AI integration is the next big thing. Here, we'll discuss essential dos and don'ts you should follow to make yours a success.
5 best practices in AI app development
AI app development is quickly becoming the go-to way to take your software product to the next level.
But, you need to do it right.
Just thoughtlessly adding AI to your product won’t cut it.
That’s why you need to follow AI integration best practices which will help you maximize your chances of success.
Here, we’ll discuss 5 best practices you should follow.
Understand the purpose and limitations of AI app development
It’s tempting to assume that AI can solve every problem.
But, AI integration isn’t a silver bullet that can instantly solve every problem you’re facing and make your product successful.
Of course, the pros of AI integration outweigh the cons.
Still, you should understand the purpose of the AI you’re integrating and its limitations.
In simple terms, you need to know when and how to integrate AI into your software product.
The numbers back this up.
According to Gartner, only 53% of AI and machine learning projects make it from the prototype stage to production.
Fortune reports that the failure rate of AI projects is between 83% and 92%.
source: AI Multiple
These figures underscore just how important getting AI integration right is if you want it to deliver business value.
Take IBM’s Watson, for example.
One of its main proposed use cases was real-time cancer diagnosis.
However, it faced criticism after offering unsafe and incorrect recommendations for treatment.
While it may have failed to revolutionize cancer treatment, that doesn’t mean that Watson is dead and buried.
IBM pivoted and turned Watson into an AI and data platform, Watsonx, designed to help businesses scale their AI efforts.
Another AI use case where it’s wise to understand your AI’s limitations are chatbots for customer support.
They can handle common customer problems and queries quickly and efficiently.
But, they’re not suited for more complex and nuanced problems.
If you’re planning to integrate an AI chatbot, you should consider those limitations.
Zappos, an online retailer, recognized this limitation and uses AI to enhance, not replace, their customer support.
But, what can you do to avoid the common pitfalls of AI integration?
First, ask yourself these questions: “Will integrating AI solve a real problem for my users?” and “will integrating AI enhance the user experience?”
If the answer is yes, you’re on the right track.
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Integrating AI into your product should serve a purpose and not just be a meaningless buzzword.
Also, you need to know the limits of your AI’s capabilities.
Remember, AI models operate based on the data they’re trained on.
They don’t understand context and nuance like a human can.
So, don’t expect miracles and make sure you don’t overpromise and under-deliver.
It’s also a good idea to prototype early.
Test your AI module in a controlled environment or with a smaller group of users before full integration.
This way, you’ll gather feedback and identify any problems before you deploy it in your product.
To sum up, taking a measured approach to AI integration is the best way to make the most of it.
If you’re clear on its purpose and understand its limitations, you’ll get the most value out of it.
Invest in data quality
“Good data is more valuable than gold.”
This quote by AI pioneer, Andrew Ng, is key to understanding the field of artificial intelligence.
Data is the bedrock of AI.
If you want your AI integration to be successful, investing in quality data is an absolute must.
Without accurate and representative data, even the most sophisticated AI algorithms and models will.
Poor-quality data can cost you a lot of money, too.
According to Gartner, organizations believe that poor data quality is responsible for an average of $15 million per year in losses.
But, the impact it can have isn’t just financial.
Poor-quality data can lead to flawed insights and even brand damage.
Take Apple Card’s credit limit controversy, for example.
In 2019, Apple Card faced allegations of gender discrimination as it offered lower credit limits to some women compared to their husbands.
This happened because of unintentional biases in the data their AI algorithms used.
Although they weren’t found to be breaking any laws, this case shows just how damaging poor data quality can be.
Amazon faced a similar controversy with their AI-powered recruiting tool.
It showed bias against female applicants because it was trained on resumes that mostly came from male applicants.
Amazon had to scrap the tool because of these inherent biases.
But, what can you do to ensure the quality of your data?
First, you need to make sure that the data you use comes from diverse sources.
This way, you’ll reduce biases, and your AI models will be more accurate and representative.
Another vital step is accurately labeling the data you use to train your AI models.
source: Bridge Money
This’ll ensure your AI models are trained on relevant and precise data sets, minimizing the risk of mistakes happening.
It’s also important you regularly audit your data.
You should update and cleanse the data to maintain its quality and reduce mistakes your AI model makes.
In short, the quality of your AI depends on the quality of the data it’s trained on.
Making sure you use high-quality data will set the foundation for the success of your AI integration.
Use AI to focus on user experience
Your users’ experience should be the main focus of your software product.
That’s why the focus of your AI integration should be on improving your product’s user experience (UX).
As Steve Jobs once said: “You’ve got to start with the customer experience and work back toward the technology, not the other way around.”
Statistics back that up, too.
According to PwC, 32% of customers will leave a brand they love after just one bad experience.
If they have 2 bad experiences, that number rises to 59% of customers.
These numbers go to show just how important good experiences with your product are to your users.
And AI integration is a great way to take your UX to the next level.
source: UX Collective
Take MyFitnessPal, for example.
MyFitnessPal uses AI algorithms to analyze their users’ diets and exercise patterns to make suggestions for meals and workouts.
This ensures that their recommendations are personalized and tailored to each user’s specific needs.
It also significantly improves their UX and provides value to their users.
Another good example are Nest’s smart thermostats.
