What’s the State of Embedded AI Tools in Apps?

Did you ever notice how your phone seems to know what you need, like the brightness of your phone can change automatically based on your environment or your health app gives you a reminder to wake up and drink a glass of water?

Some of the apps even do this without the internet. That’s not just cool features or a good design. It is embedded AI doing the work in the background.

Embedded AI offers great results, such as quicker responses, smarter decisions, and better privacy. As speed, convenience, and security have become important, technology is getting better, and devices are becoming powerful, more apps are starting to use embedded AI.

It is changing how apps are built, how we use them, and what we expect from them. This blog will address what embedded AI is, what is the current state of embedded AI tools in apps, where it is used, and more.

What is Embedded AI?

Embedded AI is a type of smart technology that works inside your phone, tablet, smartwatch, or other device. It runs directly on the device. It doesn’t rely on cloud servers to process data and give results. This means all the thinking happens on the device, and the best part is that it doesn’t need constant internet access.

For example, if your phone gets unlocked by seeing your face or enhances photos instantly, that is embedded AI. It is much faster and often more secure, as your information doesn’t have to leave your device because the data is processed locally.

More and more apps are now using embedded AI, as it allows apps to work smoothly, respond quickly, and even work when you are offline, which helps make your everyday experience smarter and more convenient.

Key Features of Embedded AI Tools

Embedded AI tools help make apps smarter and faster. These tools are built into the device. Below are some key features that make embedded AI useful:

1. Works in Real-Time

The biggest advantage of embedded AI is that it works straight away. It doesn’t need to wait for a server or the internet to respond. For example, you are using the camera on your phone.

You can see that it can quickly improve your photo or blur the background. Here you can see embedded AI at work. It can understand and respond to your actions immediately.

2. Can Be Used Offline

For embedded AI to work, it doesn't always require an internet connection. Thus, you can still use its smart features when you are not online.

If you are in an area that is remote or your mobile data has run out, the app can still perform tasks like correcting grammar, recognising faces in photos, and more. This becomes helpful when you travel a lot or are in places with poor network coverage.

3. Learns What You Like

Over time, embedded AI can learn from the way you use your apps and devices. Your habits are noticed by AI tools to offer you suggestions that match your style. It notice:

• Which apps do you open the most
• Which words do you type often
• The type of music you listen to

That is why you can see that your keyboard suggests words you often use. This makes your experience more friendly and personalised.

4. Keeps Your Data Safe

AI is built into the device, so most of the processing happens right there. This means the data and your info don’t always have to be sent to the internet or shared with outside servers.

This helps keep your data more secure. This gives users satisfaction when using the apps, as they know that the app they are using can manage private or sensitive information.

5. Uses Less Internet

Embedded AI does not depend too much on the internet to work. As most of your tasks are done on the device, it doesn’t need to send and receive as much data. This helps save mobile data and reduces your internet usage. It also helps apps run better in places where the internet is slow or unstable.

6. Saves Time

With the help of embedded AI tools in apps, you don’t have to wait for your app to load or process online because everything happens on the device.

For instance, when you use a photo editor, filters can be applied or lighting can be adjusted immediately by AI. It makes apps faster and more responsive, which in turn, helps improve your experience.

Examples of Embedded AI in Popular Apps

Embedded AI is already being used in many apps. These apps have features built into them that work directly on your device. Some of the examples of embedded AI in apps are as follows:-

1. Voice Assistants (like Siri and Google Assistant)

The best example of embedded AI in an app is the voice assistants on your phones and various smart devices. They can do the following within seconds:

• Listen to your voice
• Understand what you are saying
• Give you a quick reply

Many voice tasks, like playing music, are handled right on the device. This makes the assistant respond quickly and even work without internet for basic commands.

2. Photo Editing Apps (like FaceApp and Lensa)

Embedded AI is often used in apps that help you improve or change your photos. This allows them to do the work quickly. Therefore, when you use a filter to make your selfie look even better or remove any background from a photo, the app uses AI on your phone to make those changes.

3. Fitness Apps (like Fitbit and Apple Health)

Embedded AI is used by fitness and healthcare apps. These apps use it to track your movement, heart rate, sleep, and more. You can get feedback right away on your daily activity with the help of these apps.

For instance, you want to know how many steps you have completed in a day when you are walking. The app, using embedded AI, can do that for you, so you can check the calories you have burned for the number of steps you have walked.

The AI can also give suggestions for your health based on your habits. All of this is done on your device, which makes embedded AI quick and private, and it also works when you are offline.

