Can Travellers Trust an AI Recommendation?

“This hotel is a good choice for your weekend” sounds like one recommendation. It can conceal several separate claims: that the property exists, suits the traveller's needs, has suitable rooms, is available on the requested dates and can be booked at the displayed price.

An AI assistant may have a reasonable basis for some of those claims and no current evidence for others. The interface needs to make the difference apparent before the traveller relies on it.

For a travel operator, this creates two connected questions. How should the business govern an assistant it provides? And how should it maintain the information that external assistants may use to describe its offers?

Both begin with the status of the information. An idea for a destination can remain useful without live inventory. A room price or departure availability needs a relevant, current source. A confirmed booking needs evidence from the systems that create the commitment.

The OECD's 2024 report on AI and tourism identifies recommendation accuracy, bias and accountability as sector concerns. Those concerns should influence the design of the service, including the language used around an answer.

The practical task is to decide which claims the assistant is allowed to make, what evidence supports them and what it should do when that evidence is missing.

A suggestion for a scenic stop and an assurance about entry requirements should not pass through the same review process. One may waste an afternoon. The other may disrupt a trip or expose a traveller to a serious problem.

A small business can begin by classifying the information its assistant handles.

This is a proposed design aid, not a universal risk classification. The consequences depend on the trip, the traveller and the service the business provides.

Avoid treating a citation as proof by itself. A linked page may be outdated, concern a different location or fail to support the precise answer. Evaluation should check whether the source actually establishes the claim, not merely whether the answer contains a link.

Where current verification is unavailable, the assistant needs a useful response. It can explain the limit, preserve the traveller's request and offer an appropriate next step. Filling the gap with a plausible answer transfers the uncertainty to someone who may not recognise it.

Recommendations can be constrained by commercial reality. A small agency may sell from a selected set of suppliers. A hotel may recommend activities it can arrange. A booking service may have different commercial relationships with different providers.

The assistant should not imply that it has searched the entire market when it has considered only a limited catalogue. Nor should it describe a promoted placement as an impartial judgement of suitability.

Useful explanations can be brief. A traveller might be told that the suggestions come from the operator's available partners, that a particular option matches the dates and stated accessibility needs, or that the system cannot verify a feature and further confirmation is required. The wording should match what the system actually did.

This does not require exposing proprietary ranking code. It requires presenting the scope and limitations that matter to the customer's decision.

The same care applies to inferred preferences. An assistant that assumes a traveller's budget, mobility or preferred pace may narrow the options in a way the traveller did not request. Offer a practical way to correct the assumptions. A recommendation should remain open to revision when the user explains what matters.

That is especially relevant to multilingual and cross-border travel. A fluent answer does not establish that local names, service details or the traveller's requirements have been interpreted correctly. Evaluate the languages and journeys the business actually intends to support.

The riskiest moment may be the transition from discussing a trip to acting on it. If an assistant can place a booking, request a change or start a cancellation, it needs defined authority and a clear record of what the traveller authorised.

The user should understand the action, the relevant terms and whether the result is final or pending. Confirmation language must follow the actual transaction status. It should not be generated simply because the conversation reached a reassuring conclusion.

Booking.com's reservation API overview provides a concrete example of structured reservation messages and acknowledgements. It illustrates why conversation and reservation processing are separate responsibilities. Other suppliers have different interfaces and rules.

Human help also needs a specific boundary. “Contact support” is incomplete if the support team cannot see the recommendation, its source or the attempted action. Preserve the information required to investigate, subject to an appropriate data policy.

For a modest initial service, a narrow assistant that helps travellers compare a maintained set of offers may be easier to govern than one that can amend trips. Expanding its authority should depend on evidence that the business can evaluate errors and recover from them.

NIST's AI Risk Management Framework core supports defining context, oversight and ongoing evaluation. It is a voluntary risk framework, not a statement that a particular travel product is legally compliant.

A travel business cannot control every external assistant that discusses it. It can improve the information it publishes and the route through which a traveller verifies an offer.

Start with durable facts: the correct business and property names, location, contact route, service descriptions and clearly identified terms. Keep time-sensitive offers distinguishable from general destination content. When an offer expires or changes, update the underlying page rather than leaving contradictory versions easy to find.

Where the business supplies inventory through a partner or feed, establish who maintains it and how errors are corrected. Information quality is an operating responsibility even when discovery happens elsewhere.

These measures do not guarantee that an AI system will recommend, cite or accurately describe the business. They make the operator's own account clearer and give travellers and intermediaries a more useful verification point.

The distinction between discovery and verification should also be visible when the traveller arrives. A visitor following an AI-generated suggestion may hold an incorrect assumption about an inclusion, date or price. The relevant landing page should make the actual offer easy to check without requiring the visitor to reconstruct it from several unrelated pages.

Evaluate incomplete and conflicting information alongside ordinary requests. Try unavailable dates, an outdated offer, ambiguous place names and a requested feature the supplier has not confirmed. For an assistant allowed to act, test a delayed response and an unsuccessful transaction in a safe environment.

Record whether the assistant acknowledges uncertainty, seeks the right clarification and preserves the difference between a suggestion and a commitment. Include a route for customers and staff to report misleading answers after release.

The project brief for AI and automation should therefore describe the allowed decisions, authoritative sources, evaluation cases and escalation responsibilities. The model is one part of that service.

A useful travel assistant should help someone make a better-informed choice. The operator remains responsible for defining what its own assistant may say and do, and for making correction possible when the service gets something wrong.