Quality management is where human expertise stays decisive in AI-assisted localization, from LLM-powered evaluation to the rise of post-AI editors.

Large language models are reshaping data services, from collecting low-resource language data to comparing models and automating quality assurance.

Accuray cleaned translation memories in five languages with an AI-driven approach, lifting trained MT engine quality from 76% to 92%.

Six practical moves for localization teams to own multilingual AI in their organization, from early wins to building internal AI operations expertise.

Practical talking points for discussing generative AI with management, plus two predictions on GenAI tooling and the expanding role of translators.