7 mistakes to avoid in AI app development

15 min read
September 7, 2023

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

But, just randomly adding AI to your product won’t cut it.

If you want it to be successful, you need to do it right.

Here, we’ll discuss 7 common mistakes you should avoid when integrating AI into your software product.

Let’s go!

Ignoring data quality

Data is the lifeblood of AI.

Without good-quality data, even the most sophisticated AI app development will fail.

The “garbage in, garbage out” principle is particularly true for AI.

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

Bad data quality cost

source: Capella Solutions

As you can see in the above picture, the average annual cost of bad data quality for companies is estimated to be $14.2 million.

And that’s without going into the cost of training and deploying an AI model.

That’s why you can’t ignore data quality when integrating AI into your product.

A good way to think about it is like mathematician Clive Humby said: “Data is the new oil. Like oil, data is valuable, but if unrefined it cannot really be used.”

If data is oil, then AI is a refinery.

It can extract information from raw data and turn it into usable knowledge.

But if the data is bad, the outcomes will be bad, too.

That’s why you need to make sure the data you use is cleaned and formatted correctly before you use it to train your AI model.

Tools like OpenRefine or Alteryx can help you clean up your data and format it correctly.

Alteryx UI

source: EDF

Also, you need to use relevant data.

Focus on collecting data that aligns with the goals of your AI integration.

In other words, filter out noise and irrelevant information.

This’ll help you avoid skewed outcomes.

But, what are some practical tips you can use to ensure data quality?

First, you need to develop a data management strategy.

It should cover how you:

  • Collect data
  • Process data
  • Analyze data

This way, you’ll set quality standards before you even begin integrating AI.

If you don’t already employ them, it’s also a good idea to hire data scientists.

They’ll know how to clean and prepare the right data for your AI model.

You can hire external experts or freelancers if you’re not ready to hire them permanently.

Another good practice is to make sure you’re using reliable data sources.

You need to verify that the datasets you’ll be using come from verified sources to avoid misinformation and poor-quality data.

Also, make sure you continuously monitor the quality of the data that goes into your AI model.

This way, you’ll prevent the degradation of your model’s outputs.

Censius UI

source: Censius

Tools like Censius and Arize are good choices for that task.

In short, ignoring data quality is a potentially very costly mistake.

But, if you’re meticulous about data quality, you’ll increase your AI integration’s chances of success.

And that’s why it’s worth it.

Underestimating resource usage

One of the biggest mistakes you can make in AI app development is underestimating its resource usage.

According to a survey by Deloitte, 47% of companies faced higher-than-expected costs when implementing AI.

Underestimating how many resources you’ll need can cripple your progress.

But, it’s not just about the money you need to invest.

AI models can take up a lot of computational resources like CPUs and GPUs.

According to Bloomberg, training a single AI model can use more electricity than 100 US homes use in a year.

Of course, those models are at the higher end of the range.

With cloud-based AI platforms like AWS AI or Watsonx, you’ll use fewer resources to train your AI model.

AWS ML stack

source: AWS

Still, you shouldn’t underestimate how many resources you might end up using.

You’ll need to do a detailed resource analysis early on in your AI app development project.

This way, you’ll prevent shortages down the line.

But, what are some other practical tips to accurately estimate your resource needs?

To start, it’s a good idea to consult with AI specialists.

They’ll be able to accurately estimate the resources and money necessary for integrating AI into your software product.

If you’re using an AI platform, such as Azure ML, to train your models their experts will be able to help you with that estimate.

Azure ML services

source: Microsoft Tech Community

Take their estimate as the minimum amount of resources and money necessary for a successful AI integration.

It’s also a good idea to create a pilot program for your AI integration.

That way, you’ll get a realistic estimate of the resources needed before you fully launch your product.

You should also adopt flexible budgeting.

That way, you’ll account for unexpected spikes in resource usage and costs that might crop up during your integration process.

Also, you should continuously monitor your model’s resource usage, just like you should monitor its data quality.

Tools like MetricFire and OpManager Plus are good choices for that task.

In short, underestimating the resources required can be a major obstacle to your AI integration.

You need to make sure you’ve got all the necessary resources before you start integrating AI into your product.

If you don’t, your AI integration could be dead on arrival.

Failing to adequately test your AI model

If you fail to thoroughly test your AI model before integration, you’re drastically increasing the chances that it’ll fail.

Thorough testing is an absolute must.

You should have a methodical approach to quality assurance (QA) as standard if you want your AI integration to be successful.

If you don’t, bugs that mess with your product’s functionality and user experience (UX) are much more likely to happen.

And it can cost you a lot of money, too.

