AI-Powered Mobile App Development: Architecture, Use Cases, and Best Practices

Nowadays, a good deal of efforts in app development is directed towards adding a predictive edge to apps. Consequently, several apps have the ability to proactively interpret a user’s intent. Some even go a step further and anticipate the user’s request. Examples of such requests abound. For instance, an app can automatically arrange photos in a particular order, or even suggest an appropriate response to a message. Similarly, an app can alert the user about unauthorized transactions on her/his credit card. The underlying technology for such advanced functionalities is AI.

The focus of this article is to provide insights about AI in mobile app development. We will cover various components of such apps, the manner in which they interact with each other and the various use cases of such apps across different industry verticals. Further, we will touch upon some of the best practices which increase the chances of the successful completion of such projects. In this context, we will discuss the role of AI development services.

What is an AI Powered App?

AI powered apps can gain knowledge and make their own decisions. These apps can be developed and designed similarly to conventional apps. Behavior of a conventional app is determined by the commands programmed in it, and commands are designed in a way that a certain action is always followed by the same consequence. An AI app can not be designed by just specifying its input and output. Therefore, skills and knowledge learned by the AI app through training and examples determine the behavior of that app. Designing and developing AI apps can be challenging and complex.

An app can be designed with AI to give suggestions to the user about what word comes next, based on what word comes next in what other word in a large body of text. Current mobile app development with AI focuses on developing apps which can do two things.

First, apps need to be designed with conventional UI/UX and other conventional app features. Second, AI developers need to integrate AI models which will perform the intended intelligent tasks. Therefore, developers need to select an appropriate AI model based on the use case and integrate it in the app.

This helps determine whether an app includes true AI. An app that uses AI to perform spell checking doesn’t merit a spot in the AI App category. An app that uses AI to take meeting notes and summarize merits consideration. This is especially true if the main reason for opening the app is to access the AI model.

The same can be said for the features implemented by an AI app. If these features prove popular and increase user engagement, more are rapidly added. Developing these features often requires the services of specialized AI companies. AI development companies assist with the model development process including data cleaning and result interpretation. Many companies outsource the development of AI components involving model architecture.

The Architecture of AI Apps

AI apps are built of components and structures referred to as architecture. The building blocks of these apps are relatively few. Understanding these components is essential to designing AI components of a mobile app.

The first layer is the interface. This is what users touch, click, type on, or speak to. An interface is made up of a user input system, a way to get information to the model, and a way to give a result back to the user. An example of this is a user taking a photo. The model decides how to handle the photo. The model then gives the user the result.

The second layer is the code that makes up the app. This code tells the app the correct approach to take in making a request to the model. This code also implements app behaviors, user controls, and other app features. This code also makes conclusions about user actions and behaviors and takes app actions based on the user behavior. This code, along with the interface and the model, implement the app’s logic.

The third layer is data. Models can’t think without data the same way a car can’t move without fuel. This data can be user preferences, user settings, user history, user data, and other files. Models can read this data and use it to give the user an answer. This layer determines the quality of answer a user gets.

The fourth and final main component of an AI system is the model layer. The model is the trained component of the system that makes predictions. Models can perform a variety of tasks such as classifying objects in an image, reading handwritten text, or forecasting trends. Training models is typically the most computationally expensive and time consuming aspect of building AI systems. For this reason, most of the work in developing an AI system occurs on a server and is completed prior to deploying the AI system on a mobile device.

The fifth component is the backend. The backend of a mobile AI system is comparable to a traditional software system and includes servers and databases. The backend receives a request from the mobile device, processes the request using the AI model, and returns the results to the mobile device. The development of the backend is unavoidable and is seen with software teams of all sizes.

How Data Flows in a Mobile AI System

Data flows through a Mobile AI system in two primary directions. The secondary path is from the model to the user. After the AI system makes a prediction, the system must present the results to the user in a format that is understandable.

In addition to providing a solution to the user, the model must learn and adapt to the user in order to improve its efficiency. Feedback from the user is captured and used to further train the AI model. In the absence of this feedback, the model is powerless to learn and adapt to the user.

APIs and Model Serving

An AI app comprises a mobile app and models, which need to communicate for the app to work. Communication between the mobile app and model is facilitated through an API. Model serving, on the other hand, refers to the process of making a model available to be consumed by a client, in this case, the mobile app. The model is made available by a model serving system. AI development companies concentrate on response times, to ensure that the features developed will not put off users with long wait times. This is because, in mobile app development, it is good practice to ensure APIs are clearly defined.

Mobile App Development: Limitations Lead the Way

There are differences between mobile phones and servers. A mobile phone can lose a signal and drain its battery. Older phones have less memory than newer phones. For these reasons, mobile app developers test their apps on older phones. A good mobile app is one that can be used on phones of any release year. These limitations are particularly relevant to AI mobile app development. Most AI development companies test your app on older phones.

Examples of AI Use Cases

Below are some examples that illustrate how various kinds of AI improve the services offered by mobile apps. Each example represents how user expectations have changed due to the prevalence of AI, and how mobile app development companies must integrate AI in their offerings to serve users better.

