Ресурсы Crowdin

Translation management systems used to be the center of everything in localization. If you wanted to translate, review, manage, or ship content, you went through your TMS. However, in The Agile Localization Podcast, Jourik Ciesielski, CTO at ELAN Languages, makes it clear that this model is starting to break. AI hasn’t just added new capabilities. It’s exposed deeper limitations in how TMS platforms are designed and how teams think about them.

Based on original research combining a B2B survey of localization, engineering, product, and security professionals, Reddit community discussions, and publicly available industry data. The question is no longer "whether" but "how". AI translation is no longer an experiment. It has become an operational baseline for enterprise teams managing multilingual content at scale. Based on our original survey of 152 B2B professionals across the US and Canada, roughly 95% of respondents already use AI or machine translation in some capacity – with nearly half doing so frequently and about 18% using it for every translation task. Nearly 9 in 10 require or prefer bring-your-own API keys. Over 91% already have governance frameworks in place or underway. And 1 in 5 report quality incidents since introducing AI translation. But adoption alone tells only part of the story. What matters now is how companies implement AI translation when the stakes include data security, regulatory compliance, brand consistency, and production reliability. The central finding of this research is clear: enterprise teams do not treat AI translation as a plug-and-play capability. They treat it as a managed process – one that demands governance, quality controls, cross-functional oversight, and platform-level orchestration. In other words, the industry has moved past the debate over whether AI translation works. The real conversation is about how to make it work safely, predictably, and at scale.

If you’ve ever managed multilingual content, you know what a grind it can be. It’s tedious, time-intensive work that’s prone to errors and ultimately isn't scalable unless it's automated. Traditional content management system (CMS) platforms haven’t helped. Because of their architecture, content often gets tangled up in templates or the presentation layer, making it hard to extract. Headless CMS architecture is different. Content lives in structured, API-accessible fields, which means translation can be treated as a data operation rather than a manual one, making translation much easier and scalable. In this guide, we'll cover how headless CMS architecture sets you up for multilingual content from the start, what teams forget to localize beyond body copy, best practices for developers, and how ButterCMS + Crowdin work together to automate the translation workflow.

To work well, AI shouldn't guess; it should gather context. This month, we’re presenting a combination of our already released (AI Pipeline) and new tools (Crowdin Copilot) to eliminate repetitive manual tasks for managers, and provide production-ready translations. The highlight is our new Crowdin Copilot. One of its key use cases is analyzing hundreds of translation issues and synthesizing them into a few high-level questions for you to answer. We’re also debuting Dual Preview mode in the Editor to shift from segment-by-segment editing to full document review, and enhancing security in task-based mode.

Shipping multilingual content is rarely ‘translate and done’. Pages change, UI space is tight, legal terms must be exact, and markets never sleep. In The Agile Localization Podcast, host Stefan Huyghe talks with Putri Kumala (AI Localization Operations Team Lead) and Arshaad Mohiadeen (Senior AI Engineer) from Deriv about how they run 20+ languages with automated, AI-driven workflows while staying in control of quality, brand, and compliance.