Integrating AI into your software product can be challenging. Here, we'll discuss 6 major AI integration challenges and how to solve them.
Pros and cons of AI app development
AI app development is a great way to improve your product.
But, integrating AI into your product isn’t a decision that should be taken lightly.
You need to be clear-eyed about the pros and cons of AI integration.
Here, we’ll discuss some of those pros and cons to help you decide whether or not AI integration is the right choice for you.
Let’s dive in!
The pros of AI app development
First, let’s explore some of the pros of AI app development.
You should use AI because it:
- Improves efficiency
- Enhances user experience
- Saves you money in the long run
Let’s explore each in more detail.
One of the best use cases for AI is improving the efficiency of your software product and your development process.
The numbers back that up, too.
According to McKinsey, automating processes with AI on average reduces business costs by 30% within 5 years.
It doesn’t just reduce costs, though.
It also frees up your time so you can focus on more important tasks, like strategic initiatives to grow your company.
But, the main benefit of integrating AI is improving the efficiency of your product itself.
Take Intuit’s TurboTax, for example.
They integrated AI into TurboTax, branded as the “Express Lane”.
This allowed their users to file their taxes in 10 minutes or less, a significant improvement over the IRS estimate of 13 hours.
So, if you’re basing your product around data entry, integrating AI can help you make that a much more efficient process.
Another way AI can increase efficiency is quick data analysis.
source: IBM Developer
Both have natural language processing abilities, meaning that you can ask questions in conversational language and the AI will find the data you’re looking for.
This significantly speeds up the process of finding data.
Also, these tools can analyze vast amounts of data in a short amount of time.
They’re also very good at finding patterns in data, some of which a human might overlook.
Of course, if you’re dealing with a lot of sensitive data, you can train your own data analysis model.
Regardless if you choose a third-party tool or train your own model, AI will help you analyze data more quickly and accurately.
And that’s a great way to improve not just your product’s efficiency, but also the efficiency of your organization as a whole.
Enhances user experience
User experience (UX) should be the focus of your product.
And AI app development can help you take it to the next level.
Like Steve Jobs said: “You’ve got to start with the customer experience and work back toward the technology, not the other way around.”
This is important because your users care a lot about their experience when using your product.
Research shows that 88% of users are less likely to return to a website after a bad experience.
Also, 62% of users who had a bad experience on mobile are less likely to purchase from that brand in the future.
If that doesn’t convince you, for every dollar invested in UX, you get $100 in return – that’s a ROI of 9,900%.
source: Adam Fard
So, how can integrating AI help you improve your product’s UX?
One way is through AI-powered personalized recommendations and content curation.
Take Netflix, for example.
Netflix uses AI algorithms to suggest films and shows to their users.
And the algorithm works – 80% of the content their users watch comes from their recommendation system.
Amazon is another good example.
They use a sophisticated AI algorithm to recommend items to their customers.
And each user’s recommendations are personalized according to their specific needs and preferences.
This gives them a much better experience compared to a generic recommendation system.
Amazon also offers personalization as a service through AWS and Amazon Personalize.
You can train one of their AI models with your data and customize it based on your unique business needs.
This way, you can get a ready-made and proven AI-powered recommendation system integrated into your product in no time.
Another great use case for enhancing UX with AI are AI-powered chatbots.
They’re especially useful for customer service, as they can handle the most frequently asked questions.
Learn from a software company founder.
Make your software product successful with monthly insights from our own Marko Strizic.
This’ll let your customer service teams focus on solving more complex problems your users are facing.
A good example is Sephora’s chatbot.
The chatbot, Sephora Assistant, helps their customers book in-store appointments and pick out the right products to suit their needs.
It delivers results, too – it has an 11% higher conversion rate compared to other channels.
That’s because it improves their users’ experience.
And that’s what AI app development should be about.
Saves you money in the long run
The goal of every business is to increase revenue and reduce costs.
AI app development can be a great way to reduce costs and save you money in the long run.
Although your initial investment in AI could be high, the long-term benefits are much larger.
According to a report by Accenture, AI has the potential to increase business profitability by an average of 38% by 2035.
One way it can do that is by reducing your operational costs.
Take General Electric, for example.
They use their AI platform, Predix, to analyze the vast amount of data their wind turbine sensors generate.
This allowed them to implement predictive maintenance, which reduced the risk of failure and costly repairs.
Also, they used AI to optimize the logistics and installation of the turbines, reducing crane working hours by 20%.
As logistics are a significant part of the overall cost of installation, reducing the cost even by 10% amounts to a huge amount of money.
The key to reducing operational costs with AI is identifying areas where AI can optimize your processes.
You should find where you can automate repetitive tasks and use AI to analyze your processes to find inefficiencies.
Another way AI can save you money in the long run is by minimizing human errors.
This is most obvious when it comes to data.
The data loss rate due to human error comes out to around 25%.
And bad data can cost you a lot of money.
source: Capella Solutions
Bad data costs businesses a staggering $3.1 trillion every year in the US alone, according to IBM.
Integrating AI can help you solve that problem.
AI can analyze huge amounts of data very quickly and automate data entry.
This’ll help you clean up your bad data and reduce the associated costs.
In the long run, the savings from that can reach millions of dollars.
And that’s a very compelling reason to go through with AI integration.
The cons of AI app development
Now, let’s cover some of the cons of AI integration.
- High initial investment
- Complex to maintain
- Difficult to integrate with existing systems
Let’s cover each in more detail.
