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7 AI app development trends to look out for in 2023
AI app development is the next big thing.
And as new AI use cases gain more traction, the market is only going to grow.
But, to get the most out of it, you need to know the market.
That’s why we’ll discuss 7 top AI app development trends you should look out for in 2023.
Let’s dive in!
Table of Contents
Conversational AI is a type of AI that can recognize, process, and reply in natural language.
In simple terms, it’s AI that can interact with humans in a natural way.
So, how does it work?
Conversational AI works by combining:
- Natural language processing
- Foundation models
- Machine learning
The AI models are trained on a large and diverse text and speech dataset.
That data is used to train the system to understand and process human language.
Now, let’s discuss some examples of conversational AI.
Conversational AI examples
The most well-known examples of conversational AI are voice assistants like Siri and Alexa.
They use voice recognition to understand user questions and answer with pre-programmed answers.
But they also use natural language processing to learn how to give better, more human-like answers.
And while they’re not as capable as generative AI, they still have their place.
Another good example of conversational AI are chatbots.
Take Sephora’s chatbot, for example.
It helps their users pick out products and book in-store appointments.
The chatbot works, too – it has an 11% higher conversion rate than other channels.
And that’s a pretty good return on investment.
How you can use conversational AI
We’ve mentioned that the 2 most common use cases for conversational AI are voice assistants and chatbots.
Now, let’s talk about how you can use them in your app.
Integrating a customer service chatbot in your app is a great way to use conversational AI.
source: Sprout Social
The chatbot can handle the most common customer problems and your customer service team can focus on handling more complex issues.
And the best part?
The chatbot is available 24/7 and can provide round-the-clock support.
As for voice assistants, you can integrate them in your AI app, too.
One major benefit of voice assistants is that they improve your app’s accessibility.
And even just that makes it worthwhile.
AutoML and no-code AI platforms
One of the major drawbacks of AI app development is that building and training AI models is a difficult task.
And to do it, you need to have a solid understanding of data science and a number of other key skills.
But, AutoML and no-code platforms are changing that and making AI app development easier than ever.
So, how do they work? And what’s the difference between them?
Let’s start with AutoML.
AutoML platforms help automate the end-to-end process of training machine learning models.
source: Microsoft Learn
Their main benefit is automating their routine tasks and helping them deploy AI models much faster.
No-code platforms, on the other hand, use a no-code development environment to quickly train and deploy AI models.
They’re aimed at non-experts and help beginners start working with AI models.
Now Let’s take a look at some of the AutoML and no-code platforms on the market today.
AutoML and no-code platform examples
The most well-known AutoML platform is Google Cloud AutoML.
Google AutoML automates the creation of AI models that can analyze:
- Image data
- Video data
- Text data
- Tabular data
There are other AutoML platforms on the market, too:
As for no-code platforms, some of the top platforms on the market are:
Now, we’ll dive into how you can use them when developing an AI app.
How you can use AutoML and no-code platforms
The main benefit of both AutoML and no-code platforms is that they make deploying AI models in your app much easier.
AutoML platforms are geared towards data scientists and engineers just getting started with AI development.
With them, you can make developing your AI app easier and faster.
You’ll be talking with our technology experts.
No-code platforms, on the other hand, are meant for beginners and you don’t need development skills to use them.
They often use a visual, drag-and-drop interface to deploy pre-trained AI models.
Both have the benefit of lowering the barrier of entry to AI and making it easier for companies to integrate AI models into their apps.
That’s why they’re a worthwhile investment, especially if you’re just getting started with AI.
With every passing year, cybersecurity is becoming more and more important – and AI can help you take it to the next level.
And there’s a simple reason why that’s the case.
Cybercrime is expected to surge in the coming years, as you can see below:
The estimated cost of cybercrime worldwide is expected to rise to $23.82 trillion by 2027 – that’s a 107% increase from the 2023 numbers.
But, these figures are so high they’re almost abstract.
On an individual level, the average cost of a data breach is $4.35 million.
