AI localization: the definitive guide for 2025

Let’s say an automotive software provider needs to keep up with weekly app updates across 25 languages.

Using a translation management system (TMS) integrated with AI ensures fast, accurate translations at scale without overburdening the team. Instead of manually localizing each string for every release.

Fewer delays and more efficient updates mean a smoother experience for global users.

AI localization tools with built-in style guides and glossaries help lock in your brand voice and approved terms.

Instead of sounding like 12 slightly different companies, you sound like ‘you’ in each.

Take a pharmaceutical company that launches a new treatment across Europe and Asia.

With AI-powered termbases, ‘treatment protocol’ doesn’t become ‘healing schedule’ in one region and ‘medication plan’ in another.

The message stays clear and compliant for every global audience (because ‘guess what we meant’ isn’t a great regulatory strategy).

An AI localization workflow should be a straightforward, repeatable formula that helps you translate content quickly and accurately.

A proper TMS like XTM is at its heart.

XTM combines AI-driven workflows (XTM Cloud) with software localization (Rigi) and project management (XTRF):

Before you can localize anything, you need to pull (ingest) the right content from wherever it lives. Whether that’s your CMS or code repositories.

Parsing then breaks that content down.

It separates what you should translate (like product descriptions and button labels) from what you shouldn’t. Think HTML tags or code snippets.

Without proper parsing, you risk:

Neither is a good look for global launches. Unless you’re aiming for ‘lost in translation’ as a design aesthetic.

Let’s say an enterprise retailer managing 100,000+ SKUs across multiple regions needs to localize every product and sizing guide correctly.

With a cloud-based TMS, the brand can automatically pull product content from its CMS.

Then, use built-in parsers to separate translatable text from code elements accurately.

Here are three tips to get it right:

If your site has lots of dynamic content (like personalized offers or dynamic sizing charts), double-check how your TMS parses those fields.

A little setup work now can save thousands of edits later.

Before your content touches a translation engine, smart pre-processing cleans it and checks for errors.

This step uses AIto automatically harvest key terms and run pseudo-translations (quick dummy translations to spot structural issues).

Then, it checks source quality for things like typos or missing context.

If you skip this step, even the best AI can stumble. Messy inputs almost always lead to messy outputs.

Imagine a pharmaceutical enterprise preparing to localize new patient education materials across 15 countries.

If their glossary isn’t locked down before translation, key terms like ‘clinical trial’ could end up mistranslated or misinterpreted. Hello, regulatory headaches.

Here are three tips to get pre-processing right and avoid future disasters:

Good pre-processing can save you hours (or days) of painful rework later.

Engine routing automatically chooses the best MT engine for each type of content. While some are better for technical manuals, others are stronger with conversational marketing copy.

Getting the right match upfront saves a ton of time (and money) on post-editing later.

For example, a manufacturing enterprise may need to localize detailed maintenance manuals and social media ads.

If it sends both through the same MT engine, the manual might turn out fine. However, the ad copy could sound robotic and weird.

Intelligent engine routing sends:

1) Technical docs to a technical-specialist engine

2) Creative copy to an engine trained on marketing language

No one wants an assembly manual that sounds like the script for a TikTok trend.

Here are three ways your TMS can help:

Routing engines can improve turnaround and quality, meaning less human editing and faster launches.

Human post-editing involves skilled translators reviewing and refining AI’s work to make it sound natural. This way, content will be accurate and fit your brand voice perfectly.

It’s how you go from ‘technically correct’ to sounding human.

Think of it as AI doing the heavy lifting, with humans polishing up cultural nuances and inclusive language before you send it live.

Imagine a biotech company releasing a patient-facing brochure.

AI translation might technically get the words right but miss critical tone (e.g., sounding too clinical when it should be reassuring).

A human post-editor makes sure it sounds warm but compliant. No confused patients. No awkward headlines. No regulators at your door.

Here’s how to make sure your human post-editing process is solid:

Here’s what translation memory looks like in XTM:

Set clear guidelines for this editing stage. Is the goal just to make it understandable (light editing) or publication-ready (full editing)?

Define this upfront to save back-and-forth later.

Automated LQA scans translations for typos, inconsistent terminology, formatting mistakes, and more.

While human eyes are invaluable in localization efforts, they can miss things when dealing with thousands of words.

Automated LQA is your last line of defense before localized content goes live.

Think of it as setting up tripwires to catch the little mistakes before they snowball into big, embarrassing issues post-launch.

For example, a global software company may be about to push a major app update in 15 languages.

Without automated LQA, a mistranslated button or broken character in Korean could ruin the user experience (UX) and flood support tickets.

With built-in LQA in its TMS, the enterprise catches and fixes issues automatically before users ever see them.

Here’s how to bake LQA into your localization process the smart way:

Instead of trusting a ‘100% quality score’ at the end of this workflow, you may want a linguist to give it one final read.

LQA flags mechanical errors, but humans can spot if a sentence feels wrong for the target audience.

Continuous delivery means your localized content moves automatically from translation to deployment through smart triggers (like webhooks or branch updates).

This process keeps global launches lightning-fast and ensures every language version stays in sync with original content updates.

Let’s say your mobile app rolls out new features every two weeks.

Instead of waiting to collect all translated content manually, you use a TMS with continuous delivery.

As soon as a localized string passes QA, it flows straight into the right build. No hold-ups, no version control nightmares.

Here’s how to set up a continuous localization flow that actually works:

Make sure you build in regular human and AI-assisted QA checkpoints.

Even automated flows should leave a little breathing room to catch last-minute issues before content goes live.

AI localization is powerful, but it’s not foolproof.

Left unchecked, it can produce anything from mildly confusing copy to full-on international embarrassment.

For example, billionaire investor Ray Dalio once gave a speech using voice-to-text and MT-backed subtitles.

English and Chinese transcriptions were both wrong and confusing, updating “Ray is a man with a dream” to “That. One in Switzerland. Dreamer.”

Facebook’s AI translation even got one Palestinian man arrested when his “good morning” post was mistranslated to “hurt them” and “attack them.”

Here are six of the most common pitfalls and how smart teams sidestep them:

You don’t have to choose between speed and quality.

Use AI for efficiency and humans for accuracy. Let both do what they’re good at to ship confidently in any language.

AI localization can take the stress out of your translation efforts. However, it can also lead to risks that cost you more than just time without proper guardrails.

That’s where the right platform steps in.

With everything from glossaries to vendor management, XTM speeds up processes while ensuring quality.

Use it to blend AI-powered automation with the human contextual understanding you need to localize your content accurately and consistently at scale.