Eighty-three percent of enterprises have yet to adopt modern language artificial intelligence (AI) tools, despite widespread AI investment elsewhere. The gap signals a structural weakness in global business operations, particularly across multilingual workflows tied to revenue and compliance.
DeepL’s “Borderless Business: Transforming Translation in the Age of AI” report, published March 10, draws on survey data across the United States, United Kingdom, France, Germany, and Japan. It finds 35% of firms still rely entirely on manual translation, while 33% use legacy automation with human review. Only 17% deploy next-generation AI, including large language models and agentic systems.
Why Are Enterprises Lagging In Language AI Adoption?
The lag contrasts sharply with broader AI deployment trends across enterprise systems. Content volumes have increased 50% since 2023, yet 68% of organizations continue operating workflows built for lower scale and slower turnaround. But, demand drivers are shifting toward core business functions such as global expansion, cited by 33% of respondents, followed by sales and marketing at 26%.
Adoption disparities are also emerging across regions. DeepL data shows 54% of executives expect real-time voice translation to be essential in 2026, up from 32% today, with the United Kingdom and France leading uptake at 48% and 33%, respectively. Japan trails at 11%, highlighting uneven readiness across major economies.
“AI is everywhere, but efficiency is not,” said Jarek Kutylowski, CEO of DeepL.
He added that most companies fail to achieve productivity gains because workflows remain “designed around people, not systems,” limiting scalability despite increased AI spending.
The next phase centers on execution, as enterprises shift from pilot programs to production-scale deployments, with 71% of business leaders prioritizing workflow transformation in 2026 and agent-based systems emerging as the key infrastructure layer to watch.
