How AI search changes destination discovery — and what AIEO means for tourism

The question circulating through destination marketing offices and DMC boardrooms right now is some version of the same thing: how do we get listed on ChatGPT? How do we show up when someone asks an AI assistant where to go in East Africa, or which operator to use in Sri Lanka, or what the best time to visit a particular region is?

It's a reasonable instinct. More than half of US tourists now use AI tools at some point in their travel planning.¹ Google AI Overviews appear in most travel-related searches. The anecdotal evidence is everywhere — travelers are asking AI assistants for recommendations, and arriving at opinions that influence where they go and who they book with.

But the framing of the question is wrong, and getting it right changes what you should actually do.

There is no submission form for ChatGPT. There is no tourism listings directory on Perplexity. You cannot apply to appear in a Google AI Overview the way you submit a sitemap to Search Console. AI systems don't curate directories — they synthesise answers from what they have already read, across the accessible web, weighted by signals that have very little to do with your homepage.

That distinction is where the useful thinking starts.

What's actually happening when AI recommends a destination

When someone asks an AI assistant to recommend a destination, the system isn't consulting a ranked list. It's constructing an answer from patterns in its training data and, for tools that search in real time, from what it finds across the current web — synthesised into a response that sounds like a knowledgeable person speaking from experience.

The sources that feed that synthesis differ by platform. ChatGPT, when using search, draws heavily on Bing's index and shows an 87% alignment with Bing's top results.² Wikipedia accounts for nearly half of ChatGPT's top citations across topics.² Perplexity cites an average of 6.61 sources per response³ and favours recent content, first-hand accounts, and conversational writing — weighting these above formal institutional language. Google AI Overviews draw from Google's own travel data, its knowledge graph, and a selection of high-authority pages; pages scoring well on semantic completeness are significantly more likely to be included than those simply ranking at the top of search results.⁴

What this means in practice: ranking first in Google does not guarantee citation in a Google AI Overview. A page ranking fiftieth can be cited if its content is clearly structured and directly answers the query. And a page that ranks well but buries the relevant information inside narrative the AI can't easily extract may not appear at all.

The more significant implication, though, is about where citations actually come from.

The distribution finding that changes everything

Research tracking AI citations across major platforms found that 91% come from sources other than the brand's own website.⁵ For destinations, this means tourism board content is rarely the primary source when AI talks about a place. For operators and DMCs, it means the well-maintained company website is, from AI's perspective, one of the least influential things you own.

This is the central reframe for anyone trying to understand AIEO: it is not primarily an optimisation problem. It is a distribution problem.

The platforms AI systems read — OTA listings, review aggregators, Wikipedia, major travel media, social discussion forums — are where your visibility is actually built or lost. Your own site matters, but as one signal among many, and a relatively weak one compared to the weight AI gives third-party sources.

The practical implications differ meaningfully between destinations and operators. Both are dealing with a distribution problem, but the platforms that solve it are not the same.

For destinations: where AI actually finds you

A destination's AI visibility is built from the composite picture that emerges when an AI system reads everything publicly available about a place. The coherence of that composite — whether sources converge on a consistent description of what the destination is and who it's for — matters more than any single optimised page.

Wikipedia is foundational. It accounts for close to half of all ChatGPT citations² and is presumed to be heavily represented in the training data of every major language model. A destination's Wikipedia entry — and the entries for its key attractions, regions, and cultural assets — is not an afterthought. Destinations with thin, uncited, or contested Wikipedia pages start from a structural disadvantage that no amount of schema markup on the official tourism website will overcome. Keeping Wikipedia entries accurate, well-cited, and substantively written is among the highest-leverage activities available to a destination's digital team.

Authoritative third-party coverage shapes the narrative. AI systems weight citations from high-authority sources — Lonely Planet, Condé Nast Traveler, National Geographic, The Guardian Travel — more heavily than content on most destination websites. The editorial decisions these outlets make about your destination, the framing they use, the attributes they highlight, shape what AI extracts and repeats. This is not a new observation: earned media has always shaped perception. But AI amplifies it. The framing in a Lonely Planet country guide may be reproduced in AI answers to thousands of travel queries, making the quality of that coverage a more consequential input than it was when travelers read it once and moved on.

