Travel is still driven by human emotion, but the decision pathway is becoming algorithmic. More journeys now begin with a prompt rather than a search query, a brochure, or a recommendation from a friend. When an AI system produces a destination shortlist, an itinerary, and a rationale in seconds, it does more than speed up planning. It reshapes what even enters the frame.
The scale of outbound travel makes this shift meaningful. UK residents made 94.6 million trips abroad in 2024 and spent £78.6 billion doing so. The appetite to travel is well established. What is changing is how quickly people move from “maybe” to “booked”, and who is doing the filtering along the way.
From experimentation to delegation
Consumer trust in AI travel tools is no longer confined to early experimentation. Industry tracking shows the use of AI for holiday inspiration has risen sharply, and confidence levels are moving with it. A meaningful share of travellers now say they would trust an AI to plan a holiday, and a growing number are comfortable letting it handle the booking as well.
The same pattern appears across consumer research more broadly. A significant minority of travellers are already using AI to plan trips, and satisfaction among those users is high. The story here is not novelty. It is a growing comfort with delegating parts of the planning process to systems that feel fast, consistent, and dependable.
Discovery is now conversational, and already embedded
This shift is accelerating because AI is being embedded into tools people already rely on. Search remains the dominant gateway to travel research for UK consumers, used by the vast majority of adults and generating billions of queries each month. Increasingly, those searches return AI-generated summaries rather than traditional lists of links, and many users now encounter these summaries as a routine part of browsing.
That matters because it changes what “research” looks like. Instead of comparing multiple sources, travellers are presented with a single synthesised answer that blends recommendations with reasoning. Travel brands are responding accordingly, investing in how they surface within AI summaries and chatbot recommendations, and treating this as a new discovery layer rather than a passing feature.
Why prompts change what gets recommended
Prompts are not neutral. A broad request such as “best places to visit” tends to reproduce familiar, over-indexed destinations. A more specific prompt, for example “quiet two-week trip in May, good food, walkable cities, under £X”, forces a system to prioritise different attributes. In effect, the traveller supplies a product brief.
Prompt quality often predicts output quality because it encodes trade-offs. Signal price sensitivity and the system leans towards value and shoulder-season logic. Signal crowd avoidance and it shifts towards second cities and alternatives. The system is not guaranteeing outcomes, but it is responding to intent patterns at scale.
The five countries that keep surfacing for 2026
AI recommendations are not driven by a single dataset. They reflect blended signals from behavioural patterns, trend reporting, and the content models have absorbed over time. When multiple forward-looking outputs for 2026 are compared, a consistent shortlist of countries appears, often paired with the same types of destinations.
Italy remains a constant, increasingly framed through alternatives that relieve pressure on classic hotspots. Secondary cities and southern regions surface more often than Rome or Florence.
Japan continues to appear for high-intent prompts that bundle culture, food, safety, and transport quality, with regional destinations recommended alongside traditional gateways.
Spain remains resilient because it fits common constraints: short flight times, flexible trip lengths, and a wide value range. Northern cities and regional hubs feature prominently.
Vietnam appears repeatedly when prompts combine value with variety, particularly food, culture, and coastline within a single itinerary.
Morocco rises in recommendations when prompts emphasise proximity, winter sun, and cultural contrast, often favouring lesser-known cities over the most established tourist centres.
The pattern is not about fashion. It is about fit.
Five prompt patterns that produce better answers
Most dissatisfaction with AI trip planning stems from prompts that are too broad. The most useful outputs come from prompts that specify constraints, trade-offs, and verification steps.
Budget-first shortlist
Suggest eight destinations from UK airports for seven to ten days in month X with a total budget of £X per person. Rank by value and include flight-time ranges, what makes each good value, and two quieter alternatives.
Shoulder-season optimisation
For destination X, propose three date windows in 2026 that balance weather and price. Explain trade-offs around crowds, events, and climate.
Crowd-avoidance matching
I like place X for reasons Y. Give me six alternatives with a similar feel but fewer crowds, explaining the match in two sentences each.
Routing strategies
I am based in city X. Suggest routing ideas that may reduce cost, including nearby departure airports, open-jaw trips, and one stopover option. Explain why each might be cheaper.
Built-in verification
Draft an itinerary for dates X to Y, label each item “verify required”, and list what must be checked on official sources such as visas, entry rules, operator schedules, and attraction hours.
AI systems are strong at synthesis. They compare options, draft itineraries, and explain trade-offs clearly. They also help travellers clarify priorities, which is often the real bottleneck in planning.
What they cannot reliably guarantee is real-time pricing, entry requirements, disruption, or opening hours. Those details still require verification through official sources.
The safest operating model is decision support, not unquestioned automation. Use AI to narrow choices and generate scenarios. Verify critical details before committing.
The strategic takeaway for 2026
The most important shift is not that AI builds itineraries faster. It is that prompts shape discovery, and discovery shapes demand. To understand where travellers may go next, pay attention to how they ask.
Prompt literacy is becoming a consumer advantage. It produces better recommendations and more predictable outcomes. For the travel ecosystem, it also represents a new distribution layer, one that will reward clarity, structured information, and verifiable data.
