fbpx
bahis tahminleri canlı casino siteleri Güvenilir casino siteleri Güvenilir casino siteleri
ML & AI Mobile Apps: How to Integrate the Tech
Share on linkedin
Share on twitter
Share on facebook
Share on email
Share on print

Advancements in Machine Learning (ML) and Artificial Intelligence (AI) are transforming mobile app development. ML/AI mobile apps now have the power to understand speech, discern images, interpret gestures, and more. But more than that, they allow users to connect with the world around them in new and exciting ways. And users have embraced the new technology with enthusiasm. In fact, according to a report from Markets and Markets, the global AI market is expected to reach $309.6 billion by 2026.

To stay ahead of your competitors, and keep up with your users’ expectations, now is the time to add ML and AI to your mobile apps. Here’s what you’ll need to know for a successful implementation.

The Difference Between AI & ML

Artificial Intelligence

Artificial Intelligence, or AI, is a general term for any technology that uses logic, “if-then” rules, decision trees, and machine learning to give the appearance of human intelligence.

Machine Learning

Machine Learning, or ML, is a single aspect of AI. ML is a set of methods that allow for automatic identification of patterns in data. Those patterns are then used to predict future data or to help perform further decision-making.

Data, in the form of images, text, or voice, allows ML to answer questions. The data is provided to the ML system first, and then answers, called “labels,” are provided from the system. The algorithm then “learns” from each data set to make predictions, also called inferences.

New call-to-action

Benefits of ML & AI Mobile Apps

There are many reasons to implement ML and AI technologies into your mobile apps. Here are some of the most common.

Reasoning

AI and ML are powerful tools that add the ability to reason to your mobile app. Consider apps that provide driving directions, like Google Maps. If you’ve used apps like this to travel to the same location more than once, you’ve likely noticed that it provides different routes based on traffic conditions. This is the essence of how AI works: it uses basic reasoning skills powered by real-time data to provide the best solutions.

Improved Personalization

The strength of AI is its ability to evaluate data from a wide range of sources. This includes, for example, data sources like social media, search terms, and credit ratings. This data can then be used to provide tailor-made recommendations to each user’s device.

ML provides answers to questions the AI needs to offer those recommendations, like:

  • Who is the user?
  • What are their preferences?
  • How much money do they have?
  • What vernacular do they use to describe products?

Advanced Search

All user data, including typical, routine actions, can be accessed through search algorithms. This, combined with behavioral data, can be used to enhance the search function within your app. This provides better and more contextual results, while making searching easier and less time-consuming for users. Furthermore AI and ML can aid in the implementation of gesture search or voice search to improve your app’s performance even more.

Data Mining

One of the key advantages of ML is that, in general, the algorithms it provides get better over time. This ability to learn and improve allows for the effortless gathering of data, and the identification of connections within those data sets.

Improved Security

ML powered AI mobile apps provide more efficient and secure app verification. Furthermore, this enhanced security acts as a powerful marketing tool. Users have the opportunity to use biometric data as a verification step. Recently, facial recognition has seen a jump in popularity. And for good reason: it improves security while also speeding up the login process. Additionally, it helps protect data from other, unknown sources.

Fraud Prevention

Fraud is a concern shared by all industries, but it is of particular interest to banking and finance professionals. Machine learning is especially useful here, where it can reduce loan defaults, fraud checks, credit card fraud, and more. Moreover, it provides the ability to analyze an individual’s risk, as well as their ability to repay a loan.

Additional Benefits

In addition to all these benefits, adding AI and ML to your mobile app also provides:

  • Faster completion of repetitive tasks;
  • Enhanced accuracy and completeness;
  • Improved client experiences;
  • More intelligent interactions with users;
  • Better retention of users.

New call-to-action

How to Integrate ML & AI into Your Mobile App

Integrating AI and ML into an existing app is a major change. As such, it requires a massive shift in the way the application functions. Here’s how to update your app.

Identify the Kinds of Problems ML/AI Mobile Apps Can Solve

Before you invest in ML/AI mobile apps, you must first understand what these systems can do. Both ML and AI are potent tools and are capable of supercharging your existing solutions, but only if you understand them first.

To better grasp the efficacy of AI mobile apps:

  • Consult with AI experts, online resources, and other information;
  • Improve your knowledge by exploring existing tools and technology.
  • Explore case studies from your industry to see how others have successfully implemented AI and ML into their products.

 

Know Where AI & ML May Improve Your App

Once you have a better understanding of what ML and AI mobile apps can do, it’s time to identify the key problems in your app that need to be solved. Different industries – indeed, different businesses within the same industry – face different and unique problems. That said, some of the most common problems that arise are data-based insights, customer service, recommendation systems, and security systems.

Once you feel you’ve identified areas where ML and AI can improve your app, consider running a market analysis and see if similar applications have worked – or not.

Map Out Your Plan & Budget

Your next step is to roll out your plan for ML/AI implementation. A key aspect of this implementation is deciding how much you want to pay for it. Your budget will impact how much can be done. If you have no financial constraints, you may be free to completely revamp your app all at once. However, regardless of your budget, it’s best to formulate a staged plan for your ML/AI integration. That’s because once you have your problem areas identified, and your budget worked out, you will be able to prioritize your changes.

Data Integration & Security

At this point you should have a better understanding of how AI mobile apps work, have identified where they can improve your app, and have a plan and a budget for implementation. But it’s not time to start just yet. Instead, you now must consider your data.

For machine learning to operate at its best, it requires well organized data. Old data, which may be organized differently, can have a negative impact on the efficiency of your ML/AI mobile app. For this reason, once you have your plan in place, you’ll need to focus on your database. Ensuring your data is well-organized will help keep your app performing at its peak in the long-term.

This step is also the perfect time to consider security for your ML/AI mobile apps. In order to maintain the integrity of your user’s data, you’ll need to integrate security measures.

Implementation

Finally, now that all your budgeting, planning, and pre-processing is complete, it’s time to begin your implementation and deployment. Your team will need to take extra care during this step to test all your changes before they go live to your users.

Additionally, it’s recommended that you consider adding a strong analytics system during your deployment. This can help you analyze the impact adding ML and AI to your app has, and provide insights for future updates.

Supporting Aids

Lastly, it’s wise to choose robust supporting technologies and digital solutions to back your app. This means data storage aids, security tools, backup software, optimization solutions, and more. Ensure that each is future-proof to keep your app working consistently. Without proper supporting aids, it’s possible for your app’s performance to decline significantly over time.

New call-to-action

Conclusion

There’s no doubt that artificial intelligence and machine learning are the future of mobile app development. This is because AI mobile apps offer a more personalized user experience, more advanced services, and more revenue for your company. Furthermore, implementing ML and AI solutions now, while the technology is still young, will put you on the cutting edge and ahead of your competitors.

Keep Reading:

Artificial Intelligence in Mobile Banking: Reshaping The Customer Experience

How 5G Apps are Changing Mobile Application Development

What is Enterprise App Development?