Must-Know Localization Trends for 2025
The localization industry, a a USD 41.8 billion powerhouse, is poised for continued growth. With a projected CAGR of 2.02%, what does the future hold for this dynamic sector?
To uncover the key trends shaping the localization landscape in 2025, we consulted with the experts at Milengo. In this blog post, we’ll explore the key developments set to revolutionize the industry.
AI is now part of the furniture
Language AI – the technology that delivers AI-generated content and translations – has become a top priority for global companies. For instance:
- Since September 2024, Reddit has been offering automatic translations in 35 languages for posts and comments in its forums. This allows users from Brazil and France, for example, to communicate in their native languages.
- YouTube now offers real-time video dubbing in multiple languages – including experimental functionality that would mimic the creator’s tone and intonation in the dubbed versions.
Automatic translations for apps, videos, and websites will soon be delivered seamlessly. This trend is called Translation as a Feature (TaaF) – near- or real-time interfaces that rival the Babel Fish from The Hitchhiker’s Guide to the Galaxy.
Industry transformations
Content management systems (CMS) such as Contentful aim to offer AI translation options by default in 2025. However, these tools often lack a connection to translation management systems (TMS), where translations are traditionally maintained and managed, significantly complicating quality assurance processes for multilingual content inventories.
Innovative translation software such as Lokalise or Phrase offers connectors to popular CMS tools to bridge this gap. Proprietary AI features in software are also becoming the norm. This has led to a swathe of strategic mergers across the industry, with a recent example being memoQ’s acquisition of machine translation provider Globalese. Companies that focus on AI innovation such as Blackbird.io, a platform that automates translation management processes, are also growing in stature in the industry.
As you can see from our brief snapshot, AI is making waves throughout the industry. But what should companies consider when choosing the right translation technology? Our first localization trend offers some insights here.
Localization trend #1: The battle between NMT and LLMs will rage on… or will it?
The world’s most accurate translator.
The world’s best translation AI.
The most intelligent, AI-enabled translation platform on the market.
Translation platforms such as DeepL, Phrase, and Trados all make bold statements with their slogans – which is no surprise considering that they are vying to outdo each other.
But they can’t all be the best, right? Let’s separate the fact from the fluff.
We’ll start with the core technology: neural machine translation (NMT) or large language models (LLMs).
For many years, neural machine translation providers such as Google Translate were the undisputed leaders. In 2022, ChatGPT turned this status quo on its head. Since then, the industry has been fervently debating which of these two technologies will be the future of translation.
From a conservative standpoint, NMT is still the safer choice – after all, unlike large language models, this technology was specifically developed for translations.
LLMs such as ChatGPT are notoriously unreliable – especially when it comes to “verbosity”. Put simply, this is the risk that the AI decides it wants to “have a chat” and starts explaining its decisions or providing multiple translation options in the middle of a translation.
How well can ChatGPT actually translate? Check out our Tech Talk with Milengo experts to find out.
While automation, which is common in automotive manufacturing, for instance, aims for nearly 100% reliability, unregulated AI poses a security risk. This is critical for large companies, as the AI could jeopardize their reputation or business objectives.
But once these teething problems are resolved, LLMs will undoubtedly be the future. They are more powerful, leverage larger amounts of training data, and can better grasp the context of a translation. Most importantly, they can be adapted to specific requirements through prompting.
Industry reports such as Intento’s Machine Translation Report 2024 confirm this trend and increasingly highlight LLMs such as GPT-4o as leaders in translation applications.
Lara by Translated: LLMs in translation services are trending
Our prediction
In 2025, these technologies will converge instead of compete, opening new opportunities for businesses. DeepL has recently integrated a next-generation LLM into its translation service. New translation tool “Lara” by Translated also takes the hybrid approach, boasting an impressive rate of just 2.4 translation errors per 1,000 words. Another prominent example is e-commerce giant Alibaba, which innovatively merges NMT and LLM technology to provide translations in e-commerce shops.
No matter which technology ends up dominating, there will still be another key challenge on the path to the “perfect” translation. We will address this in the next localization trend…
Localization trend #2: Context will still be key
Machine translation engines have struggled to comprehend texts properly for decades.
For a long time, sentences were translated in isolation, leading to translation errors – does the German word Fluss mean flow or river in English? What does a pronoun like it refer to in the sentence She couldn’t find it anywhere? It’s nearly impossible to know without additional context. Such context could be drawn from external documents, a video, or simply common sense.
Human translators intuitively put information into context – machines do not. But language AI developers are finding creative solutions to tackle this problem. Below are the four we’re most excited about.
Questions and A(I)nswers
Tomedes’ AI Translation Assistant generates questions based on the text the user uploads to refine the translations provided by the AI. The user then selects multiple-choice answers to set preferences for tone, target audience, or specific translation nuances. This highly adaptable method optimizes translations according to the text type.
