Artificial intelligence is increasingly reshaping how people plan and experience travel. In 2026, the Redion Holiday Barometer confirms that more travelers are using AI tools to organise and book their trips. Yet this acceleration is far from uniform. Instead, AI adoption is unfolding at very different speeds across regions, age groups and income levels, pointing to a fragmented future for travel experiences.
What emerges is a two-speed AI landscape. In India, the Middle East and North Asia, AI is rapidly becoming part of the mainstream travel journey. By contrast, Europe, North America and Oceania remain markedly more cautious, with adoption rising only gradually and clear reservations persisting.
Since 2025, the use of AI tools for planning and booking holidays has increased in every region. Growth is strongest where adoption was already higher, reinforcing the lead taken by India, the Middle East and North Asia.
In several countries, AI has already reached majority usage for travel planning or booking, including India (69%), the UAE (60%), Malaysia (54%) and Saudi Arabia (54%). Japan stands out as a notable exception in North Asia, with adoption remaining lower at 21%, illustrating that cultural factors play a role even within high-adoption regions.
Elsewhere, adoption remains significantly lower. Europe, North America and Oceania were already lagging and have not closed the gap. Usage is lowest in France, the UK and Canada, all at 19%, confirming a persistent reluctance in these markets.
Past use of artificial intelligence for holiday planning or booking (%)
| Region | Breakdown | Yes |
|---|---|---|
| EU | 24 | |
| West EU | 23 | |
| South EU | 26 | |
| East EU | 23 | |
| North Asia | 40 | |
| India | 69 | |
| Oceania | 23 | |
| Middle East | 57 | |
| North America | 24 | |
| South America | 28 |
Clear demographic divides accompany these regional differences. Across all regions, under-35s are consistently more likely to use AI for travel than older travelers. In Europe, usage among under-35s is more than twice that of over-55s (42% vs 11%), with similarly wide gaps in North America (40% vs 10%) and Oceania (39% vs 11%). Income also plays a role: with the exception of North America, AI usage is higher among high-income travelers, particularly in high-adoption regions such as the Middle East.
While awareness of AI is growing, several factors continue to slow adoption. The desire for human interaction remains the single most important consideration in most regions, reflecting a preference for reassurance and personal control in travel decision-making.
Considerations about accuracy and reliability represent a more structural factor. These are particularly prominent in Oceania and North America, where limited trust in AI-generated information is one of the main reasons for non-use. Countries such as New Zealand, Australia, the US, Poland, the UK and Canada stand out in this regard.
Age again plays a role. In Europe and North Asia, younger travelers who have never used AI for planning their trips are more likely than older ones to express trust-related considerations, reflecting higher exposure to AI coupled with more critical evaluation. This underscores that familiarity does not automatically translate into confidence.
Among those who do use AI, adoption is not limited to a single step of the journey. On the contrary, users tend to deploy AI across multiple stages of travel planning, suggesting that once trust is established, AI becomes a versatile tool.
In Europe, North America, Oceania and South America, the most common uses are itinerary planning, destination choice and identifying activities and attractions, although a substantial share also use AI for accommodation and transport planning. Usage varies widely by country: within Europe, accommodation planning via AI ranges from 60% in Switzerland to over 80% in Austria, Italy and the UK.
In high-adoption regions, usage is broader and deeper. In India and the Middle East, destination choice is the most common application, while in North Asia, itinerary planning dominates.
