Artificial Intelligence in Mobile App Development: Use Cases and Capabilities

Both mobile application and artificial intelligence are extremely popular sectors that have seen tremendous growth in a relatively short time – and are expected to grow even more in the near future. 

The mobile application sector is supposed to reach a massive $755 billion by 2027. The AI sector has even more positive predictions going for it – $1.8 trillion by 2030. While mobile apps have been around for about a decade now, AI has only begun its growth and is already predicted to earn trillions in less than a decade.

The creators of ChatGPT and Dall-E, the Open AI company, are considered to be the most funded Machine Learning company on the planet with over $1 billion of funding in 2022 (and the second place on the same list being barely above $600 million).

It should not be surprising to see both of these markets being intertwined as time goes on – especially considering how much more effective they can be together, with AI enhancing mobile app experience in many different ways. It can be somewhat challenging to implement elements of AI in a mobile app development process if the development team is unprepared – but the number of advantages that can be acquired with proper preparation is simply astounding.

How mobile apps were developed in the past?

In the initial stages of mobile technology, devices faced limitations in processing power and storage capacity. Developers operated within a relatively constrained environment, resulting in simplistic early mobile apps. These applications, such as calculators, calendars, and messaging tools, offered basic functions to users.

Furthermore, the development process during this period was labor-intensive, demanding meticulous coding and manual testing to ensure compatibility across various mobile platforms. The constrained nature of mobile devices at that time necessitated a more hands-on approach to ensure the functionality of applications.

Biggest challenges of pre-existing mobile application development processes

• The need to write complex algorithms and codes manually. 
• The development process was time-consuming and followed a step-by-step approach. 
• If you were unfamiliar with the standard app development process, a comprehensive guide was essential for building mobile apps.
• Designing user interfaces with intuitive experiences posed significant challenges, and applications struggled to adapt to individual user preferences.
• Ensuring seamless app compatibility across a wide range of screen sizes and resolutions posed a significant challenge, leading to inconsistencies in user experience.

The landscape of mobile app development was characterized by these challenges, emphasizing the need for advancements and innovations in the field. And these advancements did not take long to arrive – offering better development tools, more complex feature sets, and so on. The introduction of Artificial Intelligence, on the other hand, is slightly more unconventional in comparison.

The introduction to Artificial Intelligence

Artificial intelligence (AI) is a technology designed to reproduce the cognitive functions of a human being for tasks that mostly revolve around solving issues and making decisions. It is a multidisciplinary field that draws upon principles from computer science, data analysis, and algorithmic processing to perform actions more swiftly and accurately than traditional human methods.

The term itself usually refers to a number of different sub-fields that are all a part of the “Artificial Intelligence” term group – not the “purest form” of AI that most people have in mind as self-aware machines with human-like intelligence. Here are several examples of AI subfields:

• AI applications that enable machines to interpret and analyze visual information from the world, often used in image and video recognition – Computer Vision.
• AI techniques that enable machines to process human language (not just understand it, but also interpret it and even generate language snippets of its own) – NLP, or Natural Language Processing.
• A subset of AI that revolves around algorithms capable of learning over time and improving their results without the need for additional programming additions per se – Machine Learning.
• Automating various repetitive tasks with the help of software robots to free the work time of employees who were performing these tasks by hand – RPA, or Robotic Process Automation.
• Computing models that were commonly used in machine learning/deep learning and are inspired by the way the human brain is structured on the inside – Neural Networks.
• Several layers of neural networks allowing for more complex data to be processed – Deep Learning.

Finance, insurance, retail, healthcare, and real estate – these are just a few examples of industries that are introducing AI into their work processes in some way. Its application in mobile solutions is no exception, offering numerous opportunities to enhance efficiency, decision-making, and user experiences across different business sectors. The versatility of AI allows it to be applied to diverse domains, showcasing its potential to revolutionize the way tasks are performed and problems are addressed in the modern world.

Advantages of AI for mobile application development

Plenty of AI-related capabilities rely on extensive data sets – and mobile applications gather tremendous amounts of data regularly, making them a great option for the development of various AI features. The process of embedding AI into mobile app development is still relatively new, so there are plenty of users who are not convinced of its usefulness yet. 

