The software development industry has reached the point where ignoring AI app trends is simply counter-productive. Missing out on advancements in AI and mobile apps means you’ll fall behind competitors who leverage these technologies for productivity gains and user experience enhancements.

When it comes to AI in mobile apps, users already expect a high level of personalization, seamless interactions, and accurate image or text recognition. Meanwhile, developers increasingly use AI tools that check for code software issues, maintain infrastructure, and generate lines of code.

As an experienced development company, Techstack is no stranger to these advancements. This article will describe the current use cases for AI in mobile app development and explain how to implement the technology effectively in your apps and workflow, addressing common problems with integration. But to start, let’s go way back.

The Evolution of Artificial Intelligence in Mobile Applications

There’s often confusion around the term “Artificial intelligence” (AI). In essence, this technology allows computer systems to simulate human intelligence and problem-solving. It performs complex tasks, such as language understanding, reasoning, learning, and decision-making, without human interference.

Artificial intelligence in mobile apps progressed from what seemed to be decades away to something that routinely sits in the palm of your hand. While it feels like the evolution has been unfolding rapidly only over the last few years, let’s look back at the key milestones:

Technological advances of the past decade paved the way for current AI trends in mobile applications and software development. Let's examine them closely.

The market for mobile AI was valued at $5.58 billion in 2018 and is projected to jump to $57.45 billion in 2028, according to Technavio’s 2024 Mobile Artificial Intelligence Market report. At the same time, the software and natural language processing (NLP) segments, the latter of which allows mobile AI to learn and understand human speech, will see the highest growth.

The following trends provide insight into how AI, and mobile apps in general, are expected to evolve in the coming years:

  • Demand for edge computing. Edge computing stores and processes data near its origin, optimizing latency and response times. This makes it easier to integrate AI technologies into smartphones and other IoT devices like cameras, smart speakers, and drones.
  • Proactive assistance. By enabling real-time data analysis, AI can predict user needs and offer assistance before the user explicitly requests it. For instance, a financial mobile app equipped with AI could analyze spending patterns and predict when a user might need to transfer funds to avoid overdraft fees.
  • Integration of extended reality (XR). Developers increasingly experiment with augmented reality (AR) and virtual reality (VR) to create immersive, interactive experiences in mobile apps. Popular examples include virtual try-ons, like in the popular IKEA Place app. Evolution of generative AI. Companies are integrating AI tools and LLMs in mobile apps to produce text, images, and videos from prompts. The technology has many uses in mobile app development, too. According to Salesforce’s 2023 State of IT report, 86% of IT leaders expect to use AI for their business needs.

AI technology, in one way or another, is commonly used by software engineers to streamline the development process.

Benefits of AI in Mobile App Development

By incorporating advanced AI into the mobile development processes, companies can expect a series of business and productivity gains. The key ones are:

  • Streamlined development. Engineers use AI tools to automatically check for errors and inefficiencies. DevOps teams integrate tools like SonarQube into their CI/CD pipeline to scan code commits to repositories, identify potential issues, and provide recommendations.
  • Product quality improvement. By continuously monitoring the code during the software development lifecycle, developers can detect more issues and explore more optimization options. AI code review tools also help maintain high-quality coding standards across the teams.
  • Efficiency gains through automation. GitHub’s 2022 study found that developers who use AI-powered coding assistants (GitHub Copilot) complete their tasks 55% faster than engineers without such tools. As developers pointed out, these tools make coding less stressful when most of your day consists of repetitive tasks.
  • Higher team satisfaction. 60-75% of the developers in GitHub’s study felt that using AI in mobile development makes them more satisfied and fulfilled. About 87% of the engineers said that automation tools helped them save their mental energy and maintain their workflow.
  • AI-enhanced data security. Integrating AI and machine learning enables companies to identify suspicious activities, such as unusual login attempts or data access requests. In DevSecOps pipelines, tools like Checkmarx allow code analysis for security flaws and potential injections. In addition to facilitating the development process, AI tools better protect mobile apps against hacks and fraud attempts.
  • Enhanced decision-making. AI can analyze user behavior and predict future actions, empowering a software development company to make informed decisions about app features and user interface and experience (UI/UX). The technology also helps monitor the app’s performance after release.

AI transforms many aspects of mobile app development and post-release maintenance. Just as important is the way it enhances the user's experience with your app.

How AI enhances user experience in mobile apps

Leveraging AI in mobile apps lets you create smarter apps that meet the requirements of users with different needs.

  • Personalized recommendations. Developers integrate AI algorithms that learn from users’ interactions and historical data to personalize offers. For example, Netflix can recommend TV shows based on your viewing habits, genre preferences, and even the time of the day.
  • Smart search functionality. NLP allows the app to better understand user intent, even if the input is vague or contains spelling errors. It also learns from your past searches and transactions to optimize the search results to your liking.
  • Dynamic user interfaces. AI-powered interfaces can change based on how individuals interact with the app. For instance, if a user frequently accesses a particular feature, the AI can rearrange the interface to move it to the homepage.
  • Image and voice recognition. AI enables mobile apps to identify and classify images, videos, and real-life 3D objects. A Google Lens app, available on most Android phones, allows users to search for a product by taking its picture.
  • Human-like interactions. NLP and sentiment analysis allows apps to respond appropriately to a user's tone. Your AI chatbot may use these technologies to provide more empathetic and supportive replies to a frustrated or confused customer.
  • Context-aware alerts. A travel app might use AI to detect when a user arrives at an airport and send real-time updates about flight status, gate change, or nearby amenities.

