Describe a time you were pressured to ship an AI feature before you thought it was ready. What did you do?

Question

Accepted Answer

Marketing had committed a launch date for our document-analysis feature at a conference, and 10 days out our eval said it was not ready: 88 percent field accuracy overall, but 71 percent on scanned documents, which were about a third of real uploads. My first move was to make "not ready" concrete, because a date fight you can lose, a risk statement is harder to dismiss: at projected volume, 71 percent on scans meant roughly 200 wrong extractions a week reaching customers who would trust the numbers, in a feature whose whole pitch was accuracy. Then I offered scope instead of delay: ship on the date, but gate scanned documents behind a "beta, verify before use" banner with a review step, while digital PDFs, where we cleared 94 percent, shipped as advertised. That kept the conference demo and the launch, cut the risk surface to the segment we could stand behind, and gave us a visible path: the scan pipeline improved behind the gate, and we removed the banner 5 weeks later when the slice hit 90 percent. Two things I did that mattered beyond the compromise: I wrote the risk in one paragraph, numbers included, so the decision-maker was deciding with open eyes rather than against my mood; and once the call was made, I instrumented per-segment accuracy in production so the promotion decision would be data, not another argument. What I took from it: "not ready" is only useful when it decomposes into which slice, which failure, at what rate, and scope is almost always the negotiable axis that dates are not.

Answer

What if leadership had insisted on shipping scans ungated?

Document the risk and the expected error volume, disagree and commit, ship with maximum instrumentation and a kill switch, and pre-draft the rollback criteria so the first bad week triggers a plan rather than a panic.

Answer

How do you avoid being 10 days out before discovering this?

Per-slice eval gates wired into the release process from the start, so segment weakness surfaces weeks early, and marketing commitments checked against the eval dashboard before dates go public.

Describe a time you were pressured to ship an AI feature before you thought it was ready. What did you do?

Describe a time you were pressured to ship an AI feature before you thought it was ready. What did you do?

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