When teams use public AI models independently, they often lose control over brand tone and terminology. Once inconsistencies reach different markets, they're costly to undo.
Say a regional team translates campaign slogans with a public tool. The system ignores approved terminology or misses cultural nuance. You get conflicting taglines, or mistranslations that hurt the brand. With no shared glossary or brand guidelines, no two markets speak in the same voice.
These inconsistencies aren't only stylistic. They create governance and compliance risks. Each time someone uses a public tool, confidential data can leak, or errors can break internal guidelines. Enterprise translation needs more than speed. It needs systems that embed expertise: human review, quality metrics, and centralized governance. For the security questions to put to any vendor, see our checklist on AI translation security and data privacy.
Leena offers a telling analogy: "If a lawyer and a layman both ask ChatGPT to find a legal reference, the lawyer will get better results, because they know how to ask. The same goes for translation. Without linguistic expertise, you risk losing meaning."
Governance means visibility and control over every step: how data is stored, who can access it, and how results are audited. Enterprises in some markets now need documented accountability chains. They show what content AI generated, what humans reviewed, and how linguistic assets are protected. Frameworks like ISO 17100 and ISO 27001, plus regulations such as the EU AI Act, now expect demonstrable transparency. Without oversight, businesses risk inaccuracies, data breaches, IP misuse, and reputational harm.
"Organizations are only starting to realize how much they need to know about training data, transparency, and tracking AI use, especially with regulations like the EU AI Act."
Leena Peltomaa
