Automated translation: what to automate and what to review

Automated translation combines AI, translation memory, terminology management, quality checks, and workflow automation to translate multilingual content faster and more consistently. Instead of translating every sentence manually, modern localization platforms automate repetitive tasks while keeping translators involved where expertise, creativity, and cultural adaptation matter.

While many people use the terms “automated translation” and “AI translation” interchangeably, they are not the same. AI translation is only one component of an automated translation workflow. Modern translation management systems combine multiple technologies to improve translation quality, reduce costs, and accelerate localization.

In this guide, we’ll explain how automated translation works, the technologies behind it, its benefits and limitations, and how companies use it to scale multilingual content.

If you want to learn more about the translation industry’s history, we highly recommend reading our previous article, which overviews the latest modern period and the early 20th, when machine translation was created.

Read more in this article: Translation Industry: history, impact, and trends.

How does automated translation work?

Modern automated translation is a workflow rather than a single technology. Instead of sending every sentence directly to an AI translation engine, localization platforms combine several technologies to automate different parts of the process.

A typical automated translation workflow looks like this:

• New content is imported automatically from your repository or CMS.

• Translation memory reuses existing approved translations.

• AI translates only the remaining content.

• Terminology is applied to ensure consistent product and brand language.

• Automated quality checks detect issues such as missing placeholders, formatting errors, or terminology violations.

• Translators review only the content that requires human judgment.

• Approved translations are synchronized back to the source system.

Which translation steps can you automate, and which need a person?

You can automate the steps that follow rules: moving files, reusing approved translations, drafting new ones, and checking placeholders, tags, length and terminology. People still make the calls that need judgment: brand messaging, legal wording, cultural fit, and the segments that fail a check.

How does automated translation keep content consistent?

Consistency comes from shared sources. When every draft, from a translator or an AI model, starts from the same glossary, style guide and translation memory, a feature name gets the same translation in the app, the help center and the website. Automated checks then catch drift, such as a translation that misses a glossary term or uses one your glossary marks as not recommended.

Can you automate translation and keep it culturally right?

You can automate the repetitive work, but cultural fit still needs a person who knows the market. A style guide for each language sets the audience, formality and voice, so the first draft starts from the right register, for example formal or informal “you” in German. Send campaigns, legal text and culturally sensitive messages to a reviewer in that market, and let UI strings, documentation and support articles go through with checks and spot reviews.

In LingoHub, you decide how much its AI, LINA, does for each target language. As Translator, LINA pre-translates and pre-checks segments, and a team member approves them. As Agent, LINA translates, checks and approves, retries a segment that fails a quality check up to three times, and then marks it Blocked for your team.

Technologies behind automated translation

The translation tool market provides various solutions that are constantly improving; let’s overview the most widely used automated translation services.

AI translation

The phrase “automated translation” is often first associated with AI translation. Nowadays, OpenAI, Mistral, Anthropic, Google, DeepL, Amazon etc., provide powerful yet simple and available tools that allow even work with different content, more simple travel, and getting information from various resources for education and entertainment.

Quality differs by engine and language pair, so it pays to compare machine translation engines on your own content. Today, AI-powered machine translation is driven by both neural machine translation (NMT) and large language models (LLMs). While machine translation delivers the initial translation, modern localization workflows often combine it with translation memory, terminology management, and automated quality checks to improve consistency and accuracy.

MTPE (Machine translation post-editing)

In MTPE, people check and correct machine-translated content instead of translating from scratch. Post-editing started to replace pure human translation in the 2000s and became the standard for many content types in the 2010s. This process is more convenient for customers and translators as it saves time and requires less effort. The only challenge that can appear is an honest and effective effort calculation.

Translation memory

The other instrument which can be unfairly underrated is translation memory. Its main advantage is that translation memory can collect the content during translation to provide suggestions in the future. Based on their types, translation memories can be split into two types:

• File-based TM, which requires export of the translations into it and can provide suggestions limited by the data in these files.

• Virtual (smart) translation memory that studies during your work with content. This TM type is much more convenient as it allows users to avoid the constant uploading of new translations.

Here’s an example of what the application of the translation memory in the editor looks like:

Terminology management tools

Termbases or glossaries are fundamental instruments for consistency in the project. The correct usage of a term throughout all the content in the project (software, website, help center, social media, etc.) is impossible without it, as even one person can’t remember which exact variation of a word was used a month ago. What can we say about teamwork when different people work on the same documents?

As with all translation-related tools, the automation of the glossary is highly important. Without it, the translator can miss some terms that should be checked inside the documentation and have to spend additional time searching for corresponding translations. AI can generate fluent translations, but it doesn’t always choose your preferred product terminology. That’s why enterprise localization teams combine AI with terminology management to keep feature names, legal terms, and brand language consistent across every language. In LingoHub, terminology is highlighted during translation and quality checks automatically detect terminology violations.

How does automated translation software change the industry?

Often the translation automation tools are combined in the CAT (computer-assisted translation software) or TMS (translation management solutions) because they do not provide enough benefits for the translation process alone.

