Debunking 7 myths about AI app development

14 min read
September 14, 2023

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

But, there are already a number of myths about it that have popped up.

They might even discourage you from integrating AI into your software product.

That’s where we come in.

In this article, we’ll debunk some of the most common myths about AI app development.

Let’s go!

AI app development is only for big tech companies

That AI app development is only for big tech companies couldn’t be further from the truth.

According to IBM’s Global AI Adoption Index, 31% of respondents say their organization is using AI while 43% of respondents are exploring its use.

So, that’s 74% of companies that are either using or exploring the use of AI.

Of course, because of their vast resources, big tech companies are spearheading AI adoption.

But that doesn’t mean that startups and small and mid-size companies can’t successfully develop AI apps.

On the contrary, there are plenty of success stories from smaller organizations.

Take Grammarly, for example.

Their writing assistance software uses AI to improve their grammar and style suggestions.

Grammarly UI

source: TechCrunch

They’re a good example of how a mid-sized company can use AI to deliver value to their users.

And with all the third-party AI solutions available, even if you’re a small business, you can integrate AI into your processes.

For example, you can integrate ChatGPT to handle customer service and automate repetitive tasks.

OpenAI offers their models as APIs you can easily integrate into your website or app.

How to use OpenAI API

source: Soft Kraft

By integrating these models, you’ll end up with more time to focus on growing your business.

And those are just 2 AI use cases out of many.

But, what are some practical AI app development tips for smaller companies?

First, it’s okay to start small – it might even be better, in fact.

You don’t need to overhaul your entire system to integrate AI.

For example, you can start by integrating AI functionalities to automate repetitive tasks and expand your AI integration gradually.

Next, it’s a good idea to work with AI experts when integrating AI into your app and processes.

You can hire freelancers who’ll guide you through the integration process or you can ask for guidance on AI expert forums like Kaggle or AI Stack Exchange.

Also, you should use open-source and cloud-based AI platforms.

Cloud-based platforms like Azure Machine Learning or AWS AI offer end-to-end AI services.

In other words, with them you can:

  • Train an AI model
  • Integrate AI in your app
  • Continuously maintain your AI integration

They also make scaling your AI solutions easy.

In short, there’s nothing stopping smaller companies from integrating AI into their apps and processes.

In fact, it’s a great way you can set yourself apart from your competitors and grow your business.

Using AI results in unbiased outcomes

We often think of AI models as fully objective and unbiased.

But, that’s not true.

The AI you integrate into your app reflects the biases in the data it’s trained on.

That means that it can’t guarantee unbiased decisions and outcomes.

But, you need to make sure those biases are minimized.

Otherwise, you risk damaging your brand and your users’ trust in your product.


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Take Google Photos’ image labeling AI, for example.

In 2015, its computer vision AI models labeled some African-Americans as gorillas.

That happened because of biases and a lack of diversity in the data the models were trained on.

Another good example is AI in predictive policing.

Predictive policing AI tools like Geolitica try to predict where crimes are likely to happen based on existing crime reports.

But, because crimes aren’t equally reported everywhere, this can lead to over-policing in neighborhoods that don’t need more police presence.

Geolitica UI

source: Geolitica

The data it’s trained on also reflects existing biases in policing.

So, instead of predicting crime based on unbiased data, predictive policing tools can end up reinforcing existing biases even more.

These examples show how biased AI outcomes can hurt an otherwise promising app.

But, how can you minimize the risk of that happening to your AI app?

First, you need to make sure that the data you’re using has been cleared of biases.

For example, if your AI model relies on computer vision, make sure that the images it’s trained on are diverse and representative.

Computer vision

source: Towards Data Science

That way, you’ll get better results that are free from bias.

Next, ensure that your AI algorithms are transparent.

That means integrating an AI model whose outputs and decision-making are visible to you and your team.

This way, you can quickly identify and correct any biases that appear in its outputs.

It’s also a good idea to invest in continuous monitoring tools for your AI model.

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

In the end, you can’t guarantee unbiased outcomes from your AI app.

But, you can minimize them if you take the right approach.

AI is unreliable

AI app development is meant to be a reliable way of meeting your users’ needs.

Yet, most of your users don’t trust AI to do a better job than humans.

A survey by Krista showed that if the outcome of the decision affects them personally, most Americans trust other people over AI.

