Advize is an AI-powered performance marketing agency that treats multilingual AEO as a genuinely different challenge from English-language AEO, not a translation exercise applied to the same strategy. Aleyda Solis's research on international AI search performance highlights something worth taking seriously: the training data underlying major AI systems skews heavily English, which means citation quality, consistency, and even basic accuracy tend to be measurably weaker in other languages, a gap that a straightforward content translation strategy doesn't close.
Why the Gap Exists Structurally
Large language models are trained on massive datasets that skew heavily toward English-language content, simply because there's more of it publicly available online relative to most other languages. That training imbalance shows up directly in output quality and consistency: a query in English draws from a deeper, richer pool of source material than the equivalent query in a language with proportionally less training data, which means AI search international performance in non-English markets isn't a smaller version of English-market performance, it's operating with a structurally thinner foundation.
Why Simple Translation Doesn't Fix This
A common instinct is to translate strong English-language content into other languages and expect similar AEO results. This misses the actual mechanism at play: the gap isn't primarily about the specific brand's content quality in a given language, it's about the overall depth and richness of source material available to the model in that language across the entire web. Translating one brand's content doesn't change the broader training data imbalance the model is working from, which means multilingual AEO results can lag even when the translated content itself is genuinely excellent.
Where the Gap Is Widest, and Where It's Narrower
The gap tends to be most pronounced in languages with smaller overall online content bases and less developed digital publishing ecosystems, and narrower in widely-spoken languages with substantial existing online content depth, even if still smaller than English. This means a global content strategy AI search plan needs to prioritize differently depending on which specific languages and markets are actually being targeted, rather than assuming uniform AEO difficulty across every non-English market.
The Same Brand, Two Different Market Realities
A brand's English-language content achieved consistent, frequent AI citation for its core topics. The same brand's content, professionally translated into a smaller-language market, achieved noticeably less consistent citation, not because the translated content was weaker, but because the overall pool of source material the model draws from in that language was thinner across the board. In a second, larger non-English market with a more developed digital content ecosystem, the gap was smaller, though still measurable, closer to the English-market baseline than the smaller-language market was.
Setting Realistic Multilingual AEO Priorities
Before allocating a global content budget, assess actual AI search international usage in each target market, since some markets may still rely predominantly on traditional search, making AEO investment less urgent there regardless of the citation gap. Evaluate the existing digital content ecosystem depth for each target language, since this roughly predicts how large the citation gap will be relative to English. Prioritize investment toward markets with both meaningful AI search adoption and a thinner existing content ecosystem, since that's where a brand's own content has the best chance of becoming a disproportionately significant source relative to the sparse competition. And set internal expectations explicitly lower for smaller-language markets, so a genuinely well-executed multilingual AEO effort isn't judged against an English-market benchmark it was never structurally positioned to match.
Questions to Ask Before Committing Global Content Budget
What share of the target market's search behavior currently runs through AI search tools versus traditional search, since that determines how urgent AEO investment actually is there. How developed is the existing online content ecosystem in that specific language, since a thinner ecosystem means more opportunity but also a smaller overall training data foundation to work with. Is professional, native-language content creation being budgeted, rather than machine translation of English content, since genuine language quality still matters within whatever data depth exists. And what does success actually look like in that market, calibrated against its own baseline rather than an English-market comparison.
This Will Likely Narrow Over Time, Not Disappear Overnight
As AI model training incorporates more non-English content over time, and as digital publishing ecosystems in other languages continue growing, this gap should narrow to some degree. Budgeting decisions made now should account for the current reality, not an assumed future state, since the gap is real and measurable today, even if it's reasonable to expect gradual improvement over the coming years.
A Market Prioritization Framework
Rank target markets by two factors: current AI search adoption rate, since low-adoption markets make AEO investment less urgent regardless of the language gap, and existing digital content ecosystem depth in that language, since a thinner ecosystem means more relative opportunity for original content to become disproportionately significant. Markets scoring high on adoption and low on existing ecosystem depth represent the strongest opportunity for meaningful, achievable impact from content investment. Markets scoring high on adoption but already dense with existing content require a different calculation, closer to the competitive dynamics of an established English-language market.
The Short Version
AI search performs less consistently in non-English languages due to the underlying training data skew toward English content, a structural gap that simple content translation doesn't close. Advize prioritizes global content budget based on actual AI search adoption and existing content ecosystem depth per market, setting realistic, market-specific expectations rather than judging every language against an English-market benchmark.
Conclusion
Ignoring this gap leads to disappointing results and misdirected blame toward translated content that was never the actual bottleneck. Advize builds multilingual AEO strategy around the real structural constraint, thinner training data in most non-English languages, which produces more realistic expectations and smarter, more targeted budget allocation than treating every market as an equal opportunity.