A Complete Guide to Implement AI and Machine Learning in An App

To implement AI and Machine Learning in an app is not a new concept. Nor is it restricted to the biggest names in the industry. In fact, with the right tools, anyone can implement AI and machine learning into their apps.

For example, Regie.ai, a startup that generates marketing content through AI, recently raised $10 million in funding.

Similarly, Kumo, a platform that handles predictive problems of businesses through AI and machine learning, raised $18 million.

You will have various examples of recent startups implementing AI to create innovative solutions. The question then becomes: how can you implement AI and ML in your apps?

Let’s find out. 

How Can AI and Machine Learning be Used in Mobile Apps?

Artificial intelligence is no longer a futuristic concept. It has become part of our daily lives, with most of us using it in some way or the other. From recommending products on e-commerce websites to medical diagnoses in healthcare, AI has permeated every aspect of our lives.

Though there are various applications of AI and machine learning in apps, all of them can be boiled down to the following three uses. 

1. Reasoning 

Reasoning is a type of thinking that involves making inferences from evidence and drawing conclusions to new problems. For example, when you see a cup, you know it can hold liquid because it has a handle; this observation and conclusion are based on prior knowledge about cups and handles.

An example of it is an app like Google Assistant. It uses ML to understand what you said and respond appropriately — for example, by providing directions based on your location and time of day or playing music based on your preferences and mood.

2. Recommendation 

Recommendation is a critical use case of AI and Machine Learning. It can enhance the user experience by providing more relevant content, products, or services.

The most common recommendation we see every day is when using our mobile apps. For example, Netflix uses recommendations to show us what TV shows to watch next, or Spotify suggests music based on our tastes. Hotels like Booking use machine learning to suggest hotels with similar features to those we booked in the past.

3. Behavioral 

One of the most common ways AI is used in apps is for behavioral analytics. This type of analytics uses machine learning algorithms to detect user behavior patterns based on their actions within an app or website. This can help businesses better understand their target audience, allowing them to deliver more relevant content or tailor their marketing campaigns accordingly.

A perfect example of this includes Google Analytics. The tool tracks website visitors and collects information about them, such as where they came from, what pages they visited on your site, what actions they took on those pages, and so on. This information can be very valuable to improve your app experience because it allows you to see precisely what works well with your customers and what doesn’t.

7  Reasons You Need to Implement AI and Machine Learning in an App

AI and machine learning technology are evolving rapidly, and it’s already being used in many apps. In fact, there are many reasons why you should use it in yours.

Here are the top seven reasons:

1. Offers Conversational UI 

The best way to engage customers is through conversation — not just any kind of conversation but one tailored specifically for the customer. When your app uses AI-based chatbots, customers will feel as if they’re talking to a human being instead of having to type out long messages using emojis or pictures. This makes them feel more comfortable using your app because they can get their questions answered without waiting for someone on the support team to respond.

2. Automated Reasoning

Another reason is that AI can provide an automated reasoning tool for your app. This can help you with many things, including sorting through data and determining if there are any problems with it, as well as helping you make predictions about what might happen in the future based on statistics.

3. Personalization

Personalization is all about taking into account user preferences, behaviors, and data to provide the best possible experience for each user. It’s also about providing users with information that they will want to see.

For example, if you are creating an app meant to help people find the perfect pair of jeans, then adding machine learning algorithms can be very beneficial. These algorithms will analyze past purchases and suggest other products similar to those purchased previously by other users who have similar tastes as the current user who is viewing the site.

4. Advanced Search 

Machine learning algorithms can scan your website or app content and provide users with relevant results. For example, if someone searches for “women’s shoes,” it could suggest similar terms such as “women’s fashion” or “women’s clothing.” This helps users find what they need quickly while reducing errors caused by spelling mistakes or using synonyms.

5. More Relevant Ads

The most obvious benefit of using AI and machine learning in your app is that it will make your ads more relevant to your users. As we all know, if an ad is relevant, people are likelier to click on it. This means that if you use AI and machine learning in your app, there’s a good chance that people will click on more ads and increase their revenue from advertising.

6. Better Security Level 

Using AI and machine learning in an app can help improve security by reducing human error and helping developers identify vulnerabilities earlier than they otherwise would have been able to. This means that hackers will have more difficulty accessing your private data or exploiting any security flaws in your application.