Google's knowledge graph and destination data. Google maintains structured knowledge about destinations that feeds directly into AI Overviews and its travel planning tools. This data aggregates from multiple sources including UNWTO statistics, Wikipedia, official listings, and Google's own travel product data. Ensuring your destination's official data is accurate in the sources that feed this graph — UN Tourism reports, Google's destination pages, regional tourism statistics — is more useful than most on-site technical work.

Community and forum presence. AI search engines cite Reddit, YouTube, and LinkedIn more frequently than most destinations realise.⁶ For travel, destination-specific subreddits and general travel communities are sources AI systems read as first-hand traveler accounts. A destination that generates no organic discussion in these spaces — or whose discussion is dominated by complaints rather than recommendations — is leaving its AI representation to chance. This can't be manufactured, but it can be monitored and understood as part of the broader visibility picture.

Consistency across sources. AI systems build confidence in a description when they encounter the same information across multiple sources. A destination that describes itself as a sustainable ecotourism leader on its official site but is packaged as a budget beach destination in operator listings and reviewed as underdeveloped in travel forums is presenting AI with conflicting signals. The AI may average them, may favour the authoritative source, or may simply produce a vague answer. Narrative consistency across official channels, operator websites, and major travel media is a genuine competitive advantage — and one that most destinations are not deliberately managing.

For DMCs and operators: a different stack

For a DMC or tour operator, the AI visibility problem looks similar but the platforms that solve it are different. The goal is not to appear when someone asks about a destination — that is a destination's problem. The goal is to appear as the right company to use once someone has decided where they're going.

This is almost entirely a listing and review platform question.

OTA presence is the primary lever. Platforms like Viator, GetYourGuide, Booking.com Attractions, and Expedia Activities have explicit integrations with AI search tools, high domain authority, and the review volume that AI systems weight as a legitimacy signal. Being listed on these platforms — with accurate descriptions, good photography, and a growing review count — matters significantly more for AI visibility than anything on your own site. An operator not listed on Viator or TripAdvisor is largely invisible to the platforms AI references when recommending who to book with.

Google Business Profile has gained specific relevance. Google AI Overviews and Google's AI Mode now surface inline Business Profile cards for tours, activities, and operators — giving users direct access to reviews, photos, operating hours, and booking options without leaving the Google interface. Profile completeness correlates directly with this visibility. Operators with high review counts, recent photos, accurate categories, and consistent contact information appear more reliably than those with thin or neglected profiles.

Review volume and recency are citation signals. AI systems treat review aggregates as evidence of quality and legitimacy. An operator with 400 reviews at 4.8 stars is more likely to be recommended than an operator with an equivalent product and 40 reviews — not because AI is reading each review, but because review count and rating are structured signals that platforms expose and AI reads. Actively soliciting reviews from past clients, on the right platforms, is not a vanity metric. It is a distribution strategy.

Schema markup on your own site — specifically TouristTrip, TouristAttraction, and LocalBusiness types — gives AI systems structured data to extract rather than inferring from unstructured prose. This is not the primary lever, but it amplifies everything else. An operator with strong OTA presence and schema-marked content on their own site is better positioned than one with only the OTAs.

Entity consistency across platforms. If your company describes itself as a wildlife safari operator on your site, a luxury tour company on Booking.com, and an eco-adventure outfitter on Viator, AI systems encounter inconsistent signals about what you are. Settling on a short, specific, consistent description — and using it across every platform where you have a presence — gives AI a cleaner signal to work with and reduces the risk of being described vaguely or incorrectly in AI responses.

Where content fits — once distribution is sorted

Content strategy matters for AIEO, but sequentially: distribution platforms first, content second.

For both destinations and operators, the content AI is most likely to cite is content that answers a specific question clearly and completely. Not "discover the wonders of Rwanda" — but "the best time to visit Rwanda for gorilla trekking is June to September, when the dry season reduces trail difficulty and improves visibility, though June and July see the highest permit demand." The second version gives AI something to extract and repeat. The first gives it nothing.

Structured content performs better than narrative for AI citation: FAQ sections, comparison tables, specific "what to expect" guides. This doesn't mean abandoning long-form narrative — guides that build authority and earn backlinks remain valuable — but specific, extractable answers should exist alongside them.

