6 common AI app development questions answered

AI app development is a game-changer if you want to build an innovative app.

But, you need to get it right.

And that’s where we come in.

Here, we’ll answer 6 of the most common AI app development questions we’ve noticed keep popping up.

Let’s dive in!

Why is AI important for modern apps?

The importance of AI for modern apps can’t be overstated.

With AI, you can build an app that truly meets your users’ needs.

So, how does AI help you do that?

We’ll talk about two ways it does that in more detail: better personalization and making data-driven decisions with predictive analytics.

Personalization is the name of the game and an absolute must in today’s competitive app market. And AI can help you take it to the next level.

source: LinkedIn

The stats back that up, too.

Epsilon’s research has shown that 80% of consumers are more likely to make a purchase if they have a personalized experience.

Also, 66% of users would stop using a product if their experience isn’t personalized, with that number rising to 75% among Gen Z users.

And AI can help you deliver a personalized experience to all of your users.

Take Spotify’s Discover Weekly feature, for example.

source: The Lexington Line

They use AI to create personalized music recommendations for each of their users every single week.

And that hyper-personalized experience is one of the reasons why Spotify is the market leader in their segment.

The other reason why AI is important for modern apps we mentioned is predictive analytics.

AI predictive analytics tools can analyze huge amounts of data and help you make data-driven decisions.

And data-driven decision making is a great way you can gain the edge over your competitors.

According to a Forrester study, organizations that use predictive analytics are 2.9 times more likely to report revenue growth rates higher than the industry average.

That’s why tools like Tableau and Alteryx are so valuable.

source: Tableau

You can use them to predict a wide range of outcomes both in and out of your AI app, such as:

• User behavior
• Inventory demands
• User growth predictions
• Sales forecasts

Predictive analytics tools will help you minimize human error when making decisions which can save you a lot of money in the long run.

And that’s why they’re so important if you want to develop an AI app.

How to choose the right AI model?

Choosing the right AI model is key when you’re developing an AI app.

The right model can mean the difference between your app’s success or failure.

So, how do you make the right choice?

First, you need to set clear goals for what you want the AI in your app to do.

A good framework to set those goals is the SMART goals framework, pictured below:

source: ProductPlan

In simple terms, you need to have a clear idea of what you want the AI model in your app to do.

And that will help you determine which model is the best fit for your AI app based on your specific business needs.

Let’s say you have an e-commerce app and you’ve set a goal that you want to use AI to improve how your app recommends products to your users.

In that case, you’ll likely go with:

• Content-based filtering model
• Collaborative filtering model
• Hybrid model

The AI model you choose will depend on your specific needs and your budget.

A more budget-friendly option you can go with is using pre-trained models, like Amazon’s Personalize.

source: AWS

If you use a pre-trained model, you’ll be able to significantly reduce your AI app’s cost.

Also, keep in mind that the model you choose should be transparent and its outputs should be explainable.

According to a Capgemini report, 70% of users expect their interaction with AI to be transparent and fair.

That’s why it’s a good idea to avoid using AI models that are too complex because they’re prone to the “black box” problem.

source: Investopedia

This is when you can feed inputs and get outputs from the model but can’t examine the code or logic that produced the output.

And that can be a problem, especially if you handle sensitive user information in your app.

So, in that case, it’s a good idea to go with a simpler, more explainable model.

In short, the AI model in your app should be aligned with both your business needs and your users’ needs.

And that’s how you’ll make the right choice.

How much does AI app development cost?

Estimating your AI app development cost isn’t easy.

There’s no one-size-fits-all answer on how much your AI app will cost – the best answer is “it depends”.

A simple AI customer service chatbot will be much cheaper than an AI model in an autonomous vehicle.

On average, custom AI solutions can cost anywhere between $6000 to over $500,000. 

That’s because there are a lot of factors that influence your AI app cost, such as:

• The complexity of your AI model
• Quality and volume of training data
• Hardware and infrastructure costs
• AI development tools and frameworks
• Your AI app development team
• Continuous maintenance

Let’s take a look at just one of these factors in more detail – your AI app development team.

The team developing your AI app is a significant cost factor because they need to have the right skills for the job.

And those skills don’t come cheap.

According to Glassdoor’s data, the average pay for an AI engineer in the U.S. is $134,000 per year.

source: ARTiBA

And that’s without going into the hiring and onboarding costs of hiring a team of AI engineers.

But, there are ways you can reduce those costs.

One solution is upskilling existing team members.

There are a lot of AI engineering courses on the market, such as:

• IBM’s Introduction to AI
• Microsoft’s Azure AI Fundamentals
• Google Cloud’s Machine Learning courses

These courses will help them get up to speed and get the necessary skills to develop your AI app.

Another good solution is hiring a dedicated team.

Hiring a dedicated team can be significantly cheaper than hiring a full in-house team to develop your AI app.

But, you need to partner with the right company to make the most of it.

