10 best AI features for your app

12 min read
August 23, 2023

AI features are the way to go if you’re looking to take your app to the next level.

AI is revolutionizing almost every industry and capitalizing on these technological advancements is key to sustained future success.

In this article, we’ll discuss some AI app features you can add to your product.

Let’s go!


Personalization is the key to a successful app.

AI-powered personalization features will help you tailor your app to your users’ needs.

Personalization is important to them, too. 

According to a survey by Epsilon, 80% of customers are more likely to buy your product if you offer a personalized experience.

In the same survey, 90% of customers also said they find personalization appealing.

Take Spotify’s Discover Weekly, for example.

They use machine learning models to create personalized music recommendations for each of their users.

Spotify Discover Weekly infographic

source: The Lexington Line

They’re not the only ones, either.

Netflix uses AI to create personalized recommendations for their users, too.

According to McKinsey, 75% of what their users watch comes from recommendations based on their AI algorithms.

So, what do you need to do to implement AI personalization in your app?

First, you’ll need to identify useful user data, such as:

  • Browsing history
  • Purchase history
  • User preferences

Then, you’ll need to integrate this functionality into your app.

You can train your own machine learning model to personalize your product.

An easier way is to use off-the-shelf tools, like Adobe Target or Personalize.AI.

You can integrate both easily with your app and they’ll help you personalize your users’ experience.

Image and video recognition

Our digital landscape is increasingly visually-driven.

That’s why AI image and video recognition are a great way to get an edge over your competitors.

They open up a wide range of possibilities for your app.

You can use them from simple content tagging to complex augmented reality (AR) experiences for your users.

The market is growing, too.

It’s expected to grow to $80.07 billion by 2026.

So, how does image recognition work?

Image and video recognition are a key part of computer vision.

In simple terms, computer vision helps computers “see” and observe the world around them.

AI image recognition

source: Perficient

It enables them to get meaningful information from digital images and video and take action based on that information.

Let’s say you’ve built a property management app.

One way you can use image recognition in your app is facial recognition.

This way authorized users can easily unlock the entrance without having to take out their keys.


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Another use case is moderating user-generated content.

AI can accurately identify unsafe or inappropriate content and remove it without the need for human intervention.

To quickly implement image and video recognition, you can use a service like Amazon’s Rekognition.

If you’re working in a niche field, you can create and train an image recognition model on a proprietary set of images.

This way, you’ll ensure its accuracy.

Predictive analytics

Predictive analytics is one of the most useful AI features you can add to your app.

It’ll help you predict your users’ future behavior and habits based on their historical data.

That means you’ll deliver a better UX and retain your users.

But, how can you use predictive analytics?

Let’s say you’re thinking about features you can improve in your app.

Predictive analytics value chain

source: Qualtrics

You can use predictive analytics to identify if there are any bottlenecks in your users’ journey.

This’ll help you prioritize areas for improvement.

It can also be used to enhance personalization by analyzing your users’ habits when using your app.

This way, you can deliver a highly personalized UX to each user which will reduce your app’s churn rate.

So, which tools can you use to implement predictive analytics in your app?

There are plenty of AI-powered predictive analytics tools available on the market.

Tools like SAS Viya and SAP’s Predictive Analytics can help you quickly analyze your users’ data.

They’ll help you with app optimization and identifying areas that need improvement.

Anomaly detection

AI anomaly detection is a great way to boost your app’s security and performance.

Having a secure app will not only increase customer trust but also save you a lot of money.

Data breaches quickly get very expensive.

The global average cost of a data breach is $4.35 million, reaching $10.1 million in the healthcare industry.

That’s why it’s critical you detect anomalies early and quickly deal with any problems they might cause.

Machine learning models are great for this because they can adapt to changing patterns in your data.

So, how can you use anomaly detection in your app?

Let’s imagine you have a fintech app.

Machine learning fraud detection

source: Intellias

One major use case is detecting fraudulent activity, such as:

  • Phishing
  • Credit card fraud
  • Identity theft

Machine learning models can quickly analyze vast amounts of data to identify unusual user behavior patterns.

They’re also more efficient and accurate than a human fraud analyst.

Another way to use anomaly detection is to identify security and performance issues in your app.

AI tools like SolarWinds and Dynatrace are good choices if you’re looking for quick and accurate anomaly detection.

You can also integrate Azure’s Anomaly Detector AI service directly into your app.


AI virtual assistants and chatbots are great AI features to improve user engagement in your app.

They’re a handy customer service tool, too.

An AI chatbot is available 24/7 and can quickly solve most of your users’ problems.

In a survey by Userlike, 68% of respondents said this was the most positive aspect of chatting with a chatbot.

It can also save you a lot of money.

AI chatbots can help you reduce customer service costs by 30%, as they’re able to handle 80% of routine tasks and customer questions.

They’re also scalable and can handle hundreds of queries at the same time.

How AI chatbots work

source: Drift

So, how do you integrate a chatbot into your app?

Of course, you can train a Natural Language Understanding (NLU) model on your data and create a custom chatbot to suit your organization’s needs.

This is a good option if you’re a large business or handle sensitive data.

However, this isn’t feasible for smaller businesses and can be costly.

An easier way is integrating existing AI models into your app.

With OpenAI’s API, you can integrate GPT-4 directly into your app.

