AI localization, as a software team meets it, is machine translation and language models used inside the localization workflow: a first translation of new strings before a human reviews them, a suggestion on fuzzy matches the memory almost holds, a check that placeholders and plurals came back intact, and a draft of the context note the translator needs. Localization AI does not change what the record has to keep; it changes the cost of the new-word share and the speed of the first pass, and it makes the review step and the locale build test more important, not less. This page sets out what AI does at each step, what it does not do, and how the catalog and the memory keep it honest.
Where AI sits in the loop
At the costing, machine translation turns new words into a pre-translated share that is reviewed rather than translated, which moves the cost from the new-word rate toward the review rate; the translation memory leverage sheet on this site takes that as a larger fuzzy share and shows the effect on the release's cost. At the handoff, a model drafts the context notes. At the read-back, a model checks that placeholders, plurals and character limits survived. At the test, nothing changes: the locale build is still scored on what shipped.
What localization AI does not do
It does not know which of the three senses of a word the key means unless the catalog carries the context; it does not know the product's terminology unless the memory and the glossary are supplied; it does not know that a string is a legal notice that must not be paraphrased unless the key says so; and it does not know the string is in the build unless the catalog is complete. Every one of those is a record the team keeps, and AI localization on a catalog that lacks them produces fluent errors at scale.
The record keeps the AI honest
A machine translation recorded against its key with a status of drafted rather than reviewed is visible; one pasted into a spreadsheet is indistinguishable from a human's. Keeping the status per locale, the reviewer's sign-off and the locale build's defect counts against the release is what lets a team use AI on the first pass and still ship at its threshold. The localization testing checklist on this site scores the build the same way whoever wrote the first draft, and Xlifflane Pro keeps the statuses that say who did.
Questions people ask about ai localization
Can AI localization replace the translator?
It can replace the first draft of new strings and the suggestion on fuzzy matches; it cannot replace the reviewer who knows the product, and the locale build still has to be tested. Teams that remove the review step find the errors in the build, or the users do.
Does machine translation change the leverage sheet?
It moves words from the new-word rate toward a review rate, which the sheet models as a larger fuzzy or exact share at the review rate. The memory still matters: a match a human made is still cheaper than a draft a model made.
What should the catalog carry to make AI useful?
Context per key, the product's terminology as a glossary, the memory in an open format, and the status per locale so a draft is never mistaken for a reviewed translation.
Sources
- ICU User Guide, Formatting Messages: MessageFormat, the syntax for plurals, selects and placeholders inside a localised string
- OASIS, XLIFF Version 2.1: the XML Localisation Interchange File Format the handoff is generated in
- W3C Internationalization, Localization vs. Internationalization: what each term covers and why the second has to come first