AI and Machine Learning have revolutionized the localization process. Still, the industry seems to be stuck in a business model of cents per word and TM matching categories from the ‘90s. Neural machine translation (NMT) is approaching human parity for many domains and language pairs thanks to algorithmic progress, computing power, and availability of data. Yet executives are asking themselves, why has this break-through so far only had a marginal effect on translation costs, lag, and quality? The main reason for this is that service providers have not figured out how to merge translation technologies into a single paradigm.

By focusing on what actually makes AI enabled solutions and NMT disruptive and building on three equally important elements – bleeding-edge LangOps, automated workflow, and reliable resource management – it is possible to deliver unprecedented advantages in faster turn-around, lower costs, and consistent quality for the largest and most demanding customers in the world.

LangOps is a multidisciplinary approach to scale beyond transactional translation. It’s about deploying technologies in a disruptive way, which as always in engineering, is a question of piecing it smartly and pragmatically together. LangOps builds a translation factory optimized for specific content and costs, time, and quality needs. Once the language factory is scoped, a powerful service organisation connects the dots from defining the business purpose to delivery and continuous process innovation. With this approach we can move translation into the 21st century, where linguistic assets become differentiators to position localization on a completely new strategic level.