AI in a product stops feeling like a gimmick when it's built around a job the user already has, rather than a capability the model happens to offer. A summarize button feels gimmicky because most users never asked to summarize anything. A claims adjuster who now sees the three sentences that matter in a 40-page PDF feels very differently, because that was a job they already had and hated doing by hand.
Start from the job, not the model
The gimmick pattern is consistent. A team gets access to a capable model, then goes looking for somewhere to put it: a chat widget bolted onto a dashboard, or a 'magic' button with no clear job behind it. Users try it once, get an unpredictable result, and never touch it again.
The better approach is to start from friction that already exists, such as the report nobody has time to write, the ticket that takes 20 minutes to triage, or the spec sheet someone re-reads for the same three numbers every time. Then ask what AI removes from that specific job. The resulting feature usually doesn't look like 'AI' from the outside. The product simply gets better at something it already did.
The tell-tale signs of gimmick AI
Gimmick AI is usually optional and easy to ignore, while useful AI features tend to sit in the main flow rather than a side panel. It asks the user to write a prompt from scratch, even though most people don't want to learn prompting and the product should already know most of the context. And it has no visible fallback. When the model gets it wrong (and sometimes it will), a gimmick feature just breaks, while a well-designed one falls back gracefully to a manual path.
Design for correction as well as generation
The products that earn trust make an answer fast to check and fix, instead of just producing it. Show what the AI used to produce the output (the source document, the fields it read). Make editing a first-class action rather than an escape hatch. Log every AI action so a mistake can be traced back to its cause.
This matters more as the stakes rise. A wrong auto-complete suggestion is a shrug; a wrong quotation sent to a distributor is a real cost. The design bar for the feature should scale with what happens if the AI is wrong.
Measure adoption over novelty
A launch demo will always look good, because that's what demos are for. The real test is whether people who don't have to use the feature are still using it in week four. Track repeat usage on the specific job the feature targets. Raw 'AI feature clicked' counts mostly measure curiosity, and tell you little about value.
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