Understanding AI learning in a global, multilingual market
AI models learn mostly from English data. What that means for businesses that market to Vietnamese, Chinese and Spanish-speaking customers, and how to get better results.
Most AI models learned from data that is overwhelmingly English. They work in other languages, but not equally well. For businesses serving multicultural communities in the U.S., that gap matters.
Where AI struggles across languages
- Tone and politeness. Vietnamese and Chinese have forms of address that change meaning and respect. AI often gets them wrong.
- Slang and regional terms. Words used by Vietnamese-Americans in Orange County differ from those used in Vietnam.
- Cultural context. Holidays, colors, numbers and symbols carry meanings that a literal translation misses.
Where it helps
AI is excellent for first drafts, research on what different communities search for, and producing variations to test. Paired with native speakers, it speeds up multilingual marketing considerably.
Our approach
- Research each audience separately: what they search, watch and trust.
- Write the core message per culture, not just per language.
- Use AI for drafts and variations, then edit with native writers.
- Test and measure each language version on its own.
Learn more about our multicultural social media and ad campaigns.