At the same time, the number of advantages this union can bring is tremendous, overshadowing any of the potential risks. There are three main benefit categories that AI in app development can improve upon – Efficiency, Automation, and User Experience.

The Efficiency improvement category is relatively obvious – mobile applications powered by Artificial Intelligence can offer a much higher performance than an average human. This includes a wealth of different potential tasks – from workflow management to data processing and even responding to customer questions.

The Automation category is also rather straightforward – the addition of Artificial Intelligence allows enterprise solutions to automate a large number of different actions and tasks, more than ever before. Drastic improvements in the automation department also eliminate errors in specific fields and offer better result accuracy across the board.

The User Experience category is slightly different from the rest since it is not necessarily about obvious improvements. The user experience as a whole can be made much more personalized with the introduction of AI, offering products and services that are deemed useful to each individual user (with the help of sentiment analysis).

That’s not to say that all benefits of AI in mobile app development can only belong to one specific category out of the three. For example, the ability of Artificial Intelligence to help developers during the actual development phase by writing elements of code belongs to both the Efficiency and the Automation categories (with examples such as TabNine, Github Copilot, and more). This particular capability of AI is not just about generating code snippets, but also about automated testing, automated deployment, and many other advantages.

Developing mobile applications with the help of Artificial Intelligence

Generally speaking, the overall process of implementing AI in the mobile app development process is somewhat similar to how regular software implementations are performed. However, there are also several case-specific nuances that the developer has to remember beforehand. As such, there is a rather short list of recommendations to follow when it comes to successful mobile application development with the inclusion of an AI:

• Trying to implement AI algorithms into every single part of your mobile application is not always a good idea and may even bring more harm than good in the end. The correct option is to analyze the current state of your mobile app and figure out what specific issues or shortcomings you can solve with AI. This approach offers the biggest possible number of benefits without overextending yourself and adding features that users may find overwhelming or unnecessary.
• While it is true that AI as a whole benefits quite a lot from large data masses, the data in question needs to be structured properly to make it easier to feed it into an AI algorithm. As such, performing data governance and data organization with the help of data analytics tools is a good next step for this process.
• Setting clear goals and defining measurable metrics are crucial steps when implementing AI in applications. This approach ensures that the integration of AI aligns with specific objectives and provides tangible value. At the same time, a proper set of objectives makes it far easier to analyze and measure the overall effect of AI implementation.
• Implementing AI into your mobile app is the next step – after every previous step is complete. This process relies on both engineers and developers, as well as their experience with handling both AI and mobile development. 
• It is completely normal for an AI-based mobile app to not only use your own data in its operations but also various third-party tools or resources. As such, your development team would have to implement either APIs or custom-made integration features for that sole purpose. Custom integration technologies are usually the norm for larger and more sophisticated mobile applications, while more case-specific and small-scale apps may work with APIs just fine (since APIs are easier to work with in most cases).
• AI implementation is not a one-time process – it is a continuous chain of improvements and corrections to the app itself, to the AI algorithm, or any other part of your implementation. Monitoring the performance and other results of the solution is always a good idea, since it makes all of the modifications and changes a lot more accurate and case-specific.

Mobile app development as a whole can be a rather sophisticated topic, and the introduction of AI to the mix makes it that much more difficult. There will always be plenty of details that are case-specific or industry-specific in AI app development as a whole, but these steps above should work for the majority of use cases – since they are abstract enough to be applicable in a variety of situations.

Important use cases for AI usage in mobile apps

Now that we are aware of how AI can be implemented in mobile apps, it is a good time to go over a variety of examples of AI implementation. We can offer at least six different options here – from the aforementioned personalization to digital assistance, object detection, and more.

Security

Security stands out as a paramount concern for app users, particularly those engaging with solutions like mobile banking or online shopping that handle digital payments and sensitive data. Therefore, the more robust your app's security, the better. Unsurprisingly, AI can play a pivotal role in bolstering security measures.

