If you’ve ever been on a page in a foreign language, you’ve probably gotten a prompt to translate it. That was a classic encounter with automatic translation. Most browsers and apps use some form of it. Which means localization teams are now under immense pressure to translate everything fast and with high accuracy.
The type of automatic translation your browser does, usually via Google Translate, AI, or similar services, is indeed fast. But that doesn’t mean it’s also good. Sometimes referred to as machine translation (though, as we’ll see later, the two aren’t necessarily the same thing), automatic translation often misses nuances, expressions, and even highly specialized terms.
So how do you improve translation speed and consistency without sacrificing quality? This guide will answer all of that and more. Whether you're evaluating MT strategies or integrating automation into CI/CD pipelines, this article provides a practical, step-by-step look at how automatic translation really works.
Most people think automatic translation is when a software converts text from one language to another. While that’s a good and simple definition, the process is more complex. In reality, the term refers to the full translation workflow that is automated from content detection to publishing.
It has several key components.
Why is automation important for scaling localization teams? Because it helps you translate larger volumes of content faster, across more languages, with fewer manual steps. All that while keeping human oversight where it matters most.
Many people use the terms “automatic translation” and “machine translation” interchangeably. They may be similar, but they’re not exactly the same thing.
Machine translation (MT) is the engine that turns source text into the target text. Examples include Google MT, DeepL, Azure Translator, and custom neural machine translation (NMT) models. Machine translation produces raw output, and doesn’t include workflow management.
At first, this type of translation started with rule-based systems, then evolved into statistical MT, finally getting to NMT. Each shift came with increased accuracy. But raw MT output is still not enough for enterprise use.
And that’s exactly where automatic translation comes in, as the workflow around MT. It includes MT, routing, reviewing, QA, and publishing and helps ensure scalability, governance, and consistency.
In short, machine translation is a component of automatic translation. The latter may use one or more MT engines depending on domain, language pair, or quality needs.
Machine translation isn’t a one-size-fits-all solution, and there are various deployment models that can support different needs.
It’s fast but still not perfect, and often you’ll still need some manual intervention to make it perfect. So then, are there any benefits to using automatic translation? Absolutely.
The most obvious benefit is speed. You can translate hundreds of pages within seconds. Yes, in many situations, QA is still necessary to ensure accuracy, but even so, the entire process will take much less than it would if you did everything manually.
With automatic translation, you can create glossaries, terminology rules, and routing logic. These will ensure uniform quality and avoid those situations where an expression is translated in different ways on different pages.
Yes, you’ll still need human intervention to ensure accuracy, but the amount of labor and the subsequent costs will be significantly reduced.
For instance, Submittable, a cloud-based social impact platform, used Localize’s automatic translation solution to expand its web app into 20+ languages.
With a single code snippet and automatic detection of dynamic strings, they enabled multilingual support for dynamically generated forms and pages. The result: faster time-to-market and a > 40% reduction in human translation costs.
Automated content detection and publishing eliminate the slow, manual steps that typically hold localization teams back. Instead of waiting for developers to extract files or manually push updates, translations flow directly from workflow to production.
What does that mean? Your team will be able to release content in hours, not days or weeks, so that global customers can benefit from your products quickly.
Linguists often waste time on repetitive tasks that automation can eliminate altogether. Instead of spending hours fixing formatting issues or processing bulk strings, they can focus on nuance, accuracy, and creating high-quality output.
Despite its benefits, automatic translation isn’t without challenges.
You know automatic translation is something you want for your localization team. But how do you implement it to take advantage of all its benefits and limit the challenges and risks? Here’s an easy step-by-step plan.
Start by looking at your content types. Identify high-risk and high-volume content. Decide where you can use MT-only and where human review is mandatory. As a rule of thumb, sensitive, high-risk content will almost always require human intervention.
Then, map your current challenges. Examples can include bottlenecks, inconsistent terminology, and slow dispatching.
When choosing an automatic translation tool or platform, look at its area of expertise, language pairs, and quality benchmarks. Go for a translation management system (TMS) that can automatically send content to be translated, track it, apply the right rules, and move it through the steps.
Plus, you’ll want a tool that you can plug directly into the systems you already use, like your CMS, code repository, or product pipelines, so you can start using it with minimal effort.
Set rules so the system knows when to use MT-only, when a human should edit the machine output (MTPE), and when a human should translate everything from scratch.
To ensure consistency, create a list of important terms and writing rules, so that everyone’s on the same page from the start.
And if possible, turn on a feature that automatically finds new or updated text in your app or website and sends it to be translated, without anyone having to copy and paste anything.
Once you have your tool and know the type of content you want to use it for, it’s time to get to work. It’s tempting to do it all at once, but for best results, you’ll need to start small. For instance, translate your marketing content, then add your product UI, then customer support materials, and finally documentation, one step at a time.
Don’t forget to set up rules so everyone knows how translations should be done and who needs to sign off before something goes live.
The final step is all about making sure the translations are correct and optimizing things when needed. Track MT quality and post-editing time.
Based on these results, adjust MT engine selection, and then report on business outcomes like cost savings, multilingual SEO performance, and release cycles.
We’ve covered the core steps you need to take to implement automatic translation. Now let’s look at a few best practices that will help your team get more value out of each stage.
There are also some technical considerations to keep in mind.
You’ve done all the right things to implement automatic translation. But how can you be sure that the results are what you were expecting? There are a few ways to measure success.
How can improving some of these metrics look like in practice? Let’s check out a quick example. Bswift, a SaaS platform for employee benefits administration, used Localize to replace a manual, configuration-heavy translation process.
They automated content detection, routing, and publishing, which helped them reduce implementation from weeks to less than a day. In turn, this allowed them to support more languages faster, without increasing engineering effort, directly impacting time to market and overall localization performance.
If you look online, you’ll see there’s no shortage of options for MT tools. Here’s what to look for when evaluating them.
Other features you might want in an automatic translation tool include:
A no-code translation solution for software platforms, Localize uses the power of AI to translate your web app, UI, website, emails, help docs, and more. How does it do it? It offers several features that perfectly align with automatic translation workflows.
Thanks to these capabilities, Localize helps teams build scalable, predictable translation pipelines without losing quality control.
Let’s look at a practical example. ContractPodAI, a legal platform supporting complex, multilingual applications, needed a way to automatically detect new and updated content. To achieve that, they used Localize, routing translations through a mix of AI and human review.
The result? They were able to publish approved content without manual handoff, and scale language support across multiple applications. All while maintaining quality and maintaining security and accuracy requirements.
If you’re unsure what to do first in your automatic translation journey, start here:
Automatic translation doesn’t mean turning on your MT tool and letting it do its work unsupervised. It means creating a repeatable, scalable system that combines automation with human expertise.
With the right workflows, tooling, and governance, localization teams can deliver multilingual content faster, with higher consistency, and at a significantly lower cost.
Want to see what automatic translation looks like in practice?
Learn how Localize helps teams combine AI translation, human review, and workflow automation to scale multilingual content without sacrificing quality and control.
David is a Product Owner at Localize, where he drives product strategy and execution and works closely with engineering and design to launch impactful features. His work helps ensure Localize delivers seamless, customer-focused translation solutions.
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