The impact of multilingual SEO AI on search engine visibility: comprehensive analysis of 1.3 million citations
How translated websites get 327% more visibility in Google AI Overviews and ChatGPT – A Weglot 2025 study
How multilingual translation is redefining SEO in the age of AI
AI-generated summaries – Google AI Overviews, ChatGPT and other AI engines – have changed the way users discover information online. AI no longer simply categorizes pages. It reads, selects, summarizes and then cites the sources that seem most relevant.
In this new context, one question becomes central for any company present on the web: how do multilingual SEO AI strategies compare with monolingual sites in AI-powered search results?
An in-depth multilingual SEO AI study conducted by Weglot in 2025 provides a quantified answer. The researchers analyzed 1.3 million citations from Google AI Overviews and ChatGPT. Their conclusion is clear: translated sites gain up to 327% more visibility than sites available in a single language.
Why this study changes the way you think about SEO | multilingual translation
Modern AI systems – Google AI Overviews, ChatGPT, Perplexity and other conversational engines – no longer just list links. They select, synthesize and quote content. They behave like publishers. They choose their sources.
In this context, a simple question becomes a strategic issue: if your site doesn’t exist in the user’s search language, does the AI have a reason to cite you? The data shows that, in most cases, the answer is no.
At Koanthic, we see this every day with our customers. Website translation and multilingual SEO AI optimization is no longer a bonus for international business or a gesture of accessibility – multilingual SEO AI is essential. Advanced multilingual SEO AI strategies are now essential. It’s now a major strategic lever for digital visibility in an environment dominated by AI.
Methodology: a two-phase study, 236 sites and two Spanish-speaking markets | multilingual translation
To measure the concrete impact of translation on visibility in AI search engines, Weglot has designed a study structured in two main phases. The research targets Spanish-language sites active in two markets: Spain and Mexico.
Phase 1: Monolingual sites, Spanish only
In the first phase, the researchers studied sites without English translation:
- Sample: 153 websites
- 98 sites based in Spain
- 55 sites in Mexico
- Queries generated: 22,854 searches
- Objective: measure visibility in Google AI Overviews and ChatGPT for :
- queries in Spanish
- equivalent queries in English
Phase 2: Translated sites, Spanish + English
In the second phase, the study focuses on sites that have already been translated:
- Sample: 83 websites
- Spanish and Mexican websites with Spanish and English versions
- Queries generated: 12,138 searches
- Objective: direct comparison of performance:
- monolingual content
- translated content
How the data was collected | multilingual translation
For each site, the researchers identified the top 50 non-branded organic keywords. These keywords were transformed into realistic queries, close to those of users.
Each query was then tested:
- in its original Spanish version
- in its English translation
In total, the study draws on 1.3 million citations in Google AI Overviews and ChatGPT. It is, to date, one of the largest analyses of the impact of multilingual translation in AI search.
Key result n°1: multilingual SEO AI sites outperform monolingual ones by 327% in search visibility
The figures are clear: a site that offers no translation loses a large part of its visibility as soon as the search switches to another language.
In Google AI Overviews: a sharp drop in visibility
For sites without proper multilingual SEO AI optimization, Google AI Overviews shows very marked differences in multilingual SEO AI performance:
| Geographic market | Quotes (ES searches) | Citations (EN searches) | Loss of visibility |
|---|---|---|---|
| Spain (98 monolingual sites) | 17 094 | 2 810 | -431 % |
| Mexico (55 monolingual sites) | 12 038 | 3 450 | -213 % |
Reading these figures:
A Spanish site that performs very well on queries in Spanish almost completely disappears when the same search intention is expressed in English. The loss reaches 431% in Spain and 213% in Mexico.
In ChatGPT: a more moderate but very real bias
ChatGPT is a little more balanced than Google AI Overviews, but the bias remains clear:
- Spanish sites not translated:
-3.5% citations on English queries - Mexican sites not translated:
-4.9% of citations on English queries
These figures may seem modest, but they reflect a major trend: untranslated sites are systematically disadvantaged as soon as the query changes language.
Strategic lesson from the first result | multilingual translation
A monolingual site exists almost exclusively in its original language.
In today’s AI research ecosystem :
- an untranslated site is invisible to users searching in another language
- this invisibility results in a direct loss of traffic, leads and revenue
Key result no. 2: translated sites gain 327% visibility and perform well in all languages
When a site adds professional translations, its performance changes category. Visibility increases, not only in the language added, but also in the original language. Translation creates a multiplier effect.
