According to some recent forecasts, the global artificial intelligence (AI) market can reach the $2 trillion mark by 2030. The mobile application market sector is also growing, but not so quickly. Unsurprisingly, AI and mobile apps have become intertwined. Experts see different ways of using AI in mobile app development. Let's discuss the prospects and forecasts regarding the role of AI in this area with our recognized expert, Oleg Sukhorukov.
Oleg Sukhorukov is a professional in creating mobile apps. Working for Readdle (Portland, OR, US), he spearheaded the end-to-end design of Calendars, one of the most downloaded calendar apps on the App Store. He led the design efforts across multiple platforms, including iPhone, iPad, Mac, Apple Watch, Vision Pro, and CarPlay, ensuring seamless user experiences and high-quality visual design consistency. The app has been downloaded and installed more than 30 million times. It also has won the prestigious Red Dot Award in 2021 for “outstanding quality and communication design”. In this role, he also mentored junior designers, providing guidance on best practices in UI/UX design and encouraging creative innovation within the team. His leadership ensured that Calendars consistently met and exceeded user expectations, reinforcing its reputation as a market leader in productivity tools.
Oleg, as an experienced Product Designer, how do you envision the ideal interaction model between mobile applications and artificial intelligence technologies in today’s rapidly evolving tech landscape?
The ideal interaction model between mobile applications and artificial intelligence technologies is one that not only meets user needs and business goals but also creates an intuitive, almost invisible bridge between human intent and technological power. AI should enhance user capabilities without overwhelming them, seamlessly integrating into their daily lives. In my view, the future of AI-powered applications is built on three strategic approaches:
On-Device AI: The priorities here are privacy, speed, and offline functionality. On-device AI delivers instant value, such as photo enhancement, text translation, or health trackers that process data without cloud connectivity. This is particularly crucial for tasks requiring real-time responsiveness and confidentiality.
Cloud-Based AI: Leveraging cloud technologies unlocks the potential of large-scale data processing and complex computations. This approach is the foundation of smart assistants, recommendation systems, and analytics that enable applications to learn, adapt, and predict trends at a level unattainable for local devices due to limited computational power.
Hybrid Models: The “golden mean” is often found in hybrid solutions, where on-device AI handles fast, private actions, and cloud-based AI provides deeper analysis and long-term adaptability. For example, a fitness app might calculate steps locally while using the cloud to generate personalized recommendations based on long-term data.
The key is not just to create functional solutions, but to surprise and delight users by anticipating their needs. Well-integrated AI not only transforms the user experience but also, at the current moment, elevates businesses to the level of innovators setting new standards in the industry.
Should artificial intelligence primarily be used to enhance the development of mobile applications, or should it serve as a functional feature for end-users?
This is not an “either-or” scenario; both approaches have unique benefits and do not exclude one another - in fact, they complement each other - and combining them often delivers the greatest value.
AI in Development:
AI can significantly accelerate the app development process, improving quality. For example, AI-powered tools assist with automated testing, bug detection, or UI/UX design adjustments based on user data. In my experience, AI tools help streamline routine tasks that used to consume considerable time. For instance, structuring large volumes of user data and analyzing it helps prioritize features by understanding user behavior and preferences. Additionally, AI simplifies tasks like generating test data for exploring various scenarios, ensuring that different design states are thoroughly considered and effectively implemented. Before AI adoption, these tasks were much more time-consuming and resource-intensive.
AI as a User Feature:
In this role, AI enhances app functionality, delivering higher-quality and faster user results. Examples include personalized recommendations for music and shows (like Spotify and Netflix), grammar checking and email drafting (as seen in the Spark email client), or conversational interfaces (chatbots). These features provide users with highly tailored experiences, increasing engagement and satisfaction.
Combining these approaches achieves the best outcomes. Using AI during development ensures a smoother and faster production process, while AI-powered features make apps not just functional but indispensable for users.
