A $2 trillion industry meets AI-first discovery

The global travel and tourism industry generated $2.29 trillion in revenue in 2023, according to the World Travel & Tourism Council (WTTC). In the United States alone, leisure and hospitality spending reached $1.2 trillion (U.S. Travel Association, 2024). Online travel bookings account for a rapidly growing share: Statista estimates global online travel sales hit $755 billion in 2024, up from $475 billion in 2019.

For decades, the travel discovery funnel was straightforward: Google search → OTA or hotel website → compare → book. Travelers searched “best hotels in Bali,” clicked through the results, and eventually converted. 85% of leisure travelers use online resources to plan trips before booking (Google/Ipsos, Think with Google). The industry optimized for this funnel with an estimated $18.5 billion in digital advertising spend in the U.S. travel category in 2024 (eMarketer).

That funnel is fracturing.

Gartner forecast in February 2024 that traditional search engine volume will drop 25% by 2026 due to AI chatbots and virtual agents. ChatGPT reached 1.8 billion monthly visits by late 2024, making it one of the top 10 most-visited sites globally. Google itself now shows AI Overviews for an estimated 84% of informational queries (BrightEdge, 2024) — and travel informational queries (“best time to visit Japan,” “what to do in Lisbon for 3 days,” “family-friendly resorts in Cancun”) are among the most affected categories.

The shift is already measurable. Expedia reported that its ChatGPT-powered trip planning feature generated over 7 million conversations in its first year (Expedia Group earnings call, 2024). Tripadvisor launched an AI-powered itinerary builder. Google integrated Gemini into Google Travel. Kayak, Trip.com, and Skyscanner all released AI planning tools. The entire travel discovery experience is being rebuilt around conversational AI — and the brands that are visible in those conversations are winning.

Why travel is the killer use case for AI chatbots

Travel planning isn’t just “one of” the top use cases for AI chatbots. It is arguably the top consumer use case. Here’s why:

  • Complexity: A typical vacation involves flights, hotels, activities, restaurants, transportation, and visa requirements — across multiple days and sometimes multiple countries. This is exactly the kind of multi-step research task that AI excels at synthesizing.
  • High information volume: The average traveler visits 38 websites before booking a trip (Expedia Media Solutions). AI condenses that research into a single conversation.
  • Personalization need: “Plan a 10-day Italy trip for a couple with a $5,000 budget who love wine and history” is a prompt that would require hours of Google searching — and takes ChatGPT about 15 seconds to answer.
  • Emotional stakes: A vacation is often the largest discretionary purchase a household makes. People want confidence in their choices, and AI delivers curated, authoritative-sounding recommendations.

The data confirms this. A BCG survey found that 60% of consumers are comfortable using generative AI for travel planning (BCG, 2024). An Oliver Wyman Forum study found that 47% of millennial and Gen-Z travelers have already used AI tools to help plan trips. A Booking.com survey of 27,000+ travelers across 33 countries found 45% would be open to using AI to plan trips, with even higher rates among younger demographics.

McKinsey estimates that generative AI could create $400 billion–$700 billion in value for the travel, transport, and logistics sector (McKinsey Global Institute, 2023). Much of that value comes from exactly this: AI replacing the traditional research and discovery phase of trip planning.

For travel companies, this means the question isn’t whether AI will reshape how travelers find and choose providers. It already has. The question is whether your brand is part of the AI-generated itinerary or invisible in it.

Who AI actually recommends for travel

We tested this extensively. Across hundreds of travel planning queries to ChatGPT, Perplexity, Gemini, Claude, and Grok — using prompts like “plan me a trip to Barcelona,” “best hotels in Kyoto,” “where should I book a safari in Tanzania,” and “best platform to book vacation rentals” — the same brands dominate:

Rank Brand Monthly Visits (approx.) AI Mention Rate *
1 Booking.com ~500 million Mentioned in 85%+ of hotel/booking responses
2 Airbnb ~270 million Mentioned in 80%+ of responses
3 Expedia Group (Expedia, Hotels.com, Vrbo) ~190 million (combined) Mentioned in ~70% of responses
4 TripAdvisor ~150 million Mentioned in ~60% of responses
5 Google Travel / Google Hotels N/A (integrated into search) Mentioned in ~45% of responses
6 Kayak / Skyscanner ~80 million (combined) Mentioned in ~35% of responses
— Avg. independent hotel / tour operator 5,000–100,000 <2% of responses

