How AI in translation can help you scale localization efforts

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:

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:

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.

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:

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:

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.

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:

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.

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.

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:

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.

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.