There are two main gateways for AI to improve the security situation of a mobile application. Primarily, by swiftly processing vast volumes of data and identifying potential cybersecurity threats, artificial intelligence can proactively thwart attacks by providing timely alerts to both you and your users. Given that well-trained machine learning algorithms learn from their past incidents and problems, the security of your app will progressively grow and evolve over time.

Additionally, AI contributes an added layer of security to your app's biometric authentication, enhancing the accuracy and efficiency of the system. This is achieved by transforming biometric data, such as fingerprints or facial scans, into analyzable information that can be cross-referenced with a database.

Personalization

Since the mobile application market as a whole is rather saturated right now, standing out is a recipe for success. The introduction of AI to the mobile app is a great way to do exactly that – and even use it as a selling feature, to a certain degree.

Through perpetual analysis of your users' behavior within the app and discerning their individual preferences, you can craft a solution that provides personalized experiences for each customer. This approach leaves a lasting positive impression, enticing them to return repeatedly.

For mobile application proprietors engaged in online product and service sales, this proves particularly advantageous, as AI can furnish pertinent recommendations, consequently heightening the probability of completing a purchase.

Support Automation

The competition among mobile applications is intense, underscoring the critical importance of delivering a seamless user experience and outstanding support. However, maintaining one or more agents available 24/7 can incur substantial costs, particularly when many inquiries can be readily addressed.

Enter AI-based chatbots, a solution to this challenge. These intelligent tools enable your mobile application to automate the handling of customer queries through natural language processing. The system efficiently interprets the inquiry, retrieves pertinent information, and communicates it to the user in the most contextually relevant manner.

AI-based chatbots rank among the most prevalent applications of artificial intelligence, mainly because they can substantially diminish operational expenses for businesses. Therefore, do not underestimate the potential of this time and cost-saving AI application, as it is poised to elevate your customer service standards and enhance customer loyalty.

Object Detection

AI technology finds another crucial application in mobile phone apps through object detection. When your application involves visuals, the collaboration of image recognition technology and artificial intelligence becomes invaluable for identifying objects, people, locations, and other pertinent elements.

Healthcare is a good example of this particular use case. By implementing AI for application development, the app can enhance disease diagnosis. Healthcare staff can upload scans or photos of potential anomalies, and algorithms will swiftly analyze the visuals, providing prompt diagnostic suggestions.

Both claim processing and underwriting in the insurance industry can be sped up drastically with the help of AI. This is achieved by accurately and rapidly assessing damages through photos and analyzing risks by scanning social media, thereby streamlining critical processes.

Voice Recognition

Virtual assistants, powered by voice and speech recognition technology, excel at identifying the speaker's voice and comprehending spoken commands, eliminating the need for users to navigate through the app manually.

Beyond the evident time-saving advantages for users, virtual assistants also offer a practical solution for individuals issuing commands while engaged in activities like driving or tasks that require hands-free interaction. Furthermore, speech recognition is indispensable for inclusivity, ensuring that your application accommodates users with disabilities.

For those developing solutions to enhance internal processes, such as contact center performance, AI can contribute by providing digital assistance. This includes delivering dynamic call scripts and adjusting dialogue suggestions for sales teams in real time, enabling employees to adapt to each call and cater to customers optimally.

Analytics

Analytics constitutes a fundamental aspect of any business, regardless of the industry—be it insurance, finance, retail, or healthcare. The ability to analyze performance, understand customer behavior, and enhance service delivery is paramount.

App analytics is another field that is changed drastically when AI is involved. Intelligent algorithms adeptly sift through customer usage data, transcending traditional historical performance analysis. AI based app development brings predictive capabilities into play, offering insights into future scenarios and equipping you to proactively prepare.

AI empowers businesses with advanced analytics, enabling them to decipher intricate user behavior patterns and generate highly accurate demand forecasts for products and services. It ensures your app maintains peak business performance and constantly generates the need for improvements across the board. 

AI usage during the mobile app development process

As we have mentioned before, AI use cases are not just about the improvements to an already existing application – it can also be a great help for app developers in a variety of ways. This particular group of advantages may include automated deployment, automated testing, bug detection, code review, code refactoring, and even the ability to generate code snippets based on the previously analyzed code patterns of the project. There are several examples of such applications that can be found further into the article.