For destinations, the content gap worth filling is usually the question that major travel media hasn't answered well. Generic coverage of famous destinations is saturated; specific, authoritative answers to the questions a smaller destination's travelers actually search for are underserved, and more likely to be cited as a result.

For operators, the content worth creating is trip-specific rather than company-specific: not "why book with us" but "what a 10-day Namibia itinerary actually looks like, day by day, with honest notes on road conditions, accommodation trade-offs, and what gets cut when time is short."

The honest limits

Several things about AI visibility are genuinely outside the influence of individual destinations or operators, and pretending otherwise leads to wasted investment.

Citation volatility is real. AI Overview citations change substantially with each platform update — research tracking consecutive captures found close to half of cited URLs change between updates.⁷ An operator cited in AI answers today may not be cited next week, for reasons that have nothing to do with anything they did or didn't do. This makes AIEO difficult to track reliably and impossible to optimise in the way traditional search rankings can be targeted.

The OTA oligopoly limits independent leverage. Booking.com holds roughly 70% of the European OTA market.⁸ These platforms have negotiated integrations with Google's AI tools that smaller operators and destinations cannot replicate. Being on these platforms is necessary; being able to influence how they are integrated into AI is not within reach for most operators.

Language and geography create structural disadvantage. AI systems are trained predominantly on English-language content and Western travel narratives. Destinations with smaller English-language footprints — whether because of the language they operate in or because major travel media covers them less frequently — face a disadvantage that optimisation cannot fix. The underlying problem is data availability, not technical configuration.

AI-driven traffic is currently small. Research tracking referrals from AI platforms suggests that AI-driven website traffic accounts for a small fraction of overall visits for most industries — though cited pages do show meaningful uplift in organic click-through compared to non-cited competitors.⁹ The long-term trend is significant; the short-term pressure to optimise aggressively for AI citations is probably overstated for most smaller operators.

What comes next: when AI stops recommending and starts booking

The shift currently underway moves AI from answer engine to booking agent. Google has announced and begun rolling out AI Mode features that handle flights, hotels, and activities — integrating directly with Booking.com, Expedia, Marriott, and IHG to move travelers from research to transaction within a single interface.¹⁰

This changes the stakes. In a world where AI recommends destinations and operators but the traveler still completes the booking independently, visibility in AI answers drives intent. In a world where AI handles the booking itself, visibility in AI decision-making becomes closer to placement in the transaction.

For destinations, the implication is that being well-represented in structured, machine-readable data — accurate product listings, real-time availability, pricing — becomes as important as being described positively in editorial content. The focus shifts from narrative to data infrastructure.

For operators, the implication is more immediate: being bookable through the platforms AI agents transact with is the next version of the distribution problem. An operator not on Viator or GetYourGuide is already invisible to AI citation; in an agentic booking world, they are also unreachable by the agent completing the transaction. The OTA question becomes more important, not less.

This is early-stage. The tools are still developing, the integrations are still being negotiated, and traveler behaviour patterns are still forming. But the direction is clear, and the same distribution logic applies: AI will book through the platforms it already reads.

Destination checklist

A practical starting point for DMOs and national tourism boards.

1. Audit your Wikipedia presence. Check the main destination entry and the pages for key attractions, regions, and cultural assets. Are they substantive, well-cited, and accurate? Thin or contested entries are a structural disadvantage no other optimisation can offset.

2. Review how major travel media frames you. Read your destination's current coverage in Lonely Planet, Condé Nast Traveler, The Guardian Travel, and two or three other high-authority outlets. Note the language, the attributes highlighted, and the traveler being addressed. Is this consistent with your positioning? Is it the argument you'd make?

3. Check your data in the sources Google reads. Verify that your destination's statistics — visitor numbers, key attractions, official website — are accurate in UN Tourism databases and on Google's destination pages. Errors here propagate into AI answers.

4. Monitor community forum presence. Search your destination on Reddit (r/travel, r/solotravel, destination-specific subreddits) and note what questions are being asked, what answers exist, and what the dominant narrative is. This is a listening exercise, not a content brief — but it tells you what AI is reading about you from traveler voices.

5. Implement TouristDestination schema on your official site. If you haven't added structured data markup, do it. It's not a silver bullet, but it gives AI systems a cleaner signal about what your site is.

