What translation misses
Translation moves words. Localization moves the listing. A listing that performs well on Amazon.com often falls flat on Amazon.co.jp even after professional translation because the unit system is metric, the trust signals are different, and the buyer reads the listing in a different scanning pattern.
The model can help with both, but the brief has to specify the target market. 'Translate to Japanese' is not enough. 'Adapt for Amazon Japan with metric units, JP-style trust signals, and a softer tone in the title' gives the model a clear target.
Building a market-by-market checklist
For each target market, build a checklist with three sections. First, the units and measurements. Second, the cultural references and idioms. Third, the trust signals and proof points that matter locally. The checklist is what you hand to the AI every time you adapt a listing for that market.
The checklist lives in your project documentation, not in the prompt. It gets reused across hundreds of listings. It also makes it obvious when your product is not a fit for a market — sometimes the answer is no, and the AI cannot tell you that.
When to use a native speaker
Use a native speaker for the final read of any listing in a non-native language. AI drafts are good enough for most of the structure, but idiom, humor, and cultural nuance still need a human pass. The native speaker does not need to write the listing from scratch. They need to catch the sentences that feel off to a local.
If a market is large enough to justify a 100-listing-per-month cadence, hire a part-time local editor. The cost is small compared to the cost of a campaign that lands flat because the listing sounded machine-translated.