# Development
How Can AI Enhance User Experience for Mobile Apps? 6 Takes From Developers
Red Rocket TeamDec 23, 20244 min read
In This Article
Context-Aware Interactions with Environmental AI Dynamic A/B Testing for Real-Time Feature Evolution Enhanced Emotional Engagement with Sentiment Analysis Intelligent Battery and Resource Management Adaptive Learning Curves for Onboarding and Skill Development Cross-App Collaboration for Holistic Experiences
Entrepreneurs spend dozens of thousands of dollars on mobile app development to reach as many target audiences as possible. However, once a user gets into the app, you need to know how to retain them. This is why you should update your mobile app on a regular basis. What to add/change in the updates?
First of all, you should find existing bugs and fix them. We also recommend following the current trends in mobile development and consider implementing artificial intelligence features in your mobile app. It's actually not as difficult as it may seem at first glance.
Statistics show that about 80 percent of companies identify artificial intelligence as part of their development strategy. Actually, already most websites and apps contain some AI functions, and you use them every day without thinking about it. You can also make your mobile app more attractive and efficient for users by implementing machine learning and artificial intelligence. In this article, we've put together for you six unique ways AI can help improve user experience in mobile apps. Lots of details, features, and real-world examples are waiting for you here!
Context-Aware Interactions with Environmental AI
Each of us has at least one app on our smartphone that directly interacts with the world around us. Have you guessed what app it is? We are talking about weather apps. Some of these programs are powered by artificial intelligence and can give advice based on current/upcoming weather conditions. For example, apps can autonomously send users recommendations to dress warm before going outdoors.
Would you like more real-world examples? Let's take a look at a well-known app used by thousands of drivers around the world. Waze is a mobile app intelligence that was one of the first to introduce innovative features. It's no secret that the time to get from point A to point B can vary depending on external factors. These include bad weather, traffic jams, accidents and more. Waze monitors the situation on the roads in real-time, providing drivers with the most up-to-date information and the best routes based on various factors.
Dynamic A/B Testing for Real-Time Feature Evolution
Mobile apps are joining the Revolution of Dynamic A/B testing. Unlike traditional A/B testing methods, which take a lot of sitting down time and limited user groups, this approach utilizes AI in mobile apps to examine the way customers behave and prefer in actual time. An app can concurrently run through multiple versions of a feature (a user interface design, say, or a content recommendation algorithm) on different segments of its audience. It is an instant process where AI reads the user interactions and selects which version will have higher engagement, conversion rates, or satisfaction.
A particularly nice application of this is in e-commerce apps. We want to take a look at an app that every one of you know about – Amazon. The app is able to analyze real-time data and determine which of the versions leads to more purchases without affecting any traffic. Amazon offers two options for the recommendation section: one that features a user’s browsing history and one that focuses on trending products near the user.
This way, the results will vary depending on the interests and actions performed by the users. For the company, this helps to increase sales as they offer customers what they might be most interested in.
Enhanced Emotional Engagement with Sentiment Analysis
Surprisingly, nowadays mobile applications can identify both environmental and human factors. This is becoming a reality thanks to voice recognition, behavioral analysis, and polling capabilities. Artificial intelligence is able to determine your current emotional state using its own algorithms. What are the benefits of such artificial intelligence in mobile applications?
- Personalization. Detecting emotions allows apps to adapt their content or suggestions to deliver greater relevance.
- Adaptability. Adjustments are made quickly, which makes users happy and less desperate.
- User retention. As a rule, users spend more time in such applications.
What real-world example of improved emotional interaction with sentiment analysis do we know? Let's take a closer look at the Woebot mobile app. It's an app for monitoring your mental health that uses artificial intelligence to analyze moods. It also shares psychology tips and helps you manage stress better.
It examines user input in text conversations and detects emotional cues, like sadness or anxity. Sometimes it might recommend a calming breathing exercise, some grounding techniques, or just messages of comfort to ease the stress.
Intelligent Battery and Resource Management
Are you often annoyed that your device's battery discharges too quickly? Sometimes it is the fault of apps that heavily load the smartphone's CPU, which contributes to fast battery consumption. In fact, there are a lot of artificial intelligence mobile apps that help to extend the smartphone battery life by optimizing some processes.
It's no secret that mobile games are the apps that load the processor the most. That's why when you play your favorite mobile games, your battery percentage decreases at a rapid pace. It's better for mobile game developers to have users spend more time playing games, which is why they came up with the introduction of artificial intelligence features.
The PUBG mobile game has more than 300 million daily users from different parts of our planet. This application utilizes intelligent resource management to enhance user experience while preserving device performance. For example, when the app detects a low battery level or signs of device overheating, it automatically adjusts graphics settings, reducing resolution or frame rates to minimize GPU and CPU load. Additionally, the game offers a «Battery Saver» mode, which optimizes performance by limiting background processes and disabling non-essential features like visual effects or advanced sound.
Adaptive Learning Curves for Onboarding and Skill Development
Many students today use apps in their smartphone for learning and this trend looks positive. However, sometimes the same process for all participants of a mobile app may not be the most effective solution. It is great if the learning methodology would be variable depending on the specific characteristics of the users.
We have an example that will explain you well what we really mean. Artificial intelligence for mobile app Duolingo changes the learning process for different users. First, you have a very short introduction to the app, and then you maintain an adaptive learning curve to a good extent. The app starts with a proficiency test based on which the app assumes the skill level of a new user. According to the results, Duolingo alters the path of learning the language by skipping a basic lesson for more advanced students or by offering more practice for students who are still beginners.
Cross-App Collaboration for Holistic Experiences
Some applications on your smartphone can interact with each other to provide more useful features. This can be useful in many aspects. Imagine you have installed a trending AI app personal assistant on your device. Its purpose is to help you schedule tasks, remind you of events, wake you up at the right time and much more. It has access to almost everything on your phone, including email, calendar, and weather to provide you with helpful recommendations.
We know of another example of cross-app collaboration using AI. MyFitnessPal and Fitbit are examples of delivering a holistic health and wellness experience. Connected, Fitbit tracks users’ physical activity (steps taken, calories burned, exercise intensity) and shares that data with MyFitnessPal.
MyFitnessPal will use the activity data to suggest personalized meal plans and caloric intakes that fit with the user's fitness goals. For example, after doing a high-intensity workout, Fitbit tracked with MyFitnessPal, you may be recommended recipes that are high in protein to support muscle recovery.
Final thoughts
As you may have already realized, artificial intelligence in mobile applications is a popular trend nowadays, and it will be in demand even a decade from now. Technologies are improving at the speed of light, so be sure to keep an eye on current practices in mobile application development.
Remember how users perceived the first version of ChatGPT. It generated a lot of interest among users, developers, and business people. Since then, some artificial intelligence tools have been able to handle many more workloads in less time.
It has been proven that by introducing new innovative features into an application, users give higher scores. The most important thing is to select and implement the right opportunities of technological progress.
Frequently asked questions
What is AI in mobile apps?
Many mobile applications from different fields of activity are incorporating innovative features called artificial intelligence. They work independently without human intervention and can receive, analyze, process, and send information to users.
Can AI integration make mobile apps more secure?
AI can be applied in any area where it is needed. In some cases, such technologies can make your application more secure against hacking and data loss. For example, you can set up algorithms that analyze user actions and identify potential threats.
What challenges do developers face when adding AI to apps?
Implementing artificial intelligence features into a mobile application is not an easy task that only experienced developers can cope with. One of the biggest challenges is to properly train the artificial intelligence so that it does not cause problems, data leaks, and so on.