4. Productivity Tools (like Grammarly and Notion AI)

Some writing and work apps use embedded AI to help you write better or organise your notes. Let’s look at the example of Grammarly.

You can enter the text you have written, and it will then show you the suggestions for the spellings you need to improve or the grammar you have to correct. These suggestions appear almost instantly.

You can get faster results, and your content stays secure because some of these tools run on your device.

Benefits of Embedded AI in App Development

Embedded AI offers many helpful features to apps. It helps both users and developers by making apps smarter and quicker. Here are some key benefits of embedded AI in app development.

1. Faster App Performance

As the AI is built into the device, the device doesn't have to wait for the internet or connect to servers that are far away. Everything happens on the device, which means the app can give answers or complete tasks very quickly.

For example, a photo app can improve your picture right after you take it. This fast response makes the app more fun and easier to use.

2. Works Without Internet

Another benefit is that many apps that use embedded AI can still work when you are offline. This is very useful when you don’t have Wi-Fi or mobile data. For instance, a health app can still count your steps. It allows users to use the app whenever they want.

3. Better Privacy

Embedded AI can run on your phone or tablet, which means it doesn’t always have to send your data to the internet. This helps protect your information, like photos, messages, or health details.

It stays safely on your device. This also means users can use the app more comfortably because it ensures that their data is not being shared or stored somewhere they don’t know.

4. Saves Costs for Developers

The need for large cloud servers is reduced when AI works on the device. That is why app developers don’t have to spend as much money on storage or internet use.

It also reduces the need for apps to use a lot of internet data. For apps with lots of users, this can lead to excellent cost savings over time.

5. Improved User Experience

The app becomes more enjoyable when it offers faster responses, offline features, and smart suggestions. Users get the feeling that the app understands them and works well in many situations.

For example, an app may remember your choices and suggest things you like. When users are happy with the app, they also keep using it and even recommend it to others.

Challenges of Embedded AI Integration

Embedded AI too comes with challenges. Developers have to consider some important challenges. Here are the main ones.

1. Limited Device Power

Phones and tablets don’t have as much power as big computers because they are small. There are some AI tools that need a lot of processing power to work well.

Thus, developers have to make their AI smaller and lighter so it can run on all types of devices, even older ones. The app might become slow or not work at all if the AI is too big.

2. Battery Use

AI features can use energy in large amounts, particularly when they are working in the background all the time. For instance, the battery of a voice app can drain quickly if it listens all the time.

That is why developers need to make sure that AI tools are made in a way that doesn’t use too much battery. This will allow users to still enjoy the app and don't have to charge it often.

3. Making Models Smaller

There are many AI models that are large. That is why these models can take up a lot of space. But to run them on mobile devices, developers need to make them smaller. This is called compression.

The tricky part comes when you need to keep the AI smart and accurate while also reducing its size. It takes time and skill to do this properly so that the app runs well and still gives good results.

4. Security Risks

Although the data stays on the device, the AI model can still be at risk. Someone can try to copy it or change how it works.

Developers need to protect the AI to ensure it is not used in any wrong way. This means using safety tools to lock the model and make sure it works only the way it’s supposed to.

5. Updates and Maintenance

Updating AI that is built into an app or device can be difficult than updating AI that runs on a server.

So, the developers need to find ways to send updates, but also without using too much space or slowing down the device. They also need to make sure the updates don’t cause problems or affect other features in this AI app.

Industry Use Cases Driving Growth

Embedded AI is being used in many different industries. It’s helping businesses create apps that are smarter and tools that can work faster, more safely, and with less internet. Here are some of the main areas where it is used:

1. Healthcare

Embedded AI in healthcare is used in mobile apps and wearable devices. It can be the smartwatch you wear or your bands for fitness.

These tools can check your heart rate, patterns of your sleep, and activity levels, even without internet. Users can get instant results and keep their data private because everything works on the device.

2 .Automotive

Embedded AI in the automotive industry is helping make cars smarter. Many vehicles now use it for parking assistance, lane detection, and driver alerts.

For example, if you are driving a car and are tired or feel distracted, the AI can sense it and will give you a warning. These features don’t need a network because the AI runs inside the car.

3. Smart Home Devices

Embedded AI is also used in various home gadgets. The voice assistance you use, the smart lights that many now use in their home, etc, use embedded AI.

These devices can learn your routine and habits, which helps make the user's life more convenient. These devices respond faster because of the AI that runs inside the device.

