AI Visibility in Other Languages: Why Your Hotel's Score Changes

Ask an assistant in English where to stay in Prague for a spa weekend, then ask the same thing in Czech or German. You will often get overlapping but noticeably different lists. The engine has not changed its mind. It has consulted a different set of sources, because the sources that discuss Prague hotels in Czech are not the sources that discuss them in English.

For a hotel this means a single AI visibility score is an average over something that is not uniform underneath. A property can be well covered in English travel editorial and almost invisible in the local-language web, or exactly the reverse.

Reviews concentrate by language on different platforms. Local directories and city tourism sites publish in the local language first and sometimes only. Regional press writes in its own language and is rarely translated. All of that is material an engine reads, and it does not overlap neatly between languages.

Phrasing differs too, and not only lexically. A German guest asking about a family trip uses different framing from an Italian one, which means the question you should be testing is not a translation of your English question. It is the question that guest would actually type, which sometimes has a different structure entirely.

This one cost us real time, so it is worth passing on. We built a market keyword from a city's English name and queried it against a Czech-language corpus. The result looked plausible and was badly wrong: the same domain came back with 459 mentions on the English form of the keyword and 3,660 on the Czech one, roughly an eightfold understatement from a single word.

The general lesson is that a multilingual measurement fails quietly. There is no error message when you ask the wrong corpus the wrong question. You get a smaller number that looks like a finding about the market, and you act on it. If you are checking a non-English market, use the local name of the city and the local phrasing, and be suspicious of any figure that seems low.

This is the common case rather than the edge case. A hotel with a website in one language only, tracking questions across several, because its guests come from several markets. The measurement will show poor coverage on most of them.

What matters is reading that result correctly. A naive content check would report that your site does not cover most of the topics guests ask about, and conclude your content is thin. The real finding is usually that your content is in one language and the questions are in others, which is a different problem with a different fix. Diagnosing it as thin content leads to writing more English pages, which does not help.

Start from your actual source markets. Pull your booking mix by guest nationality for the last year, take the top few, and use the languages those guests would search in. That list is usually shorter and less obvious than the one a hotel would guess, and it occasionally excludes a language the team assumed was important.

Then accept the trade. Tracked questions are finite, so a question panel split across six languages goes less deep in each than one split across two. Staylight supports 28 languages and caps a single property at up to 8 on the top plan for exactly this reason: at some point adding another language buys breadth by taking away the resolution you need to see a change in your main market.

We do not know, and we are not going to imply otherwise. Translating a hotel website is a significant cost, and we have no data showing that translated marketing pages lift AI visibility in the target language.

What is defensible is narrower. The levers with a plausible mechanism are the ones in the target language's own sources: reviews in that language, listings on that market's directories, local-language press and guides. Those are what the engine is reading when it answers in that language. Translating your own site may help and may not, and anyone who tells you confidently either way is ahead of the evidence.

The checklist below takes about ten minutes and costs nothing. The output is worth more than it sounds: you find out whether a market you care about sees you at all, and you see which local sources the engine trusts there.

If the non-English answer cites local directories and guides you have never heard of, that list is your actual work in that market, and it is usually a shorter list than the equivalent in English.