These numbers are a good incentive to invest in cybersecurity.
Cybersecurity AI examples
One very successful example of using AI for cybersecurity are Gmail’s spam filters.
Using TensorFlow, they built an AI-powered spam filter that blocks 99.9% of unwanted emails, including:
That comes out to an additional 100 million spam emails blocked every day.
Crowdstrike’s Falcon platform offers end-to-end protection and integrates a large number of tools in a unified platform.
Darktrace’s Enterprise Immune System uses AI to detect and counter threats in real time.
And it learns from each encounter with a threat, becoming better and better over time.
How you can use cybersecurity AI
So, how can you use cybersecurity AI in your app’s development?
Aside from the AI-powered cybersecurity platforms we’ve mentioned, there’s several other ways you can use AI to improve your app’s security.
Let’s say you’re building a fintech app.
You can use AI for fraud detection and prevention.
AI can analyze huge amounts of data and detect suspicious transactions and patterns.
Another way you can use AI to improve your cybersecurity is by analyzing your app’s codebase.
It will spot any vulnerabilities you might have missed and help make your app safer.
Personalization is a must in today’s market.
And AI can help you take it to the next level.
According to a report by Twilio Segment, 92% of companies are already using AI personalization to drive business growth.
Your users want personalization, too.
Here’s another interesting statistic – 65% of customers are willing to stop using a product if their experience isn’t personalized.
That number rises to 75% among Gen Z customers.
Now, let’s discuss a couple of examples of successful AI personalization.
AI personalization examples
A good example of AI personalization are recommendation systems.
And they can be wildly successful.
Take Netflix, for example.
According to Netflix themselves, 80% of watched content on the app comes from their recommendations.
Another good example is Amazon’s recommendation engine.
McKinsey estimates that it’s responsible for 35% of Amazon’s yearly revenue.
And considering Amazon’s yearly revenue in 2022 was $514 billion, the recommendation engine was responsible for $180 billion of that number.
That’s a pretty good return on investment.
And these are just two of the highest-profile examples of AI personalization.
So, how can you use it in your app?
Let’s find out!
How you can use AI personalization
There’s a number of ways you can use AI to personalize your users’ experience.
If you’re building an e-commerce app, you can use Amazon’s recommendation engine through Amazon Personalize.
With it, you’ll be able to personalize your recommendations based on your unique data.
But, AI personalization isn’t just about recommendation engines.
You can also use AI to personalize your marketing campaigns.
And these are just a few use cases for AI personalization.
Edge AI is the combination of AI models and edge computing.
More simply put, the AI models run on physical devices rather than on cloud servers.
A great benefit of edge AI is that you can get all the AI functionalities without a constant internet connection.
That means that the AI models can consistently run no matter where the physical device is located.
source: Cardinal Peak
But, edge AI has some drawbacks, too.
One major downside is its lack of computing power compared to cloud-based AI models.
So, if you’re planning to work with edge AI, keep in mind that it can’t run complex models.
Now, we’ll cover some examples of edge AI.
Edge AI examples
A great example of edge AI is Nest’s Thermostat.
It uses AI to learn its users’ preferences and automatically adjust temperature settings.
So, if you have a 9 to 5 job, it will automatically adjust the temperature in your home during that time to save energy.
It can also integrate with weather forecasts and make intelligent decisions days in advance.
Another good example of edge AI are autonomous vehicles.
If they didn’t use edge AI and relied on a constant internet connection, they’d be far more limited.
The AI models powering them wouldn’t be able to work in areas with spotty internet connectivity.
Running the AI models on-device makes autonomous vehicles much more useful.
How you can use edge AI
One of the main use cases of edge AI is if you’re building an Internet of Things (IoT) app.
As edge AI models are simple and lightweight, they don’t use up a lot of resources.
And, since they process data on-device, you won’t need to invest as much in your infrastructure.
That can significantly reduce your costs.
Also, edge AI is a good idea if a significant number of your users live in areas with poor internet connectivity.
