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The truth about implementing AI in localization

A recent McKinsey report tells us that 92% of companies plan to pour more money into AI. Yet only 1% of leaders say their organization is mature in their use of it—where AI moves beyond a flashy experiment to being fully baked into their workflows and delivering business results.

Seems like 99% of those enterprises who are dabbling in AI are leaving value on the table.

We see this play out daily in localization. So many organizations are interested in implementing AI into their localization programs. Yet they struggle to pick the best tools to tackle the right problems and implement them without breaking existing processes and minimizing risk.

Here’s the thing: Localization is one of the best places to start getting AI right. Stick with us while we explain why that is and give you a blueprint for AI implementation designed to give you real results.

When AI Met Translation: A Friendship Story Decades in the Making

First off, why is localization such fertile ground to get started with AI? And why should you trust a language solutions provider (LSP) for tech advice, anyway?

Because localization has always been a tech-forward industry, and we’ve been doing this for years. Decades, in fact.

Machine translation, a form of AI, has been in place since the 1940’s. The history is fascinating: it started when an American scientist learned of Alan Turing’s achievements in cryptography to break the Germans’ Enigma Code. What is translation, anyway, if not a form of code-breaking?

The first problem after the war was keeping abreast of Russian scientific publications. IBM picked up the challenge…but the earliest models were only capable of swapping one word out for another while observing only the most basic grammar rules. Science fiction had everyone dreaming about a universal translator, but early machine translation was far from that.

Fast forward to the 2020’s where we have large language models. We may not have flying cars just yet, but we’re close to having that universal translator. Translation is something LLMs do quite well, in over 100 languages. If your average LLM were a person, it would’ve majored in linguistics and minored in translation studies. So yeah, this is our turf. And our customers have been using AI in the form of Neural Machine Translation for years. But smart enterprise localization teams and savvy LSPs aren’t stopping at basic translation. They are also using AI to automate workflow steps, summarize content, handle voiceovers, ‘convert’ text from one dialect to another, and estimate quality of source and translated text. Here’s how we are using it for our customers (without causing chaos).

When you implement AI in your localization program, you’re not starting from scratch—you’re building on decades of innovation and working with partners who are already familiar with the ins and outs of implementing new tech.

Next, we’ll show you what results look like, what to watch out for, and how to get your AI strategy rolling the smart way.

The Good Stuff: What Enterprises Gain from AI in Localization

There are 4 major benefits you can expect when AI stops being just another buzzword and becomes a working part of your localization strategy:

Faster turnaround: AI handles repetitive, time-consuming busy work so humans can move faster and have more fun doing it. And it’s not just about speed: when humans aren’t stuck doing the boring, labor-intensive tasks, they can focus on what requires a nuanced expertise—voice, culture, nuance.
Lower cost: AI can reduce human effort substantially, which brings down costs.
Ability to handle higher volumes: if you have millions of words to translate across service, support, marketing, training, and user content, then there is no way to get that done without some level of automation.
Higher quality: Humans make mistakes. Highly trained machines not as much. But there’s a huge caveat: AI can be more accurate than humans in specific tasks, particularly those involving data processing and pattern recognition, but humans excel in areas requiring creativity, intuition, and understanding of context.  (We’ll get back to that in a minute).

And that’s all great. But it’s also only the beginning. When it’s all implemented properly, AI isn’t just about workflow improvements and the tactical stuff. It’s a strategic lever—think faster launches, better engagement, more conversions, and more markets.

And yet, despite all this, some of our customers are hesitant to get started using AI, or struggle with implementations. There are some good reasons for that, though they need not hold you back.

What Could Possibly Go Wrong? (Spoiler: A Lot—If You’re Not Careful)

You’re being pressured to bolt AI onto your localization program? We get it. But here’s what you need to know first:

In translation, you can get bias and stereotyping: LLMs train on the internet. The internet is a dumpster fire of stereotypes (and worse). Left unchecked, that bias flows into your localized content.
Workflows can be disrupted. Drop AI into a traditional workflow and you can get chaos. If tools aren’t properly integrated, they’ll clash. If you try to automate too much for the wrong reasons, everything good about your existing program will be at risk.
Inaccuracy can happen at scale. Yes, I know above I asserted that AI can improve quality. But maybe not with highly nuanced or emotional content (like marketing copy). Also, AI translations might be “pretty close,” which is a huge problem when legal, safety, or branding is on the line.
There’s a security risk with some tools. Feeding proprietary data into third-party models without oversight? That’s not innovation, that’s a disaster waiting to happen.