7. Forecasting User Behaviour

With AI and machine learning, you can predict user behavior and make better business decisions based on this data. These tools allow you to understand which features users will like, how they will use your app, and what they want. The more you know about users’ needs and wants, the better you can tailor your app to meet those needs and wants.

8 Things to Consider Before You Implement AI and Machine Learning in an App

AI and ML offer a competitive edge by automating tasks and making intelligent decisions based on data analysis.

But before you implement AI and ML in your app, here are some things you should consider:

1. Recognize the Pain Point You Need to Solve 

You might be tempted just to start adding some machine learning algorithms into your app, but it will not be very effective if you don’t have a specific problem in mind. To get started with machine learning and artificial intelligence, you need to identify a specific pain point it could solve. 

For example, let’s say you’re developing an app for real estate agents who want to find properties quickly. You could use machine learning algorithms to recommend properties based on user preferences or search history.

2. Gather Data

To use AI or ML, you must ensure you have enough data for training purposes. If not, it can be very difficult for the systems to learn from the limited amount of data available. Also, if your data isn’t clean and organized, it’ll be hard for the algorithms to use it effectively.

3. Set Measurable Goals with Metrics

The next thing you should do before implementing AI or ML in your app is set measurable goals with metrics. It’s important to know what you’re hoping to achieve before putting any effort into implementing these tools because otherwise, you’ll have no way of telling if they’re working or not. For example, if you want users to spend more time on your app after signing up for an account, then they need to know how long it takes for them to complete certain tasks within the app — such as adding items to their shopping cart — so they know what can be improved upon.

4. Get Data Scientist’s On-Board

Data is the key ingredient in any ML or AI solution. You need data scientists who can collect data from your users, analyze it and feed it into your apps to learn from past behavior and make better decisions about future actions. If your company doesn’t have data scientists on staff yet, consider hiring one or partnering with an outside vendor specializing in AI/ML solutions.

5. APIs Are Not Sufficient

If you are planning to use APIs for machine learning, then you must have a good understanding of how they work. APIs do not offer all the features you might need for your app. They also don’t give you access to the data processing capabilities of your app. Therefore, you must understand the process before implementing it into your app.

6. Analyze the Feasibility 

What does this mean? It means that you need to know if the technology has already been implemented by other companies in similar businesses or industries and if so, how well did it work? You also need to know if the company or person who can implement said technology will be willing to do so for your company (and at what cost). Finally, if all those things check out, you need to be sure that there will be enough demand for this type of service from consumers before investing too much into development efforts.

7. Take Data Integration into Consideration 

One of the biggest challenges with implementing AI is integrating it into your existing data infrastructure. If you’re already using relational databases or NoSQL databases like MongoDB or Couchbase, it’s easy to integrate them with popular AI frameworks like TensorFlow. But if you’re using something less common, like Apache Spark or Hadoop, integrating these data sources with your AI framework of choice may take some extra effort.

8. Technological Aids 

In most cases, you will be using AI and Machine Learning as additional features in your app, so it makes sense to use a platform that already has these technologies implemented or is capable of integrating them with ease.

8 AI Technologies Popularly Used in Mobile Apps

According to Gartner Survey, one-third of organizations implement AI and ML across various business units. How? By implementing one of the following AI technologies into their apps and platforms. 

1. Speech Recognition Technology 

Speech recognition is the process of converting audio signals into text. The technology has been around for decades, but it’s only become viable for consumer use in the last few years.

Speech recognition is probably best known as an accessibility feature, helping people with disabilities who struggle to type on virtual keyboards or touchscreens. But it’s also one of the most versatile AI technologies because it can be used in any app where typing would be inconvenient or impossible (for example, when driving).

Apple’s Siri, Google Assistant, and Amazon’s Alexa are some apps that use speech recognition technology.

2. Chatbots

Chatbots are automated programs designed to simulate human conversation through voice or text. These bots can be used as customer support tools and help with sales by creating leads and closing deals faster than humans can. 

Chatbots have been around for decades, but they were not widely adopted until recently due to their inability to understand the context of conversations. Nowadays, chatbots have evolved enough to understand conversational nuances and respond accordingly.

Various apps, such as Zendesk, Duolingo, Uber, PayPal, etc., employ chatbots in their apps. 

3. Natural Language Technology

Natural language technology is a branch of artificial intelligence that deals with the understanding and processing of human language in computers. It is the ability to analyze, understand and derive meaning from human language in any form. 

