What is AI app development? All you need to know

12 min read
October 5, 2023

Developing an AI app is a great way to stay competitive in today’s crowded app market.

But, what exactly is AI app development? And how do you develop an AI app?

Don’t worry, we’ve got you covered.

Here, we’ll discuss what AI app development is and give you a step-by-step guide on how to do it.

We’ll also discuss the main benefits of AI app development and some best tips to help you make yours a success.

What is AI app development?

AI app development is the process of creating apps empowered with AI capabilities.

In other words, it’s the process of adding AI models to an app during the development process.

And AI models are in a lot of apps already, such as:

  • Netflix
  • Spotify
  • Youtube

All of these apps rely on AI models in one way or another.

And there’s a good reason for that.

The AI models in these apps allow them to deliver a better user experience (UX).

And UX should be your focus when developing an AI app, too.

That’s because for every dollar invested in UX, you get $100 in return – a ROI of 9,900%.


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And AI is a worthwhile investment if you want to improve your app’s UX.

But, that’s not the only reason why you should consider developing an AI app.

Another major advantage of AI apps is their adaptability.

The AI model in your app can learn from user interactions and make decisions based on patterns it recognizes from those interactions.

That means it can continually evolve and improve, without human intervention.

Benefits of AI app development

Now that we’ve defined what it is, let’s discuss some specific benefits of AI app development.

They are:

  • Enhanced personalization
  • Increased efficiency
  • Improved security

Let’s dive in!

Enhanced personalization

Developing an AI app is one of the best ways to improve your app’s personalization.

And personalization is a must in today’s market.

The stats back that up.

According to a report by Twilio Segment, 92% of companies are already using AI personalization to drive business growth.

Your customers want personalization, too.

65% of customers are willing to stop using a product if their experience isn’t personalized.

And that number rises to 75% among Gen Z customers.

These stats show just how important personalization is for your app’s success.

And AI can help you deliver a tailored experience to each of your users.

AI personalization

source: LinkedIn

A good example of this is Netflix’s recommendation system.

Netflix uses AI to fine-tune each users’ recommendations – and it works.

80% of what Netflix users watch comes from their recommendation system.

That’s one of the reasons why they’re still market leaders in an increasingly competitive industry.

And you can use AI personalization to stand out from the crowd, too.

Increased efficiency

One of the main benefits of AI is increasing the efficiency of your app and your development process.

McKinsey estimates that automating processes with AI on average reduces business costs by 30% within 5 years.

AI improves the efficiency of the app you’re developing, too.

This is especially true if your app is based around data entry.

Intuit’s TurboTax is a good example.

TurboTax Express Lane

source: Intuit

They integrated AI into their app with the “Express Lane” feature.

It allowed their users to file their taxes in under 10 minutes – much quicker than the IRS estimate of 13 hours.

But, that’s not the only way AI can increase your efficiency.

You can also speed up your app’s development process with coding assistants like Github Copilot and Tabnine.

These tools can help your engineers by writing boilerplate code and automating other routine tasks.

This will allow them to focus on solving more complex tasks and increase their productivity.

And that’s barely scratching the surface.

In short, AI can improve the efficiency of both your app and your processes.

And that’s a good reason to embrace it.

Improved security

There’s no such thing as a 100% secure system.

Gene Spafford, computer security expert, once said: “The only truly secure system is one that is powered off, cast in a block of concrete and sealed in a lead-lined room with armed guards.”

Still, AI can significantly improve your app’s security.

Let’s say you’re building a fintech app.

You can use AI for fraud detection and prevention.

AI can analyze huge amounts of data and spot suspicious transactions and patterns.

Another good example of how AI can improve security are Gmail’s spam filters.

They built an AI-powered spam filter that blocks 99.9% of unwanted emails, including:

  • Spam
  • Phishing
  • Malware

And these are just a few of the ways AI can improve your security.

Improving your security is especially important as cybercrime is expected to surge in the coming years.

Cybercrime costs by year

source: Statista

Globally, the cost of cybercrime is predicted to rise from $8.44 trillion in 2022 to $23.82 trillion in 2027.

That’s why investing in improving your security is an absolute must.

And AI can help you take it to the next level.

How to develop an AI app

We’ve covered the why, now let’s cover the how of AI app development.

The steps you need to take to successfully develop an AI app are:

  • Choose the right AI tools
  • Collect and prepare high-quality data
  • Design and train your AI model
  • Integrate the AI model into your app

Let’s discuss each step in more detail.

Choose the right AI tools

The first step when developing an AI app is choosing the right AI tools and frameworks.

Your app’s success depends on it.

If you and your team are new to AI app development, cloud-based AI platforms are the best choice.

Some of the most popular platforms are:

All of these are end-to-end platforms, which means you can use them to:

  • Build AI models
  • Train AI models
  • Deploy AI models

Their main advantage is that they make AI app development easier and quicker.

That’s because they also offer pre-trained AI models you can easily integrate into your app.

But, if you need a more complex AI model for specific use cases, AI frameworks are the better option.

Some of the most popular AI frameworks are:

The framework you choose will depend on your specific business needs.

For example, Google AutoML is a good choice for beginners while TensorFlow is more suited to complex projects.

Collect and prepare high-quality data

Without high-quality data, the AI model in your app won’t work well.

And bad data can cost you.

IBM estimates that the yearly cost of bad data is $3.1 trillion in the U.S. alone.

That’s why data quality is so important.

But, first you need to choose which datasets you’ll use to train your AI model.

Let’s say you’re training an image recognition model.

You can use publicly available datasets such as:

If you need more specific datasets, you can find them on:

Once you have your datasets ready, you need to prepare them for use.