AI trust survey

source: Krista 

That even goes for mundane tasks like picking their outfit for work or choosing the right gift to give someone.

And there’s a reason for that mistrust.

Take deepfakes, for example.

They’re a great example of how bad actors can misuse AI technology to spread disinformation and slander.

Highly publicized AI incidents and failures also contribute to a lack of trust towards AI.

One such failure was IBM’s Watson Health.

Watson Health was meant to revolutionize by recommending optimal treatments for each patient.

But, it didn’t catch on because doctors found its outputs unreliable or irrelevant.

Yet, this doesn’t mean AI is inherently unreliable and that users aren’t interested in AI features.

According to a survey by for Business, a significant number of business travelers are interested in and have a high degree of trust in AI features:


It’s all about having the right approach and using AI well.

If you do AI app development right, you’re likely to increase your users’ trust in the reliability of your AI models.

So, how can you make that happen?

First, you need to integrate a transparent and explainable AI model.

Your users should be able to understand how the AI makes its decisions.

Explainable AI

source: Datanami

Also, user education is key.

Make sure you educate your users on the functionalities and limitations of your AI integration.

After you’ve integrated AI into your app, make sure to set up a tutorial to help your users get to grips with its features.

That way, you’ll increase their trust in your AI solutions.

You also need to implement strong security measures to protect your AI model from misuse.

So, if you’re integrating a generative AI chatbot, make sure you have active filters for offensive and inappropriate content.

As for cybersecurity in general, AI-powered tools like Darktrace Respond and Cybereason are good investments.

In short, AI is reliable but only if you use it right.

And for that, you need your users’ trust.

AI app development forces you to overhaul your product

You don’t need to overhaul your product’s entire infrastructure to develop an AI app.

Think about it like changing the electrical system in your house.

Sure, it might be time-consuming and more complicated than you expected.

But, you don’t need to rebuild your entire house to do it.

AI app development works the same way.

You don’t have to change your app’s entire architecture and infrastructure to do it.

Let’s say you have an e-commerce app.

AI-powered recommendation systems are a good place to start with your AI integration.

Amazon’s Personalize and Google Cloud’s Recommendations AI are good choices.

Google Cloud Recommendations AI

source: Google Cloud

And the best part?

You don’t need to change your app’s infrastructure to integrate them.

Another good example are Customer Relationship Management (CRM) tools which have integrated AI.

Pipedrive’s Sales Assistant and Salesforce’s Einstein GPT are AI features they’ve seamlessly integrated into their product.

And they didn’t have to revamp their product to do it.

Salesforce Einstein GPT

source: Bacancy

So, what do you need to do to make sure your integration is seamless, too?

It’s a good idea to start small.

Start by integrating a single, non-essential AI feature or functionality.

This way, you’ll see how AI can work in your product and your team will get experience with integrating AI in a low-stakes scenario.

Then, you can build on that and integrate additional AI functionalities.

Also, make sure you do pilot testing before fully integrating AI into your app.

By doing that, you’ll be able to assess its performance and compatibility with your existing systems and infrastructure.

It’s also a good idea to consult with AI experts before you integrate AI into your app.

The AI forums I’ve mentioned before have lots of resources and experts who can help your team through the AI app development process.

In short, a complete overhaul of your product isn’t necessary to integrate AI.

All you need is a gradual, well-planned process guiding you through it.

And with that approach, you’ll get the best results.

AI is only good for data analysis and automation

Too many people think AI is only good for data analysis and automation.

And yet, it can do so much more than just that.

Of course, data analysis and automation are AI’s bread and butter and it excels at them.

But, its use cases extend beyond those tasks.

Just look at the impact AI has had on creative industries.

AI tools help:

  • Create content
  • Compose music
  • Create art

That’s a long way from just crunching numbers.

Another good example is using AI in education.

So, if you have an educational app, you can use AI to create personalized learning experiences for each user.

Or you can integrate a generative AI chatbot like Duolingo’s Max to add a new dimension to your product.

Duolingo Max

source: Duolingo Blog

Of course, data analysis is at the heart of all AI models.

Without analyzing data, AI models can’t be trained or even function at all.

But, their outputs go far beyond simple data analysis.

So, how can you start innovatively using AI in your app?

To start, encourage your teams to innovate with AI tools and functionalities.

