One AI Expert’s Advice for
Localization Professionals

Which AI trends are shaping the future of the localization industry?

Neural machine translation (NMT) is definitely on the list. The localization industry is increasingly focused on aligning tone and handling regional context consistently across languages. These models are slowly picking that up. Researchers are focusing on customizing models to fit each culture; for instance, I’ve worked on Meta’s No Language Left Behind (NLLB) model and IndicTrans2 for Indian languages and for Dzongkha, a Bhutanese language.

AI-powered transcreation platforms are getting trendy too. Smartling AI can transcreate slogans, ads, and campaigns. Speech recognition systems are also getting customized to adapt to code switching (mixing one or more languages) and colloquial language use.

How will AI transform the roles of localization professionals?

AI is really reshaping the localization industry, and over the next three to five years, I see roles shifting quite a bit. Localization experts won’t just be translating; they will be working more strategically — refining AI outputs, adapting tone and context, and making sure the content truly connects with local audiences.

Although I don’t come from a traditional localization background, as someone working in generative AI (GenAI), I’ve been exploring how open-source AI tools can support and enhance localization workflows. I’ve had the chance to experiment with customizable translation models that can be adapted for specific domains or language pairs, and I find it fascinating how these models can bridge gaps in communication.

I’m also interested in tools that prioritize user privacy, especially those enabling translation directly in the browser. In the context of audiovisual content, I’ve explored speech-to-text systems and alignment tools that assist in creating subtitles or captions more efficiently. I’m gradually learning about tools that handle different localization file formats and automate quality checks — areas I hope to explore further.

From a GenAI perspective, I’ve been experimenting with multilingual language models and techniques like entity recognition and glossary extraction to improve contextual understanding in translations. I’m still learning, but I’m excited about the possibilities that AI brings to localization, and I look forward to collaborating with domain experts to build meaningful, scalable solutions.

On top of that, collaboration with AI teams is becoming more common. Whether it’s curating high-quality datasets, giving feedback for model fine-tuning, or aligning brand voice across languages, localization experts will be more involved in the AI training loop.

So it looks like the future localization professional will have a hybrid role — part linguist, part cultural consultant, part tech collaborator. It’s exciting because we’re not being replaced by AI; AI and localization experts will evolve together.

How should we balance human expertise and AI tools in localization workflows?

AI is for redundant tasks, and humans are meant for creativity. Localization is an industry that demands a human touch. Experts should focus more on the creative aspects of localization and on ensuring quality, while AI might support 30% or 40% of the workflow, attending to repetitive or mundane tasks.

What technical skills should localization professionals learn to stay competitive in an AI-driven market?

Many localization experts are already well-versed with computer-assisted translation tools. Maybe they should get comfortable using localization application programming interfaces (APIs) and handling structured content like JSON or XML, or even pick up some light scripting: Python or regular expressions can go a long way. And as AI becomes more integrated, skills like post-editing and data annotation, as well as familiarity with AI evaluation metrics, will set people apart.

Are there specific programming languages or tools that would benefit localization professionals?

Definitely. For localization engineers or even project managers who want to stay ahead, learning some technical tools can be a game changer. Again, Python is a great starting point — it’s widely used in natural language processing (NLP) and automation. Even basic scripting can help automate repetitive localization tasks, such as file formatting, quality assurance (QA) checks, or text extraction.

NLP frameworks like spaCy, Natural Language Toolkit (NLTK), or even Hugging Face transformers are useful to understand how language models work under the hood, especially if you’re collaborating with AI teams.

Machine learning (ML), deep learning, and transfer learning basics can help you understand how machine translation (MT) engines are trained and fine-tuned and can give you better feedback or even contribute to model customization.

Familiarity with tools like regex and Git, as well as with JSON and XML, is helpful for handling content files and version control in localization workflows. Even a little hands-on experience with these tools can improve localization professionals’ effectiveness in AI-enhanced environments.

How important is it for linguists to understand AI/ML concepts like NMT or language models?

To address gaps in AI models, it’s important for linguists to know how these models inherently understand language. This knowledge will help them determine in which use cases and at what levels these models are to be used. Otherwise, the models might look like a black box, which can leave linguists frustrated and lead to an aversion to using these models.

Which AI-powered tools or platforms should localization professionals experiment with right now?

I believe it’s important to stay ahead by experimenting with cutting-edge, open-source AI tools that can significantly streamline and enhance the localization workflow.

Tools like OpenNMT for building and customizing NMT systems are particularly useful for domain-specific and offline scenarios. Bergamot, developed by Mozilla, is impressive for its privacy-preserving, client-side browser translation capabilities. For high-quality multilingual models, Opus-MT offers extensive language coverage using Marian NMT. In audiovisual localization, tools like Whisper by OpenAI provide robust automatic speech recognition and translation, while Aeneas is helpful for forced alignment in subtitle generation.

I also find Translate Toolkit valuable for handling various localization file formats, automating QA checks, and streamlining conversions between PO, XLIFF, and TMX. In addition, resources like Tatoeba are excellent for training low-resource language models, and Tensor2Tensor supports experimentation with advanced multilingual models. I’m also exploring bidirectional encoder representations from transformers (BERT)–based models to enhance tasks like glossary extraction and named entity recognition, improving the contextual quality of translations. Leveraging these tools not only increases efficiency but also ensures more accurate and scalable localization outputs.

Is there a growing need for knowledge in prompt engineering or AI-assisted QA in localization?

Yes. That’s a good question. Prompt engineering or AI-assisted QA comes in handy if someone knows how these models work so that they can tweak the prompts for a better response. Knowing how to give a detailed prompt that addresses all requirements will also save time. Sometimes the models know the answer, but we aren’t asking the right questions.

Can you recommend any open-source AI tools that localization teams can experiment with for tasks like translation quality prediction or terminology extraction?

There are several open-source AI tools that localization teams can explore, especially for tasks like translation quality prediction, terminology extraction, and even custom MT workflows. Some good ones include

• OpenKiwi — Developed by Unbabel, it’s one of the most popular tools for translation quality estimation. It helps predict how good a machine-translated sentence is, even without a reference translation.
• T2T-TM — This terminology extraction tool leverages transformer models. It’s helpful for building and maintaining consistent termbases, especially in technical or specialized domains.
• NLTK and spaCy — While not specifically built for localization, these NLP libraries are great for building custom pipelines like term extraction, sentence segmentation, or even basic linguistic analysis.
• OpenNMT and Marian NMT — These open-source NMT frameworks allow teams to train or fine-tune models on their own data, which is great for domain adaptation.
• Whisper by OpenAI — This powerful speech-to-text model supports multiple languages and is great for experimenting with multimedia localization, subtitling, or transcription.

These tools are ideal for micro, small, and medium enterprises or research-oriented teams that want flexibility without the cost of enterprise licensing. Plus, they’re customizable if you have access to technical support.