If we talk a lot about generative AI or cloud safety, automatic translation remains a major blindness. Invisible but omnipresent, it still circulates too often out of any IT framework.
A collaborator receives a legal document in English. He translates it in a few seconds via an online service, then shares the version with his team. The gesture is fast, fluid, efficient. It illustrates a reality: collaborators find in these tools an immediate response to understanding and reactivity needs.
But this document contained a sensitive clause, a precise contractual formulation. The translation was approximate, and no one saw it. What was a well -intentioned daily use has become a point of vulnerability.
The CIOs know this: they must respond to a permanent double tension. Massively automate uses while strictly controlling data flows. And if we talk a lot about generative or cloud safety, automatic translation remains a major blindness. Invisible but omnipresent, it still circulates too often out of any IT framework. It is the perfect terrain of Shadow AI: a massive, non -framed use, with high drift potential.
Daily use but excluding control
Automatic translation improved by AI has become a banal productivity gesture: emails, internal notes, support tickets, customer content. According to a study carried out in February 2025 by Deepl and the Opinea Institute, 8 in 10 professionals are now used daily. But in many companies, this use remains informal. The tools used are not approved by the organization, non-security and sometimes hosted abroad. Result: data leak, inconsistencies, and loss of control.
This dissociation between real use and IT framework is dangerous. For 6 in 10 professionals, writing in another language remains difficult, pushing them to look for new solutions. And for lack of integration, they find them elsewhere. The linguistic AI has been installed in browsers, extensions, unornected tools, but rarely in architecture.
Switch from individual use to governed service
This is where the translation API takes on its full meaning. It makes it possible to transform these uses dispersed into a structured, secure and piloted service, capable of registering in the company processes, while meeting the expectations of instantaneity and fluidity expressed by the teams. Concretely, it allows you to integrate the translation where it is necessary: CRM, Help Desk, intranet, publication pipelines or Devops.
It provides a complete technical response: authentication, traceability, volume control, segmentation of uses, security of exchanges. IT no longer undergoes the translation, it pilots it, and thus leaves the gray area.
But above all, the API makes it possible to reconnect the translation to its business objectives: context injection, activation of specific glossaries, adaptation to the target tone or public. A security alert does not have the same requirements as a push marketing. Thanks to these parameters, the language becomes a precision tool, not just a mode of expression.
Translating is no longer simply transmitting a meaning. It is to respect an intention, terminology, a strategy. It is to make the business speak with accuracy, whatever the language.
Generalist or specialized: Choosing the AI that serves the objective
Today, generalist AIs fascinate by their power. They know how to do everything: summarize, code, write, translate. Their promise is attractive: a unique, versatile, conversational tool, capable of responding to all use cases. And their popularity is based on this impression of infinity.
But this versatility has a setback. To be everywhere is not to be precise somewhere. In translation, this results in approximations, misinterpretations, inconsistencies. The generalist AI generates “plausible” text, but not always usable. It adapts, but does not anticipate. It reformulates, but does not integrate the business codes.
Conversely, an AI specializing in translation is designed to restore exactly. She works with a framework, a goal, a vocabulary. She knows how to take into account the context, sectoral constraints, linguistic history. She adjusts her answers according to the recipient, the canal, the expected tone. She is not content to translate: she prepares usable, reusable content, consistent with what the organization really means.
Also according to the study mentioned above, 7 out of 10 managers note that AI already improves the linguistic capacities of their teams. But this improvement has value only if it is based on the right tools, integrated into the right place, with a clear intention. It is not a matter of raw power, but of alignment with business issues.
What is played out here goes beyond the translation. It is a transformation of how companies manage their multilingual communication, their customer experience, their documentation, even their products. Tomorrow, Co -Pilotes will not only attend employees in their mother tongue. They will have to speak to everyone, in all languages, with the same precision and the same relevance.
Taking out the linguistic AI of Shadow it is preparing this future. And the API is not a technical convenience: this is the condition of a controlled, secure and efficient use. Translating with AI is not enough. You should know why, how, and with what.