According to CISQ, poor software quality cost the US economy at least $2.41 trillion in 2022 alone.

And lack of testing is one of the main causes of poor software quality.

But, it’s not just about the money.

A lack of thorough testing of your AI model can cause brand damage, too.

The perfect example of that is Microsoft’s AI chatbot, Tay.

Tay chatbot

source: TechRepublic

Launched in 2016 on Twitter, it was supposed to learn from its interactions with other Twitter users.

But, Tay began sending inappropriate tweets soon after launch.

It was promptly shut down after that happened, less than 24 hours after launch.

So, what can you do to make sure you thoroughly test your product?

To start, it’s a good idea to start testing early in the development process and make it an ongoing process.

This way, you’ll catch bugs and errors quickly and minimize their impact on your AI integration.

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It’s also a good idea to use automated testing if you don’t already.

This’ll improve the efficiency of your whole testing process.

Another good idea is to do user acceptance testing (UAT) before you launch your AI integration.

With UAT, you’ll see how your users actually interact with your product and the AI functionalities you’re adding to it.

You’ll also be able to spot and fix any issues before launch.

You should also set up a feedback loop with your users, which we’ll discuss in more detail later.

In any case, integrating AI without adequate testing is a gamble.

It’ll potentially cost you a lot of money and can do serious damage to your brand’s reputation.

And it’s not worth the risk.

Neglecting scalability

Scalability is key to a successful software product.

It’s also important for the success of your AI app development.

If you neglect scalability, you’ll bottleneck your product’s growth and cripple a promising AI project.

Scaling your AI solutions will increase your revenue, too.

According to a joint study by IBM and Forrester, organizations that have scaled AI are 7 times more likely to be the fastest-growing organizations in their industry.

But planning for scalability isn’t easy.

It’s one of the major hurdles you’ll face when integrating AI into your software product.

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 also lists scalability as one of the top 5 hurdles for AI adoption.

If you neglect scalability, your product is more likely to have issues as your userbase grows.

Take Snapchat, for example.

Originally built on the Google App Engine (GAE), Snapchat faced scalability issues as its userbase grew.

That was until they prioritized scalability and started switching to cloud-based microservices in 2017.

And it worked.

Snapchat daily active users by year

source: Statista

Snapchat’s number of daily active users rose from 187 million at the end of 2017 to 397 million in 2023 without major problems with stability and quality.

These figures show how impactful prioritizing scalability can be.

So, how can you make sure your AI app development prioritizes it, too?

A good place to start is designing your AI integration to be modular.

With a modular design, you can easily add new features and functionalities without affecting the rest of your system.

And that makes scaling in the future easier.

Next, it’s a good idea to use a cloud-based infrastructure to integrate your AI.

Platforms like Google Cloud AI and Azure Machine Learning make training and deploying AI models in your product much simpler.

Using them also means that scaling your infrastructure is a much easier task.

Another good idea is doing regular load testing.

This way, you’ll see how your AI model performs under increased load and you can adjust it accordingly.

You should also continuously monitor your AI model’s performance with tools like Neptune.ai and Evidently AI.

If you do, your engineers will be able to proactively solve any scaling issues.

In short, neglecting your AI integration’s scalability can stall your product’s growth and performance.

That’s why it’s so important to adopt a scalability-first approach from the start.

Not preparing for AI model drift

One of the biggest problems you’ll likely face with your AI app development is AI model drift.

Model drift is when AI models lose their accuracy over time.

You need to account for model drift when integrating AI into your product and be prepared to solve it.

Otherwise, it can have serious consequences for your model’s performance and usability.

Let’s imagine you have a stock trading app and you’ve integrated a stock market prediction model.

If you don’t account for model drift, it can lead to significant financial losses.

But, why does model drift happen in the first place?

One of the main reasons is the difference between real-world data and the data it’s trained in.

That’s why regularly retraining your model is so important.

AI model drift

source: Evidently AI

Another reason why AI models might drift is because of unexpected changes to your users behavior.

Take Instacart, for example.

Before March 2020, their AI model for predicting whether an item was available in a given store had an accuracy rate of 93%.

But, as the COVID-19 pandemic hit and their customers’ shopping habits changed, the model’s accuracy rate plunged to 61%.

And how did Instacart solve that problem?

They started refreshing their model with new data every 10 days.

This allowed them to be more responsive to changes in their customers’ behavior.

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But, what can you do to minimize model drift in your AI models?

For starters, you should choose an AI model with a flexible architecture.

That way, updating your model and adapting to changing data patterns is much easier.

It’s also important to monitor the quality of the data that goes into your model, which we’ve discussed in greater detail earlier.