The pattern is stark. When a traveler asks ChatGPT “where should I stay in Rome,” the response recommends neighborhoods and then says “you can book through Booking.com or Airbnb.” When asked “plan a Thailand itinerary,” the AI generates a detailed day-by-day plan — and links to Booking.com, Viator, and GetYourGuide for bookings.

Individual hotels, resorts, tour operators, and travel agencies are almost never mentioned by name unless the user specifically asks about a well-known luxury brand (Four Seasons, Marriott, Hilton) or a famous individual property. Even then, the AI typically follows up with “you can compare prices on Booking.com or Expedia.”

For the vast majority of travel companies — boutique hotels, independent resorts, regional tour operators, local travel agencies, vacation rental managers, DMOs — they simply do not exist in AI-generated travel advice. To understand what this means, read our explainer on how brands actually appear in AI responses.

Why independent hotels and tour operators are invisible

AI chatbots generate recommendations based on patterns in their training data — billions of web pages, review sites, travel blogs, Reddit threads, news articles, and forum discussions. The brands that appear most frequently, authoritatively, and consistently in that data are the ones AI recommends.

The math is brutal for independent travel companies:

  • Booking.com lists over 28 million accommodation options across 228 countries and generates ~500 million monthly visits (Booking Holdings annual report, 2024).
  • Airbnb has 7.7 million active listings and ~270 million monthly visits (Airbnb SEC filing, 2024).
  • TripAdvisor hosts over 1 billion reviews across 8 million businesses (TripAdvisor, 2024).
  • The average independent hotel website receives 5,000–50,000 monthly visits.
  • The average tour operator website receives 10,000–80,000 monthly visits.

That’s a 5,000x–100,000x gap in web presence. And web presence is what AI systems learn from.

Three specific factors determine whether AI mentions your travel brand:

  1. Corpus frequency: How often your brand appears across the web. Booking.com has billions of indexed pages. An independent hotel in Tuscany might have a few hundred mentions. The GEO research from Princeton/Georgia Tech (2023) found that content with statistical citations and clear factual claims was up to 40% more likely to be cited by generative AI systems (Aggarwal et al., “GEO: Generative Engine Optimization,” 2023) — and OTAs are packed with structured data.
  2. Source authority: AI weights authoritative sources more heavily. A mention in Condé Nast Traveler, Lonely Planet, or Travel + Leisure carries more weight than a mention on a personal travel blog. OTAs dominate high-authority travel content because travel journalists link to them constantly.
  3. Content structure: OTAs have highly structured data: property names, star ratings, prices, amenities, guest ratings, location coordinates. Independent hotel websites often have beautiful photography but very little structured, extractable content that AI can parse and cite.

For a deeper look at how this visibility gap works and what drives it, see our guide on how AI visibility scores are measured.

What AI gets wrong about travel brands

When AI does mention a travel brand, there’s a significant chance the information is wrong. AI chatbots hallucinate facts about brands frequently, and travel is especially prone to errors because of seasonal pricing, property renovations, changing ownership, and the sheer volume of similar-sounding properties.

The most common errors we find in AI responses about travel companies:

Fabricated property details

ChatGPT confidently describes hotel amenities that don’t exist. We’ve seen AI claim a hotel has a “rooftop infinity pool with city views” when the property has no pool at all. It invents restaurant names, spa services, and room categories. For boutique hotels with limited web content, the hallucination rate on specific amenity claims exceeds 50%.

Outdated and fabricated pricing

AI cannot access real-time pricing. When asked “how much does it cost to stay at [hotel],” chatbots frequently confabulate specific nightly rates that are months or years out of date — or entirely invented. A traveler making a $3,000+ booking decision based on AI-provided pricing is working with unreliable data.