4. Retail & E-commerce

In shopping apps and stores, embedded AI is used to make suggestions, recognise items through the camera, or more.

For example, some clothing apps allow you to see how the clothes you have selected will look on you before you purchase them. AI also helps scan barcodes, track stock levels, and give quick answers to customer questions.

Tools and Frameworks Powering Embedded AI

Developers use various tools and frameworks to build apps with embedded AI. And that is what we are going to learn here. Below are some of the popular embedded AI tools and frameworks.

1. TensorFlow Lite

TensorFlow Lite is a smaller version of Google’s TensorFlow. It is made for mobile and edge devices. It also allows developers to run AI models quickly and with less battery use. It works on both Android and iOS, supporting features such as image recognition, speech detection, and more.

2. Core ML

Core ML is Apple’s AI framework made especially for iPhones, iPads, and other Apple devices. It lets developers include machine learning models into apps easily.

Core ML is fast, safe, and works well without needing the internet. It supports many features like face detection, handwriting recognition, and natural language processing.

3. ONNX Runtime

ONNX (Open Neural Network Exchange) Runtime is a tool that allows developers to use AI models across different platforms.

It becomes useful when you want to build your model in one tool but use it in another app. It supports many devices and can work with different AI frameworks like PyTorch.

4. Edge Impulse

Edge Impulse is a platform that focuses on embedded machine learning for edge devices. It helps developers:

• Collect data
• Train AI models
• Deploy them quickly

It is designed for use cases like health monitoring, smart farming, and industrial automation, where AI needs to work right on the device.

How Developers Are Embedding AI Today

Developers are finding smart ways to bring AI directly into apps and devices. It has become easier to make apps that think and respond like humans. Here is how developers are doing it.

1. Using Lightweight AI Models

Developers now create small models that can work on various devices, like phones, tablets, and smart devices.

These models are trained to perform various tasks, such as recognising faces, suggesting words, etc. These are then “compressed” to take up less space. This allows them to run on everyday devices.

2. Choosing the Right Tools and Platforms

There are many tools available that help developers add AI to mobile apps. For example:

• TensorFlow Lite for Android apps
• Core ML for iOS apps
• Edge Impulse for devices with sensors or low memory

You should choose the right tool because it will make it easy for developers to train AI models and run them on devices.

3. Training AI on Real User Data

Another way the developers are embedding AI is by helping AI learn and get better. They do this by collecting user behaviour (with permission).

If you use your phone and type a message, you will see that the keyboard is suggesting the words you often type after that particular word. This is what AI is trained to do.

The training of the AI model is done safely and privately on the device itself using on-device learning. This means the data doesn’t have to leave the user’s phone.

4. Adding AI Features That Work Offline

Most of the apps that we use today have smart features that can work without the internet. Developers do this by embedding the AI directly inside the app.

For instance, you click a photo and then use a photo app to improve your pictures instantly, which is also the work of embedded AI. This allows users to use the app whenever they want.

5. Testing and Updating AI Models

AI testing is done by developers on different types of phones and under different conditions after the AI is added. This helps make sure it works well for all users. They also send updates regularly to improve accuracy or fix bugs.

However, the updates must be small and not use too much space, as the AI runs on the device. This requires more planning, but it helps keep the app running well.

6. Balancing Speed, Accuracy, and Battery Life

Developers also have to make sure the AI runs fast and correctly, and is not using too much battery. This means they need to choose the right time to activate AI, like only when the app is open or when the user gives permission.

When AI is used smartly, it keeps the app useful. It also ensures the app is saving power and works well on the phone.

Future Trends in Embedded AI for Apps

Embedded AI is set to become an essential part of how your app will work in the future. Mobile devices are becoming faster and smarter, which calls for more apps that use AI features on the device directly and also don’t need the internet if the user is not able to access it.

Apps will get better at understanding users, offering relevant and smart suggestions, and will work even when offline.

Another trend will be the greater focus on privacy. In the future, more apps will use embedded AI so that the information can stay on the device. Also, developers will use small and fast AI models that don’t drain the battery and can run on every device.

We can also expect to see more voice features, live translations, and personal assistants built right into apps. Even small teams will be able to use embedded AI using better tools and simple platforms. This will help them make their apps smarter and more secure, and useful for everyone.

Should Your App Have Embedded AI?

If you want to have embedded AI in your app, you can add it. It can be a great idea, but it also depends on what your app does and who will use it. It is important to understand when embedded AI can work best for you and when you should avoid it before you decide. Let’s look at both sides.

