Top 7 trends in mobile app development in 2024

AI is not merely a buzzword. It might seem that way currently, given the rush of companies eager to jump on the AI bandwagon by "injecting" AI features into their products, incorporating it into their processes, or simply adding "AI-first" to their marketing offers, but the shift is real. AI— including Generative AI, particularly Large Language Models (LLMs)— has a real chance to completely redefine the way we interact with and develop software.

Of course, this won't happen overnight; there are still numerous challenges— legal, technological, and business— but the changes have already begun and AI will continuously affect almost every aspect of software development, mobile apps included. It is not merely a "thing" or a stand-alone feature (although it can serve that purpose), but rather a method to create and improve things, features, and processes.

Moreover, many AI-driven features are already part of our daily lives - such as face recognition lock-ins, camera filters, machine learning automation, or AI-driven personalization. However, with Generative AI, these features will become more advanced, sophisticated, and, as we predict, common.

But let's not be too general and break it down to actual use cases.

We all know that eCommerce recommendations are made by algorithms based on our previous shopping history, preferences, and demographic data, but it can definitely go further.

With AI-driven sentiment analysis, mobile app development can discern user emotions and assess their feedback on products, services, or information. Moreover, combined AI technologies such as Natural Language Processing (NLP) and image recognition analyze a much larger volume of data coming from different sources and various formats to deliver more accurate product recommendations, and with Generative AI, personalization will be even deeper.

Generative AI can create - in real-time - personalized messages tailored to the customer not only in terms of product recommendations but also in style and tone in order to boost engagement and brand loyalty.

That might be a long game, but Generative AI has a chance to address a main pain point of online shopping, which is impulse purchasing, reducing time spent on reviewing and comparing products before finalizing transactions.

The recent advancements in AI have breathed new life into chatbots and voice assistants. Now, powered by AI, chatbots have become ubiquitous in mobile app development as they excel at engaging with users. By leveraging chatbots, businesses can streamline their customer service operations, reducing reliance on large support teams, especially for handling routine and commonly asked questions.

But why now? Previously, rule-based chatbots operated within predefined parameters, recognizing specific keywords in user queries and delivering predetermined responses. For instance, when a user asked about their order's delivery status, the chatbot identified relevant keywords and provided a standard reply based on pre-established templates. However, problems occurred when users had uncommon issues. Rule-based chatbots often failed when not provided with very specific scenarios.

AI-driven - to be specific, LLM-driven - chatbots represent a significant leap forward. Empowered by natural language processing capabilities, they can tackle complex queries and adapt their responses dynamically. With their self-learning capabilities, they improve over time, continuously enhancing their conversational abilities.

Visual search is the next frontier in mobile app development, revolutionizing the way users interact with search engines. Unlike traditional keyword-based searches, visual search harnesses the power of AI to understand rich media content, offering a more intuitive and seamless search experience.

Visual search breaks language barriers, as - while keyword-based searches rely heavily on text input - visual search enables users to simply upload an image or capture a photo.

It is especially valuable in retail. By allowing users to search for items using images, visual search removes friction from the shopping process, empowering users to find what they want quickly and effortlessly. This enhanced discoverability not only boosts conversion rates and sales but also cultivates a more personalized and engaging shopping experience.

Low-code or even no-code app development has been emerging as a game-changer in mobile app development, where often being first is more important than being the best. The rise of Generative AI, embodied as different kinds of co-pilots and code generators, can accelerate this trend.

Low-code and no-code platforms (such as WebFlow or Mendix) have already simplified development by harnessing visual modeling or, to be precise, drag-and-drop mode. In contrast to traditional application development, which involves writing lines of code, testing, and management by experienced developers, low-code platforms use pre-tested and connected visual components to rapidly build and implement successful and secure applications.

Using ready-made components boosts the development process and frees developers' time, enabling them to focus on the most unique and specific app features while ensuring app scalability at the same time.

AI-driven co-pilots play in the same field as they also speed up development time but take a different approach.

Generative AI, properly prompted, can generate code, test cases, and documentation; their output is, well, lines of code. Evaluated by developers, they work quite well for developing stand-alone apps from scratch.

Will no- and low-code platforms be replaced by AI? It doesn't seem likely; a more plausible scenario is where these two methods will be combined.

As we said, while AI can handle doing things from scratch, it may struggle with integrating with other technologies. Low-code platforms can connect and govern many AI-built apps, all to ensure you aren’t creating data silos and security issues.

Augmented Reality (AR), despite experiencing a few false starts in previous years, with Google Glass being the most infamous example, can be viewed as a controversial trend in 2024. However, there are strong indicators suggesting that it will finally carve out a place for itself, perhaps not as a leading trend but certainly as a valid one.

Why do we believe in AR? Simply put, it possesses the ability to create immersive experiences that blur the lines between the digital and physical worlds. With AR's capacity to provide lifelike and interactive encounters, mobile apps are evolving into platforms facilitating practical solutions in navigation, retail, and business environments.

AR has become synonymous with premium app experiences. Whether it's enhancing shopping experiences with virtual try-ons or revolutionizing education with interactive learning modules, AR-enabled apps are redefining the way users interact with technology.

Additionally, there's a significant AI booster driving the popularization of AR. AI models have reached remarkable levels of proficiency in executing various tasks crucial for crafting immersive AR encounters. Deep neural networks excel in identifying vertical and horizontal planes, gauging depth, segmenting images to simulate realistic occlusion, and even deducing real-time 3D object positions. Thanks to these capabilities, AI models are gradually supplanting some of the conventional computer vision methods that form the foundation of AR experiences.

The growing healthcare app market is expanding, reflecting the increasing reliance on mobile technology to manage health-related aspects, and there is no sign of stopping.

Patient-facing apps encompass a wide range of functionalities, including condition management, appointment booking, and fitness tracking. On the other hand, provider-facing apps facilitate remote patient monitoring, telehealth consultations, and streamlined communication channels.

Advancements in AI, AR, and wearables enable the development of more sophisticated, personalized, and secure medical apps. AI facilitates enhanced personalization through customized health tracking, medication reminders, and treatment recommendations. Additionally, predictive analytics integrated into medical apps assist with early disease detection, symptom analysis, and remote patient monitoring, thereby enhancing the patient experience.

Incorporating AR technology into medical apps allows for the overlay of real-time patient data, such as CT scans or MRI images, onto the patient's body during surgical procedures. This provides surgeons with enhanced visualization and guidance, improving surgical precision and outcomes.

Furthermore, AR-powered medical training is gaining traction, with medical apps leveraging AR to create immersive, interactive training simulations for healthcare professionals. These simulations enable practitioners to practice procedures and techniques in a safe, virtual environment, enhancing their skills and preparedness.