Expanding into a new language market looks straightforward on a spreadsheet. You pick a country with strong demand, hire a translation agency, and assume the rest is execution. But one variable almost nobody checks is whether the language you are targeting is actually practical to translate into accurately and affordably.
That gap between the language you want and the language you can realistically serve at scale is one of the most expensive blind spots in international business planning. And it tends to surface well after the decision has already been made.
Most guides on global expansion, including those covering planning the early stages of your business, address market sizing, legal setup, pricing strategy, and distribution channels. What they rarely address is linguistic feasibility: a technical question that sits upstream of all the others, and that most businesses skip entirely.
The Assumption Nobody Questions
Not all language pairs are equally difficult to translate between. English to Spanish involves two languages with shared Latin roots, comparable sentence structures, and decades of machine translation training data. English to Japanese involves a completely different script, a sentence structure that places the verb at the end, and cultural registers with no direct English equivalent.
An AI model trained primarily on European language pairs will perform inconsistently on Japanese, particularly for technical or domain-specific content. The error rate is not the same. The cost to fix it is not the same. And the reputational damage when it goes wrong is not the same.
The Hidden Cost of Bad Translation Choices
Operational decisions made without full information tend to surface as cost overruns later. In translation, that usually means one of three things: higher post-editing costs when machine output is too rough to publish, longer review cycles that delay product launches, or customer trust erosion when translated copy reads as obviously non-native.
The commercial stakes are well documented. Research from CSA Research consistently shows that roughly 76% of online shoppers prefer to purchase from a site in their own language, and 60% rarely or never buy from an English-only site. That creates strong commercial pressure to localize. But localizing into a language pair your tools struggle with creates a different problem: content that is technically translated but practically unreadable.
The businesses that absorb these costs without understanding why are usually those that chose their target language based on population size alone, without checking how linguistically accessible that market actually is.
What Makes One Language Pair Harder Than Another
Several factors determine translation difficulty between any two languages. Linguistic distance is the most significant: how structurally similar or different two languages are in terms of grammar, syntax, and morphology. Languages in the same family, like the Romance group, share enough structural patterns that translation is generally more reliable and less expensive. Languages separated by broader typological distance, like Finnish or Korean relative to English, require more structural rearrangement and produce more errors per sentence.
Script complexity adds another layer. Moving between Latin-script languages is mechanically simpler than moving between scripts that read differently, run in different directions, or have no direct character-level correspondence. Arabic, Thai, and Chinese each present distinct challenges, and those challenges compound when AI translation engines were not trained equally across all scripts.
Domain sensitivity is the third factor. Legal, medical, and technical content are harder to translate accurately in any language pair. In structurally distant pairs, the margin for error shrinks further because the model has fewer anchor points to work with, and errors in those domains carry real consequences.
How to Check Translatability Before Committing
One AI tool that makes this question answerable before you commit budget is a translation difficulty checker developed by Tomedes, a translation company that has worked with over 120,000 businesses globally. The tool operates as a language pair difficulty matrix built on data from SMART, Tomedes’ proprietary multi-model AI system, which runs inputs through 22 leading AI models simultaneously and compares their outputs.
The tool lets you select any source and target language pair and choose a content domain, such as legal, medical, technical, or marketing. It then returns a difficulty profile covering linguistic distance, script complexity, false cognate risk, and AI model consensus data. That last factor is particularly useful: a low consensus score across 22 AI models on a given language pair is a reliable signal that the pair is genuinely hard to translate, not just unfamiliar.
For a business planning to enter a new market, this converts what was previously an opaque judgment call into a concrete data point. If you are choosing between two similarly-sized markets and one language pair scores significantly harder than the other, that difference belongs in your budget and timeline projections.
What the Data Actually Shows
The practical insight from comparing language pair scores is not always what businesses expect. Consider a market like Brazil, for example. Portuguese-speaking markets carry genuine scale, but Brazilian Portuguese involves specific regional vocabulary, informal registers, and cultural references that diverge meaningfully from European Portuguese. Treating them as one language pair is a common and costly oversimplification.
Research compiled from multilingual e-commerce data, including Shopify findings that buyers are approximately 13% more likely to purchase from a store in their own language, quantifies the revenue case for localization. What the Tomedes tool adds is the cost-of-entry side of the equation: a market where localization is both high-revenue and technically accessible is the obvious priority. A market where the language pair is difficult and translation cost is correspondingly higher needs a different business case, or a phased entry strategy.
Businesses that treat localization as a binary decision consistently underestimate the variance in what it costs across different markets. The language pair is the primary driver of that variance.
A Practical Checklist Before Picking a Target Market
Language accessibility is part of a broader set of infrastructure decisions that precede scaling. Before committing to a new language market, run through these questions:
- What is the linguistic distance between your source language and the target? Are they in the same language family, or structurally distant?
- What script does the target language use, and does your current translation tooling handle it reliably?
- What content type are you translating? Marketing copy, legal terms, and technical documentation each carry different error sensitivity.
- Have you checked AI model consensus on this pair? Low agreement across multiple models is a reliable difficulty signal.
- Does your translation budget reflect the actual difficulty of the pair, or is it based on a per-word rate that treats all languages as equivalent?
- If the pair is genuinely difficult, do you have access to human linguists who can post-edit machine output in that language?
None of this argues against entering a difficult market. It argues for entering it with accurate information. A business that knows its English-to-Korean legal content will require substantial human review can plan and budget for that. One that discovers this after launch is managing a problem, not executing a plan.
The Case for Checking First
Market selection is not only a question of where demand exists. It is a question of where you can actually serve that demand at the quality level your brand requires. Language difficulty is a real variable in that calculation, and it is now a measurable one.
Checking your target language pair against a difficulty matrix before finalizing your expansion plan takes a few minutes and can change the sequence of markets you enter, the budget you allocate to each, and the tools and teams you put in place. That is a straightforward improvement to a decision most businesses still make on instinct alone.
