There are already a number of myths out there about AI integration that might scare you away from doing it. Here, we'll debunk 7 of the most common AI integration myths.
6 main types of AI app development
AI app development is the best way to improve your product and take it to the next level.
And the best part?
There are plenty of different ways you can integrate AI, based on your business needs.
If you’re a business owner looking into AI app development and don’t know where to start, don’t worry, we’ve got you covered.
Let’s dive in!
Direct AI integration
Directly integrating AI into your software product is the go-to way to improve it.
By doing this, the AI features become a core part of your software product.
And you don’t need to rely on third-party services and platforms to support your product’s AI functionalities.
It’s becoming more common, too.
You’ll be talking with our technology experts.
A Gartner survey showed that in 2021, around 40% of new enterprise apps by service providers have integrated AI functionalities.
You should consider doing it, too.
But, before you do, make sure that your AI integration solves your users’ specific needs.
The goal of your AI integration should be to improve your product’s user experience (UX).
Let’s say you have a photo app.
You can integrate AI-powered image recognition to label and search for photos.
AI-powered image recognition can also help with your app’s accessibility like AIPoly Vision helps visually impaired people.
These features will help improve its UX and solve specific problems your users face.
And the cherry on top?
Your team doesn’t even have to train a model from scratch.
Pre-trained image recognition models like VGG-16 are good choices if you want your product to have image recognition capabilities.
Of course, your team will need to periodically feed fresh data into the model to maintain its accuracy.
So make sure they have a continuous improvement strategy in place.
To sum up, integrating AI is a great way to improve your product.
And direct AI integration gives you full control over your AI functionalities.
Cloud-based AI platforms
Cloud-based AI platforms are an increasingly popular way to integrate AI into software products.
And the market is only expected to grow in the coming years.
KBV Research estimates that the global cloud AI market will grow to $395.8 billion between 2023 and 2029 – that’s a compound annual growth rate (CAGR) of 38.4%.
But, why is the market expected to grow so much?
It’s simple, it’s because cloud-based AI platforms are:
- Easy to deploy
Compared to training and integrating your own AI model, integrating cloud-based AI solutions is a breeze.
And they’re cost-effective because you don’t need to hire AI specialists or invest as much into expanding your infrastructure.
This is also why they’re significantly easier to scale compared to directly integrated AI.
That leaves you with enough room to focus on the most important thing – how your AI integration helps your users.
As Satya Nadella, CEO of Microsoft, said:
“We are moving from a world where computing power was scarce to a place where it now is almost limitless, and where the true scarce commodity is increasingly human attention.”Satya Nadella, CEO of Microsoft
The AI functionalities you integrate into your product should draw your users’ attention.
So now you’re probably asking: “Which cloud-based AI platforms are out there and what do they offer?”
Let’s start with AWS Sagemaker.
AWS Sagemaker allows you to quickly train and deploy AI models in your product.
GE Healthcare used it to develop their AI-powered medical imaging solutions.
Another major player in the cloud AI market is Google Cloud AI.
They offer a range of AI services and tools, with a specific focus on generative AI.
Snap Inc. uses Google Cloud AI’s services to personalize their ad recommendations.
A third major player in the cloud AI market is Microsoft Azure Machine Learning.
Azure ML is an end-to-end AI platform, which means it covers:
- Data collection
- AI model training
- Deploying AI models
- AI model maintenance and monitoring
Industrial conglomerate 3M used the Azure ML platform to develop over 1,500 custom sales forecasting models, one for each of its regions and divisions.
And these are just a few of the success stories of organizations using cloud-based AI.
But, you need to keep 1 key thing in mind.
The platform you choose should align with your business goals and strategies.
If you already use a platform’s other services, pick that one.
For example, if you’re using AWS for your cloud computing needs, go with AWS Sagemaker.
This’ll make your AI integration go a lot smoother.
AI as a service (AIaaS)
AI as a service (AIaaS) is the outsourcing and integration of AI functionalities through third-party providers.
AIaaS allows you to experiment with AI with low upfront costs compared to building and training your own AI models.
It’s also easier to scale compared to direct AI integration.
With it, you can also quickly deploy ready-made AI functionalities into your product.
This makes AI integration much easier and less time-consuming.
And the market is expected to rapidly grow in the coming years.
Precedence Research estimates that the AIaaS market will grow from $6.3 billion in 2022 to $155.31 billion in 2032 – that’s a CAGR of 37.78%.
So, how does AIaaS work?
Let’s say you want to integrate a customer service AI chatbot.
Sure, you could build and train your own model.
However, that can take a lot of time and resources.
All your team has to do is integrate their API into your product and you’re all set.
Another AI functionality you can add by using AIaaS is computer vision.
