5 best practices in AI app development

13 min read
September 4, 2023

AI app development is quickly becoming the go-to way to take your software product to the next level.

But, you need to do it right.

Just thoughtlessly adding AI to your product won’t cut it.

That’s why you need to follow AI app development best practices which will help you maximize its chances of success.

Here, we’ll discuss 5 best practices you should follow.

Let’s go!

Understand the purpose and limitations of AI app development

It’s tempting to assume that AI can solve every problem.

But, AI isn’t a silver bullet that can instantly solve every problem you’re facing and make your app successful.

Of course, the pros of AI app development outweigh the cons.

Still, you should understand the purpose of the AI you’re integrating and its limitations.

In simple terms, you need to know when and how to integrate AI into your app.

The numbers back this up.

According to Gartner, only 53% of AI and machine learning projects make it from the prototype stage to production.

Fortune reports that the failure rate of AI projects is between 83% and 92%.

Reasons for AI project failure

source: AI Multiple

These figures underscore just how important getting AI integration right is if you want it to deliver business value.

Take IBM’s Watson, for example.

One of its main proposed use cases was real-time cancer diagnosis.

However, it faced criticism after offering unsafe and incorrect recommendations for treatment.

While it may have failed to revolutionize cancer treatment, that doesn’t mean that Watson is dead and buried.

IBM pivoted and turned Watson into an AI and data platform, Watsonx, designed to help businesses scale their AI efforts.

Another AI use case where it’s wise to understand your AI’s limitations are chatbots for customer support.

They can handle common customer problems and queries quickly and efficiently.

Customer service chatbot

source: ChatBot

But, they’re not suited for more complex and nuanced problems.

If you’re planning to integrate an AI chatbot, you should consider those limitations.

Zappos, an online retailer, recognized this limitation and uses AI to enhance, not replace, their customer support.

But, what can you do to avoid the common pitfalls of AI app development?

First, ask yourself these questions: “Will my AI app solve a real problem for my users?” and “will integrating AI enhance the user experience?”

If the answer is yes, you’re on the right track.


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Integrating AI into your app should serve a purpose and not just be a meaningless buzzword.

Also, you need to know the limits of your AI’s capabilities.

Remember, AI models operate based on the data they’re trained on.

They don’t understand context and nuance like a human can.

So, don’t expect miracles and make sure you don’t overpromise and under-deliver. 

It’s also a good idea to prototype early.

Test your AI module in a controlled environment or with a smaller group of users before full integration.

This way, you’ll gather feedback and identify any problems before you launch your app.

To sum up, taking a measured approach to AI app development is the best way to make the most of it.

If you’re clear on its purpose and understand its limitations, you’ll get the most value out of it.

Invest in data quality

“Good data is more valuable than gold.”

This quote by AI pioneer, Andrew Ng, is key to understanding the field of artificial intelligence.

Data is the bedrock of AI.

If you want your AI app to be successful, investing in quality data is an absolute must.

Without accurate and representative data, even the most sophisticated AI algorithms and models will fail.

Poor-quality data can cost you a lot of money, too.

According to Gartner, organizations believe that poor data quality is responsible for an average of $15 million per year in losses.

But, the impact it can have isn’t just financial.

Poor-quality data can lead to flawed insights and even brand damage.

Compounding costs of bad data

source: NewtonX

Take Apple Card’s credit limit controversy, for example.

In 2019, Apple Card faced allegations of gender discrimination as it offered lower credit limits to some women compared to their husbands.

This happened because of unintentional biases in the data their AI algorithms used.

Although they weren’t found to be breaking any laws, this case shows just how damaging poor data quality can be.

Amazon faced a similar controversy with their AI recruiting tool.

It showed bias against female applicants because it was trained on resumes that mostly came from male applicants.

Amazon had to scrap the tool because of these inherent biases.

But, what can you do to ensure the quality of your data?

First, you need to make sure that the data you use comes from diverse sources.

