AI and translation

AI and Translation: What Clients Need to Know

| alexispernsteiner

Artificial intelligence has transformed the way we talk about language and translation. In recent years, we’ve seen an explosion of tools that can produce passable translations at remarkable speed. The conversation around this technology, however, is often polarized.

On one side, the general public and many clients sometimes overestimate AI’s capabilities, assuming it can replace professional translators entirely. On the other side, many translators react defensively, focusing only on AI’s flaws while downplaying its strengths.

As we continue to transition into this new era of AI ubiquity, it’s important to communicate with our clients about the advantages and limitations of these evolving technologies, explain our role as language service providers, and guide them in making the right decisions about which types and tiers of service they require.

What AI Can Do Well

AI tools are undeniably fast and efficient, and for certain types of projects they can be a useful starting point. Drafting a rough version of a straightforward text, processing very large volumes where speed is the priority, or suggesting alternative phrasings during the creative process are all tasks where AI can provide genuine value. Used wisely, these systems can save time and reduce costs. But relying on them without human oversight is risky, particularly in contexts where nuance, accuracy, or brand voice are essential.

Where AI Falls Short

Despite their impressive output, AI systems are prone to errors that can have real consequences for clients. They can introduce factual mistakes, invent details, or mistranslate critical information. They often lack consistency, switching terminology or style mid-text. They flatten or misinterpret tone of voice, which is especially damaging for luxury, cultural, or high-profile communications. Even at the level of mechanics, they frequently mishandle punctuation, formatting, and stylistic conventions across different markets. These are not small issues: in publishing, branding, or client-facing communications, such flaws can undermine credibility and compromise a brand’s image.

The Human Role

What sets professional linguists apart is that our work goes beyond “fixing” machine output. We read the source and target texts with equal care, making sure nothing is distorted, omitted, or invented. We rework phrasing to capture nuance and tone of voice, align the style with brand priorities, and ensure consistency across terminology, formatting, and punctuation. Where AI tends to flatten or generalize, we bring back rhythm, precision, and credibility.

Equally important, we guide clients in understanding how AI fits into the process. That means showing where it can genuinely help, and where its flaws make human intervention non-negotiable. It means using AI responsibly—prompting it effectively, reviewing its output critically, and protecting confidentiality at every stage. In this way, we don’t just deliver translations; we deliver texts that are accurate, polished, and aligned with both the client’s message and their audience’s expectations.

Final Thought

AI may be fast and impressive, but it cannot replace the judgment, sensitivity, and cultural insight of a professional. The real value lies in combining technological efficiency with human expertise, so that every text remains accurate, elegant, and fully aligned with a brand’s identity.

quick thoughts on ai & translation

| alexispernsteiner

AI, Chat GPT: these are the buzzwords of the year. The machine mind has gotten smart enough to score in the 90th percentile on the Bar Exam and capable enough of human mimicry to offer plausible self-help advice. There is of course considerable debate as to what its real impact will be: a complete societal revolution followed by the machines taking over the planet and relegating humans to a subaltern role? Or another over-hyped technology, akin to the self-driving cars that were supposed to have put us all in the passenger seat by now?

But there is at least one arena that seems to keep coming up as an accepted point of agreement: translation. Open AI CEO Sam Altman recently mentioned in an interview that he enjoys being able to travel the word and communicate, thanks to technology, with people anywhere. On my favorite podcast, Vox writer Kelsey Piper said yesterday how excited she is for AI in the field of translation, comparing the transition to the shift in the nineteenth century toward machine-made rugs. Putting aside considerations on the quality of communication when traveling with Google or my thoughts on comparing rugs with language and human interaction (perhaps for another blog post), and acknowledging that despite some of my luddite tendencies I am not actually against the idea of technology used in the service of translation (we will all be cyborgs by the decade’s close), I want to take a moment to express my current reservations about these tools.

Out of curiosity, I’ve tried Chat GPT for translation. And in my professional life, agencies and direct clients often ask me to work with specialized translation tools that use both AI and more traditional machine translation methods. I would say that compared to similar systems even a few years ago, the improvements have been massive. Still, given all the hype around these technologies, I am continually surprised at how extensively I have to intervene, often to the point of completely retranslating giant chunks of text. Just this morning, I was working with one such tool that has been trained in industry-specific French to English translation, and to give you a short example of what we translators are dealing with, here are a few of the suggested translations compared to the end translations:

  • “These two models, fitting at the waist” became “These two shoes run small”
  • “Think about changing the price tags” became “Be sure to change the price tags”
  • “Put the product on the reverse side” became “Turn the garment inside out”

I’ve chosen these three examples nearly at random from a list that would be too long to reproduce here. The main criterion for selection was length: I didn’t want to do an in-depth autopsy of a long passage (again, perhaps for another day), preferring to give you a sample that could be grasped quickly and easily. As you can see, the first translation is simply nonsense. The second makes sense but does not convey the proper thrust of the imperative: this is not a suggestion but a command! Finally, while the third translation almost passes in terms of meaning, it lacks flow and naturalness; it sounds like a machine translation.

Ultimately, my point here is this: not so fast! I think there’s a lot to be considered in terms of what will be lost when we start replacing humans with machines for language jobs, from what it will do to the economy to notions about how we communicate with each other and create shared meaning. But for now, my thoughts are very much in the day-to-day work of translating and editing, and I would say that the task of the translator is still very much translation; that is, analyzing and understanding a text in its source (original) language and then, with considerable care and reflection, carrying that message over to the target language so it can be understood by a “foreign” reader.

This is why I am careful about the kinds of ‘post-editing’ (machine translation followed by human proofreading) jobs I take on. Because the language produced by artificial intelligence and machine translation still requires a lot of work. At times, I even ask myself if it takes me longer than a traditional translation, since I have to consider not only the source text but also the suggested translation–twice the reading! Moreover, the power of suggestion is difficult to counter. If the translation provided by the machine is not good–or simply if it could be better–it takes a certain amount of almost willpower to forget it and come up with something else.

My fear is that the promises of AI in this field will serve to drive down prices, even if the “assistance” provided does not save time in a proportionate way.