Need an AI app?
We have a dedicated team just for you.

You’ll be talking with our technology experts.

Let’s talk

The right dedicated team will care like an in-house team, except they’re formed by a third-party agency or service provider.

So, as you can see, even just one cost factor when developing an AI app can vary wildly in price, depending on what you go with in the end.

That’s why it’s a good idea to get in touch with professionals and get an estimate based on your exact requirements.

Which tools to choose when developing an AI app?

There’s a wide range of tools you can choose from when developing your AI app.

And much like when choosing the right AI model, the tools you end up going with will depend on your specific needs and the type of AI app you want.

For example, if you’re looking to develop a complex custom AI model, you’ll likely end up using an AI framework like:

• TensorFlow
• PyTorch
• Scikit-learn
• Keras

These frameworks are open-source and also host many pre-trained models you can use free of charge.

But, they’re not beginner-friendly.

If you want to use them effectively, you’ll need a team that’s already skilled in developing AI apps.

That’s why cloud-based AI platforms are a better choice if you’re just getting started with AI app development.

Some of the most popular are:

• AWS AI
• Google Cloud AI
• Azure AI

They’re end-to-end platforms, which means you can go from training to deploying AI models without leaving the platform.

Also, they offer a huge range of pre-trained AI models and services you can easily integrate in your app.

source: AWS

WIth cloud-based AI platforms, you can easily add advanced AI capabilities to your app.

And the best part?

You’ll save a lot of time and money you’d otherwise spend on training your own AI model.

Also, it’s a good idea to invest in continuous monitoring tools.

You need to be able to track your AI model’s performance and solve any issues that might crop up.

There’s a number of continuous monitoring tools on the market, such as:

• Arize
• Censius
• Evidently AI
• Neptune
• WhyLabs

These tools will help you make sure your AI app consistently performs well.

And consistent performance will increase its chances of success.

How to collect data for AI models?

Data is the foundation of every AI model.

And the success of your AI app will depend on the quality of the data you collect and use to train your model.

Even the best-designed AI model won’t work properly if it’s trained on low-quality data.

That’s why the data collection process is a crucial step when developing your AI app.

So, how can you collect data to train your model?

One common method is web scraping.

source: AVI Networks

You’ll get a lot of data this way, but you need to make sure that the sites you scrape allow web scraping.

Another common data collection method is using crowdsourcing platforms.

But, both of these methods aren’t as convenient or cheap as using publicly available datasets specifically designed for AI model training.

You can find them on sites like:

• AWS Data Exchange
• Kaggle
• Google Dataset Search

There are also more specific datasets available for different AI types.

So, if you’re training an AI model for image recognition, you can find specialized datasets on sites like:

• ImageNet
• CIFAR-10
• ObjectNet

But, collecting the data is just the first step.

You also need to preprocess the data you’ve collected.

source: Serokell

Data preprocessing will increase the quality of your data and is a key step you shouldn’t neglect.

There are plenty of data preprocessing tools you can use, such as:

• OpenRefine
• Dataprep
• Altair

They’ll help you improve the quality of the data you use in your AI model and improve its performance.

And that’s the most important thing.

How to integrate AI into an app?

Integrating your AI model into your app is the make-or-break moment for your AI app.

If you want your AI model to work properly, you need to do it right.

So, how do you do that?

First, you need to decide where in your app you want to integrate your AI model – in your app’s front-end or back-end.

Learn from a software company founder.

Make your software product successful with monthly insights from our own Marko Strizic.

You’ll most likely integrate it in your app’s front-end, where your users can access it.

Back-end integration is a good option if you have a more complex model and you want to improve its accuracy.

But, there are other options, too.

You can use Google’s MLKit to deploy AI models in your app with only a few lines of code.

source: TechGig

Tools like this and Apple’s CoreML make the AI integration process much easier.

Also, if you’ve used any of the cloud-based AI platforms to train your model, keep in mind that they also have built-in integration tools.

You can also integrate their pre-trained models through APIs, which significantly simplifies the AI integration process.

And there’s one other thing that’s crucial for successfully integrating AI in your app – thorough testing.

There are 3 main types of testing you should do:

• Unit testing
• Integration testing
• User acceptance testing

Of course, there are plenty of other testing methods your QA engineers will use when testing your AI model.

But, these 3 are key to your AI app’s success.

And they’ll make all the difference once you launch your app.

AI app development questions: Conclusion 

AI app development is a game-changer if you want to build a groundbreaking app.

And if you want it to be successful, you have to do it right.

That’s why we’ve answered some of the most common AI app development questions you might have.

To recap, they are:

• Why is AI important for modern apps?
• How to choose the right AI model?
• How much does AI app development cost?
• Which tools to choose when developing an AI app?
• How to collect data for AI models?
• How to integrate AI in an app?

If you want to learn more, check out how we build AI apps and get in touch with us if you need help with building one.