While the industry debates whether AI will replace designers, a quieter–and arguably more consequential–shift is already underway in UX/UI design.
We spoke with Kateryna Orlova, a UX/UI designer at the Irish SaaS company OnePageCRM and author of academic research on the impact of design on productivity and user behavior, about why the growing accessibility of generative tools is changing what a designer’s work is actually worth.
When Figma unveiled Make Designs at its Config conference in June 2024–a feature that turns a text prompt into a finished UI mockup in seconds–the industry zeroed in on one question: would AI put designers out of work?
Within days, the conversation took an unexpected turn. Designer Andy Allen discovered that Make Designs was producing layouts strikingly similar to Apple’s Weather app, setting off alarm bells across the industry. As questions mounted over how the tool was arriving at those results, Figma CEO Dylan Field announced that Make Designs would be temporarily disabled. It returned in September under a new name, First Draft, with an option for “less templated” layouts. By year’s end, the Make Designs episode had faded from the headlines–but the question it raised hadn’t gone away.
According to UX/UI designer Kateryna Orlova, though, the industry was focusing on the wrong thing.
“I think the more important question is different: what happens to the market if thousands of companies start generating interfaces from the same pool of components at the same time? We’d end up with products that have different names but look alike, offer similar user flows, and repeat the same limitations. It becomes harder for users to tell them apart, and instead of competing on product quality, businesses end up competing on price, ad spend, and brand recognition. Meanwhile, the specific problems of a given audience can go unaddressed, because AI defaults to the most common solution–not the one that actually fits the context of that particular product,” she says.
In a Figma study of nearly 1,800 designers and developers, 89% of respondents expected AI to shape their company’s products and services within the next year. Yet only about a third of those already using AI in production reported measurable gains in revenue, cost, or market share.
That gap, Orlova argues, is the key to understanding what’s really happening to design at the turn of 2024–2025.
AI Sees the Data. Does It See the User?
AI only knows the context a team hands it. It can process a thousand user reviews faster than any human, but it can’t tell a team what data they never collected. It doesn’t know why real users abandon an app on step two if nobody has studied the problem in the first place.
That, Orlova says, is exactly where a UX designer’s value becomes clear. The most valuable insights tend to surface not from what users say, but from the gap between what they say and how they actually behave inside a product.
“A user might tell you in testing that a feature is intuitive, then fumble through it three times trying to complete the task. That gap between what people say and what they do is usually where the real problem lives. It’s especially visible in enterprise software like OnePageCRM: people come back to the product every single day, and good design there isn’t the kind that makes a strong first impression–it’s the kind that, six months into daily use, doesn’t force people into extra steps or trip them up on routine tasks. You can’t get that context from a well-crafted prompt. Observation, testing, and reading behavior correctly are still what matters most. That’s one of the core competencies of a UX designer today,” she explains.
The New Problem Facing Junior Designers
One particularly interesting question centers on people just starting out in the field.
Junior designers used to build skill by grinding through small problems over and over: sketching options, getting things wrong, taking feedback, revising, and gradually learning to spot patterns.
Now AI can handle a chunk of that work.
On one hand, that lets newcomers move faster. On the other, it risks skipping the very stage where professional judgment actually forms.
“When a tool hands you a convincing answer right away, it’s easy to mistake the quality of the output for the quality of your own decision-making. A junior designer might pick the right option without understanding why it’s right–or when that same pattern would stop working,” Orlova says.
As a result, she believes design education needs to shift away from teaching tool proficiency and toward building the ability to form hypotheses, work with user data, run research, and argue for a decision.
“The question used to be: can you do this? Increasingly, the question will be: do you understand why it needs to be done this way?”
What Businesses Should Do Right Now
Orlova offers three guideposts for product teams.
First: don’t mistake speed of production for product quality.
“AI can genuinely cut down the time it takes to produce first concepts, interface variations, and working drafts. But if the user problem was misdiagnosed to begin with, faster design just means arriving at the wrong solution faster,” Orlova says.
Second: use AI where it actually delivers an edge–exploring options, structuring information, drafting first-pass concepts, and automating repetitive work.
“AI is genuinely good at expanding the range of options on the table. It can show a designer directions in minutes that would otherwise take hours to explore manually. But the final call shouldn’t be made because an option looks convincing–it should be made because the team understands exactly what user and business problem it solves,” she says.
Third: invest in data about your own users.
This, Orlova believes, is where one of the biggest competitive advantages for product companies will live.
A competitor can use the same AI model. Buy the same tools. See the same publicly available patterns.
But they don’t have the history of how a specific product interacts with its users: research findings, observations, reasons for abandonment, accumulated analytics, and an understanding of real workflows.
“If two competitors are running the exact same AI, the advantage doesn’t go to whoever wrote the faster prompt. It goes to whoever understands, more deeply, who they’re designing for and why,” Orlova explains.
This, in Orlova’s view, is where the future of the profession is being decided. As building interfaces gets faster and more accessible, a designer’s value will depend less on mastering a particular tool and more on the ability to ask the right questions, understand human behavior, and make product decisions in situations where there’s no ready-made answer yet.
“A few years from now, we may spend a lot less time talking about who’s best at drawing interfaces. The skill that matters will be different–the ability to understand the problem before you start designing the solution. In that sense, AI isn’t eliminating the UX/UI design profession. It’s raising the bar for what counts as that profession in the first place,” Orlova says.
About Kateryna Orlova
Kateryna Orlova is a UX/UI designer at OnePageCRM (Galway, Ireland), a CRM platform for small and mid-sized businesses, where she leads interface and product design. Her work centers on how interface architecture shapes user behavior, task speed, and overall effectiveness in enterprise software.
Alongside her design practice, Orlova conducts research. Her academic and industry publications explore adaptive interfaces, personalization in user experience, and how UX design affects performance in digital products. The throughline across this work is the same: real user behavior should drive design decisions, not the other way around.
That’s the lens through which Orlova evaluates generative AI–not by how fast it can assemble an interface, but by whether it can account for how a product is actually used.
