It’s a lot more than task automation with AI in cross-platform development. For the frameworks that leverage it, developers can develop versatile - efficient applications. The popularity of cross-platform app development stems from its unique advantages. As its influence grows, so do tools and frameworks for cross-platform development in the market. This surge initially happened gradually and then suddenly, tons of mobile app development companies became eager to explore what it could do for them.
The Trajectory of Cross-Platform Apps
Internet service started in the 1980s. Back then, you needed a PC to access the web. But as technology developed, mobility became one of the most important aspects of the user experience. Users started to experience the temptation to use the internet while on the go. This impulse was increased by the introduction of smartphones and tablets in 2012. Today, your phone provides 100x the experience a PC would have back then.
According to Statista's analysis of internet accessibility, mobile users account for more than 55% of all online traffic. This information was sufficient to support a change in the way native mobile apps are developed. Mobile app development and mobile apps started to take precedence over other factors. The original response was to create distinct desktop and mobile applications. However, it didn't take long for developers to realize that this approach was not only lengthy but also expensive. As a result, cross-platform app development gained momentum.
Enter AI
AI’s role in cross-platform development frameworks is consistent when it comes to developing apps and optimizing their performance. But the true potential of AI becomes most apparent when it's integrated into the development cycle. AI in cross-platform development isn't just about keeping up with the trend; it's about pushing the boundaries of what's achievable. In the development cycle, AI does a few things; fine-tunes algorithms, automates coding tasks, predicts user behavior, analyzes data, and personalizes the user experience.
To get the most out of these features, developers need the right framework and AI/ML expertise. With that said, here are some ideal options:
For integrating AI:
TensorFlow: Best suited for tasks like image or speech recognition, natural language processing, and more complex AI tasks.
PyTorch: Ideal for developing AI-driven features within cross-platform apps. Its got a dynamic computation graph and user-friendly interface which is best for tasks demanding neural network implementation.
Keras: A high-level neural networks API that simplifies AI model integration. Great for tasks like image classification, text generation, and recommendation systems.
For Cross-Platform Development with AI:
React Native: Excellent for delivering consistent, AI-enhanced user experiences across iOS, Android, and the web.
Flutter: Offers a single codebase approach and customizable UI components, which is ideal for creating visually appealing, AI-powered cross-platform apps that prioritize performance.
Xamarin: Focuses on code reusability and allows AI functionalities in cross-platform apps. Good for developers with C# skills aiming for a smooth development experience.
For User-Friendly AI Integration:
Ionic: Great for creating aesthetic mobile apps with integrated AI features for iOS and Android.
Kotlin Multiplatform Mobile: Facilitates code sharing across iOS and Android platforms, great for tasks requiring consistent AI features across devices with native-like performance.
The Age of Personalization
Many brands struggle with personalized customer experiences, which was evident in a Gartner report that revealed the struggle in 63% of digital marketing teams. Interestingly enough, less than 20% of them have used AI or ML throughout their operations.
What machine learning does in the context of cross-platform development is make software that not only accommodates user choices but also adapts to and picks up on user behaviour. Machine learning makes judgements based on data analysis. It anticipates user behaviour and fine-tunes algorithms to maintain a customized user experience.
Again, tools like TensorFlow, PyTorch, and Keras are great because they enable features like natural language processing and predictive analytics. These technologies allow programmers to produce dynamic and adaptable applications that take into account user preferences and actions, ushering in a new era in which software not only meets user demands but also develops along with them. Machine learning is the future of cross-platform development, its ability to completely change the user experience is enough to lock that claim in for most developers.
Moving Forward
This year, mobile apps are projected to do almost a trillion dollars in revenue. With that in mind, it begs the question; What frameworks should you choose? The simple reply is that it depends on the company and the functional specifications of the app. If you want the best advice, reach out to a qualified and reputable provider who will not only put you on a path to success but also limit the headaches of getting started.
Written By Ben Brown
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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.
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