Start from the real task
Runtime performance, latency, instrumentation, and model behavior make on-device AI a budget that teams must manage.
For global mobile networks and older devices, AI failure is often about waiting time, battery, and unclear UI feedback rather than model quality alone.
A case is not yet a market
The signal matters when it clarifies a real service task, deliverable, and acceptance rule, not when it only shows a demo.
Check the delivery boundary
- Set a target duration, timeout message, cancel button, and offline or weak-network fallback for each model task
- Keep the test narrow: one service scenario with clear inputs, deliverables, acceptance rules, and human review
What still needs proof
If performance feels unstable, users interpret model delay as product unreliability. Keep the original source open so the announcement, the evidence, and this site's interpretation stay separate.