This session will explore the challenges and obstacles we face in transitioning from current SOTA NMT models to an LLM-based MT landscape for enterprise use cases. NMT models are now pervasive and utilized in many production scenarios from eCommerce, eDiscovery, and Customer Service & Support. While LLM MT shows promise with high-resource language translation there are significant latency, throughput, and adaptation challenges to resolve. The session will look at key questions like: Can LLM MT scale to the same levels as current NMT technology? What innovation can we expect from LLM MT to further the SOTA? What other impact will GenAI have on localization production practices? Will there be an interim hybrid period where both NMT and GenAI work together in production workflows? Will LLM MT be able to address low-resource language requirements? How will multilingual LLMs being developed across the world affect the Big Tech and English-centric dominance we see in GenAI today?

Although still in a nascent state as a professional translation tool, customized SMT engines already have multiple applications, each of which require clear definitions about quality and productivity. Three engine-training scenarios have emerged which are representative of real-world applications for the development and use of a customized SMT engines based on the availability of data. In the case that limited or no bilingual training data is available, a unique development process can be used to harvest and translate n-grams directly. Using this approach Asia Online and Moravia IT have successfully customized SMT engines for use in various domains. A partnership between an MT engine provider and a qualified LSP is essential to deliver quality results using this approach.