Most AI features fail for the same reason: they're built to demo, not to deliver. They look impressive in a pitch and then get used once. The features that stick share one trait — they remove a specific, painful step the user was already doing by hand.
The filter we use is simple. Does the feature eliminate a task the user hates, or does it add a new thing they have to learn? AI that removes work compounds in value. AI that adds a chatbox to a workflow that was already fine gets ignored, no matter how clever the model is.
The features worth building tend to cluster around three jobs: triage (sorting and routing the flood of unstructured input), extraction (pulling structured signal out of documents and messages), and draft generation (giving a human 80% of a first pass on something they'd otherwise stare at). Notice what's missing: open-ended chat as the product itself.
Build the guardrails first and the feature second. The difference between an AI feature users trust and one they abandon is almost never the model — it's whether you put real boundaries on what it's allowed to do and say. Get that right and even a modest model beats a frontier one with no rails.
