A strategic self-assessment for Destination Management Organisations. Map your AI maturity across 9 dimensions. Identify gaps. Prioritise the actions that move the needle.
What three years of documented AI adoption reveal about where destination organisations actually stand
Ask a roomful of DMO professionals whether AI matters and every hand goes up. Ask who has a written AI policy and most hands come down. Ask who has a formal AI strategy with budget, ownership, and milestones and you are usually left with two or three tentative gestures. This gap, between genuine enthusiasm and structural readiness, is the defining feature of where destination organisations are right now.
That is not a criticism. It is a description. The ETC study published in September 2025, drawing on data collected from European NTOs in April of that year, found that none of the surveyed organisations had a formal AI strategy in place. Only 14% had a policy of any kind. Staff curiosity was high, overt resistance was low, but the institutional infrastructure to support sustainable AI adoption was largely absent. What had grown fast was experimentation. What had not grown was governance.
The Destinations AI Canvas exists because of this pattern. Not to celebrate the organisations ahead of the curve, and not to judge those still finding their footing. It exists because there is now enough documented, verified evidence across geographies and organisation types to map the terrain clearly. And the terrain is more varied than conference presentations tend to suggest.
The ETC study is worth reading carefully because it is methodologically sound. Produced by Kairos Future and covering ETC member national tourism organisations, it assessed two dimensions simultaneously: organisational readiness for long-term AI deployment, and the usefulness already perceived from AI tools in practice. The resulting segmentation, beginners, untapped potential, opportunistic users, and early adopters, shows how wide the distribution is even within a relatively homogeneous set of European institutions.
Marketing departments are ahead of research departments by a significant margin. Seventy-two percent of marketing teams cited AI use in copywriting. Research teams found the technology useful in theory but could not point to operational adoption with the same confidence. The main barriers were not technical: limited AI expertise, sparse training, and the absence of a strategic roadmap. The OECD G7 policy paper on AI and tourism, endorsed by Tourism Ministers in November 2024, identifies market intelligence, visitor flow optimisation, and sustainability management as the highest-potential areas, while noting that many organisations lack the data infrastructure to act on that potential.
Author's composite assessment. Sources: ETC 2025, OECD/G7 2024, STB 2025-2026, CityDNA 2025, Destinations International 2025.
Not everything is cautious experimentation. Three distinct approaches have now produced enough publicly documented evidence to draw lessons from.
The most architecturally ambitious is Singapore Tourism Board. STB built a Learn-Test-Scale system: the Tcube innovation hub, an AI playbook for tourism businesses, grants and procurement support, a data infrastructure layer called STAN, and open innovation challenges. In July 2025, STB signed a memorandum of understanding with OpenAI, the first national tourism organisation in Asia to formally adopt OpenAI technology as part of its Tourism 2040 roadmap. The 2026 pilots, multilingual AI guides at Sentosa and Mandai, are the visitor-facing output of years of organisational preparation. The technology is not what makes this case interesting. The architecture around it is.
The most widely replicated model is the visitor assistant. Destination Toronto's 6ix, launched in October 2024 on GuideGeek technology, handled more than 7,500 messages from over 2,700 users in its first two months. France's MarIAnne on France.fr, Switzerland Tourism's AI-assisted travel companion, and assistants from the British Virgin Islands to Discover Santa Clara follow the same template: fast time to market, good fit for inspiration and itinerary use cases, consistent constraints around vendor dependency and attribution to economic outcomes.
VisitAarhus, through Denmark's Moving Destin(AI)tions programme, ran ten destination AI pilots and documented what went wrong as carefully as what went right. A custom GPT built to democratise internal knowledge ran immediately into security and privacy requirements more complex than anticipated. The published conclusion: every AI experiment needs defined objectives, evaluation criteria, and genuine openness to learning when the outcome is not what you hoped for. VisitScotland took a similar principled approach with its Business Support Hub AI chatbot in May 2025, and won the FutureScot AI Challenge in November 2025 for a multilingual travel companion concept built on the same logic.
Key documented milestones across DMO and NTO AI programmes. Sources cited below.
The adoption pattern is not random. Organisations move first into content and visitor assistance because the use cases are legible and the outputs are visible. They move last into the areas that require the most institutional pre-work: governance, data foundations, AI-ready content architecture, measurement frameworks, and ecosystem enablement.
Brand USA's internal AI framework is built on three questions: what does the organisation provide to employees, what does it protect, and what does it expect. This framework came before any public-facing AI deployment. The logic is plain: before you put an AI system in front of visitors or partners, you need a clear answer to what your organisation will do when it gets something wrong.
DEPLOYTOUR, the EU-funded initiative building a European Tourism Data Space under the Digital Europe Programme, addresses the infrastructure layer that most DMO AI deployments quietly depend on but rarely invest in. With five active pilots testing interoperability, data sharing, and AI analytics, it is working on the prerequisites that make the higher-value use cases technically feasible at destination scale.
Travellers are asking ChatGPT, Gemini, and Perplexity where to go, what to eat, where to stay. The answers they receive are drawn from the collective digital presence of every operator in a destination. A destination with a polished chatbot and unstructured, outdated content across its operator ecosystem is already losing ground in AI discovery. This is why the Destinations AI Canvas includes AI Visibility and Stewardship as a standalone ninth dimension. It is the one that most frameworks built before 2025 did not need to include.
The best-positioned destinations in 2027 will not be the ones that deployed the most impressive chatbot in 2025. They will be the ones that became the most credible, machine-readable, AI-legible source of truth about their territory. The canvas is a tool for deciding where that work starts.
Artificial intelligence is reshaping how destinations are discovered, chosen, and experienced. Not in the future — now. Yet most DMOs are navigating this shift without a map.
The Destinations AI Canvas gives you a structured, honest picture of where your organisation stands across the nine dimensions that define AI readiness for destination management. Inspired by the clarity of the Business Model Canvas — a single view that sparks the right conversations.
It is not a compliance tool. Not an audit. It is a strategic mirror — and a starting point for action.
Developed as a companion to the AI Tourism Playbook by Officina Turistica. Published under CC BY 4.0.
Nine dimensions, arranged so that Strategy is the anchor and the surrounding eight reflect the full scope of AI adoption — from internal capability to ecosystem influence.
For each dimension, read the five stage descriptions and select the one that most accurately reflects your organisation right now. Be honest — the canvas is only as useful as it is truthful.
Stage descriptors, diagnostic indicators, and the full maturity ladder for each dimension.
All materials are free to use and share under CC BY 4.0 with attribution to Officina Turistica.