Beyond neutral: how AI brings the local voice to life

Language is complicated. When we talk about good translation, we often think only about getting the words correct. But real, local communication is more than that. It encompasses not only the correct aspects of language but also all the ways the community shifts and subverts those rules to communicate nuance and therefore belonging.

 

There’s an idea in translation that neutral is polished and safe, but the reality is that it often fails to connect with the target audience for that exact reason.

 

That subtle disconnect comes at a price. It means customers might feel alienated. They notice when content feels imported. But good localization can be expensive, too, and sometimes hard to scale. As brands expand across markets and expect to work despite borders, can AI bridge that gap?

To find out, RWS conducted an unusual experiment where the goal wasn’t to test translation quality. It was to see whether AI could move from neutral to natural.

 

The team focused on variant pairs within the same language family:

Each pairing reflects subtle yet meaningful linguistic differences in idioms, tone, sentence rhythm and even emotional cadence. We wanted to know what it would take for language to feel like belonging and whether AI could accomplish this very human aspect of translation.0

 

Using large language models (LLMs), RWS evaluated how AI handled these differences. The experiment measured whether AI could capture local voice, idiomatic phrasing and the linguistic shifts in vocabulary, syntax and morphology that distinguish global content from authentically local communication.

 

The experiment told us a lot about what machines are capable of and where their limits lie. But not exactly in the way you might expect.

AI models are remarkably consistent at structural adaptation. They easily handle:

LLMs also manage general tone shifts well when guided, such as making a sentence sound friendlier or more formal.

But when it comes to cultural nuance, it looks like even the most advanced models still can’t quite replace a local human expert. AI often misses:

To be clear, these aren’t mechanical errors. They aren’t quantifiable or technically correct or incorrect. They’re human subtleties. AI can mimic them based on probabilistic decisions but doesn’t always understand when to apply them. That’s an important distinction in translation.

Humans are still an important part of the translation process, even with AI translations. That’s not surprising. What might be, however, is that the experiment revealed that results varied dramatically based on:

Simple instructions like “translate to U.K. English” produced sterile results. But when the team added context – for example, “adapt this U.S. marketing headline for a U.K. audience using natural tone and regional phrasing” – the quality improved significantly.

Prompt design, it turns out, is the differentiator between surface-level localization and authenticity.

LLMs respond to detail. In many cases, the more specific the prompt, the more natural the output. How you ask the model to accomplish a task is just as important as what you’re asking it to do.

Let’s look at how different prompts might play out:

Result: Grammatically correct but formal and stiff. You’ve provided only the barest context for the model.

Result: A better translation reflecting regional and idiomatic specificity.

 

Prompt engineering is an exercise in creativity as well as strong communication. With more context, the AI can behave more like a local copywriter instead of a translator. 

 

So, how can you get more from your prompts?

 

Here are some tips for more natural localization prompts that cover context in addition to commands:

Even better, add examples of successful phrasing for context. Again, the model works through probability rather than understanding the language itself, so showing it something to copy creates a more specific way for it to predict those outcomes.

We’re still a long way from machines understanding language enough to translate without us. Humans remain an indispensable part of the translation process and provide important cultural credibility to any translation.

 

Expert linguists and cultural reviewers understand how word choice influences trust, humor and relatability. They catch the subtleties AI can’t: a phrase that technically means the same thing but feels awkward or even off-putting in another region.

 

For example, the words “holiday” and “vacation” in U.S. and U.K. English may seem similar. However, for U.S. English, the word “holiday” evokes only a specific recognized celebration. In the U.K., holiday and vacation are more interchangeable, with vacation used far less overall. This may not seem like a big deal, but it can signal subtle differences that make content feel off.

 

The RWS human-in-the-loop model keeps experts embedded in every step of the AI translation and localization workflow. They don’t rewrite everything. They review and refine. They guide. This hybrid model helps make localization a lot more efficient without losing the authenticity that makes communication trustworthy.

 

RWS’s hybrid localization workflow pairs machine speed with human precision. AI performs the initial adaptation, rapidly converting content across markets and variants. Then, human linguists and cultural reviewers fine-tune the tone, phrasing and emotional resonance.

 

For marketers, this means faster campaign launches, higher engagement rates and stronger brand equity in every market. For localization teams, it offers scalability without the bottlenecks of manual adaptation. And for enterprises, it’s a sustainable model that makes the best use of what machines are good at and maintains human authenticity.

Neutral language once promised universality, but people value identity and connection. In practice, neutrality feels impersonal. The RWS experiment proved that AI can get us closer to truly local communication – but not without human guidance.

 

Prompt engineering, context and human-in-the-loop review transform AI from a translator into a cultural collaborator. For organizations managing multilingual content pipelines, the next step is clear. Audit your workflows. Identify where the content sounds correct but is not local. Then, explore how AI-assisted localization can help your brand sound like it belongs everywhere it speaks.

 

If you want to see this balance in action, take a look at how RWS blends machine learning with human expertise to create localized content that feels native from the start.