Five AI and ML Features Worth Adding to Your Mobile App

Most of the apps on your phone already use Artificial Intelligence (AI) and Machine Learning (ML), often without saying so: the feed that learns what you like, the search that understands a half-typed query, the payment check that flags an odd transaction. Users now expect the same from every app they open.

If you already run a mobile app, you do not need to rebuild it to offer these features. Most of them can be added to the app you have, with the AI running as a separate service that the app calls.

In this post, we look at five AI and ML features that make an existing app more useful, and what each one needs. First, a quick look at the two terms.

Artificial Intelligence

AI is simply a modern technique that empowers computers to imitate human behavior. Today, we see AI’s most prevalent examples in self-driving cars, chatbots, virtual agents, and similar technologies, which extensively depend on deep learning and natural language processing. Through the utilization of deep learning and NLP, along with the integration of algorithms, computers can learn to perform precise tasks by processing vast datasets and analyzing specific patterns within them.

Machine Learning

People commonly refer to Machine Learning as a subset of artificial intelligence primarily focused on developing algorithms that enable computers to independently learn from data and past experiences.

Using sample historical data, referred to as training data, these algorithms create mathematical models to facilitate predictions or decisions without explicit programming. Machine Learning merges computer science and statistics to craft predictive models. Machine Learning either constructs new algorithms or employs existing ones to glean insights from historical data. The greater the volume of information we furnish, the better the performance.

Five AI and ML features you can add to an app you already have

1. Enhanced User Experience

Successful mobile applications revolve around the user experience (UX). Developers leverage AI and ML algorithms to analyze user interactions, allowing them to pinpoint pain points and recognize areas for enhancement. This data-driven approach results in smoother navigation, faster load times, and ultimately boosts overall user satisfaction.

It also utilizes customer data, including demographics such as age, gender, and location, to understand user behavior and preferences through analysis of their search history and usage frequency.

2. Fraud Detection and Security

In an era where cybersecurity holds paramount importance, AI and ML actively contribute to safeguarding user data. They empower mobile apps to establish a robust security framework by incorporating app authentication features such as facial recognition, voice recognition, and more. Users can personalize their app authentication by setting their own biometric data as a security measure.

These technologies have the capability to detect unusual patterns, behaviors, and anomalies, thus bolstering fraud detection efforts and thwarting security breaches.

3. Advanced Search and Voice Recognition

AI and ML enable you to optimize the search options in your mobile applications. The main concept behind enhancing advanced search is to provide search results that align with the context and are intuitive. AI algorithms discover the most contextually relevant results by analyzing various queries that users have posed.

The integration of natural language processing (NLP) and voice recognition has revolutionized how users interact with their mobile devices. Siri, Google Assistant, and other AI-powered voice assistants have introduced entirely novel methods of utilizing apps. Whether users issue voice commands for tasks or perform searches within applications, AI-driven capabilities offer an intuitive and convenient user experience.

4. Relevant Ads

According to statistics, 38% of marketers are currently employing AI and ML to deliver targeted ads.

AI functions as a powerful tool for displaying the most relevant ads to each individual customer, instead of overwhelming them with irrelevant ads that merely cause irritation and undermine their engagement with the mobile application in use.

App publishers have managed to save money and enhance efficiency by analyzing data using AI, thereby eliminating all non-useful, irrelevant advertisements and ultimately cultivating a stronger brand reputation.

5. Augmented Reality (AR) and Virtual Reality (VR)

AR features rely on AI and ML to recognise surfaces, objects and faces, so the app can place content in the user’s surroundings. They are used in retail (trying a product at home), education, real estate and gaming.

Conclusion

None of these features needs a new app. Start with the one that solves a clear problem for your users, such as better search or relevant recommendations, measure the result, and add the next one once it has proved itself.

How AppsInAi can help

We build the AI layer, not the app itself. That usually means a model or assistant running as a service with a simple API, which your app team calls from the app you already have: recommendations, an in-app chat assistant, image recognition or GPT-powered features. Book a free consultation and tell us what you want your app to do.