UX design is evolving alongside artificial intelligence. Today, designers are no longer just working with screens, buttons, and user flows; they’re also designing how people interact with neural networks. This shift has become one of the central topics in the professional community. At IXDC in China, one of the largest international conferences in experience design, this year’s theme was “The Symbiosis of Humans and Machines.”
We spoke with Julia Tolmacheva, a leading product designer and Human-Centered AI specialist, about the new role of UX designers in the age of AI. Julia has worked with emerging technologies and machine learning on projects for Microsoft HoloLens, Škoda, Porsche, Société Générale, and other international companies.
Her professional focus centers on the ethics of working with artificial intelligence and how technology shapes user experience and decision-making. This is the subject of her concept, The Ethical Spectrum of the Interface, which examines how responsibility is distributed between humans and systems in the creation of digital products. Julia has served as a reviewer for academic and applied research in artificial intelligence and UX/UI design, including studies on AI’s impact on design processes and the ethics of data use.
Drawing on Julia’s experience, this article explores how AI is changing the work of UX designers, why interface design is becoming a more ethically complex task, and whether neural networks actually help designers become more creative or, on the contrary, make their thinking more formulaic.
UX Design Before the Neural Network Boom
According to Julia Tolmacheva, new technologies began appearing actively in UX design long before generative AI became widespread. But what mattered most was never the use of an innovative tool for its own sake; it was finding the right scenario in which the technology actually benefited people. For example, on one project for Microsoft HoloLens and Škoda, Julia and her team developed augmented reality solutions to visualize car components and design vehicles more efficiently.
By the late 2010s, artificial intelligence was also gradually being integrated into workflows, though at the time this mostly meant machine learning and data analysis. “Designers analyzed data alongside analytics and engineering teams, then translated complex technological solutions into clear user scenarios,” Julia recalls.
In many ways, this period laid the foundation for today’s approach to AI and emerging technologies: value came not from simply using innovation, but from the ability to embed it into real processes and make it genuinely useful for people.
A New Approach to UX Design
By the early 2020s, Julia notes, AI stopped being an invisible mechanism and became a full participant in the user experience. The very way interfaces are designed has changed too. In the past, a digital product’s user journey could be mapped out almost entirely in advance: what a person would see, what they would click, and what result they would get. That approach no longer works for AI products. A user might come to the system with completely different goals: finding information, making a decision, writing text, planning a trip, or making sense of a complicated situation. Along the way, they keep refining their request, shifting context, and evaluating the response in real time.
“So now the UX designer is designing the frame of the dialogue itself: how the neural network helps a person articulate their request, how it interprets their goal, what options it offers, how it explains its recommendations, and how it responds to follow-up questions,” the expert explains. In her view, when a dialogue is designed correctly, with attention to user psychology, it can do more than make interacting with technology more convenient; it can help people change their habits and improve their quality of life.
Fintech projects offer a good example of this approach. They help people save money systematically, visualize progress, and reach specific financial goals, from vacation savings to building an emergency fund.
One such project is Julia Tolmacheva’s Saving Goals for Sber, recognized at the international Red Dot Award for its innovative approach to building financial habits. In developing the service, she deliberately moved away from the idea of a “smart assistant” that makes decisions on the user’s behalf. Instead, AI technologies were used at the research and design stage: they helped identify behavioral patterns and understand which savings scenarios would feel most comfortable to the service’s clients. Those findings shaped the final design.
Why UX Designers Need to Be Ethical
But AI can do more than help users, Julia points out; it can also quietly influence their behavior. An interface can be designed to nudge people toward specific actions: signing up for a subscription, making a purchase, handing over more personal data, or simply spending more time on the product. Users often don’t even notice this kind of influence, since dark patterns are built directly into the interface logic, the algorithm’s recommendations, and the interaction flow itself.
According to Julia, people often expect a system to hand them a ready-made answer and tend to treat it as an objective recommendation rather than something to question or analyze independently. It’s precisely this tendency that unethical design can exploit, gradually eroding a person’s ability to make informed, independent decisions. That’s why Julia’s work emphasizes transparency: people should understand the logic behind a service, see how recommendations are formed, and retain control over their own choices.
“It matters that a recommendation genuinely helps someone make a decision rather than quietly steering them toward a pricier plan or a partner product. The same goes for requests to access data: users should clearly understand why a system is asking for information and how it will be used,” she explains.
Part of a modern designer’s professional responsibility, she says, is knowing how to engage critically with technology and stay conscious of its effects while working with it. This is exactly the subject of her Ethical Spectrum of the Interface concept, which examines the boundaries of design’s influence on human behavior.
Similar mechanics show up widely in retail and enterprise products. In retail, this takes the form of personalized recommendations, dynamic pricing, and user-journey optimization aimed at boosting conversion. In enterprise systems, it appears through task prioritization, data-driven recommendations, and decision support, all of which directly shape business processes and user behavior.
What Else Is Changing in the Design Profession as AI Advances
As AI develops, more routine design tasks are becoming automated: neural networks already help generate visual concepts, produce solution variants, and speed up certain stages of the work. But this doesn’t diminish the designer’s role. If anything, skills that can’t be delegated to technology matter more than ever: the ability to analyze context, understand user needs, frame the problem, and explain one’s own reasoning. As a result, employers are looking not just at the case studies in a designer’s portfolio, but at how those cases are structured.
“Many highly skilled designers struggle to package their experience correctly. They can put together a portfolio, but they don’t always know how to show their actual contribution to a product: what role they played in the process and how the work was structured at every stage,” Julia notes.
In response to this need, educational and mentorship initiatives have emerged to help designers structure their experience, build strong portfolios, and position themselves for international careers, including programs developed by industry experts. Julia Tolmacheva created one such project herself, called “Portfolio Power.” The program brings together product thinking, storytelling, UX methodology, portfolio structure, presentation psychology, and preparation for an international career.
Will AI Kill Creativity?
A common concern today is that AI might reduce designers’ creativity, since it takes over idea generation and makes finding solutions easier, in turn reducing the need to search for unconventional approaches on one’s own.
Julia disagrees. In her view, nothing but burnout can kill creativity, if a person has it in them at all. “If you’re creative enough, you’ll figure out how to ask a neural network the right kind of oddball question to get it to come up with something radically strange and new,” she explains. This idea holds up at the industry level too. International competitions, including Code & Create, where Julia’s work in digital solutions was recognized, show that human vision is still especially valued in technology projects.
“Today, with AI being adopted so widely, attention, empathy, and the ability to think critically about how technology affects users matter more in a designer’s work than ever,” Julia concludes. Designers like her are shaping a new approach to AI: using it not as a replacement for creativity, but as a tool to amplify it. The person and their needs remain at the center.
