Last updated: 27 October 2026

OpenAI released ChatGPT in late November 2022. Just a few months later, in January 2023, it had an estimated 100 million monthly active users, making it the fastest-growing consumer app at the time.

Other AI tools quickly followed, and search engines moved just as fast. Microsoft added AI chat to Bing in February 2023 (later rebranded as Copilot), and Google opened Search Generative Experience (SGE) to testers in Search Labs in May 2023.

Travel brands need to understand whether people will start (and continue) asking an AI assistant for a two-day city plan instead of clicking through to a destination guide, an online travel agency (OTA), or a tour operator’s site.

Alphabet and Meta are both trying to speed up adoption by putting AI in front of the billions of people who already use their products, from Google Search to WhatsApp. We look at both below.

Trust is one of the key friction points in the AI adoption curve.

For AI adoption at scale, users need to overcome preconceived biases and general perceptions of AI, including how safe it is to use and how reliable and trustworthy the information it provides is. There’s still a long way to go, though, as Ofcom found in June 2023 that only 31% of UK adult internet users had used generative AI, and almost a quarter of those who hadn’t didn’t know what it was.

At the end of March, Google’s blog (The Keyword) focused on ways Google can support users in finding their 2024 travel plans. The first method they outlined is to use Search Generative Experience (SGE).

Google has a history of targeting the travel sector with different SERP features and products, such as the Flights and Hotels features. And so, one of my longstanding beliefs is that any “free to use” public AI tools released by Google will likely be rolled out to and target the travel sector first.

This targeting hasn’t gone unnoticed, especially since the Digital Markets Act (DMA) came into force. Article 6(5) of the DMA bars companies such as Google from ranking their own products and services more favourably than similar third-party services.

That matters because AI gives Google a way to keep users in its ecosystem, presenting its own answers and paid placements alongside Flights, Hotels, and Maps, as regulators tighten rules on how those products rank. Whether AI Overviews (AIO), which Google announced on 14 May 2024, and AI Mode become a route around the DMA in travel search results remains an open question, and one we’ll keep testing.

On 23 July 2026, the European Commission fined Google €460 million for favouring its own shopping, hotel, transport, and sports results in Google Search, and said talks would continue on how the decision applies to AI Overviews and AI Mode.

Key findings at a glance

  • Meta AI suggested the most itinerary stops: 742 across 50 cities (about 15 per city).
  • Google’s SGE suggested the fewest itinerary stops: 344 (about seven per city).
  • Hallucinations were rare. Meta AI was the worst offender, with 14 of its 742 suggestions (1.9%) pointing to places that don’t exist.
  • Copilot, Meta AI, and ChatGPT-4 all suggested the most stops for European cities, averaging 13, 20, and 13 per city. SGE was the exception, suggesting slightly more for North America and Asia/Oceania.

How disruptive could AI be to itinerary research queries?

All the travel companies I’ve worked with rely on organic traffic at every funnel stage, and AI could affect the entire user journey, from destination discovery to itinerary planning. Because of this, travel companies stand to lose both direct bookings and sales, on top of missing brand touchpoints during the discovery-focused phases.

To analyse the potential impact, we’ve taken a sample of 50 destinations across Europe, North America, the Middle East, Africa, Asia, and Oceania and asked four generative AI tools to plan two-day itineraries for the destinations.

The four AI tools we’ve used are:

  • ChatGPT (GPT4, plus browsing)
  • Search Generative Experience (Labs)
  • Copilot
  • Meta AI

Testing Google’s tool alongside OpenAI’s, Microsoft’s, and Meta’s shows how much a traveller’s plan can change depending on which assistant they ask.

Methodology

We asked the four LLMs to outline two-day itineraries for 50 cities across Europe, North America, Africa, the Middle East, and Oceania/Asia.

We chose the cities based on their reported popularity for 2024, using articles from The Times, TripAdvisor, and USNews to compile the list of 50.

We ran the prompts in all four LLMs on 9 May 2024.

Our findings

From the data we analysed across the four LLMs, we found that:

  • SGE recommended the fewest itinerary items across the 50 cities, with 344, and was the only one not to make it into the double digits for each region.
  • Meta AI recommended the most itinerary items across the 50 cities, with 742.
  • Meta AI was the worst offender regarding hallucinations, by recommending 14 of 742 places that didn’t actually exist (1.9%).
  • Three LLMs produced more itinerary items for Europe than any other region, except for SGE, which returned five more items for North America than Europe.
  • All four LLMs produced fewer itinerary items for Africa than any other region, with the Middle East consistently producing the second fewest.

From a personal perspective, SGE’s restrictive results were surprising given how much Google has invested in its AI technologies, the volume of data Alphabet has access to, and the perceptions of each LLM’s capabilities.

Average Places Mentioned SGE      Copilot Meta AI ChatGPT-4
Europe 7 13 20 13
North America 8 10 19 12
Middle East 7 7 11 10
Asia/Oceania 8 7 11 12
Africa 7 7 13 11

Hallucinated (fake) places

In our test, AI recall was generally hallucination-free.

When using the previous model of ChatGPT, ChatGPT-3, in initial testing, the results were largely inconsistent. The LLM struggled quite a bit more, and results varied greatly – from highly accurate to highly hallucinated.

