How AI Is Changing the Way We Plan Trips in 2026

The version of trip planning we lived with for fifteen years

For most of the 2010s, planning a trip looked the same. You opened Google, ran four or five separate searches across hotel, flight, transit, and activity aggregators, and reconciled the results yourself in a Google Doc or a Notes app. Sometimes you used TripAdvisor for the reviews and Skyscanner for the flights. Sometimes you started in Booking.com and ended in Reddit, scrolling threads from people who had been there in 2019 and might or might not still be right.

The whole thing was a meta-task: assembling a single coherent plan from sources that didn’t talk to each other. The structure of the work was the same whether you were planning a long weekend in Porto or three weeks in Vietnam. The amount of work scaled with how careful you were, not with how complex the trip was.

That’s the baseline. It’s what anyone over 25 has internalized as how this works. It’s also what AI is — finally, actually — starting to change.

What changed in 2025

Three things, in roughly this order.

Models got cheap enough to run with retrieval at every turn. Until late 2024, hooking a language model up to a real-time inventory API was something only a research demo would do. The latency was too high and the costs were too high to put it in front of users. By mid-2025, both numbers dropped by an order of magnitude, and suddenly it was feasible to query Booking.com (or a wholesaler) live inside a conversation without making the user wait twenty seconds for each turn.

Tool-use got good enough to chain. “Find hotels, then check flights to match those dates, then look up tours that fit those nights” — the kind of multi-step planning a human does without thinking — became something a model could actually do reliably, instead of getting lost in the middle and inventing an itinerary that doesn’t exist.

Constraint-holding got better. The third change is the quietest, but the most important. The new generation of models is much better at holding a long list of constraints — your budget, your dates, the fact that one of you has a knee that doesn’t do stairs, the strong preference for shoulder season, the dietary restriction — across many turns of a conversation without dropping any of them. That’s the underlying capability that makes “AI travel agent” a real product rather than a marketing line.

You can feel this in the difference between asking a general-purpose chatbot to plan a trip in 2023 and doing the same thing with a purpose-built tool today. In 2023, the answer looked plausible and was largely fictional. Today the answer is much more grounded and still occasionally wrong about the small things.

Where the new tools still break

Anyone telling you AI has solved trip planning is selling you something. Here’s where these tools — including Mezin — still struggle, in order of how much they should worry you.

Taste is not a tool call

A language model can find you the highest-rated mid-range hotel in Alfama. It cannot reliably tell you which of two highly-rated hotels has the soul you’ll want and which is a slightly nicer Hampton Inn. Taste is the residual that doesn’t reduce to ratings, prices, or neighborhood. For now, it’s still the part you bring.

The long tail is undertrained

Lisbon, Tokyo, Paris — fine. The model has seen ten thousand essays about each. Tirana, San Sebastián, Yamagata — it knows the headline facts and not much else. For shoulder destinations and second cities, AI gives you a competent first draft, not the actual answer. You still need a trusted blog or a local friend for the last 20%.

Real-time edge cases

A flight gets cancelled at 2am the day before you fly. A festival turns the city you planned around into a logistical knot. A typhoon closes the airport. AI travel agents are not, yet, the right tool to manage your trip while it’s happening — they’re the tool to plan it before you go.

Confidence is still the failure mode

Modern models hallucinate less. They have not stopped hallucinating. Any AI travel tool worth using should be constrained to ground its recommendations in verified, queryable inventory — meaning the model can only recommend a hotel if it just confirmed the hotel exists and has rooms for your dates. Without that constraint, you’ll get a beautifully written itinerary for a hotel that closed in 2019. With it, you get something you can actually book.

The honest case for using them

So why bother? Because despite the limits, the new tools are dramatically better at the four parts of trip planning that humans hate most:

1. Holding all your constraints at once. A search box can hold dates and budget. It can’t hold “dates, budget, one of us has a knee thing, we prefer family-run places, no early flights, and we want to spend more on food than on rooms.” A conversation can.
2. Pricing the trade-offs. When you ask “is it worth the extra $30/night for the place near the trams?” a chatbot can answer that contextually. A filter UI can’t.
3. Generating real alternatives, fast. “Swap the third day to something quieter” — and the rest of the plan updates around it. That’s the part that, two years ago, you’d have done with a spreadsheet.
4. Surfacing the parts you didn’t know to ask about. Visa rules, ferry timetables, the obscure local fact that the museum is closed on Tuesdays. A good travel agent volunteers the things a search box doesn’t prompt you for.

The shorter version: AI travel agents are bad at being your guide. They are good at being the friend who knows everyone, who has the spreadsheet open, and who will keep all of your weird requirements in their head while they hand you three options.

That description is doing real work. It maps to the parts of a human travel agent’s job that scale poorly to the web. The booking aggregators of the 2010s never solved this — they were a search engine for inventory, not a partner in the planning. The new tools, when they’re built with care, can be.

What we’d ask of the rest of the booking web

If you’re building one of these tools, three things matter more than anything else, and the industry is not yet uniform on them.

Constrain the model to verified inventory. A travel agent that invents hotels is malpractice. Make hallucinated availability impossible by construction; don’t hope your prompt engineering catches every case.

Don’t optimise the ranking on commission. This is the easy money — surface the option that pays you the most — and it’s the same trap that turned booking aggregators into ad-sales businesses. The whole reason a thoughtful AI travel agent feels different is that the recommendation is supposed to be for the traveller. Make the rule explicit, write it on the site, and hold yourself to it.

Disclose the affiliate relationship in plain English. Not in 9-point gray. Not buried in a footer link. A small banner on every blog post, a paragraph in the chat, a real affiliate-disclosure page that says exactly who pays you and how.

We do all three at Mezin, and we built the product around them — which sounds like a sales pitch, but it’s less of one than you’d think, because none of those three are technically hard. They’re business-model choices most of the booking web is not willing to make.

What this means for you

For the next year or two: use AI to do the parts of trip planning you hate (holding constraints, pricing trade-offs, generating real options) and keep doing the parts you’re good at (taste, knowing what you actually want, talking to a friend who’s been). The new tools are a force multiplier, not a replacement.

And when you do use one, ask three questions. Where does the pricing come from? What stops the model from inventing a hotel? How does the company make money? If a tool can’t answer those clearly, treat it with the same caution you’d give a confident stranger in a hostel bar.