UX & Product
AI generates the interface. What do UX designers do now?
When AI starts generating parts of the interface, UX does not disappear. It moves deeper into context, trust, control, and the rules behind dynamic product experiences.
This talk is for UX, product, and web teams that want to understand AI interfaces and generative UI beyond the chatbot trend. It starts from a simple observation: users often see the answer, but not the context, sources, uncertainty, tools, or data behind an AI decision. The talk shows why UX becomes more important in AI products: as the design of transparency, control, follow-up questions, correction capability, and trust. Participants learn practical patterns such as answer layers, trust layers, optional debug layers, source panels, and context transparency. The topic works for conferences, product teams, design-system teams, frontend teams, and internal workshops.
When the surface is no longer fully static
- AI products often show a polished answer without showing which sources, tools, data, or assumptions shaped it.
- Generative UI can feel useful, but loses trust when users cannot understand or correct what happened.
- UX, product, and frontend teams need new rules for transparency, control, uncertainty, and context.
Audience
Who this is for
- UX teams, product designers, and design-system teams
- Product owners, AI product teams, and digital product organizations
- Frontend, web, and engineering teams integrating AI features into real products
- Conferences, meetups, and internal enablement around UX, product, and AI
FAQ
What is generative UI?
Generative UI describes interfaces where parts of the surface are dynamically derived from context, rules, or model output instead of only predefined states.
Does AI replace UX designers?
No. The work shifts: less pure screen production, more design of context, transparency, control, trust, and correction capability.
What does context transparency mean?
Context transparency means showing users, at the right level of detail, which information, sources, assumptions, or tools an AI feature used.
Is the talk only relevant for chatbots?
No. The patterns apply to web products, internal tools, assistive features, search experiences, and dynamic product surfaces.
Which UX patterns are covered?
The talk covers progressive disclosure, answer layers, trust layers, optional debug layers, source panels, follow-up questions, uncertainty cues, and correction mechanisms.