How to Implement Fit-For-Purpose Quality Management in the Current AI-Scape
In this webinar, we'll explore the critical topic of implementing fit-for-purpose quality management in the current landscape.
As AI and LLMs continue to revolutionize the localization industry, understanding their impact on quality is paramount.
We'll explore how AI-based features influence the fit-for-purpose quality framework, examining both the opportunities and challenges they present. From reducing manual workload to improving consistency and accuracy, AI offers numerous benefits for translation and quality management.
However, it's essential to consider the associated risks and the need for ongoing human oversight. By striking a balance between risks, benefits, and cost savings, quality managers, localization managers, and project manages can effectively leverage AI to enhance their translation and quality management processes.
Takeaways
• Understand the impact of AI-based features in the fit-for-purpose quality framework
• Discuss how to balance, risks, benefits and cost savings
• Discover use cases that illustrate which AI-based features can be considered in different steps of the production workflow
Audience
• QA managers
• Localization managers
• Project Managers
Host organization: Spotify
Event Speakers
Katerina Gasova
Argos Multilingual
Katerina has over 25 years of experience in the industry, predominantly in quality management. In 2004, she founded Moravia Linguistic Services, a team of linguistic experts located around the globe. Under her leadership, the team designed and successfully implemented numerous customized and often complex quality management programs for both top industry brands and start-ups. In 2022, Katerina joined Argos Multilingual, a global language solutions provider with clients in the high-tech, life sciences, human resources, and financial industries. As the Global Quality Solutions Strategist, Katerina focuses on transforming the traditional approach to quality management seen as a set of separate quality-control tasks into client-centric quality programs. The shift involves engaging, empowering, and holding accountable key linguistic stakeholders throughout the project life cycle, while leveraging the cutting-edge technologies and innovations. Katerina actively engages in various initiatives aimed at exploring AI-assisted solutions in quality management, scrutinizing NMT and GenAI output quality and (auto-)post-editing data in relation to client-defined quality expectations. Her regular presentations and webinars are dedicated to sharing best practices in dynamic, use-case-driven quality management and advocating for user-focused quality principles. Katerina is a member of the MQM Council, an active member of Subcommittee F43.03 on Language Translation at ASTM International, and a regular contributor to various GALA's expert groups.
Jennifer Vela-Valido
Spotify
Jennifer is a Localization Strategist with over 10 years of experience in the industry. She works as a Localization Quality Program Manager at Spotify. Jennifer recently completed her PhD in Localization Quality Management and she also works as a researcher specializing in localization strategies, vendor management and linguistic quality management. She is an active member of Subcommittee F43.03 on Language Translation at ASTM International and the ISO/TC 37/SC 5/WG 1 – Translation group at ISO.