Does AI Search Work Differently by Country? Regional Variations Explained

How AI Search Results Vary by Geographic Location

AI search engines do not deliver uniform results across countries. Research analyzing over 56,000 citations across six major AI search platforms and four international markets reveals that geographic location fundamentally shapes which sources AI systems prioritize and cite. When users search from different countries, they receive dramatically different answers—even when asking identical questions. This geographic variation stems from two primary mechanisms: the user’s IP address (which signals location) and the language of the prompt (which determines which content sources the AI model prioritizes). Understanding these differences is critical for businesses operating globally, as your brand’s visibility in AI search results depends heavily on where your customers are searching from.

The implications are substantial. A user searching for “best restaurants in Barcelona” from Spain receives neighborhood favorites and local dining spots frequented by residents, while the same query from the United States returns well-known establishments featured in English-language travel guides catering to tourists. This geographic split creates two entirely separate realities for brands depending on which region customers use to search. For companies monitoring their presence in AI search, this means you cannot rely on a single set of results—you must track visibility across multiple countries and languages to understand your true global footprint.

Regional Localization Performance Across AI Platforms

Different AI search engines demonstrate vastly different approaches to geographic localization. Perplexity leads the market with 56.5% of citations coming from non-global (localized) sources, consistently surfacing local domains and country-specific information rather than defaulting to US-based alternatives. Microsoft Copilot matches this performance at 56.0% non-global citations, actively seeking out regional domains when users search from specific countries. However, the performance gap between leading and lagging platforms is dramatic—Gemini shows minimal localization at just 5.3% non-global citations, treating UK queries almost identically to US searches despite the developed digital economy in both regions.

This variation matters significantly because 66% of all citations across AI search engines still come from global (primarily US-based) domains, regardless of user location. Only 18.3% use proper country-code top-level domains (ccTLDs) like .fr, .de, or .co.uk that truly represent local markets. This creates a fundamental bias toward US-based content and English-language sources, even when users search in other languages or from other countries. For businesses in non-English markets, this means competing against a system that inherently favors American sources and global brands.

Country-Specific Differences in AI Search Localization

Geographic localization performance varies dramatically by country, revealing a regional digital divide in how AI search engines serve different global markets. The Netherlands leads with 54.5% non-global citations, benefiting from strong local digital infrastructure and consistent AI engine attention to Dutch domains and regional business information. Germany ranks second at 44.6% non-global citations, with decent ccTLD usage and regional source discovery. France shows moderate localization at 35.3%, with room for improvement in regional source discovery. However, the UK surprisingly ranks near the bottom at just 5.9% non-global citations, with minimal local domain preference despite its developed digital economy.

This geographic disparity creates competitive advantages and disadvantages based on location. Users in the Netherlands and Germany benefit from AI search engines’ relatively strong localization, seeing more local business information and regional sources. Conversely, UK businesses face an uphill battle for AI visibility despite their developed market, as AI engines treat UK queries almost identically to US searches. For market research, this creates blind spots—companies researching new markets through AI may miss key local competitors and regulatory requirements, particularly problematic in regions like the UK where local sources represent less than 6% of citations.

Language-Based vs. Location-Based AI Search Responses

Language choice and geographic location operate as two distinct signals that AI models use to personalize responses. Language determines which sources AI models cite in responses, while IP addresses help models understand geographic context for location-based queries. When someone asks ChatGPT “where are the best cafes around me,” ChatGPT uses IP address data to identify relevant locations nearby. However, different AI platforms handle these signals differently, creating inconsistent experiences across platforms.

ChatGPT prioritizes user location over prompt language for certain queries. When asked “what are the best grocery stores” in Japanese, ChatGPT returns US retailers like Walmart and Target for US-based users, even though the query was in Japanese. Google AI Overviews takes the opposite approach, returning Japan-specific results for the same Japanese-language query because Google caches results and assumes Japanese searchers want Japanese locations. This fundamental difference in how platforms weight language versus location signals means the same question asked in different languages from the same location can yield different results, and the same question asked in the same language from different locations can also produce different answers.

The practical impact is significant for global businesses. A restaurant brand researching its visibility might discover it appears in tourist recommendations when searched in English but in local searches when queried in the native language. This split creates two separate visibility profiles that require distinct monitoring strategies. Companies cannot simply translate their content and expect consistent results across AI platforms—they must understand how each platform weighs language and location signals and optimize accordingly.

Multilingual Query Handling and hreflang Support

AI search platforms struggle significantly with multilingual queries and show weak or absent support for hreflang signals, the standard markup that tells search engines which version of a page to serve to users in different languages. Testing across ChatGPT, Perplexity, Claude, and Gemini reveals a consistent pattern: when users search in French, Italian, or Spanish, these platforms often return English URLs despite the non-English query. Google and Bing, by contrast, consistently return the correct localized URLs, demonstrating decades of experience handling multilingual content.