Tour itinerary invention

For tour operators, AI often generates entirely fabricated itineraries. It will describe a “7-day safari with Operator X” including specific lodges, activities, and day-by-day plans that the operator has never offered. This creates customer service problems when travelers arrive expecting an itinerary that doesn’t exist.

Star rating and review confusion

AI frequently mixes up official star classifications with user review ratings. A 3-star hotel with a 4.5/5 TripAdvisor rating may be described as a “4.5-star luxury hotel.” In markets like Europe where official star ratings carry regulatory weight, this creates genuine misrepresentation.

Location errors

Properties with similar names in different cities or countries get merged. “Hotel Navi” in Mumbai and “Hotel Navi” in Barcelona may have their descriptions blended. AI also frequently misrepresents distances to landmarks, airports, and beaches.

The compound problem: Your travel company is either invisible in AI (bad) or mentioned with fabricated amenities, wrong pricing, and invented itineraries (worse). Both cost you bookings. The first means travelers never discover you. The second means they arrive with expectations you can’t meet — leading to complaints, refund requests, and negative reviews that further damage your AI visibility.

The OTA dependency trap deepens

The travel industry already has an OTA dependency problem. And AI is making it dramatically worse.

Consider the current state of hotel distribution:

  • OTA commission rates range from 15–30% per booking (Phocuswright, 2024). For a $200/night hotel room, that’s $30–$60 per night going to Booking.com or Expedia.
  • Only 29% of hotel bookings globally are direct (Phocuswright Global Online Travel Overview, 2024). The rest go through OTAs, metasearch, and other intermediaries.
  • Hotels spend an estimated $6.3 billion annually on OTA commissions in the US alone (AHLA, American Hotel & Lodging Association estimates).
  • The “direct booking wars” that Marriott launched in 2016 with “It Pays to Book Direct” have had limited lasting impact. OTA share has remained roughly flat or increased for independent hotels without loyalty programs.

Now layer AI on top of this. When a traveler asks ChatGPT to plan a trip, the AI doesn’t say “book directly at the hotel’s website.” It says “book through Booking.com” or “check Airbnb for options.” AI is becoming a new layer of intermediation — and unlike Google, where hotels could at least buy ads or compete organically, there are no paid placements in ChatGPT recommendations.

Metric Independent Hotels Major Hotel Chains OTAs
Direct booking share ~15–20% ~35–50% N/A (they are the intermediary)
Avg. OTA commission 18–25% 12–18% (volume negotiated) —
AI mention rate <2% ~15–30% (Marriott, Hilton, IHG) 70–85%
Monthly web visits 5K–100K 50M–150M 150M–500M
Google organic visibility Moderate (local SEO) High Dominant

For tour operators, the dynamic is similar but even more severe. Viator (owned by TripAdvisor/Tripadvisor) and GetYourGuide dominate AI recommendations for activities and experiences. Local operators who create the actual experiences are invisible — while paying 20–30% commissions to the platforms that AI recommends.

Vacation rental managers face the same squeeze. Airbnb and Vrbo (Expedia) are the only vacation rental platforms AI consistently mentions. Property management companies running 50, 100, or 500 rentals — with their own websites, direct booking engines, and lower prices — are nowhere in AI responses.

Winner-take-all dynamics in AI travel recommendations

In Google search, travel companies can still compete. You can buy Google Ads. You can rank for long-tail keywords. A boutique hotel with good SEO can appear on page 1 for “boutique hotel in Santorini with caldera view.” There are 10 organic slots and unlimited ad slots.

In AI chatbot responses, there are typically 3–5 platform recommendations. No ads. No page 2. And the same brands appear in nearly every response:

Channel Visibility Slots Paid Option Independent Travel Co. Chance
Google Search 10 organic + ads + hotel pack Yes (Google Ads, Hotel Ads) Moderate — can rank locally
Google AI Overviews 3–5 sources cited No Low — OTAs and media dominate
ChatGPT 3–5 recommendations No Very low — OTAs + hotel chains
Perplexity 5–8 cited sources No Low — favors high-DA travel sites
OTA platforms Listing within marketplace Yes (sponsored placements, 15–30% commission) High — but you’re on their platform
AI trip planners (Expedia, Tripadvisor) Within platform only Indirect (OTA listing required) Only if listed on that OTA

This creates a dangerous feedback loop. As more travelers shift to AI for trip planning, the travel companies that are invisible in AI lose an increasing share of top-of-funnel discovery. Fewer direct visitors means less web content, fewer reviews, and lower brand authority — which makes them even less visible in future AI training data. Meanwhile, OTAs capture an even larger share of bookings and commissions.

