Christian Klugert, Director UX at Experience One, talks about AI as a design component in the user experience, why it forces UX professionals to delve deeper into problem solving and why it should not be used everywhere. He explains how a trustworthy AI can be designed that enables human-centered user experiences despite its growing autonomy.
Mr. Klugert, what significance do you think Agentic AI has for UX work?
Agentic AI is fundamentally transforming UX work. We're shifting away from designing traditional, static user interfaces toward creating dynamic, dialog-driven interactions between humans and machines. This shift means UX design will no longer be primarily visual or functional—it will become increasingly context-aware, systemic, and holistic, with people placed firmly at the center. Artificial intelligence will emerge as a new design element. Instead of predefining complete user flows—as we’ve done in the past, such as booking a hotel using rigid input forms where users manually enter their arrival time, room preferences, or personal details—AI will be able to infer intent, interpret context, and offer personalized guidance based on just a few pieces of information about the user and their travel plans. This won’t just make processes more efficient; it will make them significantly more personal.
The way we work in UX design is also evolving—AI is taking over routine tasks, freeing us to engage more deeply with conceptual questions. At the same time, it can serve as a valuable sparring partner in the creative process, helping us challenge our own ideas and refine them further. As a result, our focus is shifting even more toward analyzing usage contexts, gaining a deep understanding of problems, and developing intelligent, adaptive solutions. We’re returning to the conceptual core—to questions of meaning, context, and purpose. This not only makes our work more efficient, but also more intentional and substantively relevant.
How exactly does Agentic AI change the way you work in the design process?
A great example is the design of our AI-powered landing page generator. Traditionally, I would have started by asking: What content needs to be presented on the page? What structure supports that content? What should the visual layout look like? From there, I would have designed the first screens. Today, the process begins much earlier—and at a much more conceptual level. It’s not enough to simply prompt the AI with “Create a landing page for product X.” I have to engage deeply with the underlying rationale, explaining why certain elements—like a headline—exist, and what effect or meaning I intend them to convey.
This pushes us to go deeper into problem solving. AI challenges familiar patterns, forces us to continuously re-evaluate our assumptions, and requires us to articulate our intentions more clearly. Far from making design feel arbitrary, this actually sharpens it—because every decision stems from a deliberate, content-driven approach.
What can UX teams do today to prepare for dealing with Agentic AI?
The most important step is simply to get started. UX teams should actively begin gaining hands-on experience with generative AI—by experimenting, trying out small-scale use cases, and, most importantly, sharing learnings within the team. Only those who work with AI firsthand can truly understand its potential and apply it effectively. What matters most is a systematic, analytical approach. UX work has always relied on the ability to precisely understand requirements, structure them thoughtfully, and translate them into meaningful processes—and these exact skills are now more relevant than ever.
At the beginning of any new project, teams should ask whether AI can support or automate parts of the work—and if so, how. That might be through a chatbot, for example, or an autonomous agent. The goal should always be to solve a real problem or create tangible value—not just to use the technology for its own sake.
A clear example: Some departments handle 85 inquiries per day and would likely benefit from automated processes. Others, by contrast, might only hold two conversations daily—where the human touch is far more valuable. That distinction is critical. We shouldn’t adopt AI because it’s new, but because it makes sense.
A pragmatic way to get started is to systematically identify areas where AI can provide meaningful support—with a human-centered mindset, focused on impact and benefit. AI also plays an increasingly important role in modeling complex workflows: moving away from linear, step-by-step procedures toward dynamic, branching process chains, where AI assistants—or in the future, AI agents—actively participate in shaping outcomes.
Take the landing page generator example: It’s no longer just about filling in a template and publishing the content. It’s about uncovering the underlying process of how a landing page comes into being—from both the user’s and the organization’s perspective. What happens if the person responsible hasn’t yet defined a clear topic? How can the system help identify one that aligns with the overall communication strategy?
These insights lead to a much more nuanced workflow—one that ideally leverages the strengths of both humans and AI, working in sync to support each other and reach shared goals more effectively.
You can read the full interview with Christian Klugert in our whitepaper "Road to Agentic AI. The fascination of automation".