The AI Feature You Should Not Have Shipped: A Task-Shape Checklist
The demo always works. That is the most expensive sentence in AI product development. The product manager sees the model handle the happy path, the engineer ships the obvious version of the feature, and six weeks later the support queue is full of complaints that the metric did not predict. Nothing in the model regressed. Nothing in the prompt got worse. The feature was simply not the shape the model could do well, and the team did not have a way to say so before the work began.
A meaningful fraction of shipped AI features fail this way — not because the model is bad, but because the task is wrong. The output the product needs is deterministic and the engine is stochastic. The user's tolerance for the tail is one bad answer per thousand and the model's failure distribution is heavier than that. The latency budget the unit economics require is half of what the model can deliver at any tier you can afford. The ground truth required to evaluate quality does not exist and cannot be cheaply created. None of these are model problems. They are task-shape problems, and they should have been screened before the first prompt was written.
