If you've shipped an AI feature lately, you've probably seen at least one of these:
Users try it once. They never come back.
Generation counts climb. Export, save, and approve rates stay flat.
A user corrects the AI. Next week, same correction. Week after that, same correction.
Permission grant rates collapse the moment you ask for write access.
The problem isn't usually the model. It's the hundred small product decisions around it — and most generic AI UX advice doesn't help you figure out which one is actually broken.
A free tool for diagnosing it
I put together a free Adoption Triage Deck that takes about two minutes:
You describe what your users are doing, what your metrics show, or what your users are literally saying ("I didn't know what to ask," "I have to rewrite everything," "I'm not comfortable letting it act on its own"), and it maps the symptom to one of 12 diagnostic categories:
- First-Use Dropoff
- Empty Prompt Paralysis
- Trust Gap
- Output Not Usable
- Context Failure
- Overreliance Risk
- Automation Anxiety
- Correction Loop Breakdown
- Privacy Hesitation
- Retention Without Habit
- Quality Ops Gap
- Team Adoption Friction
Each diagnostic points to specific design patterns that target it. So instead of "improve trust," you get something like "your day-2 retention pattern looks like a Trust Gap — the cards that fix it are Evidence Trail, Confidence Label, and Decision Boundary."
Why this matters for engineers too
If you're the eng lead on an AI product, the language barrier between you and your PM is real. The triage gives you a shared vocabulary for the product problem your AI feature has — separate from the model problem you might be debugging in parallel.
It's free, takes two minutes. Worth running before your next sprint planning if your AI feature isn't sticking.
If you want to go deeper, there's a full deck (104 cards across 10 stacks) at aiproduct.cards, but start with the triage — it'll tell you whether the deck is even pointed at your problem.
What's the strangest AI adoption pattern you've seen in your own product? Drop it in the comments — happy to point you to the right diagnostic.
