AI localization is cheap until every market edits it

An AI translation can make a first draft available in seconds. The market may still need a local editor, a product specialist, a legal reviewer, and a publishing owner before the text is safe to use.

The translation charge is easy to report. The work around it is where localization budgets usually drift.

A language is ready when the market can accept and publish the asset, not when the translation finishes.

Build the full localization cost#

For each market, count:

• translation and generation usage;
• retrieval of the source and glossary;
• local editor time;
• product or legal review;
• changes to claims, units, dates, and examples;
• channel formatting and publishing;
• rework after a reviewer returns the asset;
• work that expires before the campaign date.

Then calculate cost per accepted and published asset by market. The same source can be inexpensive in one language and expensive in another because the approval path, terminology, or product maturity differs.

Use a market-level table#

The table is illustrative. It shows why a shared average makes the wrong decision. The German and Japanese paths need more review and publish fewer starts. Lowering the translation rate will not fix that acceptance gap.

Give the source and glossary an owner#

Localization fails when the source set is unclear. Name the owner for:

The source-freshness guide explains why an old source creates late returns. A glossary cannot repair a product claim that changed after the source was captured.

Separate reusable work from market work#

Some checks can be shared:

• product names and feature labels;
• prohibited claims;
• required disclosures;
• formatting rules;
• accessibility requirements;
• link and tracking conventions.

Other decisions belong to the market:

• examples and idioms;
• local proof points;
• channel norms;
• regulatory wording;
• customer terminology;
• seasonal timing.

Mark the boundary in the brief. A local reviewer should not need to decide whether a global product claim is still true, and a central reviewer should not rewrite every market-specific sentence.

Measure the return reasons#

Use specific reasons instead of “translation quality”:

• term not in glossary;
• source claim changed;
• local meaning is wrong;
• channel length exceeded;
• disclosure missing;
• link or tracking field missing;
• reviewer preference;
• product owner requested a new message.

The first six are process defects or required checks. The last two may be legitimate decisions. Separating them prevents the team from treating every local edit as evidence that the model failed.

The brand review guide shows how return reasons can reduce repeated review. Apply the same discipline across markets.

Plan for the last publishing step#

An accepted translation can still fail during publishing because:

• the destination has a different character limit;
• links point to the wrong regional site;
• dates, prices, or units were not localized;
• metadata remains in the source language;
• an image contains untranslated text;
• the market owner cannot find the approved version.

Count these checks in the publishing stage. The cost-per-asset guide is useful only when the accepted asset includes the work required to place it where customers will see it.

Set a market policy#

For each market, decide:

1. which content types may use automated translation;
2. which claims always require a local specialist;
3. when a stale source blocks drafting;
4. how many attempts are allowed;
5. who can approve a new term or exception;
6. how long evidence and versions are kept;
7. what happens when the campaign date passes.

The policy should produce an action. A missing glossary can pause the request. A low-risk internal message may continue with a local review. A regulated claim may require a named approver before publication.

Deixic can show spend by agent, activity, connected tools, approvals, and missing cost evidence around these decisions. Use the product view to see where a market’s work is waiting, then keep local performance and publishing results in the regional team’s systems.