How AI in translation can help you scale localization efforts
Move over Google Translate. Today’s artificial intelligence (AI) makes robotic phrases and head-scratching translations a thing of the past.
In this article, we’ll show you how AI and language translation work together. Learn how you can implement AI without derailing your localization workflows and how to tackle any hiccups along the way.
Modern AI translation systems learn from billions of translated sentences to pick up patterns that help them understand context, tone, and even cultural references.
Here’s the magic behind the curtain: AI translation uses neural networks (think digital brain cells that learn patterns) to analyze massive collections of previously translated texts.
Unlike older systems that simply substituted words individually, today’s neural machine translation processes entire sentences at once. Because it works from such a huge dataset, all kinds of industries can use AI in different ways to make their workflows lightning fast.
For example:
Language service providers (LSPs) are riding the AI wave like pros. LSPs can now offer “MT post-editing” services where AI handles the heavy lifting of initial translation, and human experts swoop in to polish and perfect.
In e-commerce, AI translation means companies can instantly translate their entire product catalog into 20+ languages. Just as well, since 75% of consumers prefer to buy products in their native language.
For life sciences companies, the stakes are even higher. Translating clinical trial documentation or medication instructions? A single mistranslated word could have serious consequences. Modern AI maintains consistency across millions of words while flagging terminology that might raise regulatory eyebrows.
Let’s say you provide backend gaming infrastructure for multiplayer matchmaking, chat moderation, and player analytics.
You’re crushing it in North America and want to expand into South Korea and Germany.
Without localization, Korean dev teams are stuck using an English dashboard with unreadable date formats and error messages.
Plus, German regulators will flag your platform for missing compliance notices in their native language.
With localization, dashboards feel familiar to each international team. You can confidently launch knowing you’ve ticked legal checkboxes and all users understand the entire platform.
AI is transforming how language translation works, but not all AI functions the same way. Here are the three most common types of AI and how they differ.
MT is like the vending machine of AI translation. Plug in what you want, and it spits it out word for word.
For example, when you use Google Translate to read a Spanish restaurant menu in English, that’s MT in action:
Source: Google
Depending on the type of MT you use, the result might be clunky and literal or surprisingly fluent. Here are the main differences:
| Type of MT | How it works |
| Rule-based machine translation (RBMT) | Relies on grammar rules and dictionaries to produce translations. Works well for simple language, but often misses idiomatic expressions and cultural nuances. |
| Statistical machine translation (SMT) | Uses large datasets of translated text to predict the most likely translation. Doesn’t understand meaning, but finds patterns based on probability. |
| Neural machine translation (NMT) | Uses deep learning and AI models to understand context and produce more natural, fluent translations. It’s the foundation of modern tools like DeepL and the latest versions of Google Translate. |
While MT is the most widely used form of AI in translation, number two on our list is quickly catching up.
Generative AI is like MT’s cooler, younger cousin. Instead of just following patterns, it creates text from scratch using advanced language models.
Gen AI models — like GPT-4 or GPT-4.5, which power ChatGPT — are trained on massive amounts of data in different languages. They understand context, tone, and even cultural references that would fly right over MT’s digital head.
This makes generative AI useful for more than basic translation.
For example, AI can help translate an English marketing slogan so it still lands in German. Not just word for word, but with the same emotional impact:
Source: ChatGPT
It’s come a long way in recent years, but gen AI’s not perfect. As with other AI translation tools, humans still need to swoop in with their red pens to get accurate translations.
Think of AI-assisted localization tools as your super-smart editing sidekick. Unlike generative AI, which creates new translations from scratch, the tech taps into machine learning algorithms and translation memory (TM) to keep content consistent.
Picture these tools as trusty grammar nerds with photographic memories to boot. They remember every translation your company has ever approved and suggest reusing those golden phrases when appropriate.
The outcome? Your content sounds natural and tailored to global audiences, whether they’re in Tallahassee or Timbuktu.
XTM is a perfect example of AI-assisted localization in action. Its Advanced AI Pack comes fully stocked with intelligent tools to keep your content organized and your workflows humming.
Take SmartContext, which goes beyond traditional translation memory. While basic TM only identifies exact or close matches, SmartContext uses deep learning to nail context and nuance:
It analyzes how terms have been translated across your entire TM. Then recognizes patterns and applies them to new content in real time.
You stay consistent and on-brand. And spend less time wading through tedious revisions.
Or take Language Guard, your AI-fueled safety net. It filters out anything inappropriate, risky, or downright offensive before your content goes live. Saving you from a potential PR nightmare.
XTM Cloud uses AI to speed up your translation workflows — without compromising on high quality or control. See SmartContext and our other AI features in action.
The biggest win from using AI to translate? The precious time you save.
Our internal evaluations have found that XTM Cloud and its AI-assisted features can save your company 2,000 hours per month. That’s like a full year’s worth of work from one person. Every single month.
