Mobile AI or artificial intelligence is being integrated in almost every new smartphone, cell phone, iPhone, tablet and iPad these days. The benefits can be advantageous and the learning curve is quickly diminishing as these devices are becoming smarter and more functional for the average user.
It is the art and science of getting computers to apply logical reasoning to solve problems, for example, to prove theorems and solve puzzles. This way AI machines beat humans at chess, stock trading and Jeopardy. Uber uses automated reasoning in order to optimize routes and get the riders to their destinations faster. The algorithm takes millions of bits of data from Uber Drivers who have traveled similar routes and learns from their trips.
Recommendations of Services
This is the simplest and most effective application of AI in mobile apps that can be used in almost any solution. The reason why most apps fail within a year of launch is that they fail to provide relevant content to continuously engage users. You may be providing fresh content regularly, but if it isn’t something that is interesting to the end user than it isn’t worth the time you spend creating it. By monitoring the choices users make and inserting them into a learning algorithm, apps make recommendations that users are likely to be interested in. This is a powerful source of revenue for such entertainment app like Netflix. Yet any business that upsells or cross-sells content can utilize this type of mobile AI, even if it’s currently a manual process handled by the sales or marketing team.
Learning Your Behaviors
Most platforms have the capability to learn users’ behavior patterns in order to make the next session more seamless. For example, Snaptravel is a half-bot, half-human hotel booking service. It uses natural language processing and machine learning to have realistic conversations with users suited to their preferences. If a user stumps the bot with a request, a human agent intervenes and teaches the bot how to not make the same mistake next time. Another classic example of AI learning your behavior is fraud detection for online payments. Pattern-detecting algorithms go through your credit card statements and purchases as they happen, and can detect if you’ve made a recent purchase out of the norm of your behavior.
First 1-5 app sessions are crucial for retaining new customers. You’re much more likely to make these sessions memorable if you use AI technology to learn their behavior and make each app session more valuable than the previous one. Data is a privilege, and you owe it to your customers to use it to improve the experience for them. The challenges that face AI may somewhat mirror those of mobile – i.e. security, adoption, usage, performance, integration, and data management.
Introducing AI to your app involves a lot of hard work. Most companies have to start with transforming their IT organizations for a digital, rapidly-evolving market and dealing with more tactical issues such as securing mobile access to data, backend integration of apps with legacy systems, implementing API-based architectures, and adopting agile development methods. But once the process started the result will follow.
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