You can organize AI-focused hackathons where each team’s goal is to create innovative features and functionalities.

It’s also a good way to promote cross-functional collaboration between your teams.

Cross-functional teams benefits

source: Quixy

Also, you should research unmet user needs in your niche and create customized AI solutions to meet those needs.

Of course, AI might not be appropriate to use in every single case.

But, if you dive deep into your users’ unmet needs, you’ll likely find ways you can use AI to solve them.

And finding innovative ways to improve your product is what AI app development is all about.

AI app development is too expensive

Cost is a significant barrier to AI app development.

Seeing headlines talking about ChatGPT’s daily running costs of $700,000 is discouraging if you’re thinking about integrating AI into your product.

Of course, your AI app’s development isn’t going to cost nearly as much.

And while the initial costs might be relatively high, integrating AI can lead to a significant long-term increase in revenue.

According to McKinsey, AI could increase corporate profits by $4.4 trillion a year.

That’s a staggering number which, if it turns out to be accurate, will make integrating AI essential.

But, even if your main concern is the initial cost of developing an AI app, there are low-cost solutions available on the market.

A good example of these solutions are open-source machine learning libraries, such as:

They host a vast amount of AI models you can use for your own business needs, free of charge.

Another low-cost solution are cloud-based AI platforms like AWS AI or Oracle AI services.

They offer pre-built AI models you can easily integrate directly into your app.

You can also manage your AI models through these platforms, too.

Even small businesses can integrate AI into their apps, which we’ve touched upon earlier.

AI chatbots like Tidio’s Lyro are a great, low-cost AI integration option for smaller businesses.

Lyro chatbot

source: Tidio

With Lyro, you get 50 free conversations (with unlimited replies) and after that, it costs up to $0.7 per new customer conversation.

If you’re a smaller business with a niche product, using it for customer service can be very cost-effective.

But, what are some practical tips to lower the cost of AI app development?

You should start by exploring the free and budget-friendly AI tools and platforms we’ve mentioned.

Have your engineers experiment with training an open-source AI model or integrating a simple AI tool.

This way, they’ll familiarize themselves with the technology without any financial risks for your organization.

To sum up, AI app development doesn’t need to be pricey.

You just have to find the right, cost-effective solutions that meet your needs.

AI operates without human oversight

Too many people think that after integrating AI in their apps it will work without any human supervision.

But, that’s not true.

Sure, AI can do many tasks on its own.

Yet, you need someone to supervise your models to get the most out of them.

Take self-driving vehicles, for example.

Even with all the advancements in recent years, they still need human monitoring to intervene in unexpected traffic situations.

Another good example is AI in healthcare.

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The diagnosis or treatment plan an AI model recommends for a certain patient doesn’t just get OK’d immediately.

You still need a doctor to confirm its accuracy and fix any mistakes that the AI model made.

So, what’s the lesson here?

It’s that you’ll get the best results if you supervise your AI model’s outputs and don’t just let it run without supervision.

Also, continuous monitoring tools like Qwak or WhyLabs will help you notice and respond quickly to any problems with your model.

But, what are some other ways you can supervise your AI model?

For starters, it needs to have a feedback loop.

AI model feedback loop

source: UX Collective

This way, it can learn from human inputs and become more reliable.

Feedback loops are especially useful for generative AI solutions like chatbots or image generators.

Also, make sure you follow ethical AI principles when integrating AI models.

Ethical AI principles

source: World Economic Forum

If you do, you’ll minimize the risk of harm and increase your users’ trust in your product.

In any case, AI can’t operate on its own – not yet, at least.

That’s why having a good supervision system in place is so key to the success of your AI app.


Even though it’s been in the public eye for a relatively short time, there are already a number of myths about AI app development.

And although they’re not true, they might influence your decision-making about integrating AI in your app.

We hope that we’ve cleared up some of the myths you might’ve believed about AI app development in this article.

To recap, they are:

  • AI app development is only for big tech companies
  • Using AI results in unbiased decision-making
  • AI is unreliable
  • AI app development forces you to overhaul your product
  • AI is only good for data analysis and automation
  • AI app development is too expensive
  • AI operates without human oversight

If you want to learn more, check out how we build AI-powered software products or read our article going over the pros and cons of AI app development.

Written by

Karlo Mihanovic

Tech Advisor

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