Organizations have spent years building valuable linguistic, regulatory and operational knowledge into localization processes. This knowledge drives linguistic accuracy, reduces review effort and compliance risks and helps to maintain quality at scale. As organizations redesign content operations around AI, the question is how this existing knowledge can be preserved, codified and extended into an environment where AI is doing more of the work.
The promise of Machine Translation and Large Language Models is the rapid production of fluent multilingual content at significantly lower cost, making it accessible to more content creators across the organization. The downside is a lack of control, poor consistency and errors, meaning that AI translation can be challenging to scale, particularly for high-risk content.
Taking an observation from another sector, a key reason behind the success of AI coding is not just the speed of code generation but the robustness of the control environment: the ability to test and debug AI code by adapting well-established DevOps systems and processes.
In LangOps, analogous control processes have been less automated and are more disparate – but they exist and can be harnessed. They exist in linguistic assets such as translation memories, glossaries and style guides, in regulatory and compliance requirements, in quality thresholds and KPIs and in operating procedures. Control also resides in the experience and expertise of individuals – the reviewers who instinctively know which phrase would trouble regulators and the project managers who quietly correct small deviations before they reach the client.
We believe that successful operational transformation relies just as much on maintaining what is proven to work and what is unique to each organization, as it does on adopting the new. This leads us to the concept of the AI Profile, a point of reference and control layer for AI translation, adapted for each organization.
