3 mistakes we keep seeing in early AI products
Hey everyone 👋
We’ve been reviewing and building AI products lately, and a few patterns keep showing up.
3 mistakes we see often in early-stage AI apps:
-
Using AI before fixing the workflow
If the process is broken, AI usually just automates the mess. -
Treating prompts as the product
Prompts matter, but architecture, data flow, and UX matter more long term. -
Ignoring trust signals
If users can’t tell why the AI responded that way, adoption drops fast.
What’s worked better for teams we’ve seen:
→ start with one narrow use case
→ use real data where possible
→ keep humans in the loop early
AI products win when they feel useful and reliable—not just impressive in demos.
Curious—what mistake have you seen most often?
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Strong list. The mistake I keep seeing most is solving for demos instead of daily use. A lot of products feel impressive in a 2-minute walkthrough, but fall apart when someone tries to rely on them repeatedly in a real workflow.
Completely agree. Demo moments are easy to optimize for, but daily use exposes everything—latency, inconsistency, edge cases, and workflow friction.
The real test is whether users come back and trust it enough to depend on it, not whether it looked impressive once.
In our experience, products improve faster when teams measure repeat usage and real outcomes early, instead of only first impressions.
Exactly. First impressions get attention, but repeat usage is where products actually prove themselves. If something isn’t reliable enough to become part of a routine, the demo doesn’t really matter.