Top 11 uses of AI in localization for 2025

AI is supposed to make localization faster and easier. So why do so many teams feel more overwhelmed when asked to implement it? Why does it sometimes create more problems than it fixes?

A couple of reasons:

1. Hype vs. Reality: Every vendor claims their model can deliver instant quality, reduce costs dramatically, and automate everything. But in practice, many solutions are still experimental, lack benchmarks, or only work well under very specific conditions. Teams are left questioning what’s truly proven versus what’s just marketing spin – and making the wrong choice can waste budget and damage your brand’s reputation.
2. Too many models, too many options: From general-purpose LLMs (like GPT-4 or Claude) to specialized MT engines, AI-powered TMS features, and even AI tools for subtitling, voice, and image adaptation – there are more options than ever. Each comes with different workflows, integration points, and trade-offs. For localization leaders who aren’t AI experts, the sheer number of models and use cases make it difficult to know where to start, what fits their business goals, and how to prioritize without spreading themselves too thin.

AI is fully reshaping how localization gets done. While Machine Translation was the first major AI application, the rise of Large Language Models, multimodal AI, and other intelligent agents is transforming every aspect of localization, from translation to project management to quality control.  As with any other tool, to deploy AI effectively, you first need to know what problems you’re trying to solve for. What’s the task you’re struggling with, and can AI be used to address it in a way that’s faster or more scalable than your current approach without creating too much risk?

We’re here to help you visualize success based on our experience implementing AI successfully for a large number of our clients.

So, here are the top 10 ways we’re seeing AI make a real impact in localization programs today.

1. AI-powered translation

When most people think of AI in localization, translation is the first use case that comes to mind. Neural Machine Translation (NMT) has been around for years and is now widely adopted, delivering fast, affordable translations that are useful for high-volume content. But NMT still struggles with nuance, domain-specific terminology, and creative marketing copy.

On the other hand, Large Language Models (LLMs) like ChatGPT or Claude bring more fluency and contextual understanding. They can adapt tone, improve style, and handle content that requires a human-like touch. The catch: LLMs are not always consistent, can hallucinate, and may introduce risks if unchecked.

The real opportunity is pairing the two. NMT engines can handle bulk translation with speed and cost efficiency, while LLMs can refine and adapt that output for tone, clarity, and accuracy. Together – especially when guided by human linguists – these AI models offer a balance of scalability and quality that neither can achieve alone.

2. AI-driven quality assurance

Quality assurance has always been one of the most time-consuming steps in localization. Traditionally, it requires multiple rounds of human review to catch errors in grammar, terminology, formatting, or cultural tone. AI models are now being trained to help automate this process, scoring translations against reference datasets, detecting inconsistencies, and even flagging potential bias or brand misalignment.

LLMs and other AI models can evaluate fluency, adequacy, and terminology use much faster than humans. They can highlight segments that likely need attention, freeing up linguists to focus on higher-value review rather than line-by-line checks. This is especially powerful for large-scale projects where manual QA would be too costly or slow.

But AI-driven QA has limitations. Models can be overly strict on acceptable variations (flagging correct translations as errors), or too lenient in other cases. They may also lack domain knowledge, missing subtle terminology issues that matter in regulated industries like finance or healthcare. For now, AI QA is best used as a “first pass” to surface likely problem areas—followed by human validation for final assurance.

3. AI-based terminology and style enforcement

Consistency is everything in a brand’s content: it preserves your brand’s voice, prevents misunderstandings, and makes sure concepts are clear across markets. Companies spend years building glossaries and style guides to ensure the right terminology and tone are used across products, marketing, and support content. The challenge is that enforcing these rules at scale is tedious and prone to human error.

AI models can help here by automatically checking translations against approved term bases and style rules. They can flag when a translator chooses a non-preferred word, suggest alternatives that match brand guidelines, and even adjust tone for different contexts—such as formal product documentation versus casual in-app copy.

The limitation is that AI still isn’t perfect at nuance. A model may enforce a term too rigidly, missing a case where context calls for a different word, or misinterpret flexible style guidelines. That’s why AI works best as an assistant—speeding up checks and highlighting issues—while humans make the final judgment calls on exceptions.

4. Real-time multilingual communication and support

Customers typing in chats or requesting help via a portal expect quick, in-language answers. AI can help your support team meet that need across languages without hiring native speaking agents for every queue.

Instead, AI can assist with drafting and translating responses so your team can reply quickly to handle tickets, portal requests, and chats in the customer’s language. In low-stakes interactions, it can even provide in-language responses automatically. But you don’t have to rely on AI alone; you can decide when a human steps in for review based on topic, risk, or channel.

Case in point: with our AI-assisted translation workflow and rapid post-editing, a global client now turns around multilingual self-service-portal tickets in under 30 minutes (record 3 minutes), boosting customer satisfaction without staffing a 24/7 in-house team.

5. AI-enhanced localization testing

Bugs and layout issues in localized software products are a serious problem for both your team and your end users. They’re also avoidable if you catch them early enough. But manual testing is time-consuming and resource intensive.

AI-enhanced localization testing speeds up this process by scanning content for missing translations, formatting errors, and linguistic inconsistencies across multiple platforms at once. It can even flag text that overruns a button or detect mismatched terminology before your QA team gets involved.

