GenAI isn’t just changing how translations are produced. It’s reshaping the entire localization workflow. Most teams didn’t build their workflows for this shift. They’re still relying on a mix of spreadsheets, standalone MT engines, and manual file handoffs. It’s familiar, and for a while, it worked. But it was never designed for the scale teams are dealing with now.
A translation changed, but no one knows who did it or when. The release is already live. What do you do? When multiple contributors work on the same project, there’s a good chance that strings get edited, approved, overwritten, or renamed. Without a clear localization audit trail, small changes turn into production issues, and tracking them down takes time you don’t have. This lack of visibility slows you down significantly. It makes troubleshooting harder, weakens accountability,
LLMs are fluent in generating outputs, but they're not faithful to your brand. When a general-purpose LLM translates your product UI into 15 languages, it doesn't know which terms are trademarked, which phrases have legal restrictions, or which features are deprecated. It makes a statistical guess. At scale, this guesswork can lead to major inconsistencies, compliance risks, and a post-editing overload. The challenge: AI models’ probabilistic outputs are not ideal
Your campaign killed it at home. Then you launched it in Brazil, and the tagline became a punchline. Sound familiar? Nearly 3 in 10 marketing and company leaders say it's happened to them, and the cleanup cost thousands of dollars. Lokalise surveyed 392 marketing and sales leaders who localize content across multiple countries and languages to find out what actually goes wrong, and what they'd do differently. What came back wasn't a polished post-mortem. It was an honest reckoning from
You're optimizing for AI search. But are you doing it in every language that matters? We surveyed 1,000 global marketing, SEO, and localization leaders and found that brands investing in localized content are already seeing stronger AI search visibility. Meanwhile, companies that haven't started optimizing say budget is the biggest thing holding them back. Here's what the data shows. Key takeaways 63% of global companies a
What you'll learn in this article: Why AI answer engines have a built-in language bias — and what it means for your brand's global visibilityThe four signals that determine whether your brand gets cited in AI search resultsHow to build the content infrastructure that earns A
AI translation post-editing tools promise 40-60% cost savings. In practice, you only get those savings when AI output is controlled. This means your terminology is enforced, risky segments are flagged before they go live, and linguists only touch what truly needs human attention. That’s why the best MTPE tools today aren’t standalone CAT tools or MT engines. They’re actually translation management systems (TMS) that orchestrate AI, terminology, and quality assurance in one place.
As of 2026, the Total Cost of Ownership (TCO) for enterprise localization has shifted from a per-word human model (~$0.20/word) to an orchestrated AI model (~$0.002/word). This 100x efficiency is driven by AI Orchestration, which automates context retrieval (RAG) and eliminates manual project management overhead. This shift comes from AI orchestration. These systems combine large language models with retrieval-augmented context, terminology databases, translation memory, and automated q
Behind the scenes of localization with one of Europe’s leading digital health providers
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