Lost In Translation: How Language Changes Which Brand AI Recommends
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SEJ ⋅ AI Search Lost In Translation: How Language Changes Which Brand AI Recommends Your English-only AI visibility checks can miss how brand recommendations change when AI answers the same buying questions in five different languages. Reza Moaiandin 11 seconds ago ⋅ 18 min read Reza Moaiandin Co-founder at SALT.agency Bio Follow Chances are you’ve already got some idea of your brand’s visibility in AI responses. But I’m willing to bet those visibility checks were carried out in just one language: English. If a customer in Singapore asks an AI chatbot a buying question in Simplified Chinese, and a customer in Kazakhstan asks the same question of the same AI in Russian, would they get the same recommendation? What about a customer in Riyadh asking in Arabic or a customer in Los Angeles asking in Persian? These aren’t hypothetical customers. According to Statcounter’s August 2026 figures, ChatGPT was responsible for roughly three-quarters of website visits that came from AI chatbots in Saudi Arabia, Kazakhstan, and Singapore. Add in Claude and Gemini, and these three U.S.-based models accounted for more than 90% of AI referrals in each market. Of course, you don’t need to leave the U.S. to find customers whose first language isn’t English. More than half of Los Angeles County residents speak another language at home, including around 74,000 Persian speakers, according to the latest Census estimates. I’d be surprised if they all switched to English every time they opened ChatGPT. But when somebody asks an AI chatbot a question in another language, the response won’t simply be a translation of the answer it would give in English. The AI reasons and composes its response in that language. And that brings a whole slew of cultural values and other factors to bear on how the model responds. I’m not the first person to write for Search Engine Journal on how brand visibility and messaging in AI can come across differently in languages beyond English. In April, Duane Forrester explained why AI visibility strategies built in English don’t transfer easily to other markets. For a start, ChatGPT and Gemini aren’t accessible in China at all, while Claude isn’t officially offered in mainland China, Russia, or Iran. Instead, your brand’s AI visibility in some countries may depend on regional, homegrown models, such as Baidu’s ERNIE in China, Naver in South Korea, Mistral in France, and so on. As Forrester explains, each of these models evolved from a different starting point entirely: a national corpus, a specific cultural identity, and in some cases a government mandate. If you haven’t read his article, I’d bookmark it now to read after this one. However, as we’ve seen, Western AI models like Claude, ChatGPT, and Gemini are still widely used in plenty of locations where other languages are spoken, not to mention all the diaspora communities around the world. In July, Anthropic published a report on Claude’s values across languages and models. By values, we’re talking about the factors that shape responses where there is no definitive right answer. For example, are responses more likely to display deference or caution, warmth or rigor, depth or brevity, candor or execution? The researchers sampled just under 310,000 real conversations across the 20 most common languages, equally split between three models (Sonnet 4.6, Opus 4.6, Opus 4.7). They found that the values Claude demonstrated shifted according to the language it was addressed in, such as leaning toward greater warmth in Hindi and Arabic and toward greater rigor in English and Russian. The effect is this: Two people could submit the exact same query in different languages – say, whether they should upgrade to the new iPhone Duo – and come away with quite different impressions of what they should do and how good it is. One thing the Anthropic research doesn’t test is whether the choice of language also impacts whether Claude mentions, cites or recommends your brand in the first place. See also: Your AI Visibility Strategy Doesn’t Work Outside English I picked six shopping or booking scenarios where someone might use an AI chatbot to research their options before making a decision: As Anthropic’s research was the catalyst for these tests, I decided to test Claude Sonnet 5 first, with web search turned off. Using DataForSEO’s LLM Responses API, I queried Claude on each of the scenarios in five languages: This gave me the core dataset: six scenarios × five languages × 40 runs = 1,200 responses. For comparison, I also used the same setup to test two of the scenarios (hotel and PM software) with web search turned on to see if allowing the model to search the open web made a measurable difference to the answers. Finally, I tested the same two scenarios in ChatGPT and Gemini to see if other AI models behave the same way. I’ll return to these comparison questions later. Luckily, most of the mentioned brand names were rendered in Latin script, even in Arabic, Russian, Chinese, and Persian responses. As there’s no Arabic or Chinese word equivalent to a brand name like “Zoho,” the model writes it as-is. This meant I could set up a straightforward text-matching pipeline to capture and build a list of the brand names mentioned in each category. However, while this held up consistently for five of the six categories, airlines proved to be an exception. Because they’re truly international businesses, some brands have established local-language forms. For example, roughly 5% of the Chinese responses wrote “英国航空” for British Airways (literally “Britain Aviation”) or “易捷航空” for easyJet, with no Latin form nearby. Some Arabic responses did the same with phonetic transliterations, writing “جت2” for Jet2 and “إيزي جت” for easyJet. Because of this, I had to add those Chinese and Arabic strings as extra patterns to the brand-matching list. Identifying the winning brand pick in each response involved looking for the typical words and phrases used by each model in each language when making a recommendation (such as “my recommendation,” “التوصية النهائية,” “最佳选择,” a trophy emoji, and so on). This method resolved cleanly for 99.8% of responses. But before I trusted the final data, I manually checked a sample of roughly 40 responses across every method tier. I’ve excluded three responses from the results because they ended without naming a final pick. As a result, two of the percentage scores in the tables below are derived from 39 responses instead of 40. (The fourth came from the web search tests.) The most startling disparities occurred in queries related to the second scenario – airline recommendations for a return flight from London to Tenerife. In Persian, Claude picked British Airways 52.5% of the time, and easyJet only 12.5%. However, in Russian, Claude overwhelmingly opted for easyJet (72.5%) – a staggering 60-point swing – while it didn’t recommend British Airways even once. Same model, same scenario, same question, same afternoon. The only difference was the language. For hotels, the number of properties in Tenerife meant Claude had a much wider field of options than it would for, say, robot vacuums, leading to a much more varied range of recommendations in each language. However, while this meant the percentages were lower overall, the disparities between languages were still quite stark. Bahía del Duque was the top pick in 43.6% of Chinese responses but only won in 7.7% of Arabic ones. And while Hotel Botánico accounted for nearly a third of recommendations in English, it disappeared without trace in Russian. If there’s one industry where a brand would need to engage with customers in multiple languages, it would be travel and tourism. But if an airline CMO or hotel marketing manager only tested their brand’s AI visibility in one language, they would get a completely inaccurate picture of how their brand is recommended (or not) in other valuable territories. The other four categories didn’t reveal the same volatility, with the same brand winning across all five languages. Here, it’s the scale of that win and the battle for second place that becomes more interesting. For example, in the headphones category, English was the outlier, as the only language where Sony had serious competition. To a lesser extent, the same is also true in the CRM software category. The final two categories saw no real outliers, with the story playing out much the same across all five languages. If you’re after a robot vacuum for under £500, it seems that whichever language you use, AI will almost always recommend Roborock.
Source: Search Engine Journal
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