Here are some ways AI technology helps improve the translation process. Plus a couple of other language translation benefits you might not have thought of:
| Benefit | How AI-powered translation delivers it |
| Cut time to market | Giving you instant first drafts so human translators can focus on content polishing, not starting from scratch Automatically handling those repeated phrases (nobody wants to translate “Terms and Conditions” a hundred times) Taking care of the tedious stuff like tagging, file prep, and formatting |
| Minimize translation errors by up to 50% | Highlighting potential mistranslations or missing segments so nothing slips through unnoticed
Applying preferred phrasing and formatting rules across all languages, even when multiple translators are involved
Keeping the tone aligned with your brand voice, not just word-for-word accuracy |
| Lower localization costs | Reducing time spent on manual tasks, so you pay less for translation services overall Avoiding costly rework cycles by reducing localization errors Building up your TM faster, making each future project more cost-effective |
| Scale faster | Automatically adapting content to different languages and regions while preserving core meaning
Handling high-volume updates like seasonal campaigns or product launches
Making it easier to onboard new markets or languages without slowing down existing localization workflows |
For translation workflows where repetitive tasks are common, those savings add up quickly. Especially within enterprise-scale operations, where speed and cost are key.
Assisted by AI features, a translation management system (TMS) like XTM slashes localization costs (by over 60%) and time to market (80%+). Companies can more than double their localized output, with an impressive 90%+ improvement in translation quality.
Take Johnson Controls, a global leader in building tech. It struggled with a slow translation process, causing more delays than a traffic jam. So it hooked up XTM Cloud to its own AI-powered translation tool and got to work.
The result? A leaner, smarter setup with centralized translation memory, visual review tools, and automated workflows. Together, the AI tech and XTM Cloud cut turnaround times by a massive four weeks. Saving Johnson Controls serious money in the process.
Now that you know how using AI for translation can benefit you, let’s break down the steps to get it smoothly into your workflow. No magic wand required. Just a bit of planning.
Before you bring in AI, take stock of what you’re translating now. And where there might be room to do more.
Start with a short working session between your localization lead, content owners, plus product or regional teams. Focus on:
Content types. List everything you currently translate. Think user guides, UI text, help center articles, emails, etc. Include content you want to translate but haven’t because of cost or time.
Complexity. Identify low-risk content that doesn’t require deep subject knowledge or cultural nuance. This is where AI shines the brightest. Especially for translating that high-volume or repetitive text that feels like pulling teeth.
Language pairs. Pull data from your TMS or translation provider to see which language pairs you use most often. Include upcoming markets or languages your regional teams have been not-so-subtly hinting about.
Content volume. Look at word counts from recent projects, or export data from your content management system (CMS) or translation software. Estimate average monthly volume per content type and language pair. Spoiler alert: it’s probably more than you think.
This step doesn’t need to be perfect. The goal is to spot high-volume, low-risk areas where AI can give you the biggest return. You can always add more as you get to grips with AI systems.
Not all AI translation tools are built equal. Run through this list to check that potential tools fit the needs you identified in step one:
Translation approach. For high-volume, simple content, machine translation may be enough. For more complex, customer-facing material, look for tools like XTM Cloud that support AI drafts with human editing.
Language support. Ensure the tool handles your key language pairs, especially less common ones or those with specific dialect needs. A tool may cover European Spanish, but what about regional variations or Catalan? XTM Cloud supports 887 languages to make sure you’re covered.
Cost and pricing model. Is the price for the AI translation tool by word? By usage? Costs can add up quickly, especially for high-volume content. Weigh up short-term savings against long-term scalability. XTM offers flexible pricing to grow with your business.
Start with a shortlist, then test one or two with real content, either through a free trial or demo project. This helps you see how the tool handles your workflow and language pairs before you commit.
Not sure where to begin? Take XTM for a 30-day spin for free.
AI isn’t a full replacement for human translators, but it can do the heavy lifting. The trick is knowing where to bring people in.
Let AI handle the first draft. Let machine translation handle high-volume, low-risk content. It’s fast and gets you a solid starting point.
Assign human reviewers where it matters. For complex or customer-facing content, involve professional translators to review and adjust tone, clarity, and cultural relevance.
Divide the workload. Let AI manage terminology consistency and formatting. Human translators can focus on nuance and accuracy.
Build feedback loops. Make sure you track human edits so the system can learn and improve over time.
You don’t have to choose between speed and quality. Just ask language service provider Tilde, under pressure to turn around high volumes of content, fast.
It hooked up its adaptive MT engine directly into its computer-assisted translation (CAT) tool, so the tech learns from its translators’ edits and gets smarter over time.
For speed like that, you need structure. That’s where XTRF by XTM comes in.
The translation business management platform handles the nuts and bolts: vendor and project management, financial admin, reporting — the boring-but-crucial stuff.
With XTRF keeping everything on track, Tilde’s teams could focus on quality and scale up without burning out.
