Mirela
A localization team typically consists in translators, cultural experts, and editors who work tirelessly to adapt content. It’s been like this since the very beginning. However, more recently, we saw an additional help entering the scene. Some are excited about it, while others remain a little skeptical. That’s why, in this article, we’re going to discuss the role of generative AI in localization, and see how it actually helps brands move faster.
About generative AI
Generative AI is a class of artificial intelligence models that’s trained on massive amounts of text across many languages and domains. The foundation of a generative AI model is a deep learning neural network. The model is trained on an enormous corpus of data (like text from the internet, a collection of images, or audio files).
During this training, the model doesn’t just memorize the data; it learns to identify and encode the underlying patterns, structures, and relationships within it. For a text model, this involves understanding grammar, syntax, semantics, and how different concepts are related.
Most modern generative AI models, especially those for language, are built on a specific architecture called the Transformer. The key innovation of the Transformer is the attention mechanism. This mechanism allows the model to weigh the importance of different parts of a sequence (like words in a sentence) when processing a particular element.
A crucial step in making these models useful and safe is Reinforcement Learning with Human Feedback (RLHF). After a model generates an output, human trainers evaluate its quality, relevance, and safety. This feedback is used to further fine-tune the model. It’s important to teach your AI tool to produce more helpful, accurate, and less biased results.
What generative AI brings to localization
So what does generative AI actually bring to localization? First, it’s a lot better at understanding context. Where older translation tools might get tripped up on a word like “pitch” (does it mean baseball, sales, or music?), AI can usually figure it out from the sentence around it. That alone saves a ton of cleanup work.
It’s also surprisingly good at shifting tone. You can ask for something casual and chatty in one language and then turn around and request something more formal in another. On top of that, AI can learn your brand’s style if you feed it examples of how you already talk. Once it gets the hang of your voice, it can apply that tone consistently across markets.
And then there’s the creative side. Anyone who’s tried to translate a pun or a slogan knows how often it just doesn’t work. With AI, you can ask for multiple alternatives that capture the same spirit, not just the same words.
And finally, if you’ve got thousands of product descriptions to roll out in multiple languages, no team of humans can do that overnight. But AI can produce workable drafts instantly, and then your human reviewers can make sure that everything looks alright.
Why it still needs humans
Generative AI can generate text that looks fluent, but in reality a word could carry a negative connotation in another country, or a color, symbol, or phrase could be politically sensitive. That’s where the risks show up. AI is also know to sometimes hallucinate (aka producing confident but incorrect information).
AI can mimic a brand voice, but it won’t always know when to switch registers for specific audiences. So you need humans to make those judgment calls based on experience and strategy, not just patterns in text. Plus, end users might notice when something feels off in their language. Human oversight ensures the final product feels authentic.
To conclude
The role of generative AI in localization is about more than speed. We’re also talking smarter translation, contextual understanding, and giving human experts better starting points. Generative AI is taking care of the heavy lifting so that humans can handle the subtleties of adapting content for audiences worldwide.