AI Translation vs Human Translation for Technical Documentation: How to Decide, and How We Use AI in Practice
For technical documentation, the real choice in AI translation vs human translation is not one or the other: it is which documents AI may draft, under which controls, and who is responsible for the result. At DEMA Solutions, AI speeds up analysis, first drafts and checks, while qualified linguists translate, revise and sign off every delivery. This guide explains how to make that decision for your own content.
Who this guide is for
This article is written for the people who will have to answer the question when someone in the company asks why manuals cannot simply be run through AI: technical documentation managers, technical writers, localization managers, and the procurement and quality directors who sign off on translation spend. Most of them work at manufacturers of machinery and equipment that ship operating, installation and maintenance manuals, spare parts catalogues, HMI strings and safety warnings into a dozen or more languages.
The pressure usually comes from two directions at once. Budgets are tight and AI looks free; at the same time, the Machinery Regulation (EU) 2023/1230 applies from 20 January 2027 and leaves no doubt about who is responsible for the instructions that leave the factory.
What we mean by AI in translation
Two technologies sit behind most of what is now called AI in translation, and they behave differently.
Neural machine translation is the engine that has powered machine translation for some years. It is trained specifically to translate sentence by sentence, it is fast and predictable, and it can be configured to respect a glossary and to reuse an existing translation memory. Its weaknesses are well known: it translates what it sees, sentence by sentence, without much awareness of the document around it.
LLM translation uses large language models, the same general-purpose systems behind conversational AI assistants. They can take more context into account, follow instructions about style and audience, and often produce text that reads more naturally. They are also less predictable. The same input can produce different output on different runs, and because the model is built to produce fluent text, it can smooth over a problem in the source instead of reproducing it, or add something that was never there.
For a manufacturer, the distinction matters less than what surrounds the engine. Most AI document translation offered online is a raw engine with no glossary, no memory and no review. The same engine inside a controlled process, fed with your approved terminology and followed by a qualified linguist, is something else entirely.
What AI does well, and where it fails on technical content
On the right content, AI is genuinely good. It handles high volumes of plain, repetitive text quickly, produces grammatical sentences in the major European languages and is very useful for understanding a document you could not otherwise read. For internal content or service notes, that can be enough.
Technical documentation is a harder test, because the errors that matter are not the ones a casual reader notices. These are the failure patterns reviewers see most often.
Terminology drift. A component called a “limit switch” in chapter 2 becomes an “end-of-travel sensor” in chapter 7 and something else again on the HMI screen. Each translation is plausible; together they confuse the technician who has to match the manual to the machine, and they undo years of terminology work. This is the single most common problem with uncontrolled AI output, and the reason keeping technical terminology consistent across translators and suppliers needs an approved glossary rather than good intentions.
Numbers, units and references. Torque values, tolerances, pressures, part numbers and figure references must come through untouched. AI engines occasionally reformat decimals, convert or round units, drop a digit or renumber a step. A reviewer reading for fluency is unlikely to catch it; a field engineer setting a relief valve will.
Omissions. A long sentence with three conditions comes back with two. A table cell is skipped. A note following a warning disappears. Fluent output hides missing content well, which is exactly why it is dangerous.
Smoothed over or invented content. When the source is ambiguous or badly written, a human translator queries it. An LLM tends to guess, producing a confident sentence that resolves the ambiguity in one direction, which may be the wrong one. It can also add an explanation that sounds helpful and has no basis in the source.
Negation and modality in warnings. “Do not open the cabinet unless the main switch is locked out” and “Open the cabinet when the main switch is locked out” are not the same instruction. Neither are “must”, “should” and “may”. AI engines occasionally lose a negation or soften an obligation, and in a safety warning that is the most serious error a translation can contain.
Inconsistency between runs. Translate the same chapter twice and you may get two different versions. For documentation that is updated every few months, that means unchanged sentences can change in the target language for no reason, which breaks your translation memory, inflates review effort and makes every revision look bigger than it is.
None of this means AI cannot be used. It means AI output on technical content is a draft, and the quality of the final text depends on the controls around it and on the person who revises it.
Where AI helps across the documentation chain
Translation is only one step in the life of a manual, and in our experience AI often adds as much value around the translation as inside it.
Before translation, AI helps analyse incoming files, spotting structural problems in Word, InDesign or XML sources and estimating scope and risk so a project is set up correctly the first time. It can extract candidate terms from a new product’s documentation for your engineers to approve before translation starts, and compare two versions of a source manual to summarise what actually changed.
During translation, machine translation running on your approved glossary and memory can produce a first draft for suitable content, which a qualified linguist then post-edits. Where that approach is good enough, and where it is not, is covered in our guide to post-editing for technical documentation.
After translation, AI supports quality assurance: automated consistency and formatting checks, flags on potential terminology conflicts, and pattern detection across large volumes of text that no person could read line by line in the time available. These checks find candidates for a human to look at. They do not decide whether a translation is correct.
Across all of this, one principle holds. AI can prepare, suggest and flag. People decide, approve and remain responsible.