It covers methods, algorithms, and techniques for dealing with human languages as computer data, especially in speech and text mining. For example, Google Now uses NLP to provide recommendations based on user behavior and location history.

NLP can be used to:

• Automatically summarize text into short summaries that are readable but not necessarily factually accurate (e.g., “Apple Inc is a multinational company based in Cupertino California.”).
• Classify documents into categories (e.g., news articles vs blogs).
• Detect spam emails or tweets written by bots instead of humans

The technology is used by various top-notch apps such as Google Translate, Grammarly, Todoist, etc. 

4. Machine Learning

Machine learning is a subset of AI that focuses on developing computer systems that can learn and improve from experience without being explicitly programmed.

It is used in many applications, including medical diagnosis, spam filtering, predictive analytics, robotics, and many others.

One of the most popular machine learning techniques is deep learning, which has been used to train computers to recognize images and speech. The top apps that use machine learning are PayPal for fraud detection, Walmart and Alibaba for data analysis, etc. 

5. Biometrics 

Biometrics is the technology that leverages the unique physical characteristics of a person for authentication. It is primarily used for mobile apps to prevent impersonation and increase security.

Biometric authentication can be performed using fingerprints, retina, voice, face, and DNA. The most common biometric technologies used in mobile apps are fingerprint recognition and face recognition.

For example, Apple’s Touch ID feature uses fingerprint scanning to unlock iPhones and iPads. It also allows users to make payments using Apple Pay by simply holding their finger over the phone’s fingerprint sensor.

6. Emotion Recognition

Emotion recognition is one of the most popular uses for AI in mobile apps. It allows you to analyze facial expressions or voice tone to determine whether someone is happy, sad or angry — and even make predictions about how they feel based on their past behavior.

Using emotion recognition can be beneficial for many different types of businesses: retail stores can use it to identify unhappy customers so they can offer them help; restaurants can use it to identify hungry customers looking for food; hotels can use it to identify unhappy guests so they can offer them amenities like free Wi-Fi and late check-out; etc.

7. Image Recognition 

Image recognition has been a buzzword in the AI world for a while. The ability to programmatically recognize objects within photographs and videos is incredibly useful and can be used to build products with real-world applications. 

For example, using image recognition to identify specific foods or objects can help categorize and retrieve images in an image storage system like Google Photos or Flickr.

8. Text Recognition

Text recognition is an everyday use of AI. It’s used to convert text into machine-readable data. For example, you can use it to automate forms or convert handwriting into typed text. The most common use of text recognition is in the auto-completion field on mobile keyboards. When you start typing an address, the keyboard will suggest addresses that match what you’ve typed so far.

Wrapping Up 

AI and machine learning are taking over the world. 

Technology is becoming more sophisticated, and it’s being used in almost every industry, including mobile apps. The two technologies can be implemented in mobile apps in several ways. They give a certain edge to your app and make it more interesting for the user. 

As a result, many businesses seek ways to use AI to improve their business processes and products. But how can you harness the power of AI to help your business succeed?

At Idea Usher, our team of AI and ML experts has years of experience developing cutting-edge apps that use these technologies in innovative ways. We’re experts at helping companies discover new uses for AI technology — improving customer service or enhancing user engagement.

You can connect with our team to explore more about how you can implement AI and machine learning in apps. 

E-mail: [email protected]

Phone Numbers : (+91) 946 340 7140, (+91) 859 140 7140 and (+1) 732 962 4560

FAQs

How to implement AI in an app?

Here are some ways to implement AI in your business:

-Chatbots to interact with customers.

-Machine learning to analyze data and make predictions about future behavior.

-Augmented reality (AR) to help people find what they need or help them identify products in their hands.

How is machine learning used in apps?

Machine learning is used in apps to make recommendations, detect patterns, sort through videos and photos, etc. 

What are the applications of AI and machine learning?

Here are some of the most common applications:

• Personal assistants that can answer questions or find information
• Speech recognition software, like Siri or Alexa
• Computer vision, which is used in self-driving cars to identify objects like pedestrians and road signs
• Natural language processing (NLP), which is used in customer service chatbots that answer basic questions about products or services
• Fraud detection systems and credit scoring algorithms

How can I implement AI on a website?

There are several ways you can implement AI to improve your website. One way is through customer service. Another great way to use AI is through chatbots, automated programs responding to customer inquiries.