As a result, the demand for combined automatic translation software constantly rises. For example, the TMS market was valued at $2 billion in 2023 and is estimated to grow over 18% yearly from 2024 to 2032.

Such a market rise is conditioned by the positive changes in the processes because of the implementation of TMS. With their support companies:

• Quickly speed up the localization/translation;

• Scale the language number without significant effort;

• Manage the content regardless of its volume without challenges;

• Build a continuous localization process;

• And many more.

We know that the actual cases say more than a hundred words, that’s why we suggest to check:

• How TELUS Agriculture and Consumer Goods accelerated the localization process three times and automated localization.

• How CompanyMood reduced the localization cost by 15%.

• And how Rocket.Chat supports over 65 languages and manages the translation volunteers’ contributions seamlessly.

Current challenges and potential issues of automated translation tools

Despite their high effectiveness, automatic translation software still requires deep human control as it can’t fully handle content adaptation.

The translation process is much more complex than finding the correct translation for the word correctly. Sometimes, it requires a creative approach (UX copywriting or content transcreation), which includes the following abilities and knowledge:

• Understanding of cultural nuances and audiences;

• Deep learning of the context;

• Handling rare languages;

• Knowledge of the ambiguous terms;

• Legal and privacy compliance;

• Etc.

AI translation has improved significantly, but it still benefits from human review for creative marketing content, legal documents, culturally sensitive messaging, and highly specialized terminology. Most enterprise localization teams therefore use AI to automate repetitive work while relying on human expertise where judgment matters.

Which automatic translation solution can provide LingoHub?

As a translation management system, LingoHub brings AI translation, translation memory, terminology management, automated quality checks, and content synchronization into one workflow. This helps teams reduce manual work across the localization process, from importing new content to reviewing translations and delivering them back to their connected systems.

Automated content import

Whenever you have your localization files in GitHub, GitLab, Azure, Bitbucket, Figma, Contentful, Storyblok, etc., you don’t have to worry about manual files going back and forth. Connect your apps and repositories and forget about the files updating.

With LingoHub, all changes in the text segments will be synchronized with the content source app. Moreover, you can set up a workflow that combines the steps for text processing, such as initial text status setup, automated quality checks running, etc.

Automated content translation

The most significant part of manual translation tasks is fully covered and takes just a few seconds. The following features were designed for maximum translation automation:

• Translation memory ™ - that collects the approved translations and allows reuse of them.

• AI translation with robust engines under the hood (like Google Gemini, Claude, Mistral, ChatGPT, and more).

• Glossary that provides the correct translation for specified terms.

• The pre-translate feature that can automatically fill all the segments you need.

• Automated quality checks that warn about any deviation from predetermined rules.

Automated translated content delivery

As we mentioned, smooth synchronization with the apps and repositories allows LingoHub users to push the changes to their systems smoothly.

For example, for the repositories, LingoHub provides Git branching support, which means you can keep your main code clean and quickly translate the separate parts of the software. Push changes, create pull requests, integrate localization in your development processes, and use REST API for customization - you are fully flexible with our platform.

Conclusion

The translation automation industry has reached historic heights and now offers tools that would have sounded fantastic even 10 years ago. But still, human efforts are required for quality control and approval in language translation, as machines can’t understand all the linguistic and cultural nuances. That’s why automation can perfectly work as the initial step and replace tedious, repetitive manual tasks like file uploading and general (pre-)translation.

At LingoHub, we provide tools that simplify the routine and allow you to focus only on the quality of translation. Try how it works with the 14-day trial right now, or book a demo call with our team, where we will guide you through all the abilities.

Frequently asked questions

What is automated translation?

Automated translation is the process of translating multilingual content using a combination of AI, translation memory, terminology management, quality checks, and workflow automation. Rather than relying on a single translation technology, automated translation combines multiple tools to reduce manual work, improve consistency, and accelerate localization.

Is automated translation the same as AI translation?

No. AI translation is only one component of automated translation. While AI translation generates the initial translation, automated translation also includes technologies such as translation memory, terminology management, automated quality checks, and human review to deliver more accurate and consistent results.

Can AI replace human translators?

AI can automate a large share of repetitive translation work and significantly improve productivity. However, human translators remain essential for creative marketing content, legal documents, culturally sensitive messaging, and other content that requires context, nuance, and cultural understanding.

How do you set up automated translation?

Connect the place your content lives, such as a Git repository, Figma or a CMS. Add your glossary and style guide, and bring in the translations you already have so translation memory can reuse them. Then choose which languages AI may pre-translate and which quality checks must pass before approval. Start with one product area, review the results over a few releases, and widen the rules once they hold.

Which content should not be translated automatically?

Content that requires creativity, legal accuracy, or deep cultural adaptation should always be reviewed by a human. This includes marketing campaigns, brand messaging, legal contracts, medical content, and highly specialized technical documentation. For repetitive UI text, product documentation, and support content, automated translation can significantly reduce manual effort while maintaining high quality when combined with translation memory and quality checks.