Sure, AI can localise – but what happens next?

That was fast!

An unexpectedly quick turnaround, substantial time savings – we’ll take it.

Accuracy, consistency and clarity? All hard requirements, of course.

Imagine that AI has translated a product page into six languages faster than the marketing team could agree who would review it. With the launch date approaching, local colleagues are asked to “look over the copy”.

One checks whether it reads naturally. Another corrects a product description. Both return their comments, but neither knows whether they are also expected to approve publication. The marketing manager now has six translations and a bunch of corrections. Are the pages ready to go live?

Producing translations with AI can be straightforward. Deciding whether they are ready for publication takes more thought. What needs checking? Are the right people involved? Who can confirm that outstanding questions have been resolved?

This is where AI governance in localisation comes in: agreeing the review criteria, assigning responsibilities and establishing how approval is given. These arrangements provide a basis for publication across all language versions.

What should an AI translation review cover?

The colleagues have done useful work. But each one may have understood the assignment differently. Were they supposed to check spelling and grammar, compare the translation with the source text, verify product terminology, consider how customers would understand it – or something else?

Consider a sentence saying that a device is compatible with a particular accessory. The translation might read naturally while suggesting that the accessory is included in the purchase. A reviewer concentrating on fluency could miss the difference. A product specialist might spot it, provided they can understand both versions well enough to assess the claim.

If the marketing manager can’t read the target language, things get even more difficult. “It’s been reviewed” doesn’t give them much to go on unless they know specifically what the review covered and whether any issues remain unresolved.

A finished translation doesn’t reveal the work behind it: checks on meaning and terminology, issues flagged and conflicting feedback reconciled. When AI changes how translations are produced, teams still need to plan for this review work and assign it to people with the right expertise.

Accuracy matters – but how high are the stakes?

Step back and think about the objectives of the copy. What should readers understand, and what should they be able to do after reading it? Then consider the consequences of getting the message wrong.

A rough translation used to understand an informal internal update may need less attention than a product page that helps customers make a purchase. On the other hand, an internal document containing safety instructions may warrant a more thorough review. So the label “internal” alone says little about the consequences of an error.

For the product pages, accuracy matters on several levels. The translation needs to convey the intended meaning and ensure that product details are correct. The wording should also reflect how much the audience already knows about the product category. Even a technically accurate sentence can leave a customer unsure which product to order or how to use it.

The broader industry discussion reflects this need for different approaches to AI governance in localisation. In its 2025 evaluation of translation management systems, research firm Forrester noted that requirements vary across functions such as marketing, legal and employee communication, and that setting standards and overseeing workflows still require human involvement.[1]

Agreeing on review expectations takes time. It involves people who understand the content, its readers and the consequences of an error. Those conversations are easier to have before translated pages are waiting for approval and everyone is working towards a deadline.

A reviewer needs more than just language skills

Local colleagues can bring valuable knowledge of customers and the market. They may also know the language well enough to recognise awkward phrasing. But that doesn’t automatically mean they can assess every technical claim or identify a subtle change in meaning from the source.

The same limitation applies in reverse. A product specialist may know exactly how a device works but be less confident judging whether its description sounds natural or could be misunderstood.

Some texts therefore need input from more than one person. It should be clear why each reviewer is involved and what they are being asked to assess. And availability is no small consideration: there’s little point assigning a review role to someone who doesn’t have the time to do it properly.

This is where professional linguistic review adds value. A reviewer compares the translation with the source to identify missing information, changes in meaning and misleading wording, while also assessing terminology and suitability for the intended audience. Product questions may still need specialist input.

An experienced agency can provide this linguistic quality control when translated content is awaiting publication. AI-assisted quality checks can flag potential problems, but a reviewer still needs to decide whether changes are needed. Moreover, the team needs to understand what has been checked and which questions remain open.

Translation approval: when does “reviewed” become “ready”?

Let’s go back to those product pages. Imagine that the language reviewer and product specialist disagree over a term. One thinks customers won’t understand it; the other considers it technically precise. Accepting one correction without taking the other concern into account leaves the problem unresolved.

The person approving publication doesn’t have to resolve every issue personally. But they do need to know that the agreed checks are complete, important questions have been answered and the corrections appear in the final version. It also needs to be clear who is authorised to give final approval: returning review comments is not the same as approving publication.

If the same terminology questions keep returning, resolving them page by page creates avoidable work. Terminology management can help teams agree which terms to use and record them in a shared glossary. Definitions, approved equivalents in each language and notes on usage give reviewers a common reference. The decisions made for these six pages can then inform the next set of translations.

AI can save time in translation, but establishing a sound basis for review takes thought and effort. That work belongs in the plan for AI localisation.

With that groundwork in place, the team has a clearer route to publication when the next six pages arrive – and greater confidence in both the content and the process behind it.

1. Kathleen Pierce, “Announcing The Forrester Wave™: Translation Management Systems, Q3 2025, Our Inaugural Evaluation Of The Market,” Forrester, 10 September 2025.