I’ve watched dozens of AI demos that impressed in a conference room and died in production. The pattern is consistent: they optimize for “wow” moments instead of workflow fit.
The Demo Trap
LLMs are incredibly good at generating impressive outputs in isolation. Ask it to write a cover letter, summarize a document, or generate code — and you’ll get something that looks great. The problem is that users don’t live in isolation. They have existing workflows, existing tools, and existing mental models.
A product that requires users to fundamentally change how they work will lose to a mediocre product that fits into what they already do.
Workflow Fit Over Feature Richness
When I built the AI writing assistant, the first version had 12 features. Usage data showed 90% of sessions used exactly one: the “sharpen this paragraph” button. We cut everything else, made that one thing exceptional, and retention tripled.
The question isn’t “what can the AI do?” It’s “what is the user trying to accomplish in the next 30 seconds, and how does AI remove friction from that specific action?”
Latency Is a Feature
AI products live and die by perceived responsiveness. Streaming responses aren’t just a nice-to-have — they’re the difference between a tool that feels alive and one that feels broken. Users will tolerate a 3-second wait if they see tokens appearing. They’ll close the tab after 1.5 seconds of a blank screen.
Trust Calibration
AI makes mistakes. The question is how your product handles them. I’ve found that showing confidence scores, offering easy correction flows, and never hiding the AI’s reasoning builds more long-term trust than pretending the model is infallible.
Build for the recovery path, not just the happy path.