Why AI Search Hits Non-English B2B SaaS Markets Twice as Hard

By Gaetano Romeo, Head of Global Digital Marketing, EasyVista

Every GEO conversation I sit in treats generative engine optimization as if it applies uniformly across markets. It doesn’t. The models doing the citing were built overwhelmingly on English content, and that asymmetry has direct, measurable consequences for anyone running B2B SaaS marketing outside the US and UK.

The Training Data Imbalance Is Bigger Than Most Marketers Realize

The scale of the English skew in LLM training data is not a minor footnote, it’s structural. Research on the Colossal Clean Crawled Corpus, one of the major filtered web datasets used to train large language models, found that 51.3% of the content is hosted in the United States, with the next largest source, India, sitting at just 3.4%. Separate analysis of the global web found that among the top 10 million websites, over 60% are in English, while German and French each represent only around 2.5% of resources.

The gap shows up directly in model training corpora too. Meta’s Llama 2 technical report disclosed that French accounts for just 0.16% of its training corpus. That’s not a rounding error, it’s a near-total absence relative to the language’s actual global weight.

What This Means Once the Model Starts Citing Sources

Generative engines don’t just answer in whichever language you ask them a question. They also decide which sources are authoritative enough to cite, and that decision is shaped by everything the model learned during training, including which languages and regions it saw the most content from. Academic research on linguistic bias in LLMs describes a documented pattern of cross-linguistic interference, where English acts as a pivot language even when generating content in other languages, and where non-English outputs show measurably higher error rates. One study found 16% of linguistic errors in French and Dutch LLM-generated text traced directly to English influence.

For a B2B SaaS brand publishing content in Italian, German, or French, this compounds two separate disadvantages. First, there’s simply less non-English content in the training data for the model to have learned your category from at all. Second, even where non-English content exists, the model’s underlying reasoning is still frequently anchored to English-language conceptual patterns, meaning locally-specific terminology, positioning, or market nuance is more likely to get flattened or mistranslated in how the model represents your space.

The Zero-Click Problem Lands Harder When You’re Already Underrepresented

Industry-wide, organic traffic has already declined by an estimated 15 to 25% as AI-generated answers increasingly resolve queries without a click. Ahrefs data shows AI Overviews reduce click-through rates by 34.5% on the queries where they appear, and research also shows that 76% of AI citations come from pages already ranking in the top 10 of traditional search, meaning visibility in classic SEO remains the gateway to GEO visibility, not a separate track.

Put those two facts together for a non-English market. If your content already has a structurally smaller footprint in the training data the model draws from, and citation still heavily favors pages that already rank well in traditional search, then the compounding effect isn’t additive, it’s closer to multiplicative. You’re competing for citation slots in a system that was undertrained on your language, using a signal, top-10 traditional rankings, that’s itself harder to win in less English-dominant search ecosystems with different competitive dynamics.

What I’d Actually Do About This

A few adjustments worth making if you’re running B2B SaaS marketing in a non-English European market specifically:

• Don’t treat GEO as a lift-and-shift of your English content strategy. Structural under-representation in training data means non-English markets likely need a heavier investment in authoritative, well-structured content per topic to reach the same citation probability English content achieves more easily.
• Prioritize getting cited in top-10 traditional search results in your native language first, since that remains the strongest predictor of AI citation regardless of language, and it’s the one lever fully within your control.
• Watch for silent mistranslation of your category terminology in AI-generated answers about your space. If a model is pivoting through English to generate French or Italian output, your carefully built local positioning language may be getting flattened into a generic, English-derived equivalent without anyone noticing.
• Build market-specific measurement. A GEO strategy benchmarked only against English-market citation rates will always look like it’s underperforming, when the underlying training data disadvantage was baked in before you published a single word.

The Uncomfortable Structural Reality

This isn’t a solvable problem in the way most SEO problems are solvable. You can’t out-optimize a training data imbalance that runs into the single digits of percentage representation for your entire language. What you can do is stop benchmarking non-English GEO performance against English-market expectations, and start treating the extra investment required as a real, structural cost of operating in these markets, not a sign that your content or strategy is underperforming.

Sources:

• Dark Social and AI Search: How B2B Buyers Are Finding SaaS Tools in 2025, GrackerAI
• AI Search, Zero-Click & the B2B SaaS Buyer Journey in 2026, Altair Media
• 38 B2B SEO Statistics and Organic Search Trends for 2026, GTM8020
• Toward Human-Centered AI-Assisted Terminology Work (linguistic bias, CCC corpus data), arXiv
• Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense (web language distribution), arXiv
• compar:IA: The French Government’s LLM arena (Llama 2 French training share), arXiv

[🤖 Co-created with the assistance of AI / Written in alignment with EU AI Act transparency guidelines]