AI works best when it fits smoothly into your current setup. Not when it’s tacked on as a separate tool. Or worse, an afterthought.
Disconnected platforms create room for errors and force your team to play digital hopscotch between systems. Here’s how to keep things flowing:
Use APIs to automate workflows. APIs can trigger actions like sending content for translation when it’s approved or syncing finished translations back to your CMS.
Choose a tool that doesn’t disrupt communication. Your translators are already using tools like Slack or Teams. If your AI solution can send updates into those same channels, you’ll speed up feedback loops and keep everyone on the same page.
Connect to your TMS or CMS. Choose AI translation tools that integrate with the software you already use. For example, XTM Cloud integrates with Hubspot, so you can add translations where you write instead of going back and forth between tools.
Build AI into your existing workflow, and it will support your translators and marketing teams without slowing them down.
Even the best AI needs a second pair of eyes. A strong QA process helps catch errors and keep your translated content sounding right every time.
Use post-editing for high-impact content. Have human translators review AI output for anything public-facing, regulated, or legally sensitive.
Create review checklists. Make sure reviewers check for things like tone and formatting, not just grammar.
Track errors and patterns. Log common issues so you can fine-tune prompts or adjust your source content.
Take steps to get the proper QA in place, and you’ll be able to put your AI-translated content into the world with confidence.
To make things easier, XTM Cloud created LQA (Linguist Quality Assessment).
This built-in translation quality scoring system tracks translation errors by volume, severity, and type (like accuracy or terminology). Helping you spot exactly where your translation process needs fine-tuning:
Your LQA is displayed on your business intelligence dashboard alongside other metrics. So you can spot and fix translation issues fast.
AI advancements have made breakthroughs that were once thought impossible. However, like any technology, it’s not without flaws.
Here are some challenges to keep in mind as you start using AI in translation, plus how to overcome them.
If you’re using AI to translate content like confidential client contracts, research papers, or sensitive business information, you’re dealing with data that needs extra protection.
AI tools must follow strict rules to keep your data safe. But here’s the catch: many AI systems process and store data to improve their translation models.
Without the proper safeguards in place, you risk exposing sensitive details. Which spells serious consequences, especially when working with intellectual property or private research. For instance:
Under Europe’s GDPR rules, fines for AI-related data breaches can cost you 7% of global revenue or €35 million, whichever is higher
In the US, state-level laws like Virginia’s Consumer Data Protection Act (VCDPA) or the California Consumer Privacy Act (CCPA) can land you fines of up to $7,500 per violation
Tip: Make sure your AI tools are transparent about how they handle your inputs. Look for platforms that guarantee they won’t store or use your content to train their models. XTM backs this up with comprehensive documentation. So you know exactly how we protect and use your translation data.
With XTM Cloud, your data stays safe. We don’t use it to train AI models. Ever.
Speed up translation while meeting the highest security and compliance standards.
Most AI is great at direct translation, but we’ve already shown that localization is a different ball game. It’s about making sure your message truly resonates with a local audience.
The problem with traditional AI? It often misses key cultural nuances. Think things like local preferences or idioms. When these aren’t translated well, it can make your content feel off.
The knock-on effect can be huge. Language barriers can lead to lost engagement or even damage to your brand’s reputation.
Tip: To combat this, choose an AI translation tool designed for localization, not just translation. XTM Cloud is a TMS built to handle both, with localization-specific functionality like an AI-enhanced TM for consistency and terminology management to align your brand’s voice across markets.
78% of organizations use AI in at least one business function. Despite its popularity, not everyone in the translation industry will immediately be doing cartwheels at the thought of bringing robots into their workflow.
It’s common to worry that new tech might replace human translators, but that’s not the case. AI is here to assist.
Tip: The key to easing concerns is clear communication. From the start, explain how AI will be used and where human input is still crucial.
Provide hands-on training so your translators can see how AI will handle the tasks they secretly dread. Like double-checking terminology consistency or proofreading large volumes of content.
Once people realize that AI frees them up to focus on the more creative and complex aspects of translation, it will be much easier to get their buy-in.
There’s a Grand Canyon-sized gap between having AI tools and knowing how to use them effectively.
Without proper guidance, your shiny new AI tools might end up like that exercise bike in your garage. Purchased with the best intentions, now gathering dust.
Tip: Bridge this gap by assigning AI champions within your translation team. Identify tech-savvy translators who can master the tools first, then serve as internal mentors and troubleshooters.
To keep up to date on the latest advancements, encourage your AI champions to watch our XTM webinars. They’re a great place to cut through the noise around AI and get practical advice.
The use of AI in translation is like a superpower for businesses looking to speed up workflows without quality taking a hit. Of course, every hero faces challenges, but you can easily leap over them with a proactive approach.
If you’re thinking about implementing AI in your translation process, start small. Identify the areas where AI can save you the most time and integrate it into your workflow. Then scale as you grow.
Seeing is believing. Book a demo with XTM to see our AI-assisted tools in action.
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