Improving AI visibility is not "post more on Instagram" or "buy more keywords." It's a specific job: make your hotel the most verifiable, best-matched answer to the questions travelers ask an AI. Models recommend what they can confirm and what cleanly fits the question. So the work splits into two halves — being findable for the right questions, and being trustworthy enough to be chosen. Here are the levers, in the order worth pulling them.

Lever 1: measure where you're invisible

Optimizing blind is just guessing. Before anything else, find the gaps: the Blind Spot breakdown shows where you're missing by theme and by engine, and the per-persona scores show which travelers don't see you. "Recommended to couples, invisible to families" isn't a vague worry — it's a specific, fixable target. Everything below should be aimed at a gap you've actually measured. If you haven't yet, a free scan is the place to start, and the methodology explains what each number means.

Lever 2: cover the questions that matter

You can only score on questions the model is actually asked. Travelers don't search by your brand — they search by intent: a neighborhood, a price band, an occasion, and above all a landmark. "Hotel near Central Park," "near the convention center," "walking distance to the beach," "close to the airport for an early flight." If you're near something travelers anchor on, make sure you're being tested on it.

Two practical moves: confirm the nearby attractions, airports, and events worth anchoring to, and confirm the things you're genuinely known for — a rooftop bar, a notable restaurant, EV charging, a spa. Those become the questions the model gets asked about you. (Remember: edits apply on the next daily scan, so changes show up the following day, not instantly.)

Lever 3: become the verifiable source

This is the foundation under everything else. When a model can confirm your facts from an authoritative source, it recommends you with confidence. When it can't, it does one of two things — omits you, or assembles a version of you from OTA listings and stale review snippets. Either way, you lose control of the answer.

  • Build a single source of truth. Room types, real availability, rates, amenities, policies — consolidated in one authoritative place, not scattered across a website, a PDF, and three OTA extranets that disagree.
  • Make the facts machine-readable. Accuracy a model can't parse doesn't count. Structured data, clean FAQ and policy content, an llms.txt that states the facts plainly — exposed in a form a model can extract and cite.
  • Keep it consistent across the web. Conflicting facts across sources make the model hedge or defer. Consistency builds the confidence that earns the recommendation.

This is the discipline of GEO and AEO together — getting recommended, and being right when you are. We separate the two in GEO vs AEO, and the cost of skipping the accuracy half is laid out in the case for AEO. If you'd rather not run it in-house, done-for-you GEO & AEO is exactly this work, continuous and managed.

Lever 4: win the contested questions

Share of Voice shows who the AI names instead of you on the same questions. That's a gift: it tells you precisely who you're losing to and on which intents. Study the competitor the model keeps preferring, find the differentiator they're surfacing that you aren't, and close it — in your facts, your content, and the draws you're tested on. You don't have to win every question. You have to win the ones your guests ask.

Lever 5: fix the booking path

Being recommended and then handed to an OTA is half a win — you got the visibility and gave away the margin. Booking Path Integrity measures how often the AI points the guest to you versus a third party. Improve it by making direct booking the clear, listed path in your facts, and ultimately by being directly bookable inside the conversation itself — the subject of AI Direct Bookings. Visibility creates the demand; the booking path decides who captures it.

Lever 6: correct what's inaccurate

If the model repeats a stale rate, a closed restaurant, or a sore point from old reviews, that's not noise — it's a fixable defect. Address it at the source so the next answer is accurate. A confident recommendation on wrong facts isn't a win; it's a complaint forming at the front desk.

"The hotels that win in AI aren't the loudest in the answer. They're the ones whose answer is true, complete, and points the guest home." — Sigtrip Strategic Analysis, 2026

Why the order — and the urgency — matter

These levers compound, and the surface they act on is hardening. Once a model has a confident answer for "boutique hotel in your city," that answer is reinforced every time it's given — and dislodging an established default is harder than becoming one (the dynamic behind the early-adopter advantage). The facts you make verifiable now are the basis of every recommendation the model makes later.

Sigtrip measures all of this on every plan, and on Enterprise we run the optimization for you — continuous GEO and AEO, no extra headcount. But the first step costs nothing: scan your property, find your blind spots, and pull the first lever.

You can't move a number you don't watch. Measure the gaps, feed the model the truth, and make yourself the answer it trusts.