Clarifai offers computer vision models you can use for features like:
- Visual search
- Image tagging
- Content moderation
They also offer a free version of their service, so you can experiment with their models before committing to a paid plan.
This means you can integrate AI functionalities even if you don’t have in-house expertise.
This ease of use, and cost-effectiveness, are great incentives for you to go with AIaaS.
And that’s why the market is booming as much as it is.
In simple terms, on-premise AI refers to AI solutions deployed within your organization’s infrastructure.
This is a great AI integration option if you’re dealing with sensitive data.
Let’s say you have a successful fintech app.
You’ll most likely have your own data center to store and manage the vast amount of data in your app.
And if you want to integrate AI-powered fraud detection, on-premise AI integration is the best choice for that task.
The aerospace industry is another great example.
A good use case for AI in that industry is predictive maintenance.
Tools like Odysight collect data from various sources, such as:
- Data from sensors
- Maintenance and logbook data
- AI computer vision images
They then use that data to prioritize and predict which parts need maintenance.
This way, they reduce downtime and maintenance costs.
And increased security isn’t the only benefit you get from on-premise AI integration.
On-premise AI models are also more customizable.
As they’re fully in your control, you can adapt them to your specific business needs more easily than cloud-based AI solutions.
This is especially beneficial if you have a niche product and pre-trained AI models aren’t available.
But, on-premise AI has some drawbacks, too.
The main drawback is that you’ll need a substantial investment to get it up and running.
You’ll have to invest in your infrastructure to make the most of your AI models.
Also, you’ll have to hire skilled personnel to manage and maintain your on-premise AI system.
Unlike with cloud-based AI platforms or AIaaS, your team has to have some experience with AI systems before you integrate on-premise AI.
But, while it demands a significant investment, you get complete control over your data and AI operations.
And that’s reason enough to go with it.
Edge AI is the combination of edge computing and AI algorithms.
In simple terms, the AI algorithms run on physical devices rather than depending on cloud servers.
This is especially useful if you’re developing an Internet of Things (IoT) app.
With edge AI, you can get all the AI functionalities without needing a constant internet connection.
One great benefit of edge AI is faster data processing.
Take autonomous cars, for example.
Autonomous vehicles would be far more limited if they had to rely on a constant internet connection.
And the AI that’s powering them wouldn’t work in areas with poor internet connectivity.
However, with edge AI, it can consistently run no matter where it is.
Another good example of edge AI is Amazon Echo.
Echo processes voice commands locally, which means it responds faster.
That’s the key to a great user experience (UX).
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One other benefit of edge AI is data privacy.
As the data is processed on-device, it’s easier to ensure data privacy than if it was processed on cloud servers.
But, edge AI has some drawbacks, too.
One major weakness of edge AI is its lack of computing power compared to cloud-based AI solutions.
So, don’t expect you can run complex AI models if you’re opting for edge AI.
Edge AI is also more vulnerable to security breaches.
That’s because it’s more vulnerable to human error.
And human error is by far the most common cause of data breaches – 88% of data breaches are caused by human error.
Still, if you have an IoT product or want real-time AI functionalities that’ll work even when there’s poor connectivity, it’s the right choice.
Operational AI refers to AI functionalities you’ve integrated into your organization’s day-to-day operations.
While you don’t integrate IT directly into your product, they still have a positive effect on it.
Operational AI will help you:
- Automate routine tasks
- Boost efficiency
- Help you make data-driven decisions
All of these benefits will help you build a better product.
Many organizations have already adopted operational AI for at least one function.
And they’re seeing the benefits, too.
According to McKinsey, a majority of organizations that have adopted AI for at least one function report cost decreases and revenue increases.
Take customer service, for example.
AI-powered customer service chatbots are a great example of an operational AI solution that can help you cut costs.
Then, your customer service team can focus on tackling your customers’ more difficult problems.
And the end result of that is increased customer satisfaction, which is a key factor in revenue growth.
They automate the creation of boilerplate code and allow your engineers to focus on solving more complex tasks.
This makes them more productive and efficient.
Engineers who use Github Copilot agree with that, as you can see in the picture below:
source: Github Blog
And these are just two examples of how you can integrate operational AI to boost your organization’s productivity.
It’s up to you to choose which part of your organization’s work you want to augment with AI.
It’ll depend on your specific business needs.
But, whichever you go with, you can expect substantial gains.
AI app development types: recap
There are a number of ways you can integrate AI into your product and organization.
The type of AI integration you choose depends on your specific needs.
To recap, the 6 types of AI integration we’ve covered are:
- Direct AI integration
- Cloud-based AI
- AI as a Service (AIaaS)
- On-premise AI
- Edge AI
- Operational AI