What Linguists Do Next With AI: Humans in Control as Translation Scales
Member Webinar
AI is reshaping translation work—and the big change is scale: human effort no longer limits translation volume, so output can grow without growing headcount.
Agentic AI is moving beyond post-editing to coordinate multiple engines, check quality, and adapt in real time—so language professionals are shifting from translating to governing quality, setting rules, and orchestrating workflows.
This webinar covers the move from traditional MT + post-editing to agentic workflows, using findings from the State of Translation Automation 2025 report, which evaluated 46 MT engines and LLMs across 11 language pairs. We’ll show what these systems can do today, where they still fail, and what that means for linguists.
To use LLMs reliably for translation, you need an orchestration layer on top of them. This is how you meet specific business and language requirements—consistently, at scale. We’ll explain how multi-agent workflows use specialized agents for translation, post-editing, terminology, tone of voice, style guide checks, source quality, and compliance—reducing manual post-editing by up to 95%.
You’ll also learn when to let AI agents run end-to-end and when to keep humans in the loop—how to set risk tiers and guardrails, and how to use editor/user feedback so systems improve over time instead of becoming a black box. Rather than asking “will AI replace translators?”, we’ll focus on what translators are becoming.
Key Takeaways:
You’ll leave with:
- A clear picture of what “agentic” translation workflows are, and how they differ from MT + post-editing
- What current MT engines and LLMs are good at (and where they break), based on evaluation of 46+ systems across 11 language pairs
- How an orchestration layer makes LLM-based translation reliable in production—meeting your business and language requirements
- How linguists’ work is shifting from translation execution to quality governance, workflow orchestration, and strategic oversight
- How to choose automation vs. human review: risk tiers, guardrails, and feedback loops
- Real enterprise examples of workflows that are working today in large-scale localization programs
Who should attend:
- Localization engineers and AI specialists implementing agentic workflows
- Enterprise buyers evaluating the future of their translation operations
- Localization managers exploring AI integration strategies
- Computational linguists and translators interested in how their roles are evolving alongside AI
- Anyone curious about the practical realities of human–AI collaboration in language work
Registration Options
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Registration Options
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Price |
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MEMBER TICKET
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FREE |
Daria Sinitsyna
Lead AI Engineer | AI Curation
Daria Sinitsyna is a Lead AI Engineer at Intento, where she leads a team researching AI developments and improving MT evaluation workflows. With an MS in Computational Linguistics from Syracuse University as a Fulbright alumna, Daria brings expertise in natural language processing, machine learning, and prompt engineering. Daria’s background allows her to effectively bridge the gap between linguistic theory and practical applications in the rapidly evolving field of computational linguistics.