Focusing purely on ChatGPT-3 as a testing source would be unreliable without repeating the same study enough times to gain statistical significance. Furthermore, this wouldn’t reflect real-world user behaviour, and the resulting data would be misleading, with hallucination percentages ranging from 5% to 95%.

“Bad” or inappropriate places

In addition to looking for hallucinations, we reviewed the itineraries for questionable places that didn’t “fit the bill”.

For example, ChatGPT-4 recommended a children’s playground in Berlin for an afternoon stop-off, which, to an AI, seems like a safe choice. But upon reviewing the playground ourselves, it looked fairly run-down, with rampant graffiti. Not really the type of place you’d usually add to your itinerary as a traveller on a weekend city break.

That said, the percentage of bad or inappropriate places was relatively stable across all four LLMs. While Meta AI returned the greatest number of inappropriate places, it also returned the most places overall, keeping its percentage in line with the other LLMs.

How does generative AI affect travel SEO and online discovery?

When we ran this test, SGE was still an opt-in Search Labs experiment, so we couldn’t yet measure its effect on clicks. Google then started rolling out AI Overviews to all US users in May 2024.

Traffic data now shows generative AI’s impact on travel discovery journeys. In February 2025, Adobe Analytics found visits to US travel, leisure, and hospitality sites from generative AI sources were up 1,700% compared to July 2024, a faster rise than retail or banking (which increased by 1,200%).

In our test, a single prompt was enough for every tool to return a complete two-day travel plan including destinations, accommodation, attractions, and an itinerary. Meta AI averaged around 15 stops per city. Previously, travellers might have pieced that together from several destination guides, review sites, and booking pages.

Each of those assets gives a travel brand a chance to earn a visit, a booking, or at least a brand touchpoint. Now, an AI answer can skip them all. Travel brands mentioned or cited in an AI answer benefit, which is where your strategy comes in.

Search Generative Experience (now AI Overviews)

As I mentioned above, Google has usually first targeted the travel sector with its innovations. Its March 2024 push of the SGE beta for trip planning came two months before it rolled out AI Overviews, SGE’s successor, to everyone in the US.

While SGE didn’t return as many itinerary items as the other LLMs in our test, its integration with Google’s other data sources (Flights, Hotels, and Maps) means it will disrupt customers’ current journeys.

If you’re interested in learning more about SGE and the impact it will have, we’ve covered Google’s AI search products on our YouTube channel:

The Meta AI threat to travel search

In our test, Meta returned more than double the number of itinerary items (742) than Google’s Search Generative Experience (344). However, it wasn’t 100% accurate and had a 1.9% hallucination rate. (Which, to be fair, is still much lower than early ChatGPT-3 tests).

Like Google (and its parent company, Alphabet), Meta products already have considerable market penetration. According to Statista’s February 2025 ranking of social networks by monthly active users (MAU), Facebook has around 3.07 billion, Instagram and WhatsApp have two billion each, and Facebook Messenger has just under 950 million.

At that scale, any Meta AI feature built into these apps could quickly change how people discover travel, and it’s already started. In April 2024, Meta AI was added to the search bars of Facebook, Instagram, WhatsApp, and Messenger. From then on, someone can ask for a weekend itinerary without leaving the app.

That could speed up AI adoption across both business-to-business (B2B) and business-to-consumer (B2C) industries. WhatsApp and Messenger products are already used for B2C communications, as well as payments and money transfers.

Further studies (including one from the Pew Research Center in 2019) highlight how millennials have historically led the technology adoption curve. Now, Gen Z is leading AI adoption. Ofcom’s Online Nation 2023 report found that 79% of online 13 to 17-year-olds and 40% of 7 to 12-year-olds use generative AI tools. In a separate survey of internet users aged 16 and older, the figure was just 31%.

Because Meta’s core products are used by people aged 18 to 35, improving AI adoption makes a lot of sense for the tech giant. Our piece on how Meta and TikTok are changing travel planning and discovery covers this adoption and shift in more detail.

Preparing your travel SEO strategy

Travel brands that don’t prepare and adapt their strategies to the impending AI disruption from various LLMs will see their traffic and overall brand visibility suffer. For brands that rely on organic search for direct bookings, lost visibility quickly turns into lost revenue.

You can’t treat LLMs like a single ranking factor, but you can improve how your travel brand appears in ChatGPT, Gemini, Copilot, and Google’s AI experiences. Start by tightening your technical foundations, clarifying your brand entity, and publishing content AI models can easily understand and surface.

Then cover any remaining gaps with broader discovery touchpoints. This approach to AI search optimisation ensures your brand is best placed to show up and be cited. We’ve set out practical steps in our guide to optimising travel brands for LLM and AI platform visibility.

A practical first step is to measure your travel brand’s exposure. List the destination and itinerary queries that drive your organic bookings, run them through ChatGPT, Gemini, Copilot, and Google’s AI Overviews, and record whether your brand is mentioned or cited. If you don’t appear, it’s an opportunity to target and grow relevant traffic and reach your audience through new queries and touchpoints.

If you found this article useful, you should also read our thoughts on how the travel sector can better use AI for data and SEO. Alternatively, if you want to develop your AI travel SEO strategy, please get in touch.