How to Supercharge Your Mobile App with Artificial Intelligence

Sector: Data Analytics and Artificial Intelligence

Author: Nisarg Mehta

Date Published: 10/29/2024

Have you ever wondered how your favorite apps somehow seem to understand what you want even before you do? Behind the scenes, we have AI that is changing the way we use mobile apps. AI is what allows apps to become smarter, and more intuitive from being recommended for personalized shopping, to your virtual assistant, predicting your next action.

Here’s a cool fact: AI-fueled personalized shopping experiences result in a 44% higher repeat purchase rate. And it’s not stopping there. In 2023, the AI app sector took $1.8B and is forecasted to reach $18.8B where there are already millions using it.

So, what does all this mean? Most simply put, if you don’t have AI in your app, you may be behind the times. So, let us move ahead and see some of the most interesting use cases of AI in mobile apps right now, and what are the next steps to take.

AI Use Cases for Mobile Apps in 2024 and Beyond

Mobile apps are already benefiting in many ways from this. So, let’s talk about some of the best AI use cases in mobile apps and what we expect to see soon.

Personalized Experiences

Personalization is one of the biggest advantages of using AI in mobile apps. AI helps apps tailor the experience, by analyzing user behavior, preferences, and patterns. To illustrate, it is important to note that AI based on shopping experience improves customer satisfaction and increases repeat purchases by 44%, which indicates how engaged users can be using personalized service.

NLP (Natural Language Processing)

You have most likely dealt with chatbots or virtual assistants who almost perfectly understand what you are looking for. That’s thanks to Natural Language Processing (NLP), a type of Artificial Intelligence that allows apps to understand human speech and react appropriately. Voice-activated apps are a game changer essentially requiring natural language processing (NLP) and by 2023, natural language processing will own a massive 35% share of the mobile AI app market. The trend should continue as more apps adopt voice and text-based interactions.

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Predictive Analytics

To the same end, AI is also being used to predict user behavior. With the power of apps, they can now predict what the user wants, whether it’s products or content. This provides a way to increase user engagement by giving users what they want when they want it, improving the experience of using the app.

Image and Speech Recognition

With image and speech recognition, AI also helps improve the security and accessibility of mobile apps. Much more than this, apps can now scan faces, recognize voices, and even read images. And this is especially applicable in areas like biometric security, where facial recognition is becoming a more common thing.

Operational Automation

Finally, AI has the potential to automate a lot of the boring stuff found in mobile apps. Whether through customer support chatbots or automated notifications, AI helps automate the redundant, tedious, ineffective parts of the business, letting them focus on the higher tasks while making sure that the app is always active, responsive, and ready to do business for them.

Real-World Use Cases of AI for Mobile Apps

The term AI is no longer just jargon because AI is already enabling the most popular apps you use every day. Here are a few standout examples of how AI is being integrated into mobile apps to enhance user experiences:

eCommerce & Retail Apps

For years, e-commerce giants, like Amazon and eBay have been using AI to recommend products based on browsing history and user preferences. These apps use AI to create a seamless shopping experience boosting both customer retention and repeat purchases. In fact, the e-commerce and retail segment accounted for as much as 26 percent share in the AI mobile app market in 2023, making it clear that AI is an important thing in this space.

Smart Home Apps & Voice Assistants

A prime example to state how AI, especially NLP is transforming mobile app concepts is voice assistants such as Google Assistant and Amazon Alexa. These assistants make it possible for users to control smart home devices, set reminders, and deliver information to them without having to see what’s on a screen. They understand natural language and know how to respond to it, using AI.

Social Media Platforms

We have AI on Instagram and TikTok for personalized feeds and content recommendations. These platforms can then analyze the user interactions and preferences to display the most relevant posts, videos, and ads to users. In addition, AI responsibly drives filters and augmented reality features that help users be more creative and engaged.

Healthcare Apps

For example, healthcare apps such as MyFitnessPal use AI to run fitness “health” recommendations using user data. AI algorithms parse through your activity levels, what you eat, and your progress to give you tailored advice on how to stay on track with your health goals.

Entertainment & Streaming Services

Netflix and Spotify use AI among many others to recommend movies, shows, and music based on user preferences. Content discovery has become a breeze as AI algorithms take what you have watched or listened to in the past, and tell you what you will like next.

