Artificial intelligence is now a normal part of the translation and localization industry. Over the past year, AI has moved from a new concept to an everyday tool, changing how people work and what clients look for.
The Translation Technology Insights 2026 (TTI) report from RWS shows how AI is being used in the industry. Based on feedback from almost 2,000 language professionals worldwide, it covers trends, challenges, and how human skills are changing.
Here are five main ways AI changed the translation industry in 2025.
1. Adoption is growing faster than understanding
More people are using AI in translation workflows. About 60 percent of respondents say they use machine translation every day, and the number is even higher for language service providers, at around 80 percent. Besides translation, almost three-quarters of professionals are experimenting with other AI tools, such as content tagging and smart terminology suggestions.
But people are using AI faster than they fully understand it. Many tools are not used to their full potential, and connecting different platforms or systems remains difficult. For example, a translator might use AI to produce first drafts, then spend extra time matching those results to glossaries or translation memories. This shows that using AI well is still something people are learning.
2. Trust, quality, and accuracy remain top concerns
Even though more people use AI, trust in the technology has not always kept up. Accuracy and quality are still the main concerns. For example, large language models can create translations quickly, but their results are not always reliable for important or technical content unless a person checks them.
Security, compliance, and bias are also ongoing concerns. Enterprise teams handling regulated or sensitive information often prioritize accuracy and reliability over speed. When AI outputs fall short, the typical response is not to abandon the technology but to apply additional human oversight, showing that trust and judgment remain critical components of effective AI use.
3. Human-AI workflows are becoming standard
Workflows that mix human skills with AI help are now common. Post-editing used to mean just fixing mistakes, but now it also involves spotting patterns, checking quality, and deciding when and how to use AI results.
The TTI report discusses the “power of four” concept, which brings together translation memories, termbases, machine translation, and large language models. This means AI is now part of a larger set of tools, not a separate solution. Translators now need to know both languages and how to use AI tools well.
4. Demand is evolving rather than disappearing
Some people worried that AI would mean fewer jobs for human translators. A few freelancers and small companies did see fewer client requests for a while. But bigger organizations say they still need just as much, or even more, multilingual content.
The type of work in the industry is changing. Regular translation tasks might be going down a bit, but there are more chances in AI-based services, new types of content, and data-focused translation. Professionals need to learn new skills and tools to keep up.
5. Skills and roles continue to change
AI is changing the skills needed for translation. Post-editing is now a basic skill, and jobs that mix language, AI management, data analysis, and quality checks are becoming more common.
Skills such as annotation, validation, and specialised knowledge in certain fields are now more important. Even experienced people say workflows still need work, which shows the industry is changing a lot. The main goal now is to build skills that work well with AI, not to fear automation.
Looking ahead
AI is not expected to replace translators in 2026. Instead, people and AI will work together, learning how to use technology well while keeping quality, trust, and good judgment.
AI is accelerating production, creating new market opportunities, and expanding the definition of language work. For professionals in translation and localization, success in 2026 will depend on adaptability, continued skill development, and the ability to integrate AI into workflows thoughtfully. Translation is not shrinking. It is evolving, and those who embrace this transformation will be best positioned for the future.
