We live in an AI-driven content transformation era that is changing how we create, manage, and localize content. Many language service providers (LSPs) have long used AI to localize existing content. For instance, Welocalize has used machine translation (MT) and neural language models for over two decades to meet the growing demand for translating large volumes of multilingual content.

However, a new frontier is emerging. “As corporations adopt generative AI technology, we expect multiple sources of content and a likely explosion in volume. This is new terrain, and most people are concerned about the quality and accuracy of generative content,” said Chris Grebisz, CIO of Welocalize.

Indeed, AI still has its limitations. Its well-documented potential for bias, occasional weakness in understanding linguistic and cultural nuances, and tendency to hallucinate (generating false or misleading statements) highlight the crucial role of human intelligence in reviewing and editing its output. Furthermore, the creation, updating, and alignment of these AI models relies on the creation of large training datasets by human annotators.

Welocalize’s teams have spent many months experimenting with these models and their various applications and deepening our understanding of how these models best apply to localization. We’ve created this guide to cut through the noise.