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AI visibility for national tourism boards

Around 40% of European national tourism organisations have a dedicated AI team or monitor AI use, per the European Travel Commission, which leaves three in five with neither. The institutional question is narrower than the vendor conversation suggests: whether a country's own authoritative facts are reachable by the systems travellers now ask.

What the data says

The best-sourced figure available on this audience is also the one most easily overstated. The European Travel Commission, surveying 29 of its 36 member national tourism organisations in March and April 2025, found around 40% with a dedicated AI team or active monitoring of AI use.

The report's own wording is "around 40%", which we do not sharpen. Read the claim precisely: having a team, or watching the field, is not the same as running AI in operations. Even on the looser reading, three in five national bodies have neither.

The demand side is further along, and the measure that bears on a national board is where discovery is attributed. 40% of US travellers say they discovered a destination through AI and 29% changed their plans on an AI recommendation, both April 2026.

Those are US figures from a 1,006-person panel and should not be read as global — but they describe a source market most national boards spend heavily to reach. More broadly, 38% of AI users across 33 markets use AI for destination research before a trip, the top pre-trip task in that study and the stage at which a country is either on the shortlist or not.

Where national boards differ from the rest of the destination sector is in what they are for. A national organisation is the authoritative publisher of record for its own country: entry and visa rules, regional seasonality, protected sites and their access conditions, official names and transliterations, safety guidance.

When that record is not machine-readable, a model answers an entry-requirement question from a forum post instead. Across the wider sector only 26% of destination organisations have a documented AI strategy, measured among 103 DMO leaders globally — but for a national body the first deliverable is not a strategy document. It is the authoritative dataset, published so that it can be read.

Policy has moved faster than practice here. The OECD's September 2026 paper on AI and tourism in APEC economies sets out six priority areas for responsible adoption, with access for small and medium enterprises at the centre, because tourism is SME-dominated and the gains otherwise concentrate among operators with capital to invest.

For a national board that framing is useful rather than abstract: the mandate is not only to be visible itself but to make the country's small operators visible, which argues for publishing open, structured, reusable destination data rather than only running campaigns.

Three things to do

  1. 1

    Publish the official record as data

    Entry requirements, seasonal access to protected sites, official place names and transliterations, public holidays, regional transport facts. Plain text and structured formats on your own domain, dated, in your main source-market languages. This is material only you can authoritatively provide, and it is the material models most often get wrong.

  2. 2

    Measure in your source markets, not at home

    Visibility is market-specific and language-specific. Run the same question set from each priority source market, in that market's language, and compare the results. A country can be well described in English and badly described in German, and a single aggregate hides exactly that.

  3. 3

    Make the national dataset reusable by the operators below you

    Regional bodies, cities and small operators mostly cannot build structured data themselves. An open, documented, regularly updated destination dataset they can embed turns one central effort into thousands of consistent machine-readable pages, which is the only version of this work that scales to a whole country.

The evidence on this page

Every figure above comes from one of these indicators. Each one links to its publisher, field period and sample, so you can check the reading for yourself.

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