Localization workflow: A step-by-step guide

A localization workflow is the sequence of steps that takes your content from the source language to every target market: planning, content preparation, translation, review and QA, deployment, and monitoring. A defined workflow keeps quality and costs under control; automating it removes the manual hand-offs that slow releases down. This guide walks through each step and shows what you can automate.

Overview:

• Why you need a workflow
• Step-by-step guide
• The tools that automate a localization workflow
• Challenges to plan for
• Localization workflows in POEditor

Why you need a workflow

Localization involves many moving parts: languages, cultural nuances, file formats, people and release dates. You need a defined workflow, otherwise things get messy.

A localization workflow will:

• Ensure accuracy and quality
• Maintain consistency
• Boost efficiency and speed
• Help manage costs
• Scale with your needs

Now that we’ve established that the need for a workflow is real, it’s time to look at the steps you need to take to reach new audiences efficiently and effectively.

Step-by-step guide

Step 1: Planning

In this first step, you need to decide which parts of the product require localization. Think of user interfaces, documentation, marketing materials, etc. This is also when you identify target markets, languages, and regions, as well as develop an understanding of the cultural norms, regulations, and technical considerations for each target market.

Consider creating a localization kit with resources and guidelines for translators and localization teams. It typically contains glossaries, style guides, translation memories, and context information. Next, choose your management tools, translation memory software, computer-aided translation tools, or any other tools that can streamline the localization process.

What you can automate: not much yet, but decide now which later steps will run automatically.

Step 2: Content preparation

Now that you’ve decided on the content you want to localize, it’s time to extract text and other localizable elements from the product. Your code, design, and content should be built to accommodate different character sets, date/currency formats, and layout variations.

Tools can automatically pull text from code or files for translation, but manual review is needed to ensure completeness and context. Define key terms and their translations to ensure consistency throughout the project. Also ensure that files are in a format that can be easily handled by translation tools (e.g., .xml, .json, .xliff).

What you can automate: pulling new and changed strings from your code repository or CMS as soon as they’re added.

Step 3: Translation

This is where you pick the most qualified linguists with experience in your industry and expertise in the target language and culture. Consider native speakers for optimal fluency. Your translators will work on the content using translation memories, termbases, and glossaries to ensure consistency.

In this process of adapting your content for foreign markets, you will most likely use a translation management system (TMS). These tools can automate parts of the workflow and help manage large projects. You may choose to use machine translation (MT) for initial drafts or less critical content, but always follow with human post-editing for accuracy and cultural relevance.

What you can automate: translation memory suggestions, and machine or AI translation of new strings as soon as they arrive.

Step 4: Editing and Quality Assurance

It’s time to revise everything. Editors come in to ensure that the translated content is grammatically correct, reads naturally, and conveys the intended meaning within the target culture. They identify and address potential cultural faux pas, puns, or humor that might not translate well. Quality Assurance (QA) testing, on the other hand, goes beyond just text. It involves testing the functionality, layout, date/currency display, and user experience (UX) to ensure a seamless experience in the target language.

What you can automate: QA checks for placeholders, terminology and formatting, plus quality scores that tell reviewers where to look first.

Step 5: Deployment

In one of the last steps of a localization workflow, the deployment phase, you integrate the translated content back into your application, website, or files. This might involve code changes or adjustments to layouts. Developers will test the localized product as thoroughly as possible to identify and fix any bugs or display issues.

What you can automate: sending translated files back to your repository, ready for the next build.

Step 6: Monitoring and improvement

Now that the localized product has been released in the target markets, you need to look at some key metrics to measure the success of your work. Track user engagement, conversion rates, and other metrics to gauge the effectiveness of your localized content. Gather feedback from users and stakeholders in the target market to identify areas for improvement. This will help refine your workflow based on experience and data, so that your future localization projects are efficient and successful.

What you can automate: progress reports and notifications when a language is complete or ready for review.

The tools that automate a localization workflow

Now let’s look at the best tools for automating your localization.

Translation management systems (TMS)

A translation management system is the backbone of localization automation: one place to manage, track and automate the process from translation to delivery, where project managers, translators, developers and QA work together. It comes with translation memory, one of its most useful features. TM suggests matching or similar translations from earlier work.

This research paper highlights that the main driver for adopting TM is the pursuit of increased productivity. The findings show that using TM can boost productivity by anywhere from 10% to 70%.

Machine and AI translation

Machine translation (MT) and large language models (LLMs) produce draft translations in seconds, which human translators then post-edit. With glossaries and prompts, AI translation can follow your terminology and tone.

Not to be seen as a replacement for human translation, MT/LLMs can be used as the first step in the localization process. This speeds up the process significantly. You can train custom MT engines on your specific content, including product manuals or marketing copy, to improve the accuracy of translations tailored to your brand/industry.

According to various reports, 95% of surveyed organizations use AI or machine translation in some capacity and 18% rely on it for every translation task. Quality perceptions are shifting just as rapidly: 66% of respondents believe AI translation outperforms human interpreters, while only 25% continue to favor human translation. Meanwhile, AI-powered localization is thought to reduce costs by 45% while maintaining quality.

Content Management System integration

For websites, integrations can detect new or changed content in your CMS, send it for translation, and publish the localized version automatically. When working with a CMS, you can integrate localization automation to ensure that your that localized content is dynamically managed and updated as part of the