AI is most valuable in mobile apps when it removes friction. Logging an expense by photographing a receipt, asking a question about your spending in plain language, or getting a workout suggestion based on your history all save effort that users would otherwise spend typing and searching.
Start with a concrete job to be done. 'Add AI' is not a feature, but 'let users log a meal by taking a photo' is. A narrow, well-defined task is easier to build, easier to evaluate and easier for users to trust.
Decide what runs on the device and what runs in the cloud. Small tasks such as simple classification can sometimes run locally, which improves speed and privacy. Larger language or vision models usually run on a server, so design the request carefully and send only the data the task needs.
Privacy is part of the product. Tell users what is sent, avoid storing more than necessary, and let them opt out where practical. For sensitive areas like personal finance or health, local-first storage with optional sync is a respectful default.
Plan for imperfection. AI responses can be slow, wrong or unavailable. Show progress, let users correct results easily, and keep a manual path so the core app never depends on the model.
Control cost and latency. Cache repeated results, limit prompt size, set timeouts and use streaming so users see answers appear progressively. Monitor usage so a popular feature does not surprise you with its bill.
Test with real users early. Collect examples where the AI helped and where it failed, build an evaluation set from them and re-run it whenever you change prompts or models. That discipline turns a clever demo into a dependable feature.