DeepL Study: Manual Translation Slows Businesses
A new DeepL study reveals that manual translation processes continue to hinder businesses despite increased AI spending. The Language AI 2026 report, titled "Borderless Business: Transforming Translation in the Age of AI," highlights this issue.
The DeepL study highlights a continued reliance on manual translation despite rising investments in AI. Specifically, 35% of global businesses still use fully manual translation workflows. Only 17% have deployed next-generation AI tools like Large Language Models (LLMs) or agentic AI for translation. This reliance on manual processes can hinder global expansion. Traditional translation models involve sequential steps, causing delays as content is prepared, sent for manual translation, reviewed, and finally published. AI translation offers faster market entry and a competitive edge. The AI in language translation market is projected to reach $8.93 billion in 2030, with a compound annual growth rate of 24.8%. In 2024, DeepL was the most-used machine translation provider among language service companies, with 82% utilizing its technology. This surpassed Google (46%), Microsoft (32%), and Amazon AWS (17%). Alternatives to DeepL include Google Translate, Microsoft Translator, SYSTRAN, and others. DeepL offers translation between 32 languages and has been expanding in the U.S. and Asia. A 2024 Forrester study indicated that companies using DeepL achieved a 345% ROI, reducing translation time by 90% and workload by 50%.