Tomedes dynamically generates questions about the translated text
Speech-to-Understanding
Subtitles in videos have a fixed text length and must be synchronized with the underlying visual content. This has always been a challenge for artificial intelligence in translation.
Now, the translation software Trados is taking a new approach with its Generative Subtitles. Instead of translating subtitle files line by line in isolation, the text is translated within the full context of the video. In addition, an AI-generated summary of the video is used to help align the on-screen text. Custom terminology can also be integrated.
Trados promises a breakthrough in subtitle translation with its AI-powered quality assurance
Thanks for the memories
Did you know that translation tools never forget? They utilize translation memories – databases where previous translations are stored for reference.
The TMS memoQ has now made it possible for AI to use this valuable data to generate even more accurate translations. With memoQ AGT, the output is automatically tailored using the existing language resources, including translation memories, glossaries, and reference documents.
AGT is integrated directly into memoQ
Translate with style
Many modern translation software solutions already offer the option to provide the AI with basic guidelines on the style and context of translations.
One example is Lokalise, which allows users to specify the desired style and tone of translations based on the target market and the content’s objective. While this may not always be enough to accurately capture a complex brand style, it can still significantly improve translation quality.
A simple context prompt in the translation tool Lokalise
Our prediction
The power of artificial intelligence lies in analyzing and interpreting vast amounts of data. If AI can learn to leverage more context data for translations, then next year will see it become an even faster and more formidable competitor to human linguists.
Localization trend #3: AI will get better at being “human”
Artificial intelligence needs more than just raw data to make it effective. That’s why researchers are taking more and more inspiration from human behavior. For instance, a research team at Google is currently developing an AI translation tool that is designed to mimic the multi-step translation process used by professional translators:
- It starts by performing background research and analyzing the text to identify potential challenges in the subsequent translation.
- Next, it creates a draft translation, staying close to the original text.
- It then refines the draft to improve the flow and structure of the translation.
- Finally, it performs a review to ensure that the translation is error-free and ready for publication.
Our prediction
In 2025, language AI will mimic human speech and replicate human language conventions with even greater accuracy. For instance, algorithms may start to consider the average amount of information in human speech.
Localization trend #4: The way we evaluate language will change
AI can translate vast amounts of text into a rapidly growing number of languages – including lesser spoken languages like Afar, a Cushitic language spoken by nomads in East Africa.
Freely available AI translators such as Google Translate are convenient and make everyday communication or travel a breeze. However, there’s also an unfortunate side effect – our standards for language quality are declining. Whether it’s auto-generated subtitles or poorly translated apps, we’re getting used to bad translations.
That’s perfectly fine if you just want to read a menu while you’re on holiday. But when translations are critical for businesses or even a matter of life and death, it becomes a whole different story.
This type of content requires strict quality control measures and transparent quality standards – especially given the growing number of risks posed by the unprofessional use of machine translation and AI.
This is where the new ISO 5060 guideline published in February 2024 comes into play. It’s the first standard that enables translations to be evaluated using an objective and systematic process.
ISO 5060 assigns errors to seven main categories:
- Terminology
- Accuracy
- Linguistic conventions
- Style
- Locale conventions
- Audience appropriateness
- Design and markup
All the error types can be configured as needed and weighted by severity. This means every type of text can have its own specific quality definition – no matter whether it’s software UI, marketing copy, or technical documentation.
The crucial thing here is that ISO 5060 focuses on the result, not the process. Previous translation standards such as ISO 17100 emphasized the mechanics of translation: What qualifications do translators hold? How and where are translations stored and managed? How are translation workflows documented? Is sensitive data adequately protected?
ISO 5060 further separates translation quality from the underlying process – a logical step in a world where the lines between human and machine-generated translations are increasingly blurred.
Our prediction
The way translation quality is evaluated will become even more standardized and refined in the future. This could be either human or automated quality assurance depending on what’s needed.
When the stakes are high, text quality should be reviewed by a human based on ISO 5060. If all that’s required is a rough assessment of whether AI output can be used for translation, automated approaches such as machine translation quality estimation (MTQE) are a better fit.
Conclusion
As you can see from our selection of localization trends, there’s a lot going on in AI and translation. However, many of the solutions that were designed in the wake of the AI hype in 2023 remain underdeveloped or are still in progress – although this could change dramatically in 2025.
In any case, any translation generated by AI in the future will still need to meet certain expectations:
- AI translations must be adaptable to customer or business requirements, as needed. More and more professional users are turning away from generic systems such as the free versions of DeepL or ChatGPT.
- AI translation services only work effectively when paired with human experts. These professionals serve as the final quality checkpoint, managing complex translation requirements related to terminology, style, branding, and legal compliance.
The evolution from barely understandable to nearly print-ready AI translations is advancing rapidly. However, “perfect” automatic translations are still just a dream. Language is not an algorithm of zeros and ones – it is irrational and evolves in unpredictable ways.
It will be intriguing to see to what extent artificial intelligence can continue to master the complex and chaotic aspects of human communication in the future.