They use AI to learn their users’ preferences over time and automatically adjust the temperature accordingly.
Their AI algorithms also help their users reduce energy consumption.
MyFitnessPal and Nest are great examples of AI integration done right.
They improve their UX without their users even noticing the technology behind them.
So, how can you use AI integration to improve your product’s UX?
The first step is making sure your AI features’ design is user-centered.
They should be designed with your users’ needs in mind.
Make sure that your AI integrations are intuitive and boost your product’s usability.
It’s also a good idea to give your users insights into how and why certain recommendations or actions were made.
This way, they’ll be able to better understand how your AI works.
Also, you should give your users the option to override or modify AI decisions.
This will ensure they feel in control and increase transparency.
In short, AI integration can be a powerful tool for enhancing your product’s UX.
And a good UX will maximize its chances of success.
Implement continuous learning and iteration
The world of AI is dynamic and always evolving.
Resting on your laurels is not an option if you want to make the most of your AI app development.
That’s why it’s so important to implement continuous learning and iteration.
Your AI should constantly evolve and improve.
It’s the only way to keep up with technological changes and make AI integration worthwhile.
Take DeepMind’s AlphaGo, for example.
It was originally designed to play Go and was the first AI model that managed to beat a world Go champion.
But, DeepMind didn’t stop there.
They iterated on the original model and built AlphaZero.
AlphaZero could play chess and shogi as well as Go, further proof of the model’s underlying capabilities.
It even beat other machine learning models designed specifically for each of those games, as pictured above.
Another good example of continuous learning and iteration is Grammarly.
Grammarly uses AI to identify writing mistakes and offer style suggestions to make their users’ writing better.
And the best part?
Their AI model learns from the huge amounts of text it processes to improve its suggestions.
These examples show how important continuous AI learning and iteration are if you want your AI model to stay relevant.
But, what are some tips you should follow when you implement these processes?
To start, you need to stay informed about new AI advancements.
As AI is a constantly evolving field, current best practices can quickly become outdated.
To make the most of your AI integration, you need to be ready to adopt new approaches to AI as they happen.
- Keep up with new AI research
- Go to AI conferences
- Follow AI forums
There, you’ll be able to find answers to any questions you might have and keep up with the news in the AI field.
It’s key you stay in the loop, otherwise you risk being left behind by your competitors.
You should also create a feedback loop for your AI models.
source: UX Collective
Your users should be able to give you feedback on your AI solutions and features.
This way, your AI model will stay aligned with their expectations.
Also, make sure you regularly update your model with fresh data.
This’ll prevent model drift.
source: Evidently AI
It’ll also keep your model accurate and relevant.
One other tip to keep in mind is that you should be ready to pivot quickly if something isn’t working.
Some AI-powered features might not work like you want them to.
But, don’t get discouraged.
You should look at failure as a valuable learning opportunity you can use to improve your AI processes.
Keep in mind that AI integration isn’t a one-time task but an ongoing commitment.
That mindset is the only way you can ensure your product remains relevant and effective.
Plan for scalability
If you decide to go through with AI integration, planning for scalability is a must.
Your infrastructure must be ready to support its growth.
But, it’s not just about being able to handle more data.
It’s also about making sure that your AI model has consistent performance and the space to evolve.
Scalability is a major challenge when it comes to AI, though.
According to Statista, scaling up tops the list of challenges organizations face when integrating AI and machine learning models.
IBM’s Global AI Adoption Index for 2022 also lists scalability as one of the top 5 challenges hindering AI adoption.
These figures show just how important planning for scalability is when integrating AI into your product.
Take Snapchat, for example.
They launched filters powered by generative AI.
And yet, they didn’t have issues with scalability after their launch.
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That’s because they use AWS microservices to handle the millions of images shared daily on the app.
Another good example is Netflix.
Their AI-powered recommendation engine has to continuously serve recommendations to their 238 million users.
Like Snapchat, Netflix also uses cloud-based AWS microservices to scale their product and consistently deliver value to their users.
So, what are some steps you can take to ensure you can scale your AI solutions?
A good place to start is using AI-specific cloud platforms to manage your models, such as:
They’ll allow you to easily scale your AI solutions and can handle large spikes in demand.
You can also train your AI models on these platforms or choose a pre-trained model to integrate into your product.
It’s also a good idea to design your AI systems to be modular.
This way, you can upgrade or replace individual components without having to overhaul the entire system.
It also makes scaling easier, as you can scale individual components as the need arises.
If you need to scale your AI’s real-time responding, you should consider investing in edge AI.
source: Cardinal Peak
Edge AI is a combination of edge computing and AI and it means that your AI model processes data on a local device.
Edge AI is especially useful in Internet of Things (IoT) applications and in autonomous driving.
Its main benefit is that it reduces latency and the load on your central servers.
This also makes scaling your AI solutions easier.
And being able to scale means that your AI solutions and your product can grow and evolve.
AI integration is a great way to improve your product.
But, if you don’t do it right, it’s not going to do that and will potentially cost you a lot of time and money.
That’s why you should follow the best practices for integrating AI into your software product.
To recap, they are:
- Understanding the purpose and limitations of AI
- Investing in data quality
- Focusing on user experience
- Implementing continuous learning and iteration
- Planning for scalability