By handling the first sweep, AI creates faster testing cycles and smoother releases in every market.

6. Source content optimization

Good translations–both machine and human– start with good source content.  If the original text is unclear or overly complex, those problems multiply across every language you work in. (Garbage in, garbage out.) Fixing them after translation takes more time and budget than addressing them upfront.

AI can review source content before it’s sent for translation, highlighting sentences that may cause confusion, identifying terminology conflicts, and suggesting simpler phrasing where needed. It can also spot formatting quirks or structural issues that could lead to errors later in the process.

Cleaning up the source this way sets up translation in every language for success. It reduces the need for rework, keeps terminology consistent, and helps translations read more naturally, all while speeding up the overall translation workflow.

7. Regional content adaptation

A word-for-word translation might be accurate, but that doesn’t mean it will work in the culture it’s intended for. Every market has its own preferred tone, cultural cues, way of interpreting certain phrases, and local idioms. Content that feels natural in one region can sound awkward, or even off-putting, in another.

AI can now help adapt content by rephrasing idioms, softening culture-specific references, or shifting tone depending on the market. It’s a powerful way to bridge the gap between translation and transcreation.

That said, AI isn’t always great with subtle cultural nuance, and without human review it can produce copy that sounds polished but off-brand and not very human. The best results come when AI does the heavy lifting and humans bring the creative spark.

The result is content that reads like it was created locally.  That makes it more engaging and more likely to connect with your audience.

8. Market & Audience Insights from AI

One of the most exciting shifts is seeing localization teams use AI for more than just translation. By analyzing multilingual customer reviews, support tickets, or social posts, AI can surface insights about what users in different markets really care about. Suddenly localization isn’t just a cost center—it’s a source of global intelligence. The challenge is that sentiment analysis across languages is still imperfect. AI may misread sarcasm, slang, or cultural references, so human analysts are still needed to interpret the results. Used thoughtfully, though, this turns localization data into strategic value for the business.

9. AI-powered multimedia localization

Video and audio are now central to how companies communicate with global audiences—but localizing them has traditionally been expensive and slow. AI is changing that. Speech recognition combined with machine translation can generate subtitles and captions almost instantly, while LLMs help refine phrasing for readability and cultural nuance. At the same time, synthetic voices are becoming natural and expressive enough to power localized voiceovers or even lip-synced dubbing. Together, these tools make it possible to adapt training videos, product demos, and marketing assets for multiple markets at a fraction of the time and cost.

As with most current AI implementations, if you need top-tier quality then you also need human experts to fine-tune tone, pronunciation, and cultural fit. Even so, AI can make the process dramatically faster. The payoff is multimedia content that grabs attention and builds your audience in every market you target, without the long waits or heavy production costs that used to stand in the way.

10. Managing terminology with AI

Terminology databases and translation memories keep your translations consistent across linguists and languages. But manually extracting commonly used terms for your database is slow. And it comes with a high risk of errors.

AI can help here by scanning through everything you’ve published, in multiple languages, and pulling out a list of key terms.  It can then compile these terms into a termbase and translate it, using your own approved previous translations and translation memories as a guide.

At Acclaro, we also use AI to update TMs, so your translations stay accurate even as times (and terms) change.

11. AI-assisted workflow automation

Some parts of localization are too expertise-driven to automate. Others are just begging for it: File prep, formatting, pre-translation, and other repetitive steps can eat up hours of your team’s time.

AI-assisted workflow automation takes those routine tasks off your plate. It can convert files into the right format, apply pre-translation using your translation memory and an MT engine, and route jobs to the right linguists or reviewers automatically. It does the heavy lifting so your team can spend more time on creative or strategic work.

The payoff is faster turnaround times and a workflow that just works. You still control the process; you’re just letting AI handle the parts that don’t need your full attention.

For example, in pilot testing, our GenAI fuzzy match enhancer improved 40 to 80 percent of segments in German, Spanish, French, Italian, and Chinese, cutting post-editing time and keeping TM terminology intact.

Why the right AI approach matters

Combine even just a few of these use cases, and your global rollouts will get much faster and easier to plan.

But AI in localization works best when it’s targeted. Big, one-size-fits-all platforms often add features you don’t need and force you into workflows that don’t fit your team. Acclaro takes a different approach.

Our modular AI micro-services let you choose exactly what you need—whether that’s machine translation, quality checks, terminology enforcement, multimedia localization, or multilingual model validation—and integrate them directly into your existing systems. We connect to the tools you already use: Jira, GitHub, WordPress, Marketo, Zendesk, and TMSs like Phrase and Lokalise, so you can add AI without building your entire process over from scratch.

The advantages are clear:

• Flexibility: Add or remove AI capabilities as your needs change.
• Efficiency: Direct effort to the tasks that benefit most from AI.
• Integration: Works with your existing tools and workflows.
• Quality: Combines AI speed with expert human review.
• Scalability: Expand to new markets without reinventing your process.
• Compliance and security: Enterprise-grade practices and data handling.

If you’re ready to make AI work for your localization program, start with the use cases that matter most to your business. We’ll help you put together a solution that meets today’s goals and prepares you for tomorrow’s opportunities.

Let’s talk about how AI can fit the way you work.

P.S. Here’s a handy checklist to see if you’re ready for AI.