AI analyzes patterns in the data streaming in from your devices. Fitness apps might detect an elevated resting heart rate, and recommend you get it checked out. Other apps in the space might help you track your medication, or log your symptoms. They might even help you take better photos of your symptoms. Most of these apps aren’t designed to diagnose or treat, but rather help you surface concerns in a timely manner. Many of the companies that develop these AI models are also active in Health, Retail and Finance.

AI is pervasive in retail and ecommerce. Recommendations are baked into search. Many retail apps use AI to predict which items will be purchased, or which will sell out. In some cases, AI is used to make recommendations about inventory. These models are also used to determine which items will be purchased, and assist with order fulfillment. AI provides retail and ecommerce apps the ability to integrate search and recommendation features quickly.

AI models can help protect your financial security. You can set up rules about how your card should be used. If your card is used to make a purchase that doesn’t seem normal, AI models can pause the transaction to make sure it’s valid. AI models in finance are used to alert you when your spending looks unusual. Since the models learn your normal spending, they can also alert you when an upcoming bill looks larger than normal. Due to tight regulation on the finance industry, there are many barriers to entry for financial apps.

Some travel apps use algorithms to determine when flight prices will change. This allows the apps to tell users the best time to make a flight reservation. Other travel apps use live data to assist users in creating an itinerary. Even more, travel apps allow users to take a picture of a foreign language menu and instantly translate it.

There are similar advances in other fields. Education mobile app development uses AI to create individualized lessons based on the user's performance. The apps cover material the user has previously mastered at an accelerated pace and focuses on new material the user has a deficiency in. Other education mobile app development covers AI using speech models to correct a user's pronunciation.

There are other mobile app developments using similar AI. For example, AI is used to create restaurant staffing forecasts based on past customer orders. Similarly, other mobile app developments use AI to create staffing and scheduling forecasts for the delivery industry. It is even used to help warehouse staff efficiently count the stocked items on a shelf.

Each feature of the app accomplishes the same thing, which is capturing a user’s input, using AI to generate meaning, and producing output that enhances the user’s experience. Each field of expertise has its own set of input signals and definitions of “useful.” This is where things can get tricky. AI development can vary by field, and many companies enlist the help of AI development services to support them.

Helping to create AI-powered mobile apps

Working with artificial intelligence can be daunting. Some of the more common issues that arise during mobile app development can be found in the section below.

Have a specific goal in mind and incorporate it in a way that solves a prominent issue that users have. A well-defined goal can be easier to incorporate and test than an overarching mission. Instead of stating that a company will “use AI to disrupt the grocery space,” a company could start with a feature like “Allow users to scan a barcode and provide a recommended shopping list based on user input and previous purchases.”

When dealing with artificial intelligence, many developers think that complicated models yield better results, when in reality, simple models can yield better results, especially if data is incorrect or noisy.

Respect user privacy. Treat data that users create and share through your app (such as their voice or location) as if it were sensitive. Collect and retain only data that is strictly necessary. Perform data processing on the device if you can. Tell users in clear language why and how your app processes their data. Users expect that your app will be able to handle their data in a way that will respect their privacy and will feel anxious if their data is in your app. This is particularly true for data that is processed by artificial intelligence. When you engage a mobile app development services company, ensure that you assess how they will protect the data of users of apps that they will develop.

Build apps that will work in low connectivity situations, which will also enhance user experience in situations with good connectivity. Build apps that will function effectively in situations where the device is not connected to the Internet. Spinning wheels give poor user experience and should be avoided. Good user experience design will ensure that your app will function effectively in situations where the Internet is not available. A good app should function effectively in situations with low connectivity. This will also enhance the experience of users of your app. The app should have good offline support.

Test your app on the device on which it will be used, rather than emulators and simulators. Real device testing is an integral part of mobile app development. Real device testing should be part of your Artificial Intelligence app development.

Let your users correct your AI. All models are wrong in some way and allowing your users to point out those errors encourages trust in your AI. If your users don’t trust your AI they won’t use it. Users will also give your development team feedback to improve the AI, which is really just training data for the next iteration of your model. Tools to provide feedback to developers are commonplace in mobile app development.

Common Problems and How Teams Overcome Them

Conclusion

AI enhances mobile app development by allowing app developers to incorporate models and data in a way that enhances the app's overall intelligence and interactivity. When these elements are integrated, the app can feel as though it is personable and interactive.

We analyzed the five layers of these applications, the on-device vs. cloud alternatives, and the application lifecycle. We reviewed common AI use cases for health, finance, shopping, food and other delivery services, travel, learning, and other apps.

There is not much mystery or life hack here. Those involved in the development of AI applications are usually highly disciplined, and often obsessive. They build AI for one clear purpose, and often, proudly ship applications containing AI for one clear case. They often introduce the AI to a problem domain rather than the other way around. Mobile applications often incorporate AI for natural language processing to strengthen the mobile application.

Mobile app development best practices include a number of practices, such as using on-device data rather than cloud data, protecting mobile app user data, and performing user acceptance testing (UAT) on end user devices.