically fine text, just wrong in a new way. Without an evaluation set that covers the range of things the feature is actually used for, teams find out about this kind of regression from a customer, weeks after the change shipped, rather than from a test suite in minutes.
What "done" should actually require
Before calling an AI feature production-ready: a written definition of what a correct or acceptable output looks like for this feature, not just "seems reasonable"; a test set built from real or realistic edge cases, not the ten examples from the demo; a sampling process that reviews live outputs on a schedule, not only when someone complains; and a way to catch regressions when the model or prompt changes, before customers do. This is a different kind of test pyramid than traditional QA, and most teams shipping AI features haven't built it yet — not because it's exotic, but because nobody told them the old definition of "tested" doesn't transfer.
This gap — a feature that looks shipped by every traditional signal but hasn't actually been evaluated against how it fails — is the most common thing I find in an AI readiness assessment. See how that engagement is scoped.
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