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Case Study: Multilingual Retail Marketing
New AI Content Creation Solutions for a Sports and Apparel Giant
- RESOURCES
As global markets evolve, translation challenges demand more innovative, more adaptable solutions. The future of localization lies in combining engineered processes with advanced AI, namely Large Language Models (LLMs), to deliver consistent, high-quality AI translation results at scale.
During our webinar, “Delivering the Next Phase of Localization Value,” Vincent Henderson, Lionbridge Chief Product Officer, explored how agentic workflows, advanced technology, and AI frameworks (the TRUST framework and REACH framework) are transforming AI translation quality, efficiency, and cost management. We distilled his insights into a whitepaper to enable you to enhance your AI-powered translation outcomes.
“The core of this webinar is to understand this whole logic of engineering agentic translation and how to get better translations faster and cheaper using agentic AI that is possible to do today.”
— Vincent Henderson, Lionbridge Chief Product Officer
What Are the Key Webinar Takeaways?
This session explored how engineered, agentic workflows are shaping the future of AI-powered translation. Key points included:
Responsible AI is essential for transparency, reliability, and usefulness in localization technology.
Lionbridge’s TRUST and REACH frameworks ensure that we know our goals and that AI translation helps us achieve our business objectives responsibly and effectively.
Agentic translation workflows with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) deliver better translation quality and continuous improvement.
AI post-editing and Auto LQA processes reduce human effort and turnaround time while improving accuracy.
Customization, benchmarking, and human-in-the-loop reviews remain critical for maintaining translation quality and linguistic authenticity.
A technology-first platform, such as the Lionbridge Aurora AI™, orchestrates advanced workflows for AI translation, AI content creation, and AI validation to deliver scalable, high-quality global content.
Your Top Questions About the Next Phase of Localization Value Answered
A: Yes, but with caveats. Different LLMs respond differently to instructions, and results may vary. Future protocols like MCP (Model Context Protocol) will standardize how other resources and LLMs can be used within one process, making it easier to integrate custom models.
A: The main impact is a significant reduction in human effort and thus cost, potentially up to 80% over time. Technology costs (tokens and compute time) will rise. However, overall translation costs will fall. Turnaround times are faster, since less manual review is required.
A: Vendors will need to adapt to new workflows, including reviewing samples, providing detailed feedback, and working within fuzzy grid pricing models. Some vendors may face challenges adjusting to this more engineered, feedback-driven approach.
A: By providing AI with high-quality glossaries, style guides, and reference materials, and by controlling instructions and context, teams can preserve authenticity. Human review and feedback remain crucial.
A: Yes, but reliability improves when using explicit glossaries and concept notes, rather than relying solely on training. For OS/menu paths, associating terms with screenshots or context helps AI make accurate decisions.
Interested in watching the webinar in its entirety? Watch the webinar recording.
If you’re interested in exploring other Lionbridge AI-related webinar topics, visit the Lionbridge webinars page for a library of recordings.
Get in touch
Ready to achieve the next phase of AI-powered translation for your organization through our tech-first orchestration platform? Reach out to learn how Lionbridge can help you engineer agentic, responsible, and highly effective localization solutions using our advanced frameworks and technology for global content that delivers sustainable ROI.
Note: The Lionbridge Content Remix App initially generated this blog post, and a human writer refined it.