Existing frameworks and toolsets for AI in mobile app development

Even though this combination of mobile apps and AI is a relatively new idea, there are already several different development frameworks that are AI-powered in some way or another. You’ll find three examples of such tools below.

• Dialogflow is a widely used tool powered by Google Cloud, providing natural language processing capabilities for creating conversational interfaces like chatbots and virtual assistants. It can use existing conversation models to streamline the development of better conversational experiences. Additionally, seamless integration with various messaging platforms facilitates the deployment of AI-driven conversational interactions.
• PyTorch is a well-liked framework that creates deep learning models and neural networks with the help of a dynamic computational graph. Its popularity stems from its robust GPU acceleration capabilities, enabling quicker training and experimentation. PyTorch's flexibility and ease of use have garnered favor among both researchers and developers in the field.
• TensorFlow is an open-source machine learning framework. It can make it a lot easier to perform tasks such as speech recognition, image classification, or language translation by leveraging its library of APIs and ready-made functions. One of its notable strengths is its versatility, as it can run on various platforms, including mobile devices, making it a versatile choice for mobile app development.

Of course, that is not the only category of AI-powered tools out there. There is also an entire market of solutions that offer substantial advantages during the actual mobile app development process by generating code snippets, automating deployment, and more. Here are a few examples of such solutions:

• GitHub Copilot, powered by OpenAI's Codex, is a groundbreaking tool in the realm of software development. Utilizing a language model trained on a diverse array of public programming repositories, Copilot aids developers by providing suggestions for entire lines or blocks of code as they type. This innovative tool supports multiple programming languages and frameworks, significantly enhancing the efficiency and productivity of developers.
• Lazy AI represents a groundbreaking approach to AI development, challenging the traditional norms. The idea of Lazy AI came from the ability of ChatGPT to offer plenty of assistance with application development (with the correct set of prerequisites). With Lazy AI, the conventional complexity and time-intensive coding processes associated with AI app development are eliminated. This innovative tool empowers users to create AI apps using natural language, marking a significant departure from the conventional paradigm.
• TabNine is an AI-powered autocompletion tool designed to seamlessly integrate with popular code editors. Leveraging a deep learning model, TabNine goes beyond traditional autocompletion by suggesting entire lines of code based on the context of what the developer is typing. This innovative tool aims to enhance coding efficiency and streamline the development process.

Conclusion

Businesses in diverse sectors are already reaping the rewards of integrating AI into their mobile applications, and indications suggest that we are just at the outset of this transformative trend. Consequently, the sooner your organization adopts this cutting-edge technology, the greater the probability of securing a distinct competitive advantage.

There are plenty of potential benefits that could happen with AI as an industry in tandem with mobile app development, including both the app development process and the actual app usage by customers.

AI is poised to revolutionize various aspects of the app development process, automating tasks such as code generation and testing. Developers can harness AI-powered tools and frameworks to accelerate development cycles, reduce manual workload, and focus on creative problem-solving. 

Whether through intelligent recommendations or personalized user interfaces, apps will establish stronger connections with users, enhancing satisfaction and fostering loyalty. Efficiency gains will be a hallmark of AI in app development, streamlining workflows, automating manual tasks, and optimizing performance. From smart chatbots handling customer support to AI-driven data analysis, apps are set to become more efficient in delivering value to users.

As for a more customer-oriented side of these predictions, AI is bound to boost user engagement, greatly improve the overall degree of personalization, and we should also see a rather drastic shift towards more voice-centric interactions with AI – since both voice recognition and voice generation are already on an impressive level and show no signs of stopping any time soon.

The implementation of AI in mobile apps has a number of risks but the potential benefits of such an integration are far too numerous for businesses to ignore such a potential advantage. With both the mobile app market and the AI market growing at an impressive pace, it is only a matter of time until AI implementation becomes a widespread commodity – so why not get a head start to have an advantage over competition in both short-term and long-term contexts?

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