Google AI Overviews: a greatly reduced visibility gap | multilingual translation
For translated sites, the gap between Spanish and English citations decreases sharply:
| Market | Quotes (Spanish) | Quotes (English) | Visibility gap |
|---|---|---|---|
| Spain (translated sites) | 10 046 | 8 048 | -22% |
| Mexico (translated sites) | 5 527 | 3 325 | -59% |
Let’s compare with untranslated sites:
- Spain: from -431% (untranslated) to -22% (translated)
→ +409 percentage points - Mexico: from -213% (untranslated) to -59% (translated)
→ +154 percentage points
Overall gains: +327% visibility and +24% citations per query | multilingual translation
In aggregate, the study shows impressive results:
- +327% global visibility for translated sites
- +24% citations per query
- a more regular presence in both languages
A true multiplier effect | multilingual translation
Translation doesn’t just benefit the added language. It also strengthens the original language:
- Citations in English (added language): +33% per query
- Citations in Spanish (original language): +16% per query
Translating data into strategy:
AI engines seem to use multilingual presence as a signal of authority and reliability.
A translated site sends several messages to the algorithm:
- “I take my content seriously”
- “I’m targeting several markets”
- “I invest in quality and depth”.
Result: the AI cites this site more often, in all its languages.
ChatGPT: language bias almost eliminated | multilingual translation
For translated sites, ChatGPT shows a very balanced behavior. The number of citations is almost identical between Spanish and English.
This linguistic symmetry is a direct competitive advantage in a world where conversational engines are taking over information retrieval.
Why multilingual translation is becoming a ranking signal for AI
Older search engines focused on elements such as :
- hreflang beacons
- localized keywords
- technical signals for internationalization
With AI, another criterion emerges: the translation itself becomes a signal of relevance and authority.
1. Linguistic alignment: same language, higher probability of citation | multilingual translation
The large language models (LLMs) that power Google AI Overviews, ChatGPT or Perplexity naturally favor content in the same language as the query.
This is not an arbitrary preference. It’s a reflection of the way these models learn.
Simple rule:
if your content doesn’t exist in the language of the question, the probability of citation plummets.
For the model, the absence of translation means: “this content is not aimed at this audience”.
2. Authority signal: multilingual sites inspire more trust | multilingual translation
The Weglot study shows one constant: multilingual sites are cited more often, even in their original language.
Why?
- they attract traffic from several regions
- they demonstrate a real investment in the quality and scope of content
- they are perceived as international industry benchmarks
For the algorithms, this type of site ticks several boxes at once: relevance, seriousness, reach, stability.
3. The Google Translate risk: when AI replaces you with a proxy
There’s a little-known danger to untranslated sites: machine translation by Google.
Typical scenario:
- your site in Spanish offers the best answer on a subject
- a user asks the question in English
- Google AI Overviews finds your content, but no English version
- the system generates a translation via Google Translate
- the AI answer quotes the Google Translate version, not your domain.
Consequences:
- you lose control over translation quality
- some traffic is directed to the Google Translate proxy page
- your domain receives less citation credit
- the user experience depends on a raw, unoptimized translation
In other words: you do the work, Google reaps the rewards.
4. Expanded semantic surface: more languages, more opportunities to be quoted
Each added language expands your “semantic surface”.
You cover more expressions, more lexical variations, more differently formulated questions.
LLMs are trained on multilingual corpora. Each language provides :
- its nuances
- idiomatic expressions
- its query structures
By multiplying languages, you multiply:
- possible angles of attack
- the contexts in which your content is relevant
- the chances of being retained as a source in an IA response
Case study: Spanish bookseller loses 64% visibility for lack of English version
Weglot highlights a case in point that illustrates the commercial impact of the absence of translation: a major Spanish book retailer.
A paradoxical situation
This bookseller owns:
- an extensive catalog of books in English
- an international clientele
- recognized expertise
- but a Spanish-only site
Observed results
| Indicator | Result |
|---|---|
| Appearances in Google AI Overviews (EN queries) | -64% compared to ES queries |
| Appearances in ChatGPT (EN queries) | also-64 |
| Quotes via Google Translate | 36% of rare appearances |
| Citations to the real world | 64 % |
| Translation quality control | 0 % |
Impact business
Direct consequences:
- English-speaking customers rarely find the site in the IA answers
- some appearances link to Google Translate, not to the real domain
- the perceived quality of the translation is random and uncontrolled
- brand image suffers: a site that sells books in English, but without an English interface, looks unprofessional
On a large annual volume of searches, this type of situation can represent hundreds of thousands of euros in lost revenue.