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Trip planning, AI tools uses, yes at least once (%)
| Use | EU | West EU | South EU | East EU | North Asia | India | Oceania | Middle East | North America | South America |
|---|---|---|---|---|---|---|---|---|---|---|
| Planning your itineraryFinding suggestions for routes, places to visit or things to do | 89 | 88 | 91 | 91 | 95 | 93 | 91 | 91 | 91 | 92 |
| Activities and attractionsDiscovering experiences and attractions at your destination | 88 | 88 | 89 | 86 | 91 | 95 | 90 | 92 | 92 | 90 |
| Choosing a destinationGetting ideas or information on security, weather, vaccinations, currency exchange, or recommendations for where to go | 87 | 86 | 89 | 85 | 90 | 98 | 84 | 96 | 87 | 91 |
| AccommodationSearching for options, reviews or opinions on where to stay | 74 | 71 | 79 | 76 | 81 | 92 | 73 | 91 | 78 | 86 |
| TransportationComparing flights, car rentals or other transport options for your trip | 73 | 71 | 77 | 74 | 84 | 91 | 75 | 92 | 79 | 82 |
AI use continues once travel has begun. During trips, AI is most often used to discover activities and update itineraries, with regional differences reflecting travel patterns and expectations. However, assistance-related uses remain comparatively low in Europe, North America and Oceania, indicating a continued reliance on human support at critical moments.
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On-trip management, AI tools uses, yes at least once (%)
| Use | EU | West EU | South EU | East EU | North Asia | India | Oceania | Middle East | North America | South America |
|---|---|---|---|---|---|---|---|---|---|---|
| Activities and attractionsDiscovering things to do on-site | 84 | 82 | 85 | 86 | 87 | 89 | 84 | 88 | 86 | 86 |
| Updating your itineraryAdjusting plans or finding new places to visit | 79 | 78 | 83 | 77 | 87 | 95 | 73 | 92 | 79 | 85 |
| TransportationFinding local transport options or comparing them | 73 | 68 | 79 | 77 | 83 | 88 | 72 | 89 | 75 | 82 |
| CommunicationTranslating information or helping communicate in another language | 73 | 71 | 76 | 77 | 78 | 92 | 63 | 91 | 69 | 83 |
| AccommodationLooking up reviews or alternative lodging | 68 | 66 | 71 | 69 | 78 | 90 | 69 | 86 | 76 | 81 |
| AssistanceGetting help or guidance in case of issues during the trip | 66 | 64 | 72 | 65 | 76 | 92 | 63 | 93 | 70 | 83 |
As AI becomes more embedded, some travelers are open to relying on it more extensively, but full delegation remains limited. A meaningful share of travelers in India (29%) and the Middle East (25%) would be comfortable relying entirely on AI to book trips. Elsewhere, enthusiasm remains very low, particularly in Europe, North America and Oceania, where fewer than one in ten express comfort with full delegation.
However, conditional adoption is more widespread. Many travelers say they would consider delegating planning or booking tasks provided they retain the ability to review and approve decisions. Younger and higher-income travelers are consistently more open to this model, especially in slower-adopting regions.
These patterns point to a multi-lane adoption model, where different segments embrace AI to different degrees, even within the same market.
Consideration of relying exclusively on AI tools to plan a trip (%)
| Region | Yes, definitely or probably | |
|---|---|---|
| EU | 30 | |
| West EU | 26 | |
| South EU | 39 | |
| East EU | 25 | |
| North Asia | 51 | |
| India | 79 | |
| Oceania | 27 | |
| Middle East | 70 | |
| North America | 31 | |
| South America | 46 |
The next phase of AI in travel involves so-called agentic use cases, where AI autonomously selects destinations, books travel and organises experiences. Yet the Barometer suggests that widespread adoption of such models remains constrained.
Across regions, the main considerations are remarkably consistent: a desire for personal agency, enjoyment of planning and limited trust in fully automated decision-making. These factors are particularly strong in slower-adopting regions, where travelers remain reluctant to hand over control of high-stakes travel choices.
AI is set to transform travel, but not along a single, global trajectory. Instead, the 2026 Holiday Barometer points to a future defined by divergence rather than convergence. In some regions, AI will increasingly represent autonomy, speed and efficiency. In others, its role will expand more slowly, shaped by the enduring importance of human reassurance and trust.
As a result, the travel landscape of tomorrow is likely to be characterised by coexisting models, digital-first in some markets, hybrid or human-centered in others, reshaping not only how travel is planned, but what travelers expect from the experience itself.
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