6. Establish one consistent short descriptor. One or two sentences that capture what your destination is and who it's for. Use it verbatim — or very close to it — across every official channel: website, social profiles, press materials, operator briefings. Consistency across sources is a citation signal.

7. Track AI visibility as a qualitative practice, not a metric. Once a month, ask ChatGPT and Perplexity the questions your target traveler would ask: "best time to visit [destination]," "what is [destination] known for," "which operator should I use in [destination]." Note what's said, what sources are cited, and where your narrative is accurately or inaccurately represented. This won't give you a number to report, but it will tell you whether the distribution work is landing.

DMC and operator checklist

A practical starting point for tour operators, DMCs, and activity providers.

1. Get listed on the platforms AI reads. Viator, GetYourGuide, TripAdvisor, and Google Business Profile are the core four. Booking.com Attractions if relevant to your product. If you're not on these, nothing else in this list matters much.

2. Settle on one entity description and use it everywhere. A short, specific description of what you are: your operation type, the destinations you serve, two or three defining attributes. Use it consistently across your site, OTA profiles, and Google Business Profile. Inconsistent descriptions across platforms generate weak, vague AI representations.

3. Add schema markup to your own site. TouristTrip for packaged itineraries, TouristAttraction for specific experiences, LocalBusiness for your company. This gives AI a structured signal to read rather than inferring from prose.

4. Build a review solicitation process. A simple post-trip email asking satisfied clients to leave a review on TripAdvisor and Google. Review volume and recency are structured signals that AI reads as proxies for legitimacy. Consistency matters more than campaign spikes — one review a week, reliably, beats ten in January and none until June.

5. Complete your Google Business Profile. Photos updated in the last six months, accurate seasonal hours, correct business categories, Q&A section populated with the questions clients actually ask. Profile completeness correlates directly with appearing in Google AI inline cards.

6. Create one piece of trip-specific content per core product. Not "why book with us" — a specific, extractable answer to the questions travelers search: what a particular itinerary actually involves day by day, honest notes on conditions, what gets cut when time is tight. AI cites specific answers; it doesn't cite brand positioning.

7. Monitor with a monthly spot-check. Ask ChatGPT and Perplexity: "best [your operation type] in [your destination]." Note whether you appear, what your review count and rating show on each platform, and which competitors come up. These are the inputs AI is reading — watching them tells you whether your distribution work is building.

The Bottom Line

AIEO is real, but it is mostly a distribution problem wearing an optimisation disguise. The destinations that appear in AI answers are the ones well-documented on Wikipedia, covered by major travel media, and discussed organically in travel communities. The operators that appear are the ones listed on Viator and TripAdvisor with strong review counts and complete Google Business Profiles.

Your own website is part of this picture, but a smaller part than most of the AIEO conversation suggests. Getting your distribution stack right — and keeping your description of what you are consistent across all of it — is where the work actually is.

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References

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2. Discovered Labs (2025). AI Citation Patterns: How ChatGPT, Claude, and Perplexity Choose Sources. https://discoveredlabs.com/blog/ai-citation-patterns-how-chatgpt-claude-and-perplexity-choose-sources ↩

3. Data Studios (2025). How Does Perplexity Choose and Rank Its Information Sources? https://www.datastudios.org/post/how-does-perplexity-choose-and-rank-its-information-sources-algorithm-and-transparency ↩

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5. Lantern (2026). 91% of AI Citations Ignore Your Website. https://www.asklantern.com/blogs/91-of-ai-citations-ignore-your-website ↩

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7. Dataslayer (2026). Google AI Overviews: The End of Traditional CTR and How to Adapt. https://www.dataslayer.ai/blog/google-ai-overviews-the-end-of-traditional-ctr-and-how-to-adapt-in-2025 ↩

8. WeArePlanet (2026). Top Online Travel Agents (OTAs) in 2026. https://www.weareplanet.com/blog/top-ota-travel-agents ↩

9. Seer Interactive (2025). AIO Impact on Google CTR: September 2025 Update. https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update/ ↩

10. Google (2025). Explore new ways to plan and book travel with AI in Search. https://blog.google/products-and-platforms/products/search/agentic-plans-booking-travel-canvas-ai-mode/ ↩