You can also fine-tune their base models to create a custom chatbot that’s suited to your specific needs.

Health monitoring

Health monitoring is an AI use case that can make a genuine difference in your users’ lives.

We’ve already mentioned that AI can analyze huge amounts of data in seconds and we’ve covered anomaly detection as a useful feature.

Now, imagine using those functionalities in your health monitoring device and app.

Let’s say a user has started using new medication that affects their blood pressure.

Health monitoring app

source: Engadget

AI can detect sudden changes from their baseline blood pressure and notify them.

And that’s just one trackable metric.

AI-powered health monitoring can also offer users personalized recommendations based on their individual data and goals.

It can flag potential problems early and help reduce users’ healthcare costs.

Professionals agree with that, too.

78% of British NHS healthcare workers think that AI is useful in their field of work.

It has some limitations, though.

AI-powered health monitoring is useful for general health tracking but it can’t replace a human doctor.

You should make that clear to your users from the start and direct them to visit their doctor if it detects a serious issue.

Also, it’s crucial you make sure your app follows medical guidelines and protects your users’ private healthcare data.

Language translation

If you want to open your app to a global audience, language translation is a must.

AI translation is a good way to do it quickly and accurately.

A majority of consumers prefer buying products in their own language.

According to a survey by CSA Research, 76% of respondents prefer products with information in their language.

75% of respondents also said they’re more likely to purchase a product from the same brand if customer care is in their language.

AI translation is an easy way to achieve this and expand your global reach.

And the easiest way to do it is to integrate existing software directly into your app.

Google Translate is the most well-known machine translation software and you can easily integrate its API into your app.

Google Translation products

source: Google Cloud

You can also train custom translation models with their AutoML Translation service.

This is useful if you need domain-specific translation that standard Google Translate can’t accurately translate.

Another good choice is DeepL.

Like Google Translate, you can easily integrate their API into your app.

DeepL uses neural networks to accurately translate and capture nuances in a particular text.

Also, you can create a glossary to specify how certain words are translated in your app.

Regardless of the software you choose, with AI-powered translation you’ll be able to reach a much wider audience.

Gesture recognition

Using AI gesture recognition in your app is a great way to deliver a unique and interactive user experience.

This’ll help you stand out from the crowd and provide additional value to your users.

Gesture recognition doesn’t just provide a unique experience.

Gesture recognition

source: Google AI Blog

It’s also a great way to enhance your app’s accessibility.

Around 16% of the global population has some form of disability and gesture recognition can help them navigate your app more easily.

It’s also useful if you’re creating Internet of Things (IoT) products.

So, if you’re developing a smart lighting system, with gesture recognition your users can control it without needing to get up.

Another use for gesture recognition is hands-free navigation when your users have their hands full and can’t conventionally navigate through your app.

Let’s imagine you have a cooking app.

It can be inconvenient to use when preparing a meal, as your users’ hands are likely to be busy most of the time.

Gesture recognition is a handy way you can solve that problem.

Autocorrect and autocomplete

Making your users’ lives easier is key to your app’s success.

AI-powered autocorrect and autocomplete are a great way to do just that.

And that’s not just idle talk.

Typewise, an AI-powered keyboard app, reduces typos by 80% and increases typing speed by 33%.

Now, you might be wondering why you should go to the trouble of implementing these features since they’re standard on most mobile devices.

And it’s true, for most apps they’re unnecessary.

But they can be very useful if your app is geared towards a more niche audience with specific domain knowledge.

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The phrase “jack of all trades, master of none” is fitting for the standard autocorrect and autocomplete features.

You’ve probably noticed they struggle with industry-specific jargon.

That’s where AI-powered autocorrect and autocomplete come in.

Let’s imagine you have an app geared towards chemistry students and hobbyists.

You can create an AI model that automatically corrects and completes common chemical formulas.

This way, you can save your users a lot of time and improve their experience.

And the greatest thing about these features?

AI can learn from your users’ inputs.

So, the more your users use them, the better they get.

Emotion recognition

Emotions are a major driver of user behavior.

That’s why AI emotion recognition can be useful for your app.

But, how does it work?

It works by analyzing and interpreting someone’s facial expressions or voice to determine their emotional state.

It’s especially useful for personalization.

With emotion recognition, you’ll be able to offer your users personalized content and recommendations based on their emotional state.

Emotion recognition

source: Visage Technologies

Another great way you can use it is for qualitative research.

So, when you’re conducting user interviews, emotion recognition provides extra data points to consider.

Let’s say you’re thinking about adding new features to your app.

You can use emotion recognition during user interviews to gauge which feature got the most positive reactions.

Keep in mind that the technology is not perfect and has been criticized by some scientists.

Human emotions are much more complex than just facial expressions.

So, you shouldn’t fully rely on emotion recognition to understand your users.

But, if you use it to complement your app’s existing features, you’ll improve your app’s UX and make it more user-centric.


Adding AI features is a great way to improve your app and gain an edge over your competitors.

If done right, they can improve your app’s UX and provide extra value to your users.

If you’re interested in learning more about AI, check out how we build AI-powered apps or read our article about 5 ways to use AI in product discovery.

And if you need a development partner for your AI app, get in touch and we’ll discuss your needs in more detail.

Written by

Ivan Kardum

Lead product manager

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