You can check out a full exploration of all the risks of bad AI implementations.

Why Localization Teams Need a Plan

We talk to so many teams that are ready to dive in, and we applaud this. But without a plan, you’re basically committing random acts of technology, and there are a lot of potential problems with that. Here’s how we’ve seen it play out in the past:

Most of the push to implement AI comes from the top down. Execs want it in place now, without specifying what that actually means. Then comes budget pressure, because reinventing your localization program should be all savings, no costs, right? Meanwhile, localization teams are left scrambling to keep up with tech that’s moving at warp speed that may break processes that are already in place.

Then the IT teams waltz in—bright and well-meaning, but dangerously unaware of how localization works. They start building AI-powered tools with zero context about linguistics, culture, brand, or technical localization challenges. Localization teams have to step in to do damage control, fixing translations or website builds that just don’t work in another language or backing up and re-inserting a human when things go wrong.

We have seen messy AI implementations, and we don’t want that for you. We’ve seen some shiny tools that don’t work, cause problems, or that no one wants to use. We are all in on implementing AI where it makes sense, and doing it in a thoughtful, strategic way—and this isn’t it.

The fix? Communication, shared knowledge, training, and shared planning.

Matt Rodano, our VP of Account Management says “Everyone wants to move fast on AI, but speed without alignment is a recipe for chaos. I’ve watched execs push for quick implementation, assuming it will save money and solve everything. Meanwhile, localization teams are left to clean up the mess. AI isn’t a silver bullet. Businesses need a plan, shared goals, and a serious dose of cross-team education. When we work with our customers to connect the right people, clarify outcomes, and build smartly, AI becomes a powerful tool. But if teams skip any of those steps, they may be just adding complexity and creating risk—and no one has time for that.”

Jump in…but with a life vest

We call those bad AI implementations the enterprise version of running with scissors.

Here are our top 7 tips for putting down the scissors and getting your AI implementation right.

Get aligned. Get everyone involved in localization in one room: your execs, PMs, linguists, product team, and engineers. Decide which localization problems you truly need (and want) AI to solve. Rank them in order of importance. This list becomes your roadmap.
Start mapping tools to those problems. Do you need single modular AI services (microservices) or do you need one do-it-all tool? (Our view: probably the former if you are implementing AI for a specific use case.
Take a hard look at your current tech stack. Will it work with the AI tools you’re already using? You may choose to upgrade (or replace) your infrastructure. Sometimes upgrading is harder than ripping out the old stack and starting fresh.
Where translation is concerned, make sure you have clean data. Dirty translation memories (TMs) make dumb models (and bad output). Invest in data hygiene before anything else. Bonus: AI can actually help clean up messy TMs and term bases. (We do this all the time. Just ask!)
Revisit your quality requirements. Define what you really need in terms of linguistic and localization quality in 2025. Especially if you’re in legal, medtech, finance, or any place where a hallucinated term could cause real damage. What processes do you need to put in place to make sure the quality is what you need it to be?
Lock down your security. You possibly want on-site implementations of AI workflows instead of cloud versions, especially in regulated industries. Cloud tools, especially public ones, can expose you to major risk.
Take care of your people. AI can stand for Alienating Implementations, but if you play it right, your team can be happier and more productive than before. Upskill like you mean it. Train your internal folks and involve them in the process.
Call an expert! We wouldn’t write this blog post if we weren’t all in on helping you with AI. Our tech consultants know the problems and how to fix them, the risks and how to mitigate them, and they never, ever will recommend implementing AI just for the sake of it. It should go without saying, but we show up to solve problems, not create them.

Whether you are curious about AI or under great pressure to implement it yesterday, we’re here for you. Check all out all the things we are doing with AI, like translation, multimedia, glossary and source file cleanup, data validation, and multilingual LLM model testing.

And if you’re intrigued, reach out to connect with an expert here.