For DMOs (Destination Marketing Organizations), the dynamic is slightly different but equally concerning. When a traveler asks “should I visit Portugal or Greece,” the AI’s response shapes destination choice before any DMO marketing reaches the traveler. Portugal’s tourism board and Greece’s tourism authority are competing for AI mindshare they didn’t know existed. The destinations with more structured, data-rich content across authoritative travel media will win more AI recommendations — and more visitors.

What actually works: the AI visibility playbook for travel

The good news: AI visibility is a solvable problem. And because almost no travel companies are working on it yet — they’re still focused on SEO, OTA optimization, and social media — early movers have a disproportionate advantage.

Here is what moves the needle, based on the AI visibility action plan framework:

1. Audit what AI currently says about you

Before fixing anything, you need to know what’s broken. Query ChatGPT, Perplexity, Gemini, and Claude with prompts your guests and customers would actually use:

  • “Best hotels in [your destination]”
  • “Plan a trip to [your destination] for [traveler type]”
  • “Tell me about [your property/company name]”
  • “Best tours/activities in [your destination]”
  • “Where should I stay in [city] — boutique hotel or Airbnb?”

Document every mention (or absence), every error, and every competitor that appears instead of you. Or run a Metricus AI visibility report that does this across hundreds of query variations automatically. For a quick manual check first, try our free AI visibility audit guide.

2. Publish data-rich, citable content

AI systems cite content that contains structured claims, statistics, and authoritative data. Generic “book now” landing pages are invisible to AI. What works:

  • Destination guides with specific data: Average temperatures by month, travel costs, peak vs. off-season pricing, visa requirements, transportation options with specific times and costs. Include the numbers, not just “visit us in summer.”
  • Property-specific factual content: Room categories with exact sizes, specific amenities with details (not just “spa” but “2,500 sq ft spa with 4 treatment rooms, sauna, and cold plunge”), distance to landmarks in specific numbers.
  • Comparison and ranking content: “Top 10 family-friendly activities in [destination] with pricing” or “Bali vs. Thailand for honeymoons: a 2026 data comparison.” This positions your brand as the authoritative local source.
  • Guest data and statistics: Average guest rating, percentage of return guests, most popular room type, average booking lead time. Specific numbers that AI can extract and quote.

3. Build citations on authoritative third-party sources

AI doesn’t just read your website. It reads everything about you across the web. The sources that carry the most weight for travel:

  • TripAdvisor: Over 1 billion reviews. AI heavily weights TripAdvisor content. Keep your listing complete, respond to reviews, and ensure all information is current.
  • Google Business Profile: Complete, accurate information including photos, hours, amenities, and regular posts.
  • Travel media mentions: Coverage in Condé Nast Traveler, Travel + Leisure, Lonely Planet, Fodor’s. Even a single mention in an authoritative travel publication dramatically increases AI visibility.
  • Booking platform profiles: Ironic but true — your OTA listings contribute to your AI visibility because of their domain authority. Keep descriptions detailed and accurate.
  • Reddit and forums: AI heavily weights community discussions. Genuine mentions in r/travel, r/solotravel, or destination-specific subreddits carry significant weight.
  • Wikipedia: If your property or destination has notable history, a well-sourced Wikipedia article is one of the highest-weight sources for AI training data.

