Artificial intelligence, or AI, has recently made headlines due to a host of powerful and engaging tools that have gone viral across social media. Take ChatGPT, the chatbot whose revolutionary technology allows it to do everything from answering complex queries to generating song lyrics; and Lensa AI, the digital art app that uses AI to digitize portraits, resulting in a lineup of “Magic Avatars” across Instagram.
While we have been introduced to AI in small doses, such as those mentioned above as well as virtual assistants like Siri or Alexa, more companies are starting to incorporate this technology into their business models for everyday use. AI is becoming increasingly accessible in the UX industry, with designers becoming more interested in using such technologies to enhance their research and design process.
Let’s explore exactly why and how AI is being more widely used in UX research and design.
What is AI?
According to TechTarget.com, AI, or artificial intelligence, is the simulation of human intelligence processes applied to machines, usually computer systems. Basically, AI tries to make computers able to do the things human minds can. Most AI systems work by taking in large amounts of “training” data, learning to recognize patterns from this data, then applying these patterns to make predictions in real world settings. For example, chatbots learn to participate in life-like conversations by analyzing data from an extensive amount of sample texts. Uses of AI like chatbots save companies time to dedicate to other issues.
Why and How is AI being used in UX?
In the context of User Experience, AI is slowly becoming more popular as a supplementary tool used in research and design phases. While AI has the potential to be used in creative processes, designers have mainly used the tool to increase automation, productivity, and personalization of user experiences. The use of AI amongst teams can also promote collaboration and communication, enrich research development, and improve design efficiency.
Analyzing Big Data
AI has the ability to analyze large data sets faster than humans, allowing companies to analyze data from millions of sets to uncover more insightful and in depth conclusions. For example, AI could be used to analyze hundreds, thousands, or millions of qualitative and quantitative user experience studies and discover patterns and insights more quickly than a human UX researcher could. In terms of design, using AI to analyze large quantities of data reduces human error and saves time, letting designers concentrate on creating more personalized interfaces. A painless, personalized experience = greater customer loyalty and willingness to recommend.
As mentioned above, using AI can dramatically improve UX personalization. This provides companies the opportunity to get ahead of the competition, as users will flock towards companies they feel a closer connection with. AI can quickly analyze huge pools of data and segment customers into smaller groups, giving UX designers more insights to work with, and, ultimately, the ability to provide a more personalized experience to more people. For example, a larger group of customers entitled “sneaker wearers” could be broken down into smaller groups by brand, purpose, or even style preference.
AI can optimize the design process by quickly creating visualizations, and even wireframes, from designer ideas. Using interfaces such as DALLE2, designers can either articulate or submit rough sketches of their ideas and quickly be given preliminary designs or wireframes in return. Designers can also use AI to leverage historical data in order to quickly create user flowcharts. This capability of AI in UX will further streamline the design process, allowing more time to be spent on creative materials.
In the coming years, expect a surge in the number of companies using AI in such capacities previously mentioned. The automation of repetitive or tedious tasks optimizes the research and design phases of UX development, and makes a more personalized experience possible. When UX designers can allocate tedious tasks to AI, such as data analysis, or using Adobe features to resize and crop images, it frees up time to focus on the creation and personalization of additional interfaces, as well as promoting a more efficient process overall. From a UX research perspective, leveraging AI to identify patterns and key “Moments of Interest” (MOI) can allow researchers to quickly sift through the increasingly massive amounts of qualitative and quantitative data being generated by advanced remote UX research platforms.
How will AI Impact the Future of UX Design & Research?
Here’s a peek at a few expected future uses.
“Auto Pilot” User Research
AI has the potential to drastically change how we conduct research. With the automatization of research, UX researchers can still control the questions, but will be free from the task of actually conducting the fieldwork. The use of AI in this capacity can vastly augment sample sizes and the number of studies conducted, providing quantities and varieties of data that will be far superior to what we currently have, all while still cutting down on research time.
AI also has the potential to become a creative asset for UX designers. We already see AI being used in such capacities on social media, mainly through filters. When questioned in a 2018 study regarding AI and UX, designers maintained that they wouldn’t rely solely on AI for creative materials, but would be willing to collaborate with AI systems in such a capacity when stuck, kind of like a virtual coworker. For now, AI will remain a revolutionary tool in functional operations of UX design while the creative potential of AI is further explored and understood.
AI has the potential to revolutionize the UX research and design process. This technology could become so widely used, due to its speed and analysis capabilities, that in the future, UX designers will potentially only have to focus on creative materials. Since UX researchers will be able to manage much larger data sets in an efficient manner, AI could also take UX design from fitting generic, larger consumer groups to designing UX interfaces specifically for individual customers.
Cautions/AI Best Practices
Due to the nature of AI, many people, especially consumers, will be wary regarding the use of such technology. Here are some cautions/AI best practices to keep in mind if you’re going to explore UX research and design with AI.
Establish trust through transparency
The best way to maintain consumer trust is to keep them informed. Publish educational materials on your website, send email announcements about how your company will be adopting AI and what exactly that means for them. Do your best to make sure your customers know what exactly AI is and how you use it.
Many people are skeptical of AI and data collection, so emphasize to your customers that their data isn’t being used in a compromising way. Also, always be able to explain exactly how and why you use AI so you can effectively answer any customer concerns that arise.
Don’t Overuse AI
Once AI becomes more widely accessible and used, there may be a desire to think of it as a ‘magic marker’ that will help you solve all your research and design problems. AI, just like anything, is great in moderation. For every project, ask yourself if AI needs to be used and what it would add to the project.
After reading this, as a designer or user experience researcher you may be worried about AI taking over your job. No need to! While this technology will be revolutionary in an operational and functional sense to explore larger data sets and automate tedious tasks, AI does not (yet) have the ability to think freely or solve complex design problems as easily as humans can.
However, the introduction and acceptance of AI in UX could change the role of designers in the future. Designers could potentially change from the role of creator to collaborator as AI learns to automate the more complex steps of the UX research and design process. Designers could potentially use AI to help them with larger, more complex architectures and code. AI could even fully automate the process so that designers only have to worry about perfecting the creative work. And for UX researchers, the sometimes tedious tasks of watching every video session and identifying MOI and patterns, and collating with quantitative data can be replaced with managing the AI systems and study designs, as well as tweaking the follow on studies based on the iterative results delivered by AI analysis.
The Bottom Line:
AI is currently changing the UX research and design landscape through automation and personalization. As this technology becomes more accessible and widely used, we could see AI taking over larger chunks of the process. That said, although AI may reduce human error when dealing with large amounts of data and allow for massive scaling of both design and user research, a personalized, human touch will always be needed to manage a project successfully.