In one comprehensive test, when searching for “Comment creer un sitemap XML” (French for “How to create an XML sitemap”), ChatGPT provided a French-language answer but linked to the English URL. Perplexity showed the same mismatch—correct response language but incorrect link language. Claude required explicit prompting to return sources and still defaulted to English versions. Only Google, Bing, Copilot, and Google AI Mode returned the correct French URLs consistently. This multilingual weakness creates a critical gap for publishers with translated content, as AI search engines cannot reliably identify and surface the correct language versions of pages.

The implications extend beyond user experience. Hreflang appears weak or absent across ChatGPT, Perplexity, and Claude, meaning these platforms do not recognize the structured signals that tell search engines about language relationships between pages. This suggests AI search engines rely more heavily on US-English training data and lack the sophisticated multilingual indexing mechanisms that traditional search engines have developed over decades. For international businesses, this means AI search platforms may systematically misrepresent your content by returning the wrong language version, potentially damaging user experience and trust.

How IP Address and Language Signals Shape AI Results

AI models use two primary signals to personalize responses: the language of the prompt and the user’s public IP address. These signals work together but sometimes in conflicting ways, creating unpredictable results. Language choice fundamentally shapes which content AI models prioritize in responses, creating distinct content ecosystems for each language market. English prompts surface English-language sources like travel blogs and tourism sites, while Spanish prompts prioritize Spanish content from local food critics and regional publications, even when answering identical questions about the same city.

IP address detection provides geographic context that helps AI models understand location-based intent. When a user asks “where are the best cafes around me,” the AI system uses IP data to identify the user’s approximate location and return nearby results. However, this geographic signal is not always reliable or consistently applied. Some platforms weight IP address heavily, while others prioritize language signals. This inconsistency means the same user searching from the same location might receive different results depending on which AI platform they use and which language they search in.

The practical challenge for businesses is that you cannot predict which signal an AI platform will prioritize for your target audience. A user in France searching in English might receive US results (language signal dominates) or French results (location signal dominates), depending on the platform. This unpredictability makes it difficult to optimize for AI search across multiple countries and languages, as the rules differ by platform. Monitoring your brand’s visibility requires testing across multiple language-location combinations to understand how each platform treats your content.

Citation Positioning and Top-Rank Localization Bias

Global domains dominate top citation positions even more than their overall representation suggests. While global domains represent 66% of all citations across AI search engines, they account for 66.5% of top citations—actually slightly higher than their overall share. This means that when AI systems choose which source to cite first or most prominently, they show an even stronger bias toward global sources. Local sources struggle for top positions: ccTLD domains drop from 18.3% overall to just 17.6% of top citations, while subdomain localization nearly disappears at just 0.9% of top citations.

This top-rank bias has significant implications for visibility. Even if your local domain appears somewhere in an AI answer, it may not appear in the most prominent position. Perplexity shows the strongest localization for top citations at 60.4%, even stronger than its overall 56.5% localization rate, suggesting the platform actively prioritizes local sources for its primary recommendation. However, Gemini shows the opposite pattern, with even worse localization for top citations (1.2%) than its overall rate (5.3%), indicating the platform becomes even more US-centric when selecting its most prominent citation.

For businesses competing in AI search, this means localization alone is insufficient—you need to ensure your content ranks prominently within the localized results. A local domain that appears fifth in an AI answer provides less value than a global domain appearing first. This creates a two-tier competition: first, competing to be included in localized results, and second, competing to be the top-ranked source within those results. Understanding which AI platforms your target customers use becomes critical, as the rules for achieving top-rank visibility differ significantly across platforms.

Practical Implications for Global Brands and Businesses

The geographic variation in AI search results creates real competitive implications for global businesses. Companies researching new markets through AI may miss key local competitors and regulatory requirements, particularly problematic in regions where local sources represent less than 6% of citations. Partner discovery becomes biased toward US-based alternatives, as local suppliers get systematically overlooked in favor of global options. Regional competitive advantages emerge for companies in markets with stronger AI localization (Netherlands at 54.5%, Germany at 44.6%), while businesses in markets with weak localization (UK at 5.9%) face an uphill battle for AI visibility.

The 53-percentage-point difference between the best (Perplexity at 56.5%) and worst (Gemini at 5.3%) performing engines creates a fragmented global market where your choice of AI dramatically impacts the regional relevance of business information you receive. For businesses, this means monitoring which answer engines your target customers use is essential, as Perplexity and Copilot users see dramatically different local business representation than Gemini or Google Search users. Customer intelligence failures occur when 66% of all AI citations default to global sources, causing potential customers researching local solutions, compliance frameworks, and market-specific services to miss critical regional information.

To address these challenges, businesses should audit their presence across multiple AI platforms in different countries and languages, test multilingual visibility across ChatGPT, Perplexity, Gemini, Claude, and Copilot, strengthen core search visibility (which remains more consistent), and keep monitoring AI search evolution as these platforms continue to improve their localization capabilities. Understanding your brand’s regional visibility in AI search requires moving beyond single-platform monitoring to a comprehensive, multi-country, multi-language tracking strategy.