Is AI visibility software worth it for an independent hotel?

For an independent hotel that still takes a large share of bookings through Booking.com or Expedia, usually yes, at the entry price. The bar is low: on the assumptions our ROI model publishes, about two bookings a month that move from an OTA to your own website cover a $79 plan.

The software does not win those bookings for you. It shows which guest questions AI assistants answer without your hotel, which sites they rely on instead, and what to fix first. The work after that is yours, or your agency's, and if nobody has time for it the subscription is a monthly report you will stop reading. And a hotel that rarely uses OTAs, or sells out every season without them, can skip it.

Partly. The fixes overlap a lot. A website that search engines can crawl and read, accurate listings, good guest reviews and structured data (code on your pages that states facts like your address and check-in time in a format machines read directly) help with Google rankings and with AI answers alike. If an agency already looks after your SEO, some of the work AI visibility asks for is work you are already paying for.

The measurement is where they part. An SEO platform tracks where your pages rank in a list of links. A large language model (the technology behind ChatGPT, Gemini and Claude) does not return a list. It writes a short answer that names two or three hotels, or none, and a traditional rank tracker cannot see that answer at all. AI visibility software asks real guest questions to each assistant and records whether your hotel is named, where in the answer, and which sources were cited. People call the practice of improving that GEO, generative engine optimization; the software is the measuring side of it.

So the question "do AI visibility tools work better than traditional SEO platforms" has a plain answer: they measure a different thing. Neither replaces the other. Our comparison of AI visibility and traditional SEO goes through the overlap in more detail.

We publish full scan results for four hotels, and they say more about whether to pay than any general argument.

Golden Well Hotel, a boutique hotel in Prague, was named in 21 of 295 answers to questions that did not mention it by name, a 7.1% detection rate. Its technical readiness score was 77.1%, the highest of the four. The split by engine was uneven: Gemini named it 9 times in 60 answers, Perplexity 7, ChatGPT 5, Claude 0, and Google AI Overviews 0 in 55. It was named for occasion questions such as "best hotel in Prague for a honeymoon" and missing from broad ones such as "good hotels to stay at in Prague".

Mosaic House Design Hotel, also in Prague, scored lower on readiness at 60.9% and higher on visibility, named in 25 of 200 answers (12.5%). Gemini named it 11 times in 40, ChatGPT 8, Perplexity 5, Claude once and AI Overviews never. It came up for identity questions like "unique hotels to stay in Prague" and not for location questions like "hotels in Prague city centre".

Olympia Hotel, a spa hotel in Mariánské Lázně, was named in none of 299 checks across five engines. Bouda Mama, a family mountain hotel in Pec pod Sněžkou on the Radar plan, was named in 12.5% of 24 checks across two engines, and was the only one of the four that Google AI Overviews mentioned at all, once in 12 answers.

Three lessons carry over to most hotels. A good technical score did not predict visibility, so a website audit alone will not tell you where you stand. Results varied so much by engine that checking ChatGPT by hand would have given Golden Well a misleading picture in either direction. And when the hotels were left out, the assistants cited Booking.com and TripAdvisor most, with Expedia, thehotelguru.com and a French travel blog also appearing. Those are the sites a guest is sent to instead, and a rank tracker shows none of it.

Staylight's entry plan, Radar, costs $79 a month. It asks 15 guest questions once a month on ChatGPT, Google AI Overviews and Google AI Mode. To judge whether that is worth it, you need two numbers: what a direct booking is worth to you compared with the same booking through an OTA, and how many bookings the work has to move.

Our revenue calculator rests on four published assumptions, and they are assumptions rather than measurements. It assumes 38% of travellers use an AI assistant somewhere in their booking journey, and that a hotel the assistant leaves out loses 35% of that AI-influenced demand to competitors. It puts OTA commission at 18% unless you enter your own rate, and it charges 5% for the payment and booking-engine fees you pay when a guest books on your own website.

Take the modest case first: a guest who would have booked you anyway, but on Booking.com, books direct instead. On those assumptions you keep the 18% commission and pay 5% in fees, so you are 13% of the booking value better off. On a $300 stay that is $39. Two of those in a month come to $78, within a dollar of the plan's price.

Then the stronger case: a guest who would have stayed somewhere else and now books with you. After the 5% fee, a $300 booking brings in $285, so one of those every three months covers three months of Radar ($237). That is revenue, not profit, because the room still has to be cleaned and breakfast still has to be served, but the break-even point is still a handful of bookings a year.

The calculator's figures for your hotel will be much bigger than this, because it applies the 38% and 35% to every booking you take. For a hotel taking 200 bookings a month at $300, its lost direct revenue line comes to about $8,000 a month (200 × 38% × 35% × $300 = $7,980). The headline total is higher again, because it adds the OTA commission on AI-influenced bookings. Read these numbers as the size of the question, not a forecast. Nobody can promise to win it back, and nobody can guarantee an AI recommendation. The break-even test above is the one to hold any tool to, and you can run your own numbers in the OTA commission calculator.

A hotel with a very low OTA share may not need it. If nearly all your guests already book direct, through repeat business, a strong local reputation or corporate and group contracts, the saving from moving a few more bookings off Booking.com is small, and the 13% margin above applies to bookings you are not losing.

A fully booked season may not need it either. If you sell out every night from May to September without discounting, more AI mentions add demand you cannot take. The case gets stronger in the months you struggle to fill, so it can make sense to track ahead of the low season rather than all year.

It is also a poor buy if nobody will act on it. The findings are fixes to your website, corrections to listings, replies to reviews and pages that answer questions guests ask. If your website is locked behind a developer you cannot reach, or your team has no hours for this, a report each month changes nothing. Start with the free checks instead and come back when someone can own the work.

Finally, check that the questions match your market. Radar tracks one language. If most of your guests plan their trip in German and Czech, a single-language panel shows only part of the picture, and you should judge it on that basis.

Start for free and by hand. Ask ChatGPT, Gemini and Perplexity five questions a guest might ask about your city and type of stay, without naming your hotel, and note whether you appear and which sites the answers rely on. It takes ten minutes and is noisy, because the same question can get a different answer twice in a row, but it tells you whether there is a gap worth paying to measure.

Next, run the free scan. Each account gets one Radar scan at no cost: 15 guest questions across ChatGPT, Google AI Overviews and Google AI Mode, with no card required. It gives you a baseline, the sites AI cites for your market and a first list of fixes, and you can stop there if the gap is small.

If you go further, take a monthly plan rather than a yearly one. There is no contract, and a monthly plan can be cancelled at any time. Give it two or three monthly scans and judge it on two things: whether the detection rate on the same questions moved, and whether the fixes it suggested were things you could actually do.

Keep the arithmetic in mind when you read the result. Fifteen questions on three engines is 45 answers a month, so one extra mention moves the rate by about two points. A single month's change is mostly noise. A rise that holds over several scans, on questions you worked on, is what you are paying to see.