The Impact of Language on AI Brand Visibility
In today’s global marketplace, the language a buyer uses when interacting with AI tools significantly influences which brands appear in AI-generated answers. Recent research highlights that local-language queries tend to promote local brands, while English-language queries favor global brands. This phenomenon has profound implications for businesses striving to optimize their visibility in AI-driven search environments.
A controlled study conducted by a researcher in Tallinn, detailed in a paper published on arXiv, tested 234 queries across four countries and six languages. The results were striking: when the same question was asked in different languages from the same location and device, the AI recommended entirely different sets of brands. For example, a user in Oslo who asked ChatGPT in Norwegian for accounting software received recommendations for three Norwegian brands—Fiken, Tripletex, and Conta—but no American brands. When the exact same question was posed in English, the AI suggested popular American products such as FreshBooks, QuickBooks, and Wave, with the Norwegian options disappearing entirely.
This demonstrates that the language of the question effectively selects the market from which AI draws its answers. It also underscores a critical insight for companies: checking AI visibility only in English fails to accurately measure their presence in non-English-speaking markets, where they may actually compete.
Local Language Queries Favor Local Brands
AI systems like ChatGPT generate responses based on the language-specific content they have been trained on. An English-language query accesses predominantly English-language web content, where global brands have invested heavily over decades to build their online presence. Conversely, local-language queries pull from content written in those languages, where local tools and businesses receive more attention and coverage.
For instance, in the study, a question asked in Turkish failed to produce any Turkish suppliers when posed in English, while local Turkish brands appeared when the query was made in Turkish. This phenomenon, described as the “existence gap” in a separate research paper, explains how brands documented in one language exist prominently for models trained on that language but barely register in others.
These findings emphasize that English does not function as a neutral or default market. Instead, it behaves as a distinct market segment with its own dominant players, often excluding local competitors from other language markets.
Rethinking AI Visibility Metrics
Many businesses rely on monthly AI visibility reports to track how often their products and brands are recommended by AI tools. However, for companies outside the United States or those targeting non-English-speaking customers, analyzing visibility solely in English can be misleading. According to the study’s author, companies selling in markets like Germany or Norway who check their AI visibility only in English are effectively measuring a market they do not compete in. Their true rivals, predominantly local brands, remain invisible in these reports.
The reverse is also true: local champions dominate responses in their native languages but vanish when queries are made in English, which is often the language procurement teams and international buyers use. This duality means that brands must understand and measure their AI visibility in the languages of their target markets to gain a true picture of their competitive standing.
Strategies for Improving Local AI Visibility
Simply translating a website or product descriptions into the buyer’s language is insufficient to improve AI recommendations. Research shows that self-published content is one of the weakest signals AI models use when generating answers. Instead, the most effective approach is to cultivate external mentions of your brand in the buyer’s language—including coverage in local press, reviews on regional platforms, and transcripts from local podcasts. These third-party signals carry significantly more weight in AI models’ ranking algorithms.
While this strategy may seem costly, research from ESBO Ltd, a link-building and brand mentions agency operating across 53 languages, reveals that prices for sponsored articles and mentions are often lower in many non-English markets than in English-speaking ones. For example, median costs for sponsored content hover around $655 in Spanish, $480 in Polish, and about $490 across Central and Eastern Europe. Nordic and German markets tend to be more expensive, but competition is often less intense, offering opportunities for brands to gain visibility more efficiently.
To effectively measure and improve AI visibility, companies should translate a set of typical buyer questions into each target language—using machine translation for initial drafts and native speakers for refinement—and run multiple queries per language. Since AI recommendations can vary across repeated runs, logging the appearances of various brands over multiple attempts can help map out where a brand’s presence is strong and where it needs bolstering.
Conclusion
The choice of language in AI queries is no longer a neutral detail—it actively shapes which companies are visible to potential customers. The example of a user in Oslo receiving entirely different brand recommendations depending on whether the question was asked in Norwegian or English illustrates this clearly. For businesses seeking to thrive in AI-driven marketplaces, understanding and leveraging this dynamic is crucial.
By ensuring your brand is mentioned in the languages your customers use and measuring your visibility accordingly, you can better position your products where they truly compete. Ignoring this reality risks missing significant market opportunities and misinterpreting AI visibility metrics.
