AI is inseparable from hype right now, but what effect is it likely to have in user experience design? Here’s where we see it unlocking potential benefits for brands. AI feels like it’s been unleashed on us, particularly in the past year. It’s impacting lots of different industries but we haven’t felt the impact in user experience design – yet. But for any heads of digital transformation, CTOs or even marketers, some fascinating recent research by Baymard should be a sign to set our expectations properly about what AI can do – and what it can’t. After testing ChatGPT-4 for UX audits of 12 different web pages, Baymard found the AI had an 80% false-positive error rate and a 20% accuracy rate in the UX suggestions it makes. Although ChatGPT correctly identified some UX issues, it overlooked many others. Some of its suggestions were either harmful to the UX or a waste of time compared to the work of experienced UX researchers. Those headline findings show that when it comes to UX, it’s still very much a human-in-the-loop process. There’s some early promise, and judging by AI’s rapid progress in other fields, we have to assume it’s going to get better. Assessing AI’s impact At Each&Other, we love trialling new things and we’re always looking out for more efficient and effective ways to deliver world-class UX. Over the past 18 months, we’ve spent a lot of time investigating AI and how it will impact not just our industry but the sectors our clients operate in. I’ll declare my own bias here: I’m a technology optimist. I’ve always been a futurist; I tend to think further down the road than is good for me! I find AI amazing and terrifying in equal measure, but I think it will be a game changer in our industry – and I believe its effects will mostly be positive. Will we eventually get to a place where the human doesn’t need to be in the loop? Probably. Will there be so much data that you need humans in the loop? Also probably. Where automation adds value In the short term, I see some areas where AI could be incredibly powerful. Figma is the main wireframing tool we use at Each&Other, and we’re starting to see it automating tasks like padding and spacing. Another area automation can help is content creation as we build out websites. Often, the content from wireframes can end up in production. In projects I’m working on, I want people to think about the content that goes into fields on a page, or on a mobile interface. (Anyone who’s worked in UX, web design and content will be familiar with ‘lorem ipsum’ text that acts as a placeholder during the development stage. Here’s where I admit another bias: ‘lorem ipsum’ drives me crazy. But with AI, now there’s no excuse for having it. We can prompt an AI tool with: “give me labels to describe typing in this piece of information”. This gives all the stakeholders a much better sense of what the final version will look like, whereas nothing screams ‘unfinished’ like ‘lorem ipsum’. Rant over!) Speed and scale: how AI aids UX Another significant part of UX is research, and I see a lot of potential for AI tools to automate parts of this work. Last week, we took Outset for a test drive. It’s a moderated AI tool that takes a quantitative approach to qualitative interviews. The researcher plugs in questions they want to find answers to, and then the tool goes to work. We’re interested in it because in Each&Other’s UX research we prefer to do one-to-one user testing interviews between a facilitator and a participant, asking about that person’s experience of the digital interface. However, we’re constrained in the number of interviews we can do in a project. Typically, we do six or 12. Once – and this was an edge case – we did 32 interviews but it was very apparent after the first five conversations that the same themes were coming up again and again. To be clear, we’re not exploring tools like Outset because we want to make the research quicker but because we want it to have more reach, and therefore more validity. Now, we could run 200 tests in a morning using the tool, whereas if we were to run 200 face-to-face interviews, those qualitative tests would take weeks. AI can give everyone involved in a project an excellent head start on where to start improving the UX. Addressing a major challenge for brands Another area where AI can address major pain points for a brand is the ability to parse text for keywords and themes. Today, many of them are unaware of the frustrations their users feel, unless they’re spotting patterns in the data or they’re listening carefully to their customers. Imagine gauging sentiment in the feedback that comes in on your website, potentially identifying issues far earlier in the process than before, so you can step in and address them before they blow up. How powerful would it be if brands could analyse key words for positive or negative sentiment, and identify changes in website conversion rates and understand if they’re within norms or if there are patterns developing they need to worry about. Usually the marketing channel is tasked with keeping an ear open, and there are tools that will highlight when a brand or product is mentioned. My take on this is, that should ideally be part of the product team’s remit, not marketing. Instead of adding an extra layer of people to digest the feedback and make a change request, the product team could harness AI to gauge feedback, and respond by incorporating that into the product. Augmenting expertise Previously, you had to have lots of expertise to delve into this kind of data; you might have needed a web analytics expert to spot trends like people spending less time on a page, and tell whether that means they’re getting through a purchase faster or they’re abandoning a cart because the process is too difficult. Another tool we’ve tinkered around with at Each&Other is Notably. It lets you upload a video of a user testing interview and it transcribes it and analyses it. More interestingly, if you give it different videos, it’ll pull the common themes and organise them. We trialled it on one project and found we didn’t need a second person taking notes, so it turned what would have been a two-person project over one week into work for 1 person over 6-7 days. That’s a 30% saving which is powerful. The tool analyses the data at scale, which informs the necessary qualitative digging. To come back to my earlier point, there’s still a human in the loop who’s overseeing and checking the output. It’s not flawless, but it does the job quite well. I can see a lot of these tools will creep into the workflow and will make steady gains, making this necessary work incrementally less tedious. And the thing with these tools is, they keep getting better, whether they’re using OpenAI or another LLM model under the hood. Preparing for AI in your business We’re at the cusp of having AI tools that bring real business intelligence into product development in real time. And when you get real-time intelligence, it makes you truly agile and responsive. And while there’s a stampede to adopt AI in some sectors, it’s worth stating that you can’t just deploy a tool and expect savings or benefits right away. One of the best things you can do now to get ready for AI is to think practically about how your business will interact with it today. You might get things done quicker, but as a business, does quicker suit you? Are your teams and processes today set up to react to real-time insight, while also dealing with business as usual? For me, that’s the key takeaway: you need to be ready for the time that AI will give back to you. Sure, some companies will use AI to reduce development cycles, to increase profits or to take on more workload, and eventually, we will get to that stage. But I believe the true benefit for marketers or heads of product is being able to ponder subjects that you might have had to make a snap decision on in the past. That’s where I see AI’s value: it allows more time for considering the problems and the best solutions. I believe it will bring more human thought into the process, not less. And in the meantime, we’ll keep looking at exciting new technologies to make sure our service offering continues to anticipate where the market is going. We’re always open to collaborating with our customers on potential opportunities for AI. If you’re curious to explore where it might work for you, get in touch. Our team at Each&Other has more than 20 years of experience in delivering projects on a global stage and we love making better products that harness innovation.