How AI can impact the user experience

Profile picture of Coren Feldman

{date}

{#hash1}

{#hash2}

Illustrations by {name}

Artificial intelligence collects and analyzes data in most interfaces. Here’s how we can make it more transparent and user-centered.

8 min read

An illustration of a few web icons surrounding a phone that reads “You may also like”

Stay informed on all things design.

Thanks for submitting!

Shaping Design is created on Editor X, the advanced web design platform for professionals. Create your next project on Editor X. 

Get our latest stories delivered straight to your inbox →

The AI in our imaginations and the AI in use in the real world are two very different things. While science fiction depicts artificial intelligence as a borderline sentient being, today’s AI is far clunkier and requires a lot more human intervention.


AI can be a useful tool to create great user experiences, but there are a lot of hurdles to overcome when adding artificial intelligence to a product. Making sure you’re taking the right precautions, involving the appropriate stakeholders, and thinking through edge cases can be the difference between a successful product and a cautionary tale.



When used correctly, AI makes interacting with digital products frictionless, removing barriers that separate users from programs by predicting user needs. This is achieved based on data, without the need for input from the user.


How we use AI to improve our products


As our digital footprints are getting larger, we’re producing more data than is possible for humans to parse. Due to that data influx and a significant increase in computing power, the use of AI to collect and analyze information in order to better tailor experiences to users has become more commonplace.


Many large companies are invested heavily in AI to increase engagement, retention, and sales. Digital assistants like Siri, Cortana, and Google Assistant expand the ways people can interact with their devices, increasing retention and engagement. Amazon’s recommendation engine, for example, is powered by an AI that has been partially credited for a 29% increase in the company’s sales.


AI is often used to help personalize experiences for users, learning more about them as they use the product. Google can anticipate, with growing accuracy, which search results will be relevant to a user based on their past search history, and predictive keyboards on iOS and Android can suggest words you’re likely to use even as you’re typing.


When used correctly, AI makes interacting with digital products frictionless, removing barriers that separate users from programs by predicting user needs. This is achieved based on data, without the need for input from the user. These insights can benefit both users and companies, as showing relevant data to users helps them engage with products more efficiently, and more accurate data and automation increases sales and other important metrics.



Why defining success metrics is important


When working with AI, a goal and parameters must be set in order to work towards a desired result. It may seem like just setting a goal, such as “increase sales,” without specifying parameters would be productive as long as it worked, but doing so might create other issues on your platform.


At the same time, running any kind of experiment requires defining the undesirable results just as much as it requires defining the desirable results.


If you were to hastily change items around, edit copy, and alter UI with the sole intention of increasing sales, you might find that success in one metric can be failure in others. For example, changing the buttons to neon green and using the copy “Get It” instead of “Purchase” might increase the likelihood that users on a product page will purchase the item.


However, this immediate benefit may not be tenable.


First, the vague copy and new color might not be on brand with your company’s visual and copy guidelines. Second, while the percentage of users converting might be higher, it may also decrease retention in the long run, as users might find the design and language updates off-putting.


This may seem extreme, but there are many high-profile companies that have had very problematic, public misfires with AI, such as Microsoft’s Tay chatbot, which used machine learning on Twitter to learn how to speak more naturally and instead learned conspiracy theories and racism. Amazon also had to shut down an AI they created in order to streamline their recruiting process by analyzing previous job applications because it showed sexist biases.


By not defining undesirable results, these companies accidentally created harmful experiences that did real damage to a lot of people. Had Microsoft hard-coded words or phrases to avoid and users that were better not to follow, they probably wouldn’t have had a brand-affiliated product that’s using hate speech to a significant following. Had Amazon been more careful about the dataset they gave their AI, or their own existing biases when hiring, they could have hired more qualified women and avoided a PR mishap.


AI might seem like something that lies squarely in the developer’s domain, but even when creating self-learning algorithms, it’s clear that UX designers are actually critical team members to involve in the process in order to create an experience that isn’t harmful to users or the brand.





Managing expectations for AI


Introducing users to technology–especially new technology–can be alienating. As UX designers, it’s our job to put them at ease and make AI feel like something that’s making life easier for them, on their terms. First and foremost, they need to be acquainted with the concept of AI and what it means for their interaction with your product.


  • Create transparency around use of data: Onboarding a user to an AI experience, ideally, should help clarify what information the AI uses to enhance their experience and how you protect that information. This may seem like an unnecessary step, but, with constant data breaches and abuses from large tech companies, people are becoming more wary of possible uses of their data. Being upfront about how and why you use their information can help alleviate that uneasiness and potentially even gain the user’s trust.

  • Introduce the role of the AI in the product: Before anyone starts using your product, it’s critical that they understand the scope of your AI’s abilities and how they will interact with it moving forward. Being acquainted with the type of actions they can perform with the AI will make it easier to persuade them on why they should use it and reduce the need to prompt them to use it later on in the process. Outlining your AI’s abilities will also get you and your users on the same page and help avoid a negative experience, such as where the user assumes the AI can do something it can’t.

  • Reinforce AI activity with indicators: When relevant, use indicators that will show the user when the AI is on or available. The best way to approach this may differ depending on how the user interacts with it. Siri plays a short sound and opens an overlay when opened, while Gmail’s predictive text shows up in light gray with a little indicator that the user can swipe, or if on desktop, the user can press tab to accept the suggestion.