AI vs. Human Translation: What Businesses Should Actually Weigh

What machine translation is actually good at now

Modern machine translation, including the large language models now commonly used for this purpose, is genuinely strong at a specific job: producing a fast, broadly accurate rendering of Portuguese or English text for someone who needs to understand it now and does not need it to be publication- or signature-ready. Reading incoming email, getting the gist of a long report before deciding whether it merits a closer look, drafting routine internal correspondence, or translating high-volume, low-stakes content such as product descriptions or internal wiki pages are all tasks where machine translation's speed and low cost are the right tradeoff against occasional imprecision.

Portuguese-English is also a language pair with a large amount of training data behind it compared to lower-resource pairs, so the output tends to be more fluent and less obviously wrong than machine translation for less-documented languages. For a large share of the translation volume any international business generates, a fast, imperfect draft is legitimately the better economic choice than paying for professional human translation.

Where it fails, and why: false friends and legal nuance

The failures are not random; they cluster in predictable places. Portuguese and English share a long list of false friends, words that look related but carry different meanings: atualmente means currently, not actually; pretender means to intend, not to pretend; eventualmente means occasionally or possibly, not eventually. A human translator with business experience catches these automatically, because the wrong meaning is obviously wrong in context. A machine translation system trained to produce the statistically most common rendering does not reliably catch them, because in isolation the wrong word still sounds like a plausible sentence.

Legal documents raise a deeper problem than vocabulary. Brazilian contract law operates under a civil-law framework with concepts, such as the boa-fé objetiva (objective good faith) standard Brazilian courts apply to contract interpretation, that do not map cleanly onto common-law drafting conventions from English-speaking jurisdictions. A machine translation system renders the words; it does not flag that a clause enforceable as written in one legal tradition may be interpreted differently, or not enforced at all, in the other. That gap is invisible in the output, since the translated clause reads fluently either way, which is exactly what makes it dangerous in a contract nobody reviews closely before signing.

The confidentiality problem nobody asks about

Feeding a client's contract, financial statement, or personnel file into a free or consumer-grade AI translation tool is a data-handling decision, not just a translation shortcut, and it is one that often gets made without anyone asking who has access to that text afterward. Many consumer AI tools' terms of service reserve some right to retain, and in some cases use, submitted content, which means a confidential business document can end up outside the company's control the moment it is pasted in for translation.

For any Brazilian-linked business handling personal data, this also intersects with Brazil's general data protection law, the LGPD, which imposes obligations on how personal data is processed and by whom — obligations that do not disappear because the processing happened inside a translation workflow rather than a database. A human translation provider working under a signed confidentiality agreement gives a business a specific, named, contractually accountable party handling the document. A free AI tool, by contrast, gives the business only its own terms of service to rely on, and no direct recourse if that data resurfaces somewhere it should not.

Accountability when something goes wrong

A second, related gap is accountability. When a translation is delivered by a named provider under a service agreement, there is a clear answer to the question of who is responsible if it is wrong: the provider is, and a serious provider's process includes an independent second-linguist review specifically so that a single translator's error does not reach the client uncaught.

When a document is translated by an AI tool with no human review afterward, there is no equivalent chain of responsibility: no second reviewer, no named professional whose judgment can be questioned, and typically no audit trail showing that anyone checked the output before it was used. For a routine internal email this distinction barely matters. For a document that will be submitted to a regulator, relied on in a negotiation, or referenced later in a dispute, the absence of a reviewable, accountable process is itself a risk, independent of whether that particular translation happened to be accurate.

The workflow that actually fits most businesses

None of this means the answer is always full human translation, or never machine translation. The honest answer depends on what the document is for and what happens if it is wrong. A blended workflow, where a machine translation system produces a first pass and a qualified human translator edits it for accuracy, terminology, and tone, is now common practice and can work well for mid-stakes material, provided the human editor actually compares the translation against the source line by line rather than only checking that it reads smoothly.

Where that workflow breaks down is on the categories described above: legal contracts, regulatory filings, financial disclosures, and any document handling data a business is obligated to protect. For those, the case for human translation with independent review is not that machines translate badly in general — modern systems often do not — it is that the specific failure modes above are exactly the ones a business cannot afford in a document that will be signed, filed, or scrutinized later.

Where STIB fits

STIB Translations, founded in 2007 and based in São Paulo with an office in Brasília, provides certified translation with independent second-linguist review, working under confidentiality terms as a named, contractually accountable provider rather than a general-purpose tool. That structure is built for the category of document described above, where the cost of an error, a confidentiality lapse, or an unreviewed mistranslation is higher than the cost of professional review.

For lower-stakes, high-volume content, a faster machine-assisted process may well be the more sensible choice, and a credible translation provider should be willing to say so rather than positioning human translation as the answer to every document a client has.