For years, the industry debate centered on which LLM offered the best raw translation quality. According to Crowdin’s new 2026 AI Translation Enterprise Survey, that debate is over.
The survey, which polled 152 enterprise professionals across the US and Canada, reveals that while 95% of enterprises now use AI translation, they are no longer looking for a winning model. Instead, they are prioritizing orchestration, security, and multi-stakeholder governance. We are ready to share some of the key findings with you.
1. The end of the one-model strategy
One of the most striking findings is the move toward diversification. Nearly half of all enterprises (47.4%) now use a multi-provider setup, routing different tasks to different models based on language pair or content type.
Because no single model wins in every category, the value has shifted from the model itself to the Translation Management System (TMS) that orchestrates them. 65.8% of respondents report that their AI translation now happens directly inside their TMS rather than via standalone tools.
2. Security is the new quality
For enterprises, the challenge isn’t getting a good translation – it’s keeping the data private. The survey found that data sovereignty is now a non-negotiable purchasing criterion:
- 88.8% of teams require or prefer Bring Your Own Key (BYOK) to maintain control over their data.
- 80.9% of respondents refuse to send PII (Personally Identifiable Information) or user data to external AI providers.
- 91% of organizations already have formal AI governance frameworks in place or under development.
3. AI inside the quality stack, not replacing it
AI-only workflows fail the enterprise test. Enterprises are not using AI to replace their quality processes. They are using AI to augment them. The survey highlights the mandatory components of a quality stack:
- 79.6% insist on Glossary/Terminology enforcement.
- 75.7% require Human Proofreading.
- 73.0% still rely on Translation Memory (TM) to maintain consistency.
4. Outcomes: speed up, costs down, risks persist
The business case for AI is clear: 73% of respondents report faster release cycles, and 53.9% report lower costs. However, 20.4% of teams reported an increase in quality incidents or regressions since introducing AI, reinforcing the need for the platform-first approach to catch hallucinations.
Conclusion
The experimental phase of AI in localization is finished. For the enterprise, the strategy has moved from testing models to governing them. See full data in the report 2026 AI Translation Report: 95% of Enterprises Prioritize Platforms Over Models.