AI interfaces and user experience
How the visitor meets the destination when AI sits at the front door
Who builds the interface between the visitor and the destination and what is the DMO's role inside it?
The interface is the destination's new front door
From building the page to deciding what to build
Three channels
The visitor used to meet a destination through search results, brochures and travel agents. Today the visitor meets the destination through interfaces that AI helps build, that AI sits inside or that AI controls on the visitor's behalf. Three channels are taking shape.
The first is the destination's own assistant, a conversational interface that sits on the DMO's website, answers in the visitor's language and routes from inspiration through plan to book. The second is the agent layer, the AI assistants the visitor carries on their device, which research, compare and book on their behalf. The visitor never opens the DMO's website. The third is the curated content surface: newsletters, microsites, hubs and campaign pages built with AI as part of the production stack.
The destination's own chat
Alma, on the Slovenian Tourist Board website
The agent layer
The AI assistant the visitor carries on their device
Designed in an AI tool by vibe coding and then published
The XDW newsletter and the participant hub
On its own surface, the destination answers the visitor directly and keeps the conversation.
Where the work has shifted
The three channels share a working method. Building an interface starts with a conversation about the visitor, and the tools have caught up far enough that whatever a team can describe clearly, they can prototype quickly. The technology runs alongside the thinking now, which puts the visitor at the centre of every interface decision.
The conversation that matters is the one about what the visitor is trying to do and what specific question the interface needs to answer for them. Teams who hold this conversation properly produce interfaces that hold up well. The build follows the thinking, and the editorial judgement that decides what to build has become the work that matters most.
The space this opens for destinations is wider than it looks. Campaign microsites that would have needed months of planning get built and tested in a working session. Internal tools come together in an afternoon when previously they would never have made the priority list. The prototype itself becomes part of the conversation, surfacing as quickly as the ideas behind it.
The DMO's role and how far to push it
The role of the DMO is shifting towards digital architecture and the protection of brand trust. Teams now design platforms that perform beautifully for human visitors while remaining perfectly readable for the automated systems scanning the web. This dual focus requires a complete understanding of both user experience and technical data structure.
Deploying automated code directly onto a live public website creates valid operational anxieties. These concerns focus on compliance, brand consistency and long-term maintenance. A technical failure during a peak traffic period remains a significant risk if the underlying system was generated without human oversight.
A successful strategy separates initial experimentation from the final public launch. Automated coding tools work effectively for building internal prototypes and short-term campaign assets. The main consumer website requires a higher standard of engineering to handle large volumes of traffic reliably. Destinations secure this stability by using software tools for rapid concept design and then employing developer teams to stabilise the code before publication.
Accuracy and the chatbot question
Accuracy carries more weight on an automated messaging tool than on almost any other digital system. An incorrect answer is immediately visible to the visitor and directly compromises the reputation of the destination brand. Preventing these errors requires a strict management process based on verified data sources, expert reviews for complex questions and clear escalation paths to staff when the software is uncertain. Every automated platform carries this operational risk and requires an explicit plan to maintain quality standards.
Alma, Slovenian Tourist Board
A destination's own assistant built around verified information.
Pilot launched on the Slovenian Tourist Board website
The Slovenian Tourist Board built Alma as a chatbot on its website. The system runs on a ChatGPT API, sits in seven languages and was framed from the start as a pilot project, which gave the team room to test the experience with the industry first, gather feedback at events and refine the design before scaling.
The conversational flow rests on three questions. Alma asks the visitor what activity they want to do, when they want to do it and where they want to be. The three answers give the system enough context to recommend specific routes, attractions and timings. The questions also reveal what visitors are actually looking for, which feeds back into the content strategy.
Where the integrations did the work
Two integrations did the most to raise quality. The first was Google Maps, which answered the location and timetable questions visitors asked most often. The second was Outdooractive, the platform used by the Slovenian Outdoor Association, which gave Alma access to curated hiking and cycling trails. Both integrations addressed a specific weakness the team had identified through red teaming. When Alma was asked about outdoor attractions, the answers were poor because the underlying content was poor. The integrations brought in the verified, structured data Alma needed.
From 82% to 92%
Positive answer ratings sat at 82% when Alma launched. Recent measurement puts them at around 92%. The improvement came from incremental work: better content indexing, the integrations above, voice input added for mobile users and an expansion of indexed content from English-only to all seven languages. Indexing Slovene content costs around 60% more than English because of the way the language is processed, and the upgrade made Slovenian content available to international visitors through the model's translation layer.
Compliance ran alongside development
The team registered Alma on the Slovenian register of public AI systems. They published legal notices to meet the EU AI Act and the European Accessibility Act. They added optional features that let visitors receive their itinerary by email, with all the data handling that implies. The compliance work happened during the pilot, alongside the rest of the build.
What comes next
Regional DMOs across Slovenia have asked to use Alma. The legal questions around extending an AI system to other public bodies are still being worked through. The early signals are that a shared destination assistant is achievable when the underlying content and compliance layers are owned at the national level.
Selfe
Preparing for the moment when the visitor's agent arrives.
Selfe’s work highlights how automated digital assistants now handle a major portion of holiday planning for travellers. These systems scan all available online material, evaluate destination credibility and deliver a highly refined list of recommendations. This shift eliminates the traditional website visit from the research phase. Travellers form their perceptions of a location based entirely on the specific summary generated by the software.
This shift changes the exact type of content destinations need to produce. A recent green paper on the future of travel discovery highlights this requirement clearly. Remaining visible to digital planning tools requires authentic storytelling that captures a location through the experiences of the people who live there. The voices carrying the most influence belong to the chefs, makers and innkeepers who shape the daily reality of a place. These narrative details translate directly into the specific, verifiable facts that automated systems look for when evaluating a destination.
Specific stories become verified data, and verified data is what the agent trusts enough to recommend.
Selfe is building the data layer that helps that translation happen. A content surface that destinations populate with curated, verified information, queryable by the agents researching on behalf of a visitor. The principle underneath the product is the part worth holding on to. A destination is recommendation-ready when its stories are specific enough to be matched accurately and verified enough to be trusted.
The same content page, read two ways.
One surface serves both. The signals each one picks up are different.
The destination's role becomes editorial. The work is to curate the stories the agent layer can rank confidently and verify the data points underneath them. For many visitors, the DMO website now acts as a back-end service the visitor never sees directly.
Key Questions and the Path Forward
This section pairs the core challenges raised by DMOs throughout the day with the strategic conclusions reached by the group. Six practical recommendations follow.
Should we build our own chatbot or invest in the content layer that agents will read?
Both, in different proportions based on the destination's visitor volume and content depth. National tourism organisations with high inquiry volume justify a chatbot first. Regional DMOs with lower volume invest first in the structured, verified content the agent layer will rely on. The chatbot adds value when the question volume justifies the operational cost of maintaining it. The content layer compounds regardless of question volume.
Re-enter the zones from XDW 2026
Each group examined the development of digital interfaces from a distinct perspective. The summaries below outline the discussions and the practical conclusions reached by each room, while the links provide direct access to the digital environments built during the event.