Confidentiality: why public AI tools are a risk for unreleased documentation
The fastest way to create a problem with AI translation has nothing to do with quality. It is an engineer pasting the service manual of a product that has not been launched yet into a free public AI website.
Depending on the service and its terms, text submitted to public tools may be stored, logged or used to improve the provider’s models, and it leaves your control the moment it is submitted. For technical documentation that means unreleased designs, performance data, procedures that reveal how a machine is built and, in service reports, sometimes personal data.
A sensible policy covers three points. Confidential and unreleased documentation never goes into public AI tools. Any AI used on that content runs in an environment whose terms on storage, retention and training your legal team has accepted. And your translation suppliers, including their subcontractors, are bound by the same rules in writing. The question to ask is not “do you use AI?” but “where does my text go, and is it used for anything else?” Contracts deserve particular care here, since pasting a draft agreement into a free tool can breach the very NDA being translated, as our guide to translating contracts safely points out.
Responsibility does not move to the machine
Regulation is clear on one point that is easy to forget in a cost discussion: the manufacturer is responsible for the instructions supplied with its products, whatever tool produced the translation.
Under the Machinery Regulation (EU) 2023/1230, which applies from 20 January 2027, machinery must be accompanied by instructions for use and safety information in a language that can be easily understood by users, as determined by the Member State where the machinery is made available. The regulation also allows instructions to be supplied in digital form, which makes every language version easy to find and easy to compare. A translated warning that softens a prohibition, or a maintenance step with a wrong value, is a defect in the product’s instructions, and “the AI translated it” is not a defence a market surveillance authority or a court will recognise.
The same logic applies under other product legislation. Our guide to user manual translation and EU rules explains the general requirements for instructions across EU product law. The practical consequence is simple: whoever translates your safety content, by whatever means, the review and sign-off must be done by someone qualified to take responsibility for it.
Glossary and memory control: what makes AI output usable
Most of the failures described above have the same root cause: the engine does not know your terminology or your previously approved text. Fix that, and AI output on suitable content becomes far more useful.
An approved multilingual glossary tells the engine and the linguist which term to use for each component and action, in every language. A translation memory holds every sentence you have already approved, so unchanged text is reused exactly instead of being re-translated differently each time. With both in place, the post-editor starts from a draft that already uses your words.
Two conditions make this work in the long term. The glossary and memory must be approved by your own experts, ideally once, and kept up to date. And they have to belong to you. A memory locked inside one supplier’s system cannot control anyone else’s engine and cannot follow you if you change supplier, which is why owning your translation memory matters even more once AI is part of the process.
AI translation vs human translation: how to decide by document type
The useful question is how much human work each document needs on top of AI. Look at two factors together: the consequence of an error, and how controlled the content is.
Safety warnings, instructions for use, declarations of conformity and regulatory content. These carry the highest consequence of error and are where responsibility is most visible. Human translation by a specialist, or a full post-edit by a specialist linguist working from a controlled draft, followed by revision by a second qualified linguist, is the safe route. Raw AI output should never be published here.
Operating, installation and maintenance manuals. Most of the text is procedural and often repetitive, which suits a controlled draft, but these documents contain the warnings, values and steps that matter most. Full post-editing with glossary and memory control, plus revision, is the realistic minimum.
HMI strings and firmware text. Short, context-poor strings with strict length limits are where AI is weakest: a two-word label gives the engine almost nothing to work with. These need a linguist who can see the interface or at least a reference, and consistency with the manual matters more than speed. Supplying screenshots and string comments is one of the steps described in our guide to the software localization process, which applies to machine interfaces and apps alike.
Spare parts catalogues. Highly repetitive descriptions with a small vocabulary. With a strong memory and glossary, a controlled draft and a focused post-edit work well.
Internal documents and service notes. AI can often be used with light review, provided the content is not confidential and nobody outside the company will rely on it. Automatic video subtitles follow the same rule, and our guide to subtitling rates and AI subtitles shows where a reviewed draft becomes necessary.
Training and marketing material. AI drafts tend to sound generic; a linguist who writes for the audience usually adds more value.
Whatever the category, two rules apply: content written for lay users or operators who will act on it needs human review, and anything published under your company name needs a person who has signed it off.
The questions to ask a supplier about AI use
Many translation suppliers now use AI somewhere in their process, and a few use it without saying so. Before you hire one, or renew a contract, ask directly. These questions separate a controlled process from a cheap one:
- Which of my documents would you translate with machine translation or AI, and how do you decide?
- Does the engine run on my glossary and translation memory, and is every approved translation added to a memory I own?
- Where is my text processed, is it stored or used to train any model, and does that apply to your subcontractors too?
- Who post-edits the AI output, what are their qualifications, and is a second linguist revising it?
- How do you check numbers, units, negations and omissions, which fluent output can hide?
- How is AI use reflected in the price, and can I see how much of each job comes from reuse?
Good suppliers answer these without hesitation, and in writing. Our guide on how to choose a technical translation company covers the wider set of questions, and how to check a supplier’s quality before you hire them explains how to test the answers.