This way, you’ll reduce biases, and your AI models will be more accurate and representative.

Another vital step is accurately labeling the data you use to train your AI models.

Data labeling

source: Bridge Money

This’ll ensure your AI models are trained on relevant and precise data sets, minimizing the risk of mistakes happening.

AI-powered tools like Datasaur and Labelbox can help you do that quickly.

It’s also important you regularly audit your data.

You should update and cleanse the data to maintain its quality and reduce mistakes your AI model makes.

Tools like Holistic AI’s HAI data governance platform or IBM’s Watsonx.governance are good choices for that task.

In short, the quality of your AI depends on the quality of the data it’s trained on.

Making sure you use high-quality data will set the foundation for the success of your AI app development.

Use AI to focus on user experience

Your users’ experience should be the main focus of your app.

That’s why the focus of your AI app development should be on improving your product’s user experience (UX).

As Steve Jobs once said: “You’ve got to start with the customer experience and work back toward the technology, not the other way around.”

Statistics back that up, too.

According to PwC, 32% of customers will leave a brand they love after just one bad experience.

If they have 2 bad experiences, that number rises to 59% of customers.

These numbers go to show just how important good experiences with your product are to your users.

And AI is a great way to take your app’s UX to the next level.

UX factors

source: UX Collective

Take MyFitnessPal, for example.

MyFitnessPal uses AI algorithms to analyze their users’ diets and exercise patterns to make suggestions for meals and workouts.

This ensures that their recommendations are personalized and tailored to each user’s specific needs.

It also significantly improves their UX and provides value to their users.

Another good example are Nest’s smart thermostats.

They use AI to learn their users’ preferences over time and automatically adjust the temperature accordingly.

Their AI algorithms also help their users reduce energy consumption.

MyFitnessPal and Nest are great examples of AI app development done right.

They improve their UX without their users even noticing the technology behind them.

So, how can you use AI to improve your app’s UX?

The first step is making sure your AI features’ design is user-centered.

User-centered design

source: Justinmind

They should be designed with your users’ needs in mind.

Make sure that your AI integrations are intuitive and boost your product’s usability.

It’s also a good idea to give your users insights into how and why certain recommendations or actions were made.

This way, they’ll be able to better understand how your AI works.

Also, you should give your users the option to override or modify AI decisions.

This will ensure they feel in control and increase transparency.

In short, AI can be a powerful tool for enhancing your app’s UX.

And a good UX will maximize its chances of success.

Implement continuous learning and iteration

The world of AI is dynamic and always evolving.

Resting on your laurels is not an option if you want to make the most of your AI app development.

That’s why it’s so important to implement continuous learning and iteration.

Your AI should constantly evolve and improve.

It’s the only way to keep up with technological changes and make AI integration worthwhile.

Take DeepMind’s AlphaGo, for example.

It was originally designed to play Go and was the first AI model that managed to beat a world Go champion.

But, DeepMind didn’t stop there.

They iterated on the original model and built AlphaZero.

AlphaZero stats

source: DeepMind

AlphaZero could play chess and shogi as well as Go, further proof of the model’s underlying capabilities.

It even beat other machine learning models designed specifically for each of those games, as pictured above.

Another good example of continuous learning and iteration is Grammarly.

Grammarly uses AI to identify writing mistakes and offer style suggestions to make their users’ writing better.

And the best part?

Their AI model learns from the huge amounts of text it processes to improve its suggestions.

These examples show how important continuous AI learning and iteration are if you want your AI model to stay relevant.

But, what are some tips you should follow when you implement these processes?

To start, you need to stay informed about new AI advancements.

As AI is a constantly evolving field, current best practices can quickly become outdated.

To make the most of your AI app, you need to be ready to adopt new approaches to AI as they happen.

You should:

  • Keep up with new AI research
  • Go to AI conferences
  • Follow AI forums

You should follow forums and AI expert communities like Kaggle and AI Stack Exchange.