The message is simple: in the AI ecosystem, language is no longer a detail, it’s a filter of existence.
Strategic recommendations by company type
1. SMEs and startups: getting off to a good start
Context: limited resources, need for growth, untapped international opportunities.
Priority actions:
- Linguistic traffic audit
Analyze your data (Analytics, Search Console):- what languages do your current visitors speak?
- where do the sessions come from?
- Translate high-value pages first
Prioritize:- home page
- main product or service pages
- top-performing blog posts
- contact page and FAQ
- Choose a strategic first language
English is often the best place to start:- North American market
- UK / Ireland
- countries that use English as their business language
- Invest gradually
Start with a small, clean perimeter.
Then expand according to the results you see.
Expected result:
many SMEs see a 15 to 30% increase in organic traffic within 3 to 6 months on translated pages, with conversion rates comparable to the original language after optimization.
2. Established companies: correcting inconsistencies
Context: international presence, several languages already in place, but variable quality and completeness.
Priority actions:
- Content parity audit
- which pages exist in one language but not in others?
- which translations are obsolete or partial?
- Clean up negative signals
- replace unedited machine translations
- complete partially translated pages
- update key content in all languages
- Stabilize the process
- each new page must have a translation plan
- updates must be synchronized by language
- translation must be integrated into the editorial flow, not processed as an afterthought
- Strengthen your technical infrastructure
- clean hreflang
- Consistent URLs
- multilingual sitemaps
- localized metadata
Expected result:
+25 to +45% of citations in AI engines,
+20 to +35% international traffic in 6 to 12 months.
3. Growing companies: putting multilingualism at the heart of strategy
Context: geographical expansion, need for sustainable differentiation.
Priority actions:
- Include translation in your SEO plan from the outset
- keyword search by language
- multi-market content production
- Multilingual KPIs integrated into dashboards
- Exploiting the multiplier effect
Every new language improves:- global reach
- authority signals
- performance in the original language itself
- Stand out from the competition
Most players are not yet fully exploiting multilingualism in AI.
A well-executed strategy becomes a sustainable advantage. - Planning language expansion
- Years 1-2: main languages
- Years 3-4: Secondary languages
- Year 5+: Niche languages, consolidating multilingual leadership
Expected result:
+50 to +100% overall organic traffic in the medium term,
and lower customer acquisition costs in non-native markets.
The four pillars of a successful multilingual strategy in the age of AI | multilingual translation
Pillar 1: High-quality professional translation
LLMs differentiate very well:
- native text
- professional translation
- raw machine translation
They spot:
- fluidity or heaviness of syntax
- the correct use of business vocabulary
- consistency of terminology
- cultural adaptation
Recommended approaches:
- Professional human translation
For high-value content: sales pages, technical pages, pillar articles. - Hybrid approach
Neural machine translation + human revision, for large volumes. - Qualified post-editing
Use tools such as DeepL or Google Translate, then have your text professionally edited.
Error to avoid:
publish unedited machine translations, even for secondary pages.
In the long term, this drags down the perceived quality of the entire domain.
Pillar 2: Real content parity between languages
Content parity means:
each language offers a comparable level of richness.
What the AI is looking at:
- volume of pages per language
- depth of content
- coherent menus and itineraries
- completeness of translated versions
A site with a beautiful “main” version and poor secondary versions sends out an ambiguous signal:
“we’re serious… but only for certain audiences”.
Pillar 3: A clean multilingual technical base
Even the best content remains invisible if the technical structure is weak.
Technical highlights :
- correctly implemented hreflang tags
- clear URL structure (
/fr/,/en/, etc.) - XML sitemaps by language
- localized metadata
- similar performance across languages (Core Web Vitals, CDN, etc.)
Good technique doesn’t create demand, but it does enable AI to understand how your site is structured and who each version is aimed at.
Pillar 4: Continuous updating of all languages
Translating once and then forgetting the secondary versions is a classic trap.
