On-device vs. server-side processing
Some AI features (basic classification, simple filtering) can run on-device for speed and offline availability; most genuinely useful AI features (the ones covered in AI-Powered Application Development) still call a server-side model — this tradeoff is decided per feature, not as a blanket rule.
What happens offline
An AI feature that depends on a server call needs a defined, honest offline state — a clear message, not a silent failure or an infinite spinner. Same discipline as What Happens When an AI Automation Fails, applied to intermittent mobile connectivity specifically.
Latency and cost in a mobile context
Mobile users are less tolerant of multi-second waits than desktop web users in most contexts — this affects which AI features are worth adding and how they're built (streaming responses, optimistic UI) more than it would on a dashboard.
Common genuine use cases
Document/photo scanning and extraction, in-app search, and a bounded conversational assistant for a specific task — the same underlying patterns as AI-Powered Application Development, adapted for mobile constraints.
Questions
Does every AI feature need internet connectivity?+
No — some basic processing can run on-device, though most substantive AI features call a server-side model.
What happens to the AI feature if the user is offline?+
A defined, honest state is shown rather than a silent failure — designed per feature during scoping.
Is this different from adding AI to a website?+
Yes — connectivity variability and latency tolerance are more central mobile-specific constraints than on a typical web session.