AI or Artificial intelligence is a hot buzzword these days especially among various businesses that are collecting big data. Currently, data has got an increase in speed, size, and variety. And it’s not so much about how to collect data than how to analyze and use it. AI can help since it’s basically the simulation of human intelligence processes by machines, more concretely by computer systems. These processes include learning, reasoning, and self-correction.
AI and machine learning
Machine learning, for instance, is an example of AI technology. It is basically the science of getting a computer to act without programming. In addition, there is a subset of machine learning known as deep learning. In the simplest terms, it is the automation of predictive analytics. There are three types of machine learning algorithms:
- Supervised learning – data sets are labeled so that patterns can be detected and used to label new data sets
- Unsupervised learning – data sets are not labeled and are sorted according to differences and similarities
- Reinforcement learning – data sets aren’t labeled, however, after performing an action/actions, the AI system is given feedback
Thus, artificial intelligence is not machine learning. It’s more of a broader concept of machines being able to carry out tasks in a “smart” manner. As for machine learning, it’s all about giving machines access to data and letting them learn from it.
As a result, the data patterns detected with the help of AI and ML can actually be useful for improving anything, even user experience.
In this respect, artificial intelligence is revolutionizing the way we create user experiences. The AI we are discussing here is not the one you have seen in movies like Terminator. It’s something totally different in real life. And, no, robots are not replacing designers (I am sure you have asked this question in your mind).
In this scenario, they are rather helping predict user behavior and provide useful insights into improving the user experience. In fact, AI’s capabilities allow automating functions, enabling greater accuracy, and delivering measurable benefits.
The old way vs. the new way
Traditionally, UX teams would turn to metrics and tools such as heat maps, A/B testing, usability tests, usage data in order to understand how to boost user engagement in their products. At the age of AI, we now have tons of empirical and actionable data that we can use to detect user behavior patterns and to eventually optimize the user experience.
Let’s imagine a scenario where you have an eCommerce app and where you want to find out how to track user behavior to optimize the purchase flow. An AI engine will be able to track user behavior in your app and provide you with human language tips on how to create a smoother purchase experience and eventually achieve more sales.
Besides, AI will help to individually tailor the design for each user making them easily convinced to take action. It’s all about making sense of the data and coming up with perfect interfaces, messages, push notifications, colors, and fonts to suit each and every user’s needs.
You might ask how will AI achieve this all. The answer is simple – through the application of the methods of deep learning. It combines data to make inferences.
AI systems can analyze huge amounts of data quickly and effectively. They can also learn from it and adjust their behavior according to it in real-time. This way, they can help designers map best-practice interfaces within a really short timeframe.
AI-powered journey mapping can make it possible to create simple, engaging, and profitable user experiences. So, AI is not about replacing people but helping them with research or better put – carrying out proper research and analysis for them.