The continuous monitoring tools we’ve discussed earlier can alert your team if your model starts drifting.

This means that they can start solving the problem as soon as it happens.

In short, if you don’t prepare for model drift, your AI model’s performance will degrade over time.

But, if you proactively prepare for it, you’ll ensure that your models stay accurate and reliable.

Not setting up a feedback loop

“Feedback is the breakfast of champions.”

This quote by business consultant, Ken Blanchard, is a great mindset to adopt when it comes to your users’ feedback.

User feedback is key to improving the AI model you integrate into your product and it’ll help you deliver more value to your users.

Setting up a feedback loop is the best way to make the most of it.

AI model feedback loop

source: UX Collective

And that’s not just idle talk.

Take Amazon’s AI-powered recommendation engine, for example.

Amazon has implemented a feedback loop to improve it based on user feedback.

They track:

  • Product ratings
  • Product reviews
  • Purchase history

Then, they use that feedback to refine future recommendations and adapt to their users’ preferences.

And it works, too.

According to McKinsey, 35% of customer purchases on Amazon come from their product recommendations.

That number would be much lower if they didn’t continuously improve them with user feedback.

But, what steps should you take to create an effective feedback loop like Amazon’s?

To start, make sure you plan for a feedback system before you begin integrating AI into your product.

Your users should be able to give you feedback on the AI you’ve integrated right from the start.

Make sure you collect feedback from various sources, such as:

  • User surveys
  • Feedback forms
  • Problem reporting
  • User reviews

That way, you’ll collect representative feedback and get better insights from your users.

You can use AI-powered sentiment analysis tools like Lexalytics’ Semantria and Qualtrics’ XM.

Qualtrics XM TextIQ

source: Qualtrics

With these tools, you’ll be able to quickly find and prioritize negative feedback.

This way, you can quickly solve any problems your users have with the AI you’ve integrated in your product.

But, the most important thing is adopting an iterative approach to your product’s development.

Your engineers should use the feedback you’ve gathered in each development cycle.

If you do that, you’ll stay on top of your users’ changing needs and continuously improve your product.

Remember, setting up a feedback loop isn’t just a one-off task.

It should be a key part of a continuous product improvement process.

And it’s a great way to ensure the AI you integrate into your product stays relevant to your users.

Neglecting user experience

A good user experience (UX) is key to your product’s success.

And it’s key to your AI app development’s success, too.

If you neglect UX when integrating AI into your product, the risk of failure rises significantly.

That’s because your users care a lot about their experience when using your product.

The statistics back that up.

Research shows that 88% of users are less likely to return to a website after a bad experience.

And according to Emplifi, 86% of customers will leave a brand they trusted after only 2 bad experiences.

And your users talk more about negative experiences, too.

On average, they tell 9 people about a positive experience with a brand, but they tell 16 people about a negative experience.

That goes to show how much damage a single bad experience can do to your brand.

A great way to think about UX is this quote by Evan Williams, Twitter co-founder:

 “User experience is everything. It always has been, but it’s still undervalued and underinvested in. If you don’t know user-centered design, study it. Hire people who know it. Obsess over it. Live and breathe it. Get your whole company on board.”

Evan Williams, Twitter co-founder

If you adopt this way of thinking when integrating AI, you’ll increase its chances of success.

But, what are some practical tips to make sure you enhance your AI integration’s UX?

First, make sure your AI functionalities’ design is user-centered.

You should take into consideration:

  • Ease of use
  • Accessibility
  • Simplicity

Like Martin LeBlanc, CEO of Iconfinder, said: “A user interface is like a joke. If you have to explain it, it’s not that good.”

Also, you should use the feedback loop that you’ve set up to find ways to improve your AI integration’s UX.

Using your users’ feedback proactively is key to making sure it keeps up with their needs.

Another good idea is encouraging cross-functional collaboration between your teams.

Your engineers working on developing your AI integration should intensely collaborate with your UX designers.

This way, you’ll end up with a product that’s both user-friendly and technologically sound.

And that’s what AI integration is all about.

AI app development mistakes: conclusion

AI app development is a great way to improve your product.

But, you need to do it right.

And that means avoiding the most common mistakes organizations make when integrating AI.

To recap, they are:

  • Ignoring data quality
  • Underestimating resource usage
  • Failing to adequately test your AI model
  • Neglecting scalability
  • Not preparing for AI model drift
  • Not setting up a feedback loop
  • Neglecting user experience

If you want to learn more, check out how we build AI-powered software products or read our article on the best practices for AI app development into your product.

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Written by

Karlo Mihanovic

Tech Advisor

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