A campaign that works beautifully in English, translated literally into Dutch, typically feels slightly off. The words are right but the register is wrong. Dutch B2B communication is more direct; English corporate hedging sounds formal and distant in Dutch. The brand voice, which felt confident and approachable in English, becomes stiff. The content is technically translated but not genuinely localised, and audiences notice even if they can't articulate exactly why.
Translation and localisation are not the same task
Translation converts words from one language to another. Localisation adapts content to feel natural and appropriate for a specific market, which includes language, but also tone conventions, cultural references, regulatory norms, and audience expectations. For brand content, localisation is the only standard that matters. Translated content that doesn't feel local fails the brand in that market, regardless of how accurate the translation is.
This distinction matters for how you brief AI localisation. A prompt that says "translate this to Dutch" produces translation. A workflow that specifies Dutch tone conventions, local cultural references, regional vocabulary preferences, and audience register produces localisation.
How brand voice survives localisation
Brand voice elements that survive localisation well are those that are expressed at the level of values and intent rather than specific language patterns. "Direct, evidence-based, without hyperbole" survives localisation because directness, evidence, and avoiding hyperbole are values that can be expressed in any language. "Short punchy sentences with contractions" is a language-specific implementation of those values, and that implementation needs to change per language to feel natural.
A good localisation workflow separates these layers:
- Brand values: what the content must communicate, these are fixed across markets
- Brand personality: the character of the communication, translated into market-specific expression norms
- Language implementation: the specific vocabulary, sentence structure, and idiomatic choices that express personality naturally in that language
Market-specific brand rules
Brands that localise effectively with AI build market-specific rule sets that sit on top of their global brand parameters. For a Dutch market, this might mean: shorter sentences than the English version (Dutch B2B tends to be more concise), fewer formal honorifics ("je" not "u" in most B2B contexts), specific product terminology that differs from the global default, and local references that resonate in the Dutch market context.
Companies like Coolblue are an instructive example, their brand voice is unmistakably Dutch in its directness and warmth, while being recognisably consistent with what the brand stands for globally. That's not an accident; it's a designed localisation of brand values into a specific market expression.
Variant generation at scale
One of the practical advantages of AI localisation in a structured workflow is the ability to generate multiple market variants simultaneously. A campaign brief that would previously have required sequential agency work in each market, brief the Dutch agency, wait, brief the German agency, wait, can now produce all variants in parallel, each operating within market-specific brand parameters. For a campaign rolling out across five European markets, the time saving is significant. More importantly, because all variants are generated from the same strategic brief, they share a conceptual coherence that sequential agency work often loses.
Quality control across markets
Native-speaker review remains important in localised content, particularly for detecting idiomatic errors and cultural missteps that automated systems may miss. The goal of AI localisation is not to eliminate native review, it's to produce a starting point close enough to final that native review is a refinement pass rather than a reconstruction effort. Measure this: how many edits does a native reviewer make to AI-localised content compared to a literal translation? If AI localisation requires significantly fewer edits, the workflow is working. If it's comparable to translation, the localisation parameters need refinement.
Further reading
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