With these use cases and benefits in mind, it’s essential to understand how to integrate AI technology correctly.

Implementing AI in Mobile App Development

Companies should follow these essential steps to effectively integrate AI into mobile app development or the apps themselves.

1. Identify opportunities for AI integration

You should evaluate existing features, user interactions, and performance metrics to find inefficiencies and opportunities for enhancements. Study competitors who have integrated AI to adopt good practices and avoid potential pitfalls.

Analyzing feedback from employees and customers can point toward common pain points and desired features. A comprehensive analysis will help you understand what users want in your product and how to use AI in mobile app development truly effectively.

2. Define objectives and success metrics

Set specific, measurable, and achievable goals for your project that AI can help you achieve. These can include accelerating development, improving bug detection, reducing customer churn, or expanding market reach. You need to determine key performance indicators (KPIs) to understand whether the implementation has been successful.

3. Choose technologies and tools

Outline the tech stack based on project needs and target audience. First of all, decide if you want to develop a custom solution or get an off-the-shelf AI model. For example, you may opt for a ready-made AI tool for CI/CD automation or invest in a custom solution with a more isolated infrastructure and tailored workflow scenarios.

If you choose to go with existing options, you need to compare them based on the deployment type (on-premise or cloud-based), scalability, support, and payment model. Also, check what other companies say about customizability and the ease of integration.

4. Build and train AI models

Gather high-quality data from IoT devices, user interactions, transaction logs, and external datasets. The data must be cleaned, preprocessed, and diversified to ensure accuracy and prevent biases (more on it later). It’s worth creating a feedback loop to continuously train and refine AI models based on new data and performance outcomes.

5. Validate the AI model

Develop a Proof of Concept (PoC) with rudimentary UI/UX and core features to validate your project. Use a more advanced Minimum Viable Product (MVP) to test the concept on a small group of users. The results of these tests will tell you if the project is worth pursuing further and, if so, act as a basis for full-scale development.

6. Integrate AI into the app

Ensure AI components integrate smoothly with the app’s existing architecture by using APIs and SDKs for communication. At this stage, you need to optimize algorithms to handle large-scale user interactions. You may use on-device and cloud-based processing to scale resources dynamically.

7. Monitor and optimize

AI and mobile apps are never truly done. You should set up performance monitoring systems, continuously gather feedback, and use this data for improvement. Establish automated testing for your DevOps and QA pipelines to streamline updates and new feature deployments.

As with all technologies, AI integration is always easier said than done. You should be aware of several potential problems.

Challenges and Solutions for AI Integration

Integrating AI into mobile apps can require both technical and organization-wise adjustments.

  • Ethical concerns and bias in AI models. The accuracy of AI in mobile apps depends on the quality of training data. Poor data can lead to biased recommendations that unfairly favor certain demographics or flat-out wrong (but very confident) predictions. To prevent these issues, you should use data augmentation techniques that artificially increase the diversity of training datasets and regularly test model output for accuracy.
  • Scarcity of relevant training data. Companies often lack high-quality data for AI models. What’s more, privacy laws may prevent you from openly sharing such data. You can avoid these issues by using synthetic data generation tools that create realistic, compliant data for software testing and AI training.
  • Data privacy and security. The AI helps produce usable testing data that adheres to data privacy laws, like GDPR, CCPA, and HIPAA. If handled without care, personally identifiable information in your systems may leak and lead to fines. Companies must implement robust data encryption and authorization measures within mobile app infrastructure. Finally, you should invest in anonymization tools that replace sensitive information in your databases with realistic data that are not regulated by the law.
  • High computational demands. AI models can require significant processing resources and storage, which is challenging for mobile devices. Offload heavy computations to cloud-based AI services while keeping less intensive tasks on-device. For instance, only some of Samsung’s AI features are processed on smartphones, while the rest are connected to cloud servers.
  • Compatibility problems. Differences in codebases, data formats, and exchange protocols can cause problems during integration. It’s recommended to follow an incremental approach: start by integrating less critical features and scale up over time.
  • Lack of talent. About 65% of Salesforce’s 2023 survey respondents don’t want to implement generative AI tools for engineering just yet. One of the reasons is the lack of skills in how to use AI for app engineering.

To solve most of these problems with the least amount of effort, companies should collaborate with experienced AI professionals. Reputable software development companies can help you solve the issues they’ve already handled before, provide the necessary tools to facilitate the development and train your teams.

Collaborate with Experienced Mobile AI Developers

Missing out on AI for mobile app development can set you back. With the right implementation strategy, you can gain remarkable benefits with minimal risks. If you have any doubts about what and how to implement, it’s best to turn to experts for guidance.
Techstack provides a comprehensive range of mobile application development services, from technology integration and UI/UX design to custom solutions. Eager to learn more? Leave a contact request to get a response right away!