1. When to Consider It

You should think about using embedded AI in your app if:

Your App Needs to Work Without Internet

If your users are in areas with poor or no internet, then embedded AI is a good choice. For example, some apps help translate languages or enhance your photos with the help of filters. These apps can work better offline with on-device AI.

You Want Faster Responses

Some tasks need to take place almost instantly on the device, like your phone getting unlocked immediately after it recognises your face. Embedded AI handles these jobs quickly because it doesn’t need to send data online. If speed is important to your app, it can be helpful for you.

You Handle Private or Sensitive Data

Users care about privacy, especially when they are using your app that deals with health info, users' pictures, or messages. Embedded AI keeps more data on the device, which helps offer users satisfaction and build trust with them.

You Want to Save Costs on Cloud Use

If you are using the cloud to run AI models, it can become expensive, especially with many users. If your app can run smart features on the device, you will save money on server costs and data usage.

Your App Can Learn from the User

Some apps work better when they learn what each user likes. Embedded AI helps the app learn and adjust, making it feel more personal and useful.

2. When to Avoid It

Embedded AI isn’t always the right fit. You might want to skip it if:

Your App Needs a Lot of Power to Think

Some AI tasks are very difficult, and they require more memory or speed than a phone or tablet can handle. If your AI model is large and needs good computing, it is better to use cloud-based AI.

You Need to Update the AI Often

If your app relies on the data that is fresh or models that are updated frequently, then using the cloud can be easier. It can be harder to update embedded AI, and it also requires extra planning.

You’re Still Testing Your AI Model

If your AI is still under training or you are not sure how well it works, it is better to test it in the cloud first. Once it is stable and you know it works the way you expected, you can move it onto the device.

Your App is Very Simple

If your app is just showing information, helping with bookings, or offering basic tools, adding AI might not be that much beneficial. In some cases, it could make the app heavy or slow for no real reason.

You're Limited on Budget or Team Skills

It takes time and skill to add embedded AI. If the size of your team is small or they are not experienced in AI, it can be better for you to start without it. You can add it later after your app is already working perfectly.

Final Thoughts

Embedded AI is no longer a nice feature to have on your app; instead, it has become an essential part of the app. It has various features that help make your app faster and smarter.

We are seeing it drive major change across industries, like healthcare, retail, automotive, and more. Tools like TensorFlow Lite, Core ML, and Edge Impulse are helping low-power devices to even offer high-end AI experiences.

Developers are also embedding lightweight, fast, and secure AI models on the device directly and don’t even drain the battery.

However, it also has some challenges. That is because if you want to build AI that fits perfectly inside your app, works offline, protects user data, and also delivers smart results, it will require more skill, experience, and planning.

This is when you will need a company like Arramton that can help you build smarter, faster, and more secure digital solutions. We can convert your AI ideas into an actual and working app. Partner with Arramton to bring your app that is run by AI to life.

Frequently Asked Questions

Q1 Is embedded AI better than cloud-based AI?

Ans Embedded AI and cloud-based AI offer different benefits and have unique strengths, which makes both of them different. Embedded AI works on your device directly. This makes it more quick and even private because your data doesn’t need to be sent over the internet. If you give commands using voice, edit photos, etc., then you are using embedded AI for such tasks. However, cloud AI is useful when strong computing is needed, like data analysis on a large scale. That is why it depends on what the app needs to do.

Q2 Can embedded AI work offline?

Ans Yes, embedded AI also works offline. It doesn’t need to be connected to the internet every time, as it works on your device. You can still use its features if you are in an area where the internet connection is poor or not at all available.

Q3 What apps benefit most from embedded AI?

Ans There are many apps that benefit most from embedded AI. These apps are the ones that need to offer quick responses, work without the internet, or keep your data protected. For example, pictures can be edited quickly on photo apps, or language apps can translate speech on the spot. These types of apps often use it to make your experience more smooth and fast.

Q4 Does embedded AI increase battery usage?

Ans Yes, it can use more battery. It can increase, especially if it is running tasks like image processing or constant background monitoring. However, developers are looking for ways to ensure it works more effectively, like using AI models that are small or only running tasks when required. Most modern phones can handle this, which shows the effect on battery life is usually small.

Q5 How can developers start integrating embedded AI?

Ans Developers can start by using embedded AI tools. These tools include TensorFlow Lite, Core ML, or Edge Impulse. This helps them build and run AI models on various devices. They also don't need power or storage in large amounts. They can also use models that are pre-trained and adjust them for their apps. This helps save time and resources.