High initial investment
As beneficial as AI integration can be, it’s not without its faults.
We’ve already mentioned that the high initial investment can present a significant barrier to your AI integration.
That’s because training an AI model can be very costly as it requires a lot of computational resources.
source: Epoch AI
Take OpenAI’s GPT-3 model, for example.
Training the model cost $4 million, and that’s without factoring in the costs of model tuning and further training.
There are other costs associated with AI models, too, such as their continuous maintenance.
Sticking with OpenAI, they estimate that running ChatGPT costs them $700,000 a day.
Of course, it’s unlikely that your AI integration will cost anywhere near as much to train.
But, this figure illustrates the high costs of AI integration, especially the training AI models.
Another contributor to the high initial costs is the cost of hiring skilled AI specialists.
The average yearly salary for an AI specialist in 2023 is around $127,000, according to PayScale.
As there’s a shortage of AI specialists on the market, even finding an AI specialist can be challenging.
A lack of skilled professionals and the high cost of AI integration are the two main barriers to AI adoption, according to IBM’s Global AI Adoption Index.
But, how can you limit the impact of these high initial costs?
One way is using off-the-shelf, cloud-based AI and machine learning solutions.
You can use platforms such as:
They offer pre-trained models and a huge number of other AI solutions.
Using these platforms is significantly cheaper than training your own AI model from scratch.
Also, remember that you should run a thorough cost-benefit analysis before integrating AI into your product.
If there’s a simpler solution available that achieves similar results, go with that instead.
And regarding the shortage of skilled professionals, a good solution is upskilling members of your existing team.
source: Microsoft Learn
This way, you’ll save money on hiring costs and cause a minimal amount of disruption in your existing teams.
You can also hire an agency who can assemble a dedicated team of engineers to help you with your AI integration.
In short, the high initial costs are a significant barrier to AI integration.
But, the good news is that as AI models get more efficient, those initial costs are going down every year.
That’s why you should still seriously consider AI integration, even if it’s initially expensive.
Complex to maintain
Another drawback of AI integration is the complex maintenance of AI models.
Your AI models have to be continuously updated and refined to maintain accuracy.
If you don’t, they’ll experience model drift.
source: Evidently AI
If you don’t continuously refresh your AI models with new data, they’ll begin to decay.
You should make sure that you track your model’s performance and have a regular retraining schedule.
Continuous maintenance can cost a lot of money, too.
We’ve already mentioned ChatGPT’s $700,000 daily running costs.
Of course, your model is unlikely to cost you anywhere near that much, but it’s a great example of how complex and costly running AI models can get.
A good solution for mitigating the complexity and cost of maintaining AI models is investing in AI management tools and platforms.
Some of the biggest on the market are:
These tools will allow you to manage your AI model’s performance and retrain it when necessary.
But, even though regular AI model maintenance is complex, debugging it is even more so.
The cost of debugging is estimated to account for 50-75% of the total budget of software development projects.
With the complexity of AI models, that figure might rise further in case of any issues.
Take the well-known “black box” problem, for example.
This is when you feed inputs into your deep learning AI and it generates outputs but you can’t examine the code or logic that produced them.
That’s one reason why it can be challenging for your team to identify and fix bugs and errors in AI model.
One way you can make maintenance easier is by using visual debugging tools like Neptune.ai.
Visual debugging simplifies the process of finding and fixing bugs in AI systems.
That makes the whole debugging process much easier.
AI integration is complex to maintain.
But, it’s still worth it.
Difficult to integrate with existing systems
AI models are a new, cutting-edge technology.
And that means they work best with equally new, modern systems.
This is a significant challenge for AI integration.
Even regular data integration is a challenge for IT professionals.
According to Mulesoft, 89% of IT professionals say that data integration challenges hinder digital transformation.
With AI, those challenges become even harder.
This is especially true in sectors which still run a lot of outdated, legacy systems.
You’ll be talking with our technology experts.
Take banking, for example.
If you wanted to implement AI-powered fraud detection, the biggest obstacle would be integrating it with the existing systems.
You’d need a complete overhaul of the legacy system for the AI integration to work.
Another good example is healthcare.
According to a HIMSS survey, 73% of healthcare providers still operate legacy systems.
This means that integrating cutting-edge AI can present a significant challenge, especially when it comes to electronic medical records.
Also, integrating AI can lead to a disruption in operations.
That’s why it’s a good idea to gradually integrate your AI.
Before you even begin integrating AI, make sure it can work with your existing infrastructure.
If it can’t, you’ll have to decide if an overhaul of your infrastructure is worth it.
Then, start with the functionalities that are easiest to integrate and which won’t disrupt your operations too much in case they don’t work.
Also, make sure that you adequately prepare your teams for the AI integration.
If you’re having trouble, using middleware could be a solution to bridging your compatibility gaps.
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.
Another good tip is that you should work closely with your AI vendors if you’re using third-party AI solutions.
They’ll have the necessary expertise and experience to guide you through your AI integration.
In any case, difficulty integrating AI with your existing systems can be a major problem.
That’s why you need to adequately prepare before jumping in.
AI app development: the verdict
AI app development is quickly becoming the go-to way to upgrade your product’s capabilities.
But, like with anything, it has its pros and cons.
To recap, the pros are:
- Improving efficiency
- Enhancing user experience
- Saving you money in the long run
And the cons are:
- High initial investment
- AI is complex to maintain
- Difficult to integrate with existing systems