The Role of Artificial Intelligence and Machine Learning in Enterprise Mobile App Development

The Role of Artificial Intelligence and Machine Learning in Enterprise Mobile App Development

Machine learning (ML) has innumerable applications in an enterprise, including mobile application development. Applications today meet numerous needs and can be deployed to the necessary platforms and devices for use by employees or customers. One of the ways in which machine learning in enterprises works today is by enhancing the mobile app development process and capabilities.

When it comes to the process, machine learning provides personalized services within apps in order to increase user engagement and retention. Custom mobile app development is a complex process. On-device ML implementation can make mobile development processes simpler and more cost-effective while also enhancing the user experience. Machine learning for mobile app development should therefore be considered by Singapore businesses.

The purpose of this post is to provide you with information that will help you make decisions on embedding AI and machine learning in your enterprise mobility strategy. We take you through the use cases of AI for mobile apps, enterprise use of AI, the potential for deep learning (a subset of machine learning) in your business, and the future of AI and ML. Let’s begin!

Machine Learning and AI in Enterprise Applications: Use Cases

The increasing adoption of AI and machine learning in enterprises can be attributed to their sheer number of practical applications, some of which are explained below:

User behavior analysis

ML algorithms can understand user preferences and behaviors based on the data from the company’s CRM and users’ social media conversations. They also pick up cues from app users’ behavior patterns to form a better understanding of each individual user. The cues can be used to create personalized in-app offers, tips, and recommendations that drive up the user experience. An example is the Netflix app – the movie and series choices you see are curated by an ML-based recommendation algorithm that considers your viewing habits, comments, and ratings on movies.

Chatbots for assistance

The friendly robots that pop up on your mobile apps to answer your questions and provide assistance are powered by machine learning and natural language processing (NLP). The ML component extracts insights from patterns in data while NLP understands texts and spoken words. The chatbots allow businesses to provide customer support without having to employ human agents 24/7. For gaming apps, chatbots provide player support and help companies learn about the questions being asked frequently by players, bugs, and other areas of improvement, which can be fixed quickly to keep app engagement, reputation, and retention high.

Voice interface for the IoT

Voice recognition is increasingly being used as an IoT authentication method. A speaker or microphone captures a user’s voice and sends it over to a software that responds with an actionable command. While this is a convenience feature, individual support for each device user is a challenge. Using AI with an IoT voice interface will provide a common interface for better device control and ease of operations across devices. This trend is likely to catch up as speech-operated IoT devices increase in importance with the growth of IoT devices and also as a way to cater to physically and visually challenged users.

Examples of machine learning and AI in the enterprise

The variety of artificial intelligence applications in business is vast and growing. The social media app Snapchat uses ML algorithms to find a face in photos, differentiate facial features, and add filters like evil cheeks, neon horns, 3D cartoon styles, fire sunglasses, and others. Music app Spotify leverages ML algorithms to suggest users songs with similar music popular among other users. Dating app Tinder uses reinforcement learning for its Smart Photos feature that notes which picture people were looking at when they swiped right and arranges the more successful ones closer to the top of a user’s profile.

Machine learning use cases in eCommerce can also be seen in eCommerce apps. eBay’s AI-based personal buying assistance ShopBot helps shoppers narrow down deals from the millions available on the online marketplace. AI can be used in conjunction with augmented reality for virtual try-ons and placement previews wherein users can test out how a product (like furniture, lighting, or decor) will look in their home or other environments.

Apart from mobile app development, businesses are using machine learning for predictive analytics. Businesses can, for example, use ML algorithms to identify behaviors that affect app retention or product growth.

Benefits of Deep Learning in Enterprise Application Development

Deep learning is a subset of machine learning that examines algorithms to automatically learn and improve functions. It is based on an artificial neural network, a series of algorithms that attempts to process data in the way a human brain would. Deep learning enables self-driven cars to recognize traffic lights, stop signs, and such. It is used for automated speech translation in smart devices at home, and in the medical industry, to help find lung cancer on CT scans.

Deep learning offers distinct benefits for enterprise application development. Deep learning models can create new features by themselves rather than requiring those features to be identified by users, as is the case in traditional machine learning. They can solve problems end-to-end unlike machine learning, where the problem is divided into tasks and results combined into one solution. These capabilities can help boost the speed and accuracy of app development.

The human brain becomes tired at some point. A neural network can go on and on, performing tasks faster than humans without a drop in quality. Another key advantage is the ability of this artificial brain to detect errors that may otherwise escape the human brain. Complete removal of errors from the product development process will improve the overall quality and eliminate costs of rework.

The Future of Machine Learning and AI for Enterprises

Machine learning for business today has taken off in a big way and continues to evolve thanks to the immense attention and contribution it has garnered from individuals in various industries, from computer scientists, statisticians, engineers, and neuroscientists to biologists, physicists, philosophers, and high-level thinkers. But alongside the immense potential of machine learning solutions for enterprises, lie certain challenges that businesses must reflect on.

What AI and machine learning will enable in the future

These new technologies can be huge drivers of business productivity and efficiency. They can boost application development and app quality while reducing development costs related to infrastructure and team size. They can be utilized for improving the in-app experience and creating new features that are backed by user preferences and behaviors.

Using machine learning also reduces reliance on humans, and in a good way. It can reduce the human effort for the creation and innovation that only the sophisticated and unparalleled human mind can perform.

What to watch out for

Where humans cannot be present, AI can get the job done for most tasks and activities at least. At the same time, ML algorithms require abundant amounts of good training data to solve problems. Human operators must be careful to train algorithms on the right data that is bias-free and has rich human decision labels. Ultimately, intelligent decisions made by humans will determine the success of machine learning models.

There have been concerns about an over-reliance on AI that can create complacency in human developers and cause them to lose touch with their skills. To avoid a degradation in performance and continue making the most of human talent, businesses must support employees in continually implementing and evolving their skills for more complex and challenging work.