That means you need to preprocess and wrangle your data.

Data preprocessing

source: V7 Labs

In simple terms, data preprocessing is about improving the quality of your data.

You can use tools like Altair and OpenRefine for that task.

Finally, you need to wrangle your data.

This means that you need to turn raw data into a usable format for your AI model.

Once you’ve done that, you can start training your AI model.

Design and train your AI model

The AI model is the heart of your AI app.

That’s why you need to properly design and train it.

So, how do you do that?

For starters, you need to design your AI model’s architecture.

Some of the most common architectures are:

  • Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
  • Generative adversarial networks (GAN)

Each of these have specific use cases and your choice will depend on your specific business needs.

For example, you can use:

  • CNNs for recommendation systems
  • RNNs for speech recognition
  • GANs for image generation

You also need to think about your AI model training approach.

The 3 most common approaches are:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Again, your choice of training approach will depend on your business needs.

For example, unsupervised learning is better for fraud detection models while supervised learning is better for image recognition.

Once you’ve decided on your model’s architecture and training approach, you can start training the model.

In simple terms, this means feeding the data you’ve prepared into the model.

After you’ve trained the model, you can start integrating it into your app.

Integrate the AI model into your app

Integrating the AI model is the sink-or-swim moment for your AI app.

That’s why you need to get it spot on.

In essence, you need to answer 2 main questions: 

  • Where will you integrate the app?
  • How will the AI model process data?

But, what exactly do we mean by that?

Let’s start with how the model processes data.

Your choice will come down to processing data on the cloud or on-device.

Cloud computing vs edge computing

source: Cardinal Peak

On-device processing, or edge AI, is useful if you’re developing an AI Internet of Things (IoT) app.

But, cloud-based data processing is the more common approach.

You’ll also need to decide where to integrate your AI model – in your app’s front-end or back-end.

Most likely, you’ll integrate it into the user-facing front-end of your app.

But, integrating it into your app’s back-end is a good option if you need improved accuracy.

Once you’ve answered these questions, you can start integrating and deploying your AI model.

Best tips for AI app development

To round things off, let’s cover some best tips for AI app development.

They are:

  • Set clear goals
  • Plan for scalability
  • Implement continuous learning

Let’s go!

Set clear goals

If you want the AI app you’re developing to be successful, you first need to set clear goals you want to achieve.

And that means your AI should solve a problem for your users.

With the incredible hype surrounding AI, it’s tempting to just add AI to your app and ask questions later.

But, if you do that, you risk failure.

That’s why you need to be clear about how AI can help your users and improve their experience.

Let’s say you want to add a customer service AI chatbot to your app.

You need to have a clear idea of how it can help your users.

Sephora’s chatbot is a good example.

Sephora chatbot

source: Lengow Blog

It helps users pick out products and book in-store appointments.

And the result?

The chatbot has an 11% higher conversion rate than other channels.

That’s because Sephora set clear goals they wanted to achieve with it.

And you should do the same for your AI app, too.

Plan for scalability

When you’re developing an AI app, planning for scalability is essential.

And that’s for a good reason.

Scaling up is one of the biggest challenges companies face when adopting AI and machine learning models.

Machine learning challenges

source: Itransition

IBM’s 2022 Global AI Adoption Index confirms that, too.

It lists scalability as one of the top 5 challenges holding AI adoption back.

But, what exactly do we mean by planning for scalability?

To start, your infrastructure should support your AI app’s growth.

AI models work by processing and analyzing huge amounts of data.

And that can eat up a lot of resources.

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That’s why you need to make sure your infrastructure can handle that before you develop an AI app.

Another way you can plan for scalability is by making your AI model modular.

This way, you can upgrade individual components when necessary without disrupting the entire system.

Also, the cloud-based AI platforms we’ve discussed can help you with scaling your AI solutions as they can more easily handle large spikes in demand.

In short, planning for scalability from the start will help your app more easily grow and evolve.

And that will help make it a success.

Implement continuous learning

The world of AI app development is constantly evolving.

If your app doesn’t keep up, you’ll be left in the dust by your competitors.

And that’s why continuous learning is so important.

With it, your app and your AI model will continuously improve.

A great example of this is Grammarly.

Grammarly uses AI to check their users’ writing for grammar and style errors.

At the same time, their AI learns from the vast amount of text it processes to improve the suggestions it gives.

And that’s how it continuously improves its output.

But, how can you implement continuous learning in your AI app?

A good place to start is by adding a feedback loop for your AI model.

AI feedback loop

source: UX Collective

Your users should be able to leave feedback on your AI model’s outputs.

That way, it can learn from that feedback and become more reliable.

This is especially useful for generative AI models like chatbots or image generators.

Also, make sure you retrain your AI model with fresh data to prevent it from drifting.

AI model drift

source: AIMultiple

This will keep your model accurate and relevant.

And that’s how you’ll get the most value out of your AI app.


AI app development is the next big thing in the app development world.

And you need to ride the wave or you risk getting left in the dust.

Luckily, you’re in the right place.

If you want to learn more, check out how we build AI apps and reach out so we can make your idea a reality.

Written by

Ante Baus


Ante is a true expert. Another graduate from the Faculty of Electrical Engineering and Computing, he’s been a DECODEr from the very beginning. Ante is an experienced software engineer with an admirably wide knowledge of tech. But his superpower lies in iOS development, having gained valuable experience on projects in the fintech and telco industries. Ante is a man of many hobbies, but his top three are fishing, hunting, and again, fishing. He is also the state champ in curling, and represents Croatia on the national team. Impressive, right?

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