There is a crucial difference between translating and localising:

• Translating: Converting the message from language A to language B
• Localising: Adapting the message to the cultural context, expectations and conventions of the target market

A literal translation of "We're killing it in sales" into another language rarely produces usable text. Good localisation requires more than linguistic knowledge — it requires cultural understanding.

Modern LLMs are surprisingly good at localisation, not just translation. They know cultural nuances, idioms and market-specific conventions — provided you explicitly ask for this in your prompt.

AI multilingual content works best for:

• Structured content: Product descriptions, FAQs, specifications — content with a fixed structure that scales well
• SEO texts: Informative content around search terms that differ per language market
• Email campaigns: Variants for different markets based on one briefing
• Interface and microcopy: Buttons, labels, notifications — short texts that need to be consistent

Branding and tone of voice: Your brand identity must be consistent across languages. AI tends towards a neutral, formal register unless you explicitly instruct otherwise.

Domain-specific jargon: Technical terms, legal formulations or sector-specific language requires specific instructions or a glossary in your prompt.

Cultural context: Humour, references and metaphors do not work universally. What resonates in the Netherlands may not land in Germany or Spain.

Quality assurance per market: You cannot evaluate output if you don't speak the language. This requires native speakers for random checks or specialised review workflows.

The most effective approach for multilingual AI content production combines:

1. Master version in one language: Write or generate source content in the most manageable language
2. Language-specific system prompts: Each language market has its own instructions for tone, formality and cultural nuances
3. Glossary per language: A fixed word list for brand names, product terms and sector jargon
4. Native review on random sample: Not every text needs review, but a percentage per category is essential

Using this approach, one of our clients produced product descriptions in seven languages for thousands of products — 90% cheaper than manual translation.

A common mistake with multilingual content is translating search terms. Dutch users search for different terms than French or Spanish users for the same product. Effective multilingual SEO requires:

• Keyword research per market
• Language-specific meta titles and descriptions
• Local URL structures (/fr/, /de/, etc.)
• Hreflang tags for search engines

AI can help generate the content, but keyword research is market-specific and must be done separately.

Multilingual content production with AI is realistic and scalable, provided you put the right structure in place. The combination of strong system prompts, market-specific glossaries and targeted human review makes it possible to serve multiple language markets without scaling costs or capacity proportionally.

Want to know what a multilingual content workflow would look like for your company? View our services or get in touch.