Human-Centric Design: Four Ways AI Will Make UX Smarter
Cygnis Media Editor

Human-Centric Design: Four Ways AI Will Make UX Smarter

User Experience (UX) designs, by and large, aims to make digital experiences smooth and pleasant. Everything from the interactions on a user interface to the colors and features used in the product impact conversions.

But there always remains a gap in how efficient or intuitive these interactions ought to be and how effective they actually are in converting visitors. It is probably why no UX design is perfect. There will always remain a friction point in the journey that keeps users from staying engaged.

But, what if designers can fill this gap by making UX intelligent?

Now the product designers have started to rely on AI predictions to make products more engaging while interacting with the users.

For example, there are conversational interfaces available that intuitively know which traffic routes to avoid, based on conversations you have had in the past – pretty much like how personal butlers know your tastes based on previous interactions and personalize future wants accordingly.

However, AI has yet to be fully implemented in UX design. Here are a few of its implications.

1 – UX will become thinner

UX will become thinner

The ‘thinness’ of a user experience refers to how fewer hindrances users face while scooting through the product’s design before they can get any value from it.

For example, purchasing an item online is a thinner experience than going out to an actual store, looking through the aisles and waiting in line to do the same.

To make user experiences even better, designers try to anticipate user needs before they start designing. It’s what anticipatory design is all about. But, the only problem with this approach is that it still fails to predict the requirements of every user. Because some users may have experiences that are even more expressive of their needs.

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We have already made some headway here. Consider chatbots, automated chat programs, which require minimum learning from the users. To interact with one, all you have to do is to call them through your messaging platform and a machine learning algorithm guesses the requirements by predicting the patterns from data it has on you from previous interactions.

If systems like these take off, they will improve the user experiences considerably. For example, consider that an employee has to use an enterprise software to fill a form with 30 fields. With machine learning embedded, this experience will get even thinner. It will pre-fill around 25 of the fields with the information gathered from several social APIs that were linked to the employee.

2 – Invisible interfaces will be the new norm

Invisible interfaces will be the new norm

In a previous post, we discussed challenges developers face when creating chatbots in which the interface is invisible. There are no interactive navigation links (like a ‘click here’ button) that give users direction. To create experiences that are valuable for users, developers must account for all possible use cases during the design phase like mapping scripts according to user interactions.

In time, AI tools could advance enough to eliminate these challenges completely and invisible interfaces may become the next standard. With the rise of voice interactions, users or customers might ultimately influence and change the experiences that are designed for them. Designing user interfaces will be all about predicting user intents at each point of these interactions and predicting suitable responses.

3 – Computer Vision will create superior interfaces

Computer Vision will create superior interfaces

Visual content is interwoven in every User experience (UX). Imagine trying to select a jacket online if all you have going for it is a product description and price. You wouldn’t be able to visualize it at all. Unless, it is shown on a model whose image is rendered in 3D, and provides the complete feel of the jacket.

You wouldn’t be able to visualize a jacket available online. Unless, it is shown on a model whose image is rendered in 3D, and provides the complete feel of the jacket.

Thanks to immense rise of artificial intelligence, this all is possible because now machines can make sense of data. Now we have AI systems that can identify images and use this to improve user experiences. Some search engines are already using artificial intelligence to change SEO. Google has been using deep learning algorithms to help marketers optimize online content and feature search results based on relevancy. The best example is of the Product Listing Ads (PLAs) that are only shown to a segment of the audience that is truly craving for them. Of course, the bids are also high for the same, but that is a story for another time.

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With that said, we can perfectly assume that in the coming years the designers will have AI tools to automate wireframes and interfaces based on the information fed thereby reducing the design cycle.

4 – Split testing will be obsolete

Split testing will be obsolete

No user base is alike. To create products that cater to all user bases developers often use split testing. Two versions of a website or app, for example, are tested to determine which one works the best.

The problem with this approach is that, in going with the majority vote, it totally disregards inputs from users that fall in the minority and therefore fails to accommodate the needs of these users.

This means…

  • Testing will produce more insights: With split testing, you only get insights into the needs of specific user bases and design interfaces based on intuition. But intuition can fail. A real world example is of Netflix, which uses machine learning algorithms to recommend videos to users based on data like the time of the day they watch and even the spot on the screen they find videos. With better data-insights, it may enable product developers to gain more insights for quantitative user testing.
  • Testing would consume less time: While testing has proven to be useful, it consumes a lot of time. In addition to creating two versions of your product, you will also need to determine the variables to be tested. With AI on your side, products may start improving themselves through the user experience, instead of manually testing them. Thus, saving time and money.
Wrapping Up

If AI evolves, and with time it will, it could significantly enable UX designers to create thinner and more valuable experiences. The physical interface will be vanquished by virtual and smarter interfaces and features like visual recognition could result in systems that automate interface design and reduce development cycle. Further, the AI’s ability to detect patterns in data will provide developers with insights that might render split testing obsolete.

Learn more about how AI is revolutionizing engagement and how your business can gain value from it.




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