Integrating AI into your software product is the future. Here, we bring you 7 tips that'll help you make it happen.
6 challenges of AI app development
Building a working app can be difficult at the best of times.
AI app development adds another layer of difficulty to that process.
Still, it’s worth it.
With AI integration, you’ll take your software product to the next level.
Here, we’ll discuss 6 major challenges of AI app development and how you can solve them.
Let’s dive in!
Collecting and managing data effectively
AI thrives on data.
That’s why it’s important to collect and manage data effectively.
And that’s no easy task.
The quality and quantity of data you use can directly affect the success of your AI app development.
With each passing year, the amount of data created daily grows at a rapid pace.
In 2023, that number reached 328.77 million terabytes.
And that’s just in one day!
source: Exploding Topics
Here’s another staggering statistic – only 3% of companies’ data meets basic quality standards.
Poor quality data is expensive, too.
According to Gartner, it costs businesses an average of $15 million annually.
One high-profile example of AI failure due to poor data management is Microsoft’s chatbot, Tay.
Launched in 2016 on Twitter, it was supposed to get smarter by learning from interactions with users.
However, soon after launch, Tay began sending inappropriate tweets.
It was promptly shut down after that.
The internet is a treasure trove of data but as Tay’s example shows, not all of it is useful or accurate.
Sourcing relevant, high-quality data is just the first issue you’ll face when integrating AI into your app.
Another major challenge you’ll face is managing those large volumes of data.
Storing, sorting, and accessing huge datasets requires specialized tools and infrastructure.
If you don’t have them in place already, you’ll need to implement them before you integrate AI into your software product.
Luckily, there are plenty of such tools available, such as:
But, what exactly can you do to make sure you collect and manage data effectively?
A good place to start is diversifying your data sources.
You shouldn’t rely only on one channel for data collection.
Using multiple data sources will give you a more comprehensive and unbiased dataset.
It’s also a good idea to invest in data cleaning tools.
source: Hands-On Data Visualization
Also, make sure the data you use is compliant with regulations like GDPR in the EU or CCPA in California.
You should regularly review and update your data collection practices to keep up with legislation.
If you carefully plan your data collection and management, your data will turn into your most valuable asset.
And it’ll make integrating AI into your software product easier.
High costs and resource consumption
AI app development isn’t just about models and algorithms.
It demands a lot of resources, both in terms of money and infrastructure.
Worldwide spending on AI-powered systems will reach $154 billion in 2023 – a 26.9% increase from 2022.
It’s also expected to double by 2026 and surpass $300 billion.
Let’s take a look at one specific example – DeepMind’s AlphaGo.
AlphaGo is an AI model that was trained to defeat world champions in the game Go.
According to one estimate, just training the model cost DeepMind $35 million.
This is mainly due to the huge amount of computational resources used.
This goes to show that implementing advanced AI solutions is very expensive.
Deep learning models in particular are expensive to train and maintain.
Training complex AI models requires powerful GPUs and TPUs, which can be pricy.
While the prices of GPUs have fallen from their peak, top-of-the-line GPUs will still cost you a significant amount.
It’s not just model training that’s expensive.
Data collection and management can also lead to significant costs.
Also, with the AI boom in full swing, AI specialists and data scientists are in high demand.
This means their salaries are growing, too.
If you want to hire them, you’ll need to offer a very competitive salary package.
You’ll be talking with our technology experts.
So, what can you do to reduce costs and make AI integration more financially manageable?
A good place to start is prioritizing model efficiency.
Simpler is often better.
If a basic machine learning model can achieve comparable results to deep learning models, it makes sense to use it instead.
And the best part?
You get all the AI functionalities you need while reducing resource usage and saving money.
Another good solution is using cloud-based AI tools and infrastructure, such as:
All of these platforms offer a wide range of AI tools and services and are often more cost-effective than in-house setups.
Also, you should consider upskilling your existing team through AI courses and workshops.
IBM offers a number of AI courses and certifications your team could benefit from taking.
This’ll likely cost you less than hiring external experts.
While the high costs can be daunting, if you invest smartly and prioritize efficiency you’ll maximize your returns
Complexity of AI models
“Simplicity is the ultimate sophistication.”
This timeless quote by Leonardo da Vinci also applies to AI.
The complexity of AI models is one of the biggest challenges of AI app development.
Keep in mind that the most elegant solutions are often not the most complex ones.
Take image recognition, for example.
The go-to choice for image recognition tasks are deep convolutional neural networks.
And sure, they’re great at those tasks.
They’re easier to implement, too.
Another example where simpler can be better is sentiment analysis.
Supervised learning algorithms like Naive Bayes can achieve the same results as complex deep learning models.
Of course, the choice between complex and simple AI models and algorithms comes down to your specific needs.
If you’re dealing with large datasets, complex and more capable models are the better choice.
But, if you’re analyzing small datasets, simpler models are the more cost effective option.
One other problem with complex models is interpretability.
They can act as “black boxes”, whose internal workings are invisible to the user.
In simple terms, you can feed them inputs and get outputs but you can’t examine the code or logic that produced the output.
In that case, it’s hard to understand their decisions.
And that’s a significant problem for AI integration.
After all, would you put code you don’t understand in your product?
Of course, you wouldn’t.
So, how can you tackle model complexity when integrating AI into your product?
The best move is to start simple.
Start by integrating a basic model and evaluate its performance.
Then you can decide if you even need to use a more complex model.
They’ll help you select the right algorithms and train your AI models.
They’re specifically designed to explain the decisions and output of complex AI models.
To make the best use of AI models, you’ll need to balance between complexity and practicality.
Make sure you choose the right tool for the right job.
Ethical and bias concerns
Your AI models carry the same bias as the data they’re trained on.
