"Is the model accurate enough" is one of the most common questions asked before an AI system goes into production, and it's usually not specific enough to answer well. Accurate enough compared to what baseline, on which cases, and (the question that matters most and gets asked least) what happens operationally when it's wrong, because every model deployed at any real scale will be wrong some percentage of the time.
A system that's 95% accurate sounds solid in isolation. Whether it's actually acceptable depends entirely on what the other 5% costs, and whether the failure mode is graceful or silent. A recommendation engine that's wrong 5% of the time in a way that just shows a mildly irrelevant suggestion is a very different risk profile from a system making a 5%-wrong call in an underwriting or clinical decision pipeline, even at the same headline accuracy number.
The more useful framing
A more productive set of questions to ask before deployment: what does the system's failure look like? Does it fail loudly with a low-confidence flag, or does it fail silently with the same apparent confidence as a correct answer? Who or what catches a wrong output before it causes downstream harm, and is that a real, tested mechanism or an assumption nobody's verified? And critically, does the error rate hold steady across the different populations or cases the system will actually see in production, or does the headline accuracy number hide a much worse rate for a specific subgroup that happens to matter most?
Why this framing gets skipped
These questions are more work to answer than reading a single accuracy metric off a model card, and they don't produce as clean a go/no-go number. That's exactly why they're worth insisting on before a deployment decision rather than after an incident: the cost of asking them upfront is a slower approval process; the cost of skipping them is finding out the answers in production, from a case that didn't get caught.