With it, they can still use your app’s AI functionalities without major problems.
And that’s why it’s a good investment.
Emotion AI is a type of AI that can detect and interpret human emotional cues.
It’s a growing market, too.
Allied Market Research estimates that the emotion AI market will grow from $1.8 billion in 2022 to $13.8 billion in 2032 – that’s a compound annual growth rate (CAGR) of 22.7%.
But, how does it work?
Emotion AI can find and identify emotional signals in:
Of course, the success rate varies depending on the medium.
So, while textual sentiment analysis is reliable, detecting emotions from video with facial emotion recognition is less reliable.
According to Morphcast, state-of-the-art facial emotion recognition AI has an accuracy rate of 75-80%, lagging behind the 90% accuracy rate humans have.
Still, it can be a good investment, as the examples we’ll cover show us.
Emotion AI examples
So, what are some examples of emotion AI in action?
As AI can analyze huge amounts of data, it’ll help you do sentiment analysis at scale.
This way, you’ll be able to get all the insights you need in seconds.
But, there are other, more innovative uses of emotion AI.
Take Smart Eye’s Driver Monitoring System, for example.
source: SAE International
They used Affectiva’s emotion AI technology to capture drivers’ emotions.
It can detect if a driver is distracted, sleepy, or exhibits dangerous behavior.
And these are only a few examples out of a huge range of innovative AI apps.
Let’s turn to how you can use emotion AI in your app.
How you can use emotion AI
Emotion AI has a wide range of use cases.
Let’s say you have an educational app.
With emotion AI, you’ll be able to tailor your educational content to your users’ emotional state.
So, if the AI spots that they’re getting tired, it can adjust the lesson plan or suggest they take a break.
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And this will improve your app’s user experience (UX).
One innovative way you can use emotion AI is in a gaming app.
It can adjust the game’s scenarios and difficulty based on your users’ current mood, creating a fully personalized experience.
And this will increase user engagement and satisfaction, which is exactly what you want.
Generative AI has taken the world by storm in 2023 and ushered in the AI revolution.
But, what exactly is generative AI?
Generative AI are AI models that can generate text, images, and other media from the data they’re trained on.
What makes them stand out is the fact that the content they generate is brand new and original.
And they can do all that just from natural language prompts.
But, that’s not the only reason why generative AI exploded in popularity.
Another reason why it’s so popular is its impressive impact on worker productivity and potential revenue increases.
According to McKinsey, generative AI has the potential to increase corporate profits by up to $4.4 trillion a year.
So, it won’t just make your app better but will also transform how your team works.
And that’s why it’s so valuable.
Generative AI examples
At this point, we’ve all used ChatGPT and it needs no introduction.
It’s one of the most successful AI apps ever developed, and its rise has popularized generative AI.
But, it’s far from the only generative AI app on the market.
Some others include:
Also, there are a number of more specialized generative AI apps like Github’s coding assistant, Github Copilot.
It automates the writing of boilerplate code and allows engineers to focus on more complex tasks.
And we’ve barely scratched the surface of the generative AI app market.
Now, let’s cover how you can use generative AI in your app.
How you can use generative AI
A great way you can use generative AI in your app is by integrating a generative AI chatbot.
OpenAI offers APIs of most of their models which you can use in your app.
You can also train and fine-tune the models with your own data for better results.
For example, with a GPT-powered chatbot, you can take your customer service to the next level.
And that’s just the tip of the iceberg.
One of the main benefits of generative AI is its flexibility.
Let’s go back to our educational app example.
A generative AI chatbot can serve as a learning companion, who your users can ask for clarification if they don’t understand something.
This will transform your app’s UX for the better.
And delivering a great UX is what AI app development is all about.
AI app development is the future.
And the trends we’ve covered are changing the game.
To recap, they are:
- Conversational AI
- AutoML and no-code platforms
- Cybersecurity AI
- AI personalization
- Edge AI
- Emotional AI
- Generative AI