Introducing AI safely: a step-by-step approach
Companies that get value from AI translation rarely start by switching it on everywhere. They introduce it the way they would introduce any change to a controlled documentation process.
Start with a content inventory. List the document types you translate, the languages, the volumes and how often each one changes, and mark which content is confidential, which is safety-relevant and which is internal. That inventory alone usually shows where AI can help and where it should not go near.
Next, put your linguistic assets in order. Collect existing translation memories, even if they sit with several suppliers, and build or clean up a glossary of the terms that matter most, approved by your engineers. If your documentation is spread across several suppliers, the work described in managing translation across multiple vendors and markets is a useful first step, because AI amplifies whatever inconsistency already exists.
Then write a short AI policy for documentation: which content may be processed by which kind of AI, which may not, where data may go, and who signs off. Share it with every supplier.
Run a pilot on one suitable document type, typically a repetitive manual or a parts catalogue, in two or three languages. Compare the result with your usual translation on terminology, numbers, warnings, consistency and review effort, and involve the people who will use it, such as service engineers or subsidiaries that usually re-check translations.
Finally, extend gradually. Add document types where the pilot showed AI helps, keep human translation where it did not, and make sure every approved translation flows back into your memory, so each round of drafting starts from a better base.
How DEMA Solutions uses AI in practice
We use AI where it helps the work and never where it would take responsibility away from a person. How much it is used depends on content type, risk level and the client’s own rules; it is never automatic.
When we asked our internal teams how they work, five out of seven respondents said they use AI daily or several times a week, describing it as essential for speed, scale and filtering issues in high-volume work. The same number said that human oversight remains a core requirement, and teams also reported that uncontrolled AI use lowers quality. That is why rules matter as much as tools.
In practice, AI supports our technology team in pre-analysing files and formats and detecting structural issues before production. Project managers use it to pre-analyse scope and risk and to highlight inconsistencies between source files, assets and instructions. Our quality team uses it for automated consistency and formatting checks, terminology conflict flags and pattern detection across large volumes. Our vendor management team uses it to check incoming assets for completeness and to keep briefs consistent, and accessibility checks use it to flag potential risks in source files early.
What AI never does is equally clear. It does not make final translation decisions, approve terminology, approve quality, handle regulatory or compliance decisions, or sign off a delivery. People are in control at every stage: project setup and asset selection, terminology validation, translation or post-editing, review and QA, final delivery approval, and the integration of client feedback. Every final delivery receives human sign-off.
Our safeguards are practical ones: clear internal usage guidelines, centralised translation memories and terminology, client-specific rules and exceptions, transparency about which tools are used, and ongoing team training. Underneath sits an ISO 17100 certified process in which a second qualified linguist revises every translation, described on our quality page. In sensitive projects, translations reused from the memory are checked again rather than accepted because they look familiar. Your glossaries and translation memories belong to you and are delivered in portable formats (TMX and TBX), our in-house DTP team returns files in their original layout, and our teams in Italy, Serbia and Spain work to the same rules.
When AI drafting is the right fit for your content, our machine translation post-editing services combine a controlled draft with full revision by specialist linguists. When it is not, our technical translation services cover the same documents with human translation from the start.
Where TUBE fits
For manufacturers who want AI to help without losing control of their language, TUBE keeps the rules in your hands. Your glossary and translation memory control machine translation, every approved translation goes into a memory you own and can export, and leverage reporting shows how much of each update comes from text you have already approved.
Frequently asked questions
For some content, as a first draft, yes. Raw output regularly contains terminology inconsistencies, altered numbers, omissions and weakened warnings, which a fluent reading does not reveal. For anything customers, operators or authorities will rely on, AI output should be post-edited by a qualified linguist and revised by a second one.
Neural machine translation engines are trained specifically to translate and are fast, predictable and easy to constrain with a glossary. Large language models take more context into account and often read more naturally, but they can vary between runs and are more likely to smooth over or add content. Both need the same controls.
Not if they are confidential or unreleased. Depending on the service's terms, submitted text may be stored or used to improve the provider's models. Use only environments whose data handling terms your legal team has approved, and require the same from suppliers.
The manufacturer. Under the Machinery Regulation (EU) 2023/1230, applying from 20 January 2027, and other EU product legislation, the manufacturer supplies the instructions and safety information and is responsible for them, whatever tool produced the translation.
It can reduce effort on suitable, repetitive content, especially when combined with a well-maintained translation memory. On complex or safety-critical content the savings are smaller, because careful post-editing and revision take time. Ask for pricing that shows reuse separately, not just a lower per-word rate.
Give it an approved glossary and translation memory to work from, keep both up to date, and have a linguist check terminology in every post-edit. Without them, terminology drift is almost certain.
Talk to us before you switch AI on
If you are deciding how far AI should go in translating your manuals, catalogues or HMI text, tell us which documents and languages you are planning. We will look at your content and suggest where AI drafting is safe, where human translation is needed, and how to keep your terminology under control either way.
How can we help?
Tell us what you need and we’ll get back to you with the solutions.