AI, specifically Generative AI, has been dominating IT for a couple of years now. If you’re a software vendor with services that interact with users, you’ll probably have been considering how to ensure you’re not left behind, or perhaps even how to use AI to differentiate yourself. The answer to this can be light-touch AI to make the existing application a little easier to use (smarter help documentation, auto formatting, and spelling for large text fields). Then, at the other end of the spectrum, is how do we make AI central to our application? This can be pretty radical. Both ends of the spectrum carry risks – light touch use can be seen as ‘AI whitewashing’ – adding something cosmetic so you can add AI enablement to the product marketing. At the other end of the spectrum, rejecting chunks of traditional menus and form-based UI that allow users in a couple of quick clicks or keystrokes to access or create content can result in increasing the product cost (AI consumes more compute cycles, thereby incurring a cost along the way) for at best a limited gain.
While AI whitewashing is harmful and can impact a brand image, at least the features can be ignored by the user. However, the latter requires a significant investment and can easily lead to the perception that he product isn’t as capable as it could/should be.
At the heart of this are a couple of basic considerations that UX design has identified for a long time:
- For a user to get the most out of a solution, they need a mental model of the capabilities your product can provide and the data it has. These mental models come from visual hints – those hints come from menus, right-click operations, and other visual clues. UI specialists don’t do eye tracking studies just for the research grant money.
- UI best practices provide simple guidance stating that there should be at least three ways to use an application, supporting novice users, the average user, and the power user. We can see this in straightforward things, such as multiple locations for everyday tasks (right-click menus, main menu, ribbon with buttons), not to mention keyboard shortcuts. Think I’m over-stating things? I see very knowledgeable, technically adept users still type and then navigate to the menu ribbon to embolden text (rather than simply use the near-universal Ctrl+B). Next time you’re on a Zoom/Teams call, working with someone on a document, just watch how people are using the tools. On the other end of the spectrum, some tools allow us to configure accelerator key combinations to specific tasks, so power users can complete actions very quickly.
- Users are impatient – the technology industry has prided itself on making things quicker, faster, more responsive (we see this with Moore’s law with computer chips to mobile networks … Edge, 3G … 5G (and 6G in development). So if things drop out of those norms, there is an exponential chance of the user abandoning an action (or worse, trying to make it happen again, multiplying the workload). AI is computationally expensive, so by its nature, it is slower.
- Switching input devices incurs a time cost when transitioning between devices, such as a keyboard and mouse. Over the years, numerous studies have been conducted on this topic, identifying ways to reduce or eliminate such costs. Therefore, we should minimize such switching. Simple measures, such as being able to table through UI widgets, can help achieve this.
- User tolerance to latency has been an ongoing challenge – we’re impatient creatures. There are well-researched guidelines on this topic, and if you take a moment to examine some of the techniques available in UI, particularly web UIs, you will see that they reflect this. For example, prefetching content, particularly images, rendering content as it is received, and infinite scrolling.
All of this could be interpreted as being anti-AI, and even as someone wanting to protect jobs by advocating that we continue the old way. Far from it, AI can really help, and I have been a long-standing advocate of the idea that AI could significantly simplify tasks such as report generation in products that rely heavily on structured data capture. Why, well, using structured form capture processes will help with a mental model of the data held, the relationships, and the terminology in the system, enabling us to formulate queries more effectively.
The point is, we should empower users to use different modes to achieve their goals. In the early days of web search, the search engines supported the paradigm of navigating using cataloguing of websites. Only as the understanding of search truly became a social norm did we see those means to search disappear from Yahoo and Google because the mental models of using search engines established themselves. But even now, if you look, those older models of searching/navigating still exist. Look at Amazon, particularly for books, which still offers navigation to find books by classification. This isn’t because Amazon’s site is aging, anything but. It is a recognition that to maximize sales, you need to support as many ways of achieving a goal as are practical.
If there is a call to arms here, it is this – we should complement traditional UX with AI, not try to replace it. When we look at an AI-driven interaction, we use it to enable users to solve problems faster, solve problems that can’t be easily expressed with existing interactions and paradigms. For example, replacing traditional reporting tools that require an understanding of relational databases or reducing/removing the need to understand how data is distributed across systems.
Some of the better uses of AI as part of UX are subtle – for example, the use of Grammarly, Google’s introduction to search looks a lot like an oversized search result. But we can, and should consider the use of AI, not just as a different way to drive change into traditional UX, but to open up other interaction models – enabling their use in new ways, for example rather than watching or reading how to do something, we can use AI to translate to audio, and talk us through a task as we complete it. For example, a mechanical engineering task requires both hands to work with the necessary tools. Burt is also using different interaction models to help overcome disabilities.
Don’t take my word for it; here are some useful resources:
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