Why most AI features get deleted — and how to build ones that stick
A slick demo is easy. The unglamorous 90% that survives real users is the actual work.
Every team can ship an AI demo now. Far fewer ship an AI feature people still use three months later. The gap is almost never the model — it is everything around it.
The demo trap
Demos run on clean inputs, a friendly path, and a forgiving audience. Real users bring messy data, edge cases, and zero patience for a confident wrong answer. A feature that dazzles in a pitch and misfires in production gets quietly switched off.
What sticks actually needs
- Grounded in your real data, not the model general knowledge
- Guardrails and evaluation so you see how it behaves before customers do
- A human-handoff path for the cases it should not answer
- A baseline to measure against, so better is a number, not a vibe
Start smaller than you think
Ship one narrow, high-value job done reliably instead of ten it half-does. Reliability is the feature. Earn the usage, then widen the scope from evidence.