Five use cases of AI in mobile app APIs development
The main idea behind AI is to develop algorithms that can learn from past data and predict future behavior. And the best way to do this is by using the machine learning system, which enables the AI to remember vast amounts of data to predict future trends, patterns, and outcomes.
The truth is AI has been around for many years now, but only recently has it become more popular thanks to the proliferation of mobile app development APIs and frameworks.
The demand for new and exciting features and integrations such as personalization, predictive analytics, and recommendations is high. Small and large businesses can harness the value of AI technology within their app with ease, thanks to mobile app APIs that provide these features.
1. Auto-detect and remove bugs
With the help of AI, builds can be more accurate than ever before. It allows for quick fixes and a greater understanding of precisely what’s causing errors and crashes. It can save time, money, and frustration for developers and consumers alike.
Machine learning can detect when an app is struggling to load or run properly, allowing developers to fix it before users even experience problems. Apps that run smoothly are more likely to retain users — and good reviews on the App Store and Google Play store.
For example, if an app is supposed to give a specific response when a particular button is pressed but instead gives an entirely different reaction, there is an issue with the code that needs to be fixed. With AI, the problem can be detected and corrected.
2. Automate Tasks
AI can be used for automating tasks such as data entry or making apps easier to use by learning the user’s preferences and predicting their needs.
AI is used for automating repetitive tasks involved in application development such as image resizing, background-color changing, font selecting, etc. It allows developers to spend more time on more valuable lessons than spending hours doing something basic like resizing an image.
3. Natural language processing
Natural language processing (NLP) is an artificial intelligence technique used to analyze and understand human speech. In addition to using Natural Language Processing & Machine Learning to improve their products, companies also use tools such as neural networks and deep learning to make their products more efficient.
Companies are already working to incorporate AI into app development tools. These applications could help streamline developing and testing mobile apps, making it easier for more organizations to build their software applications.
Many mobile apps use NLP to improve the user experience when interacting with an app through texts. Siri is one such example. Natural Language Understanding (NLU) allows an app to understand your text inputs and provide you with relevant information based on the context in which these inputs were given. For example, if you ask, “what’s the weather like today?” it will fetch you relevant information related to weather from different sources and display them to you in a single place.
4. App Store Optimization (ASO)
App Store Optimization is crucial for any application to thrive on the App Store, and AI can be used to make this process more efficient. The importance of ASO for each app varies based on its category, but it’s essential for most apps to at least have a presence on the first page of relevant search results.
The problem is that ASO is hard work, which means that developers usually have to rely on trial and error to develop a good strategy.
But AI could help with that by analyzing user behavior, reviews, and more to determine what strategies work best for different categories of apps. It can then apply those strategies automatically using test cases, so developers wouldn’t need to spend time manually making changes that might not even work.
This type of AI could also be used as part of a continuous integration/continuous deployment (CI/CD) system or other automated testing tools that can determine how effective new versions of an app are at meeting specific metrics like retention rate
5. Predictive maintenance
Predictive maintenance is an application of artificial intelligence (AI) that predicts the stages of wear and tear in a code, allowing for early maintenance and repairs to avert critical failures. A predictive maintenance solution can solve issues with this predictive technology. It can help to reduce the time, costs, and risks associated with unplanned downtime and improve operational efficiency by optimizing the code.
The program will analyze the faulty data from this place to predict what actions need to be taken and how to take them. It will check all possible options, such as removing a part that is not necessary at this moment or looking for a particular spare part. The program will then decide whether it should order the required spare part and schedule its delivery.
Tons of data are available to developers when creating a new app, and building a program that can analyze all those numbers can be a challenge. Predictive analytics allows developers to use algorithms to predict what users want or need: If they don’t open the app much on Saturdays, perhaps they want a reminder to check out the latest sales.
Last Words
The most significant advantage of AI lies in its ability to process vast amounts of data each second, making more accurate predictions and decisions based on user behavior and characteristics. Unlike human-made algorithms, AI can find correlations between different aspects of data and conclude by itself. Thus, AI can optimize various processes within mobile apps such as advertising, content filtering, search capabilities, or any other method that involves making decisions based on collected data or previous experience.
