The pitch for AI translation is universal: fast, scalable, affordable, any language. The reality is that quality drops sharply the moment you step outside the well-resourced language pairs. 

For brands expanding into markets where English, Spanish, French, German, Chinese, or Arabic is not the native tongue, “AI handles it” is an assumption worth testing very carefully before you publish anything that represents your brand.

Low-resource languages — those with less available digital text for training — produce significantly higher error rates in LLM translation. A 2025 study found LLMs showing frequent semantic errors in low-resource language translation tasks, and the model's confidence level bore little relationship to its accuracy. 

The AI does not sound uncertain. It sounds confident and wrong, which is considerably worse. Our Nordic languages Lingonaut works with Finnish, Estonian, and Latvian regularly. These are not obscure languages — they have millions of native speakers and active digital economies. 

However, they are, by AI training standards, low-resource compared to Spanish or French. The quality difference in AI output is visible to any native speaker immediately.

What “low-resource” actually covers

It does not only mean rare or indigenous languages. The category includes regional languages with substantial speaker populations but limited digital presence. Welsh, Basque, Catalan, and several Scandinavian and Baltic languages sit here, as well as languages with non-Latin scripts where digitized training data is harder to aggregate. 

The problem compounds when brands want to translate e-commerce sites or localize specialized content in a low-resource language: legal text, medical information, and regulatory compliance. 

Domain-specific vocabulary is exactly where training data is thinnest, so the AI is working at maximum uncertainty in the content types where errors cost the most.

For example, with a Nordic e-commerce client expanding into the Baltic market, we were asked to review AI-translated Estonian product pages. The general product descriptions were acceptable. 

The care instructions and regulatory disclosures, however, were a mix of technically correct sentences and semantically broken ones that no Estonian speaker would use in writing. A native Lingonaut caught everything in review. The AI's quality estimation scores flagged none of it.

The risk calculus nobody is doing honestly

The question is not “can AI translate into this language?” — the answer is usually yes. The question is “at what quality, for what content type, and with what human review budget?” 

For low-resource language pairs, the review scope needs to increase significantly, and high-risk content types require native review on every segment. 

Test AI output on a representative sample before committing to a full project, and use native speakers as evaluators rather than automated metrics — BLEU and similar scores are unreliable for low-resource pairs because the reference data is thin. 

For e-commerce specifically, product descriptions and customer-facing copy are your first impression in a new market. A translation that sounds slightly off to a native speaker undermines the brand signal you are trying to build before the customer has even read your About page. 

For high-resource language pairs, AI plus light post-editing is a defensible choice for volume content. But for low-resource pairs, it needs more human involvement — not because the technology is fundamentally broken, but because it has not been trained on enough data from that market to be trusted without it. 

At LikeLingo, when a client asks us to localize into a less common language, the first conversation is about what level of AI-assistance makes sense for that specific pair and content type. “AI handles it” is not an answer. “AI drafts, Lingonaut owns” usually is.