Enterprise content volume has grown 50% since 2023. The AI tools to handle that volume exist. And yet, the vast majority of international businesses are not using them.
That is the central finding of DeepL's 2026 Borderless Business report, published March 10 and based on survey data from enterprises across the US, UK, France, Germany, and Japan. The numbers paint a picture of an industry caught between the promise of next-generation language AI and the inertia of workflows that have not changed in years.
The Adoption Gap by the Numbers
The report breaks enterprise translation practices into three tiers. At the bottom, 35% of international businesses still rely on fully manual translation — human translators handling every document, email, and customer interaction without AI assistance. In the middle, 33% use traditional automation combined with human review — machine translation tools that pre-date the current generation of large language models. And at the top, just 17% have adopted what DeepL categorizes as next-gen AI: LLM-based translation, agentic AI workflows, or similar modern approaches.
That means 83% of enterprises have not made the transition to modern language AI, even as the volume of content they need to translate has surged.
The disconnect is striking. These are not small businesses lacking resources. DeepL serves over 200,000 business customers across 228 markets, and the survey specifically targeted international enterprises with multilingual operations. The 68% still running legacy translation workflows are doing so despite having both the budget and the business case to modernize.
What Is Driving — and Blocking — Adoption
The report identifies global expansion as the primary driver of language AI investment, cited by 33% of respondents. Sales and marketing follow at 26%, customer support at 23%, and legal and finance at 22%. The pattern makes sense: these are the functions where language barriers most directly impact revenue and compliance.
More telling is the demand for real-time voice translation. Fifty-four percent of global executives now describe it as essential for 2026, up from 32% in the previous survey. That is a dramatic shift in expectations — but one that the report suggests most enterprises are not operationally prepared to meet.
DeepL CEO Jarek Kutylowski framed the gap succinctly: "AI is everywhere, but efficiency is not." The tools have arrived; the organizational transformation required to use them has not.
Why Legacy Workflows Persist
Several factors explain the stickiness of legacy translation practices. Integration complexity is a major barrier — enterprise translation does not happen in isolation. It touches CRM systems, content management platforms, legal review workflows, and customer support tools. Swapping out the translation layer means touching every system it connects to.
Domain accuracy is another concern. General-purpose LLMs have made remarkable progress in translation quality, but specialized fields like legal contracts, financial disclosures, and medical documentation require precision that enterprises are not yet willing to trust to fully automated systems. The 33% using traditional automation with human review may be making a rational trade-off: accepting slower workflows in exchange for higher confidence in output quality.
There is also a workforce dimension. Translation teams and localization managers represent institutional knowledge about tone, brand voice, and cultural nuance that is difficult to encode in AI prompts. Organizations that have invested in these teams are understandably cautious about displacing them — particularly when the replacement technology is still maturing.
The Agentic AI Frontier
Perhaps the most forward-looking data point in the report: DeepL now has 2,000 customers globally deploying AI agents for multilingual operations. These are not simple translate-and-replace workflows — they are autonomous systems that can handle multi-step translation tasks, adapt to context, and integrate with business processes without human intervention at each step.
Two thousand deployments out of 200,000 business customers is a 1% adoption rate for the most advanced tier of language AI. That number will grow, but it underscores how early the enterprise market is in its adoption curve.
What This Means for the Market
The Borderless Business report is effectively a market-sizing exercise for language AI vendors. If 83% of enterprises have not adopted modern tools and content volume is growing 50% every two years, the addressable market is enormous — but the sales cycle is likely to be long and complex.
For enterprises themselves, the takeaway is more urgent. Competitors who have adopted next-gen language AI are operating with faster go-to-market cycles, lower translation costs, and the ability to enter new markets without proportional increases in localization headcount. The 83% still running manual or legacy workflows are not just behind on technology adoption — they are accumulating a competitive disadvantage that compounds with every quarter of inaction.
