AI features users now expect in mobile apps

Quick Go-Through:

Siri AI now works inside third-party apps, Gemini Nano 4 ships on new Android flagships, and on-device models cost nothing per request. Here are the AI features users now expect in mobile apps, what the data says about trust, and how to build them well.

From a chat tab to AI woven into the whole app

For a couple of years, adding AI to a mobile app usually meant one thing: a chat screen bolted onto the side. In 2026 that is no longer what people mean. The phone itself now has a capable language model built in, the system assistant can reach inside apps to find things and take actions, and users are starting to notice which apps feel smart and which feel stuck in the past.

This guide looks at what changed this autumn on iPhone and Android, the AI features people now expect, the tools developers use to build them, and the trust and reliability lines that apps cannot cross. It is written for founders and product owners planning a new app or an update, not only for engineers.

Why expectations changed this autumn

On 19 October 2026 the Times of India reported that Indian apps including District, Glance, Noteshelf, Signeasy and Mood Dial have connected their services to Apple's upgraded Siri. Users can describe what they need, or refer to something already on screen, instead of remembering a fixed command. “Earlier, users had to set up Shortcuts or remember specific phrases to trigger an action,” said Rama Krishna, founder of Fluid Touch, which makes Noteshelf. In Noteshelf, people can now ask where they wrote about a trip, ask for an explanation of a diagram, or have a summary added to an existing note.

Android is moving in the same direction. Google's developer documentation lists the Pixel 11 series and Samsung's Galaxy Z Flip8, Z Fold8 and Z Fold8 Ultra as the first phones running Gemini Nano 4, the newest version of its on-device model. In a July post on the Android Developers Blog, Google's Caren Chang wrote that Gemini Nano is now running on over 140 million devices. And Sensor Tower's State of AI 2026 report counts more than 200,000 apps that mention AI in their descriptions, a group on track for about 10 billion downloads in the first half of 2026. Sensor Tower's CEO Oliver Yeh put it simply: “These numbers make it clear that AI is no longer just reshaping how technology is built, it's reshaping how consumers discover, engage, and spend.”

The AI features people now expect

1. Ask the app, not just the search bar

Users increasingly expect to say what they want in plain language, both inside the app and through the phone's assistant. On iPhone this runs through Apple's App Intents framework, which connects an app to Siri AI features such as personal context, app actions and onscreen awareness. On Android, Google's experimental AppFunctions API lets an app expose actions, such as creating a task or a playlist, that agents like Gemini can discover and run on the device. Signeasy's head of mobile, Santhosh J, described what that took: “That made us separate what the app does from how it looks.”

2. Point the camera and let the app do the typing

Scanning a receipt, a document or a label and getting clean, structured data back is now an everyday expectation. Apple's Foundation Models framework accepts images alongside text and gives the model on-device Vision tools such as OCR and barcode reading. Google's Jetpacker demo uses Gemini Nano 4 to read a photo of a restaurant bill and fill in a title, amount and category, and Google points out that receipts can contain card numbers and addresses, which is exactly why processing them on the phone matters.

3. Short summaries that fit the moment

Long screens of information, such as an itinerary, an order history or a busy chat, are being condensed into a few useful lines. In the Jetpacker example, Google's team improved its trip summary prompt until the response arrived in under 2 seconds instead of 13, a reminder that an AI feature is only welcome if it is fast.

4. Voice notes that turn into text

Dictation and transcription have become part of normal app use, from field notes to customer support. Google's ML Kit offers a Speech Recognition API with a basic mode on most Android devices running API level 31 or higher, and an advanced mode powered by Gemini Nano on Pixel 10 and Pixel 11 phones.

5. Personal recommendations people can steer

Shopping is where users most want AI help. In Luciq's 2026 survey of more than 1,000 U.S. app users, 39.3% said AI features make them more likely to choose one app over another, and shopping (47.2%), games (41.5%) and social media (39.7%) led the categories where they want AI improvements. Glance now uses Siri to suggest products based on style, fit, budget and favourite brands.

6. Features that still work offline

Because the model lives on the phone, features such as summaries, rewriting and classification can keep working with a weak signal. Google lists three benefits of its on-device GenAI APIs: data is processed locally, the feature works without a reliable connection, and there is no server cost for each call.

On-device or cloud: the 2026 toolkit

On Apple platforms, the Foundation Models framework has become a single Swift API for language models. It gives apps direct access to Apple's on-device model, to Apple Foundation Models running on Private Cloud Compute, and to other providers such as Claude and Gemini through the same interface. Apple says developers in the App Store Small Business Program with fewer than two million first-time downloads can use the Private Cloud Compute models with no cloud API cost, and its new Evaluations framework is meant to test whether AI features behave correctly before they ship. For teams that want their own model, Apple introduced Core AI to run custom models on device.

On Android, the ML Kit GenAI APIs offer ready-made summarization, proofreading, rewriting, image description and speech recognition, plus a Prompt API for custom text and image prompts with structured output. They run through AICore, a system service that shares one Gemini Nano model across apps. The documentation is honest about the limits: support depends on the device, different Nano versions can give different answers to the same prompt, each app has an inference quota, and requests are only allowed while the app is in the foreground.

The practical answer for most products is a mix. Use on-device models for short, private, frequent tasks, and call a larger cloud model when a request needs more reasoning or a long context. Plan a fallback for the many phones that do not have a capable on-device model yet.

Where trust and reliability come in

Enthusiasm for AI comes with conditions. In the same Luciq survey, 72.4% of users named privacy and security as their top concern about AI in apps, up from 52% the year before, and 63.4% worried about losing control over how their data is used. Only 16.6% said they were very likely to trust an app that uses AI to rearrange its own layout, while 63.9% said they would be more loyal to an app that clearly explains how their data is used. Reliability matters just as much: 15.4% said they uninstall an app after a single crash.

The developers interviewed by the Times of India drew sensible lines. Signeasy keeps signing inside the app and requires a manual review before anything is sent, and says its contract summaries use Apple's on-device models so document content stays on the phone. Glance stops at recommendations. “Glance helps users discover products that match their preferences, but it doesn't complete purchases on their behalf,” said Arvind Jayaprakash, its senior vice-president of technology. Mood Dial's founder Mohit Nandwani even cut Siri's spoken replies once music starts playing: “I took the talking out.” The report also notes that it is not yet proven whether users will make these assistant routes a daily habit, so they should add to a good interface, not replace it.

A practical checklist for your next app

Pick two or three tasks your users repeat often, such as finding a past order, scanning a document or summarising a thread, and make those smarter first.
Separate what your app does from how its screens look, so each action can be offered to Siri, Gemini and your own in-app assistant.
Keep sensitive data on the device where you can, and tell users in plain words what is processed where and why.
Keep a person in control of anything that spends money, signs, sends or deletes.
Check device support at runtime and design a fallback for phones without an on-device model.
Measure speed and stability as carefully as accuracy, and test AI behaviour before every release.

Where CoreCodify fits

Good AI features start with good product design: knowing which tasks matter, shaping the flows around them and making the results easy to trust. The model is only one part of that work.

At CoreCodify we design and build mobile apps, websites and UI/UX, and we create AI solutions that fit your product and your users' privacy expectations. If you are planning an app, or want to add useful AI features to one you already have, tell us about it through our contact page.