4. Implement travel-specific structured data

Structured data helps AI systems understand exactly what your business offers:

  • Hotel schema with star rating, amenities, price range, and location
  • LodgingBusiness for vacation rentals and B&Bs
  • TouristAttraction for activities and experiences
  • TravelAction schema for tour operators
  • FAQPage schema for common guest questions
  • Review and AggregateRating schema from verified guest reviews

5. Correct errors at their source

If AI is listing wrong amenities, fabricated pricing, or invented itineraries, the error is coming from somewhere. Usually it’s an outdated OTA listing, stale TripAdvisor information, or a travel blog from 2019 describing your property before a renovation. Find the source, fix it, and the AI corrections will follow over time as models retrain on updated data. Our guide on fixing AI hallucinations about your brand covers the process step by step.

Action Effort Timeline Expected Impact
Audit AI responses Low (or use Metricus) Day 1 Baseline established
Fix factual errors at source Medium Week 1–2 Stops active damage
Update all OTA/review profiles Medium Week 1–3 Improves machine-readability
Add structured data (schema) Medium (dev needed) Week 2–4 Improves machine-readability
Publish data-rich destination content High (ongoing) Week 2–8 Highest long-term impact
Earn travel media mentions High (ongoing) Week 4–16 +10–25% AI visibility
Re-audit after 90 days Low Day 90 Measure + iterate

The case for auditing your AI visibility now

The global travel technology market is projected to reach $28.5 billion by 2030, growing at 8.6% CAGR (Allied Market Research). Phocuswright projects that AI-influenced bookings will account for 30%+ of online travel transactions by 2027. Amadeus, the travel technology backbone, reports that AI queries related to travel planning grew 3x year-over-year in 2024.

The travel companies that understand their AI visibility now — while competitors are still focused exclusively on OTA optimization, Google Ads, and Instagram — will have a structural advantage that compounds over time. Every piece of authoritative, data-rich content you publish today enters the training data that shapes AI recommendations tomorrow.

The cost of waiting is measurable. Booking Holdings (Booking.com parent) generated $21.4 billion in revenue in 2023 (Booking Holdings annual report). That revenue comes from commissions on bookings that travelers increasingly discover through AI-powered channels. Every booking that flows through an OTA because AI recommended it instead of your direct website costs you 15–30% in commission.

For a 100-room hotel with an average nightly rate of $200 and 75% occupancy, the math is straightforward: that’s approximately $1.1 million in annual room revenue. If OTA share increases from 70% to 80% due to AI-driven discovery (a conservative estimate), that’s an additional $22,000–$33,000 per year in OTA commissions — on top of the $2.3–$4.6 million already being paid. For a hotel group with 10 properties, that compounds to $220,000–$330,000 in incremental annual cost.

The same economics apply to tour operators. If AI recommends Viator and GetYourGuide instead of your direct website, and those platforms charge 20–30% commission, the margin erosion from AI-driven intermediation is existential for operators running on thin margins.

The bottom line: If you’re a hotel, resort, tour operator, vacation rental manager, travel agency, or DMO that depends on digital discovery — and in 2026, that’s everyone — you need to know what AI is saying about you. Not next quarter. Now.

This article gives you the framework. A Metricus report gives you the specific errors, exact citation sources, and prioritized actions for your travel brand — across every major AI platform. One-time purchase from $99. No subscription required.

Sources: WTTC Economic Impact Report (2024); U.S. Travel Association (2024); Statista Global Online Travel Market (2024); Google/Ipsos Think with Google travel research; Gartner search prediction (Feb 2024); BrightEdge AI Overviews research (2024); BCG generative AI travel survey (2024); Oliver Wyman Forum traveler AI adoption study (2024); Booking.com traveler survey (27,000+ respondents, 2024); Booking Holdings annual report (2023); Airbnb SEC filing (2024); TripAdvisor (2024); Expedia Group earnings call (2024); Phocuswright Global Online Travel Overview (2024); AHLA commission estimates; eMarketer US digital ad spend travel category (2024); McKinsey Global Institute GenAI valuation (2023); Princeton/Georgia Tech GEO study (2023); Allied Market Research travel tech forecast; Amadeus AI travel data (2024); Similarweb traffic estimates (2024). AI mention rates based on Metricus internal testing across ChatGPT, Perplexity, Gemini, Claude, and Grok (2026). Learn more about how we measure AI visibility.

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