Non-updated versions:
- age in indexes
- lose IA citations
- degrade the user experience
A sound strategy:
- a continuous translation flow
- translation management tools (TMS)
- shared glossaries of terms
- regular parity and freshness audits
How Google AI Overviews and ChatGPT choose their multilingual sources
AI systems follow, schematically, five stages:
- understand the request (language, intent, context)
- search for relevant content (preferably in the same language)
- assess source quality
- generate response
- allocate quotes
Multilingual sites are favored:
- in step 2, thanks to language matching
- in step 3, thanks to authority and consistency signals
AI perceives better:
- clear structures
- relationships between language versions
- the strength of the domain in several markets
The future of multilingual research (2025-2027) | multilingual translation
Next-generation models (GPT-5, Gemini 2.0, Claude 4, etc.) will further enhance :
- the finesse of multilingual analysis
- the ability to mix different language sources to generate a single-language response
Companies that already have a solid multilingual base will benefit fully from this development.
The others will have to catch up, at an ever-increasing cost.
Measuring the ROI of an AI-optimized multilingual strategy | multilingual translation
The KPIs to be tracked are evolving.
In addition to traditional indicators (traffic, conversions), it is useful to track :
- AI quotes by language
- multilingual share of voice in your sector
- distribution of international backlinks
- content parity between languages
- technical performance by language version
With regular monitoring, you can :
- identify the most profitable languages
- identify under-exploited markets
- prioritize future translation investments
Frequently asked questions | multilingual translation
Is machine translation alone enough?
No, not if you’re aiming for high visibility in AI.
Without human post-editing, the models detect :
- artificial formulations
- idiomatic errors
- lack of cultural adaptation
In the long term, this is detrimental to your quality signals.
How many languages to add?
Better:
- 2 to 3 well-maintained languages
than a dozen poorly maintained languages.
A good typical path:
- add English if not already present
- add 1-2 languages according to your key markets
- then expand based on results and opportunities
Why isn’t my current multilingual strategy working?
The most common causes:
- technical problems (hreflang, indexing, redirects)
- unedited machine translations
- lack of content parity
- no keyword search by language
A well-executed international audit can quickly identify these obstacles.
Conclusion: in AI research, being monolingual means being invisible | multilingual translation
The conclusions of the Weglot study are clear:
- +327% visibility for translated sites
- +24% citations per query
- +33% of citations in the added language
- +16% in original language
- up to -431% loss for untranslated sites in searches in another language
In a world where AI search engines choose which sources they highlight, professional translation becomes a criterion of existence.
An untranslated site is:
fewer citations → less visibility → less traffic → less revenue.
The Koanthic approach: turning translation into a competitive advantage
At Koanthic, we help Quebec and Canadian companies to..:
- understand their multilingual potential
- correct their technical foundations
- prioritize the most profitable languages
- set up sustainable translation workflows
- optimize their visibility in Google AI Overviews, ChatGPT and AI engines
Our support is structured around four main phases:
- Audit & strategy
- Technical foundations
- Translation & localization
- Continuous optimization & monitoring
The aim is not just to translate pages.
The aim is to make you quotable by AI, in several languages, in a sustainable way.
Next steps | multilingual translation
If you wish:
- find out where you are today in the AI ecosystem
- identify the most strategic languages for your business
- estimate the potential ROI of a structured multilingual strategy
you can contact Koanthic for an initial assessment.
- Website: www.koanthic.com
- Email: info@koanthic.com
- Telephone: 418-455-2259
Additional resources and references
Study sources
This analysis is based mainly on:
Weglot 2025 study: “Multilingual SEO and AI Visibility Study”.
Available at: weglot.com/blog/multilingual-seo-ai-visibility
Source article: Search Engine Journal, “Translated Sites See 327% More Visibility in AI Overviews”, November 2025
Available at: searchenginejournal.com/translated-sites-boost-ai-visibility-weglot-spa/559900/
Glossary of technical terms
- AI Overviews: Google feature that generates automatic summaries at the top of search results by synthesizing multiple sources.
- hreflang tags: HTML code indicating to search engines the relationships between different language versions of the same page.
- AI Citations: References or mentions of a web source in a response generated by an artificial intelligence system.
- LLM (Large Language Model): Artificial intelligence models trained on huge amounts of text to understand and generate natural language
- Content parity: The state where all language versions of a site offer equivalent depth and completeness.
- Post-editing: Human review and enhancement of machine translations
- International SEO: Search engine optimization practices to improve visibility in multiple countries and languages
- Authority signal: Indicator used by algorithms to assess the credibility and quality of a web source.
- TMS (Translation Management System): Software platform for managing large-scale translation projects