There, you’ll be able to find answers to any questions you might have and keep up with the news in the AI field.

As for conferences, there are plenty of conferences you can attend, some of the biggest being the AI & Big Data Expo Global and the AI World Congress.

It’s key you stay in the loop, otherwise you risk being left behind by your competitors.

You should also create a feedback loop for your AI models.

AI feedback loop

source: UX Collective

Your users should be able to give you feedback on your AI solutions and features.

This way, your AI model will stay aligned with their expectations.

Also, make sure you regularly update your model with fresh data.

This’ll prevent model drift.

AI model drift

source: Evidently AI

It’ll also keep your model accurate and relevant.

One other tip to keep in mind is that you should be ready to pivot quickly if something isn’t working.

Some AI features might not work like you want them to.

But, don’t get discouraged.

You should look at failure as a valuable learning opportunity you can use to improve your AI processes.

Keep in mind that AI integration isn’t a one-time task but an ongoing commitment.

That mindset is the only way you can ensure your product remains relevant and effective.

Plan for scalability

If you decide to go through with developing an AI app, planning for scalability is a must.

Your infrastructure must be ready to support its growth.

But, it’s not just about being able to handle more data.

It’s also about making sure that your AI model has consistent performance and the space to evolve.

Scalability is a major challenge when it comes to AI, though.

According to Statista, scaling up tops the list of challenges organizations face when integrating AI and machine learning models.

Machine learning challenges

source: Itransition

IBM’s Global AI Adoption Index for 2022 also lists scalability as one of the top 5 challenges hindering AI adoption.

These figures show just how important planning for scalability is when developing an AI app.

Take Snapchat, for example.

They launched filters powered by generative AI.

And yet, they didn’t have issues with scalability after their launch.

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That’s because they use AWS microservices to handle the millions of images shared daily on the app.

Another good example is Netflix.

Their AI-powered recommendation engine has to continuously serve recommendations to their 238 million users.

Like Snapchat, Netflix also uses cloud-based AWS microservices to scale their product and consistently deliver value to their users.

So, what are some steps you can take to ensure you can scale your AI solutions?

A good place to start is using cloud-based AI platforms to manage your models, such as:

They’ll allow you to easily scale your AI solutions and can handle large spikes in demand.

You can also train your AI models on these platforms or choose a pre-trained model to integrate into your product.

It’s also a good idea to design your AI systems to be modular.

This way, you can upgrade or replace individual components without having to overhaul the entire system.

It also makes scaling easier, as you can scale individual components as the need arises.

If you need to scale your AI’s real-time responding, you should consider investing in edge AI.

Cloud vs edge computing

source: Cardinal Peak

Edge AI is a combination of edge computing and AI and it means that your AI model processes data on a local device.

Edge AI is especially useful in Internet of Things (IoT) applications and in autonomous driving.

Its main benefit is that it reduces latency and the load on your central servers.

This also makes scaling your AI solutions easier.

And being able to scale means that your AI solutions and your product can grow and evolve.


AI app development is a great way to take your app idea to the next level.

But, if you don’t do it right, it’s not going to do that and will potentially cost you a lot of time and money.

That’s why you should follow the best practices for AI app development.

To recap, they are:

  • Understanding the purpose and limitations of AI
  • Investing in data quality
  • Focusing on user experience
  • Implementing continuous learning and iteration
  • Planning for scalability

If you want to learn more, read about how we build AI-powered software products or read our article on the 6 challenges of AI app development.

Written by

Mario Zderic

Co-founder and CTO

Mario makes every project run smoothly. A firm believer that people are DECODE’s most vital resource, he naturally grew into the role of People Operations Manager. Now, his encyclopaedic knowledge of every DECODEr’s role, and his expertise in all things tech, powers him to manage his huge range of responsibilities as COO. Part developer, and seemingly part therapist, Mario is always calm under pressure, which helps to maintain the office’s stress-free vibe. In fact, sitting and thinking is his main hobby. What’s more Zen than that?

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