Integrating AI into Your Mobile App

If you’re ready to get started supercharging your mobile app with AI, here’s a simple step-by-step guide that will point you in the right direction.

Step 1: Define Your AI Use Case

Step 1 is to find out why you require AI in your app at all. What is it that you want to solve? If you want to use personalization to improve user experience, automate support, or provide better recommendations, now is the time. Narrow down your use case. If you do this, you’ll have an idea of the right AI technology to focus on and the integration process will be easier.

For example, if you are going to build a retail app, you might care about having an AI that suggests products to your users. If it’s a health app you’re building, AI could help analyze user data and make fitness tips accordingly.

Step 2: The Right AI Technology to Choose

Now that you know what you need your AI to accomplish, it’s time to choose the right AI technology. Each AI technologies have a respective purpose. Here are a few options:

Machine Learning (ML): Best for apps in the case where data needs to be analyzed and learned over time. It’s exactly what Netflix and Amazon use for their personalized recommendations.
Natural Language Processing (NLP): NLP works if your app has to be able to understand the user input and respond in kind, be it text or voice. Chatbots, virtual assistants, voice commands — many of us encounter and use these technologies.
Computer Vision: If your app has to identify images, faces, or objects this is ideal. Computer vision is what allows apps like Snapchat with its AR filters to function.
Speech Recognition: This AI converts spoken words into text; something we all need for apps with voice controls like Google Assistant or for hands-free interaction.
Deep Learning: Deep learning is a subset of ML that replicates the neural networks of the human brain as it is best at solving tasks, like speech recognition, image classification, or even autonomous decision-making. Deep learning is used by apps that use facial recognition or automatic translations.
Generative AI: Generative AI generates new content — for instance, text and images and sometimes even music. It powers ChatGPT and DALL E, the popular apps that automatically generate content to make creative tasks easier, or improve upon user-generated content. Generative AI is game-changing if your app needs to create realistic text responses or visually unique objects.
Reinforcement Learning: Adapted via trial and error, this type of AI is. If there is a specific command, you need it to remember how to perform that command or that it used memory for an operation and it needs to make a decision on its own based on what it is learning from previous interactions. It’s often used in gaming apps or any other environment where AI has to learn from how it interacts with a user and decide for itself.

Choose the right technology for your app, you don’t need them all—you just need the one that helps you deliver the best user experience.

Step 3: Select the Right AI Frameworks and Tools

Once you have identified the correct AI technology, it’s time to select the proper frameworks and tools when building your AI features. Luckily, there are resources galore that streamline the integration of AI into your mobile app.

Leading Tools and Frameworks To Inegrate AI in mobile app

TensorFlow: Developed by Google, it is one of the most popular open-source AI frameworks. That supports deep learning and machine learning tasks and works perfectly for mobile and web apps. Designed for mobile apps, TensorFlow Lite is tuned for performance with minimum power consumption.
IBM Watson: To get started with pre-trained AI models, IBM Watson is a complete suite of AI tools and APIs, which can be used to integrate directly into your app, if you wish to build a fully functional app by integrating AI capabilities into it. It’s particularly strong in Natural Language Processing (NLP) and will work great with apps that require voice or text-based interaction.
Microsoft Azure AI: There are a good number of AI services in the Azure platform: for speech recognition, computer vision, or decision-making models. If you need cloud-based solutions and scalable AI features for your app, it’s a good choice.
Amazon Web Services (AWS) AI/ML: AWS provides all the tools you’re going to need to leverage AI, and SageMaker is an example of just that — it is an AI tool for developers to quickly build, train, and deploy ML models. NLP, text-to-speech, and even a recommendation engine are also part of AWS.
H2O.ai: H2O.ai is also known for its ease of use and offers machine learning frameworks that do not require deep AI expertise. Apps that need quick implementation of AI without a ton of customization (yet) are well suited to this.
OpenAI API: The OpenAI API (the tech behind ChatGPT and DALL-E) also gives you some of the best NLP and generative AI around. This makes for a great fit for apps that wish to mock out humanlike text or visuals of something unique.

When you choose an AI framework or tool, think about your app’s complexity, the expertise of your team, and the scalability of the solution in mind. There are tools that provide some ease of use, and others let you do some more customization for some specific use cases.

Step 4: Collect and Prepare Your Data

High-quality data is quite imperative to AI models. It is because the quality of the data you put in will have a direct impact on how well your AI features will perform. Here’s how you can go about it:

Collect Data: First, get some data that matches what you want from your AI. For instance, if you’re building a recommendation engine, you’ll want user behavior data—like browsing history, purchases, or preferences. In order to build NLP-based apps, we need data from user text interactions.
Clean the Data: Raw data is rarely perfect. Clean your data so that you have removed duplicates, corrected errors, and filled in missing information. It guarantees your AI model gets the right, related data.
Label the Data (if needed): In supervised learning, you should label your data so that your AI can learn from it. For example, when training an image recognition model, the images will need to be tagged correctly, say ‘cat’, ‘dog’, or ‘car’.
Data Privacy: Be sure to follow your data privacy law like GDPR or CCPA. Consent on data collection, as well as anonymizing of sensitive information, are to be given by the users.

The foundation for any AI integration is high-quality data. The best results do require putting some time into preparing and refining your dataset.

Step 5: Develop and Test AI Features

With your data ready it’s now time to develop your AI features for your mobile app. This is where the magic happens (the coding and integration). Here’s how to go about it:

Build Your AI Models: Begin with developing your AI model using the AI framework or tool you have selected earlier (e.g., TensorFlow or IBM Watson). This means training some machine learning models, building a chatbot with NLP or developing a recommendation engine. If the complexity is low, you can fine-tune the model on your own; however, depending on the complexity, you may have to spend a lot of time collaborating with data scientists or AI specialists to fine-tune the model.
Run Tests: Testing is critical. Once you build your AI features, test them well, and if you are reasonably satisfied with them, then you know they work as you designed. It could be checking the accuracy of your predictions, how responsive your chatbot is, or how good your personalized recommendations are. Be sure that the AI doesn’t drag down the performance of the app as a whole, especially on mobiles, where they care a lot about speed and the experience.
A/B Testing: Perhaps you would want to run A/B testing to see how well your AI features do against the non-AI features. For example, compare the number of users that interact with the AI-recommended products versus manual recommendations.
Edge Cases: Also test for edge cases—things your AI can fail at. Take, for example, your voice assistant: how does it behave when confronted with accented speech or wrong pronunciation? It is important to address these potential issues at the early stage of AI integration.

But once you’re happy that the features of your AI are working as desired, you’re ready for deployment.

Step 6: Optimize AI with Monitoring

After your AI features are live, there’s still work to be done. To keep performing well over time, the AI models need to be continually monitored and optimized. Here’s what you should do:

Monitor AI Performance: Monitor the real-world behavior of your AI features. Are you reaching the goals you set early on? An example would be if you added a recommendation engine, and tracked how it’s impacting user engagement and repeat purchases. Monitor your key performance indicators (KPIs) accuracy, response times, and user interaction rates using the analytics tools.
Update the Model: At some point, user or market conditions might change and you will have to retrain your AI model over time. Updating your data and retraining your model regularly keeps you up to speed and correct. For example, a recommendation engine should continue to learn from new user data, to continue to get better.
Fine-Tune for Better Results: Not all AI models are right the first time. Operate fine-tuning of the model based on performance data you collected. It could be changing the algorithms, retraining on more data, some more tweaking the user experience.
Scalability: As your app grows and builds a user base, your AI needs to be able to scale in tandem. Ensure your aspects of the infrastructure are able to cope with higher data loads, not at the expense of the performance of the AI features.

With continuous monitoring and optimization, your AI-powered mobile app will stay running and deliver value to your users over time.

Final Words - Seek Expert Help for AI Integration

Mobile apps can no longer afford to forgo AI when many see it as the necessity, not the luxury, that it is. AI is making apps contextual, predictive, personalized, and more – everything from recommendations to virtual assistants to predictive analytics.

It’s essential to round up the right expertise along the way with AI integration. Techtic Solutions can assist you no matter what your level of experience with AI is; whether you just started getting into AI opportunities or you’re looking to spice up some of the features you already have. We have deep knowledge of AI technologies and act as guides for app owners to help them find the most suitable solution for AI on their unique needs, and then assist them in integrating AI smoothly to maximize your app’s potential.