Why is working with AI in translation essential for efficiency and cost reduction?
After numerous waves of hype and disappointment, the adoption of GenAI is rapidly expanding across all fields. It’s well-known that, when used by skilled linguists, GenAI can significantly boost efficiency and reduce the costs of language services. Unless you specifically request to exclude AI from your translation process, no professional linguist should charge you for tasks that can be handled by the machine.
Isn't AI so good it can already replace human translators?
No. While AI continues to improve, model capabilities are plateauing and converging. Under the current technological paradigm, AI remains constrained by the datasets on which it was trained and defaults to the mean. That prevents it from matching expert human translators on critical, creative, or highly specialized work. AI can produce general translations quickly, but it lacks the judgment, cultural insight, and accountability required for high-stakes projects. Rather than replacing true experts, AI allows translators with the right skills to focus on tasks AI cannot master.
Why are machine translation engines like DeepL, or even Translated's Lara still deceptive for professional translators despite all the hype?
Whether used on an online portal or integrated into CAT tool plugins, DeepL and other traditional machine translation engines rely on general language algorithms. While they can speed up the translation process, they offer limited options to adapt to your specific tone or brand voice and cannot easily integrate with your approved terminology databases. Furthermore, although many popular CAT tools now feature AI capabilities, they still translate one segment at a time without allowing linguists to leverage the full context of the text before automation. As a result, post-editing tends to be labor-intensive and repetitive for translators, increasing the risk of errors and omissions.
But can’t GenAI agents replace professional translators?
The answer depends on your specific needs and the level of risk you're willing to accept. Not all assets are created equal. The first step is to define what "fit for purpose" means for each category of content. AI-generated translations often use a neutral, standardized language, may contain occasional inaccuracies, exhibit cultural biases, and typically overlook the intended tone or purpose of the text. Establishing a clear "fit for purpose" standard will determine the extent of human post-editing required after receiving the prompt-driven automated translation output.
When using AI in translation, how can you prevent AI mistakes like hallucinations or translation errors?
While AI streamlines and accelerates translation tasks, it can still produce hallucinations or occasional errors. To prevent these mistakes from appearing in the final output, there are two essential safeguards. First, instead of allowing the AI to generate translations autonomously, you need to guide it carefully through precise prompting. This approach ensures the automated output aligns with your desired style, tone, and terminology consistency. Second, a thorough human review of the translation is crucial before delivery, providing an essential layer of quality assurance.
How can you guarantee my data is secure when you use AI in your translation workflow?
CotranslatorAI is the only tool I use that connects to AI in my translation workflows. It is governed by OpenAI’s enterprise‑grade privacy policy. Your data is never used to train AI models, not even for reinforcement learning, and it is deleted promptly after processing. CotranslatorAI makes this possible by using an encrypted API key to access the OpenAI servers, the same trusted method used by Fortune 500 companies and tens of thousands of other businesses worldwide. Since CotranslatorAI is a standalone program that runs on my local computer and connects directly to OpenAI's services, your data never travels through the cloud or passes through intermediaries, as it would if you used a CAT tool or browser-based AI. This setup eliminates the data-sharing risks commonly found with standard chatbots and other AI tools.