And AI models trained on biased data have serious ethical implications.
Luckily, top AI researchers recognize the importance of ethics to AI.
But, that’s often not enough.
Take AI facial recognition systems, for example.
If they’re trained on biased data, this can have disastrous consequences.
According to a 2019 study by the National Institute of Standards and Technology (NIST), many commercial facial recognition systems were biased.
They were up to 100 times more likely to misidentify Asian and African American people compared to white Americans.
As these systems are increasingly adopted by law enforcement, the ethical and legal implications are obvious.
Another high-profile example of AI bias is Amazon’s AI recruiting tool that showed bias against female applicants.
The system was trained by observing patterns in resumes submitted to Amazon over a 10-year period.
And the catch?
Most of the resumes came from men.
Essentially, the system taught itself that men’s resumes were preferable.
Problems with biased data prompted Amazon to scrap the system in 2018.
So, how can you avoid pitfalls like these?
The first step is examining your data.
Historical data often carries biases from the past and your AI models can learn them by mistake.
Make sure you clean the data and check it for any biases before you use it to train AI models you plan to integrate into your product.
It’s also a good idea to regularly audit your AI systems.
source: Holistic AI
You can use external agencies or tools to audit your AI systems for fairness and bias.
Holistic AI’s HAI is an AI governance system you can use to audit your AI solutions.
Also, ensure that the data sources you use are transparent.
This way, they can be externally evaluated and validated.
You should also encourage diversity in your AI development teams.
Having diverse perspectives on board can help you more easily spot and correct biases.
Proactively addressing biases and ethical concerns is essential if you want to use AI responsibly.
It’ll also increase user trust and adoption.
Dependence on external platforms or services
Many companies rely on third-party AI platforms to integrate AI into their product.
While this has many benefits, it also brings some challenges.
Depending on third-party solutions can help you fast track AI adoption.
But, it can also make in-house innovation harder.
This is part of a broader trend of migrating to the cloud – 69% of businesses are moving critical information to the cloud.
The biggest cloud computing platforms like AWS and Google Cloud are also market leaders in providing AI infrastructure and services.
A big advantage of third-party platforms and services is ease of use.
Let’s say you wanted to integrate an AI-powered chatbot into your product.
But, your customization options are limited and they might not have the flexibility to meet your unique business needs.
source: Google Cloud
Also, if you use them, you’re vulnerable to pricing shifts.
And that’s one of the main disadvantages of using external platforms for AI integration.
The initial costs might seem low, but they can skyrocket.
This is especially true if you need to scale your AI solutions or add new functionalities to your product.
Another problem that can happen with external platforms is becoming overly reliant on a specific platform.
This can make future migration challenging and costly.
But, what can you do to minimize these disadvantages?
The most important thing you can do is pick the right platform that suits your organization’s needs.
You should always evaluate your options before committing to deep integration.
Make sure that the vendor you choose aligns with your long-term goals and that their services provide your organization with added value.
But, regardless of your choice of vendor, keep an eye out for any updates to the terms of service.
You should monitor any changes they make to:
- Terms of service
- Platform capabilities
This way, you won’t be surprised if they change and you’ll be able to plan accordingly.
Also, it’s a good idea to regularly backup your data.
If your data is regularly backed up and stored securely, you’ll minimize your risks if you suddenly have to change vendors.
So, what’s the takeaway?
It’s true, external platforms can help you quickly integrate AI into your product.
But, you should always make sure your AI solutions remain flexible and under your control.
Integration and compatibility issues
AI integration isn’t just adding new code to your software product.
It’s also making sure it works well with your existing systems and platforms.
This can be a major hurdle to AI integration.
Take healthcare, for example.
Integrating AI for predictive medical diagnostics improves diagnosis accuracy which is key for effective treatment.
This can save thousands of lives every year.
But, integrating AI with electronic medical record (EMR) systems can be challenging.
EMR systems are deeply embedded and they’re often older, so hospitals face compatibility issues when introducing AI solutions.
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Banking is another good example.
Many banks still operate on outdated legacy systems and platforms.
This means that incorporating modern AI solutions for fraud detection or chatbots can present significant compatibility issues.
So, what are some reasons why integration and compatibility issues happen?
One common reason is if your AI system is designed on a different architecture to your existing systems.
This can cause significant compatibility issues.
Another reason is different data standards.
AI models need standardized data to work and your existing systems may have different data formatting or storage protocols.
Also, you need to keep in mind that AI integration doesn’t happen in a vacuum.
It can disrupt and stop essential business functions.
This is especially true if you experience issues with the integration process.
So, how can you minimize that risk and avoid these issues?
To start, you need an integration blueprint.
Before you begin integration, sketch out a detailed plan.
First, examine your IT environment and determine if there are any fundamental issues there.
Then, you should identify potential points of failure i.e. where integration issues are most likely to happen.
One way you can solve these problems is by using middleware.
Middleware is software that sits between an operating system and apps that run on it.
Think of it as a bridge between the two that allows them to communicate with each other.
Middleware can be that bridge between your AI solutions and existing systems.
Also, if you’re using third-party platforms or services, you should work closely with your AI solution providers.
Their experience can help guide seamless integration.
They’ll also be able to offer workarounds for known issues.
Compatibility and integration issues can delay AI adoption.
But, if you have the right strategy and the right tools, they’re only a temporary setback.
AI app development is the best way to take your product to the next level.
It’s not a walk in the park, though.
Integrating AI can take up a lot of your time and resources.
To recap, what are some challenges you’ll face when integrating AI into your software product?
- Collecting and managing data effectively
- High costs and resource consumption
- Complexity of AI models
- Ethical and bias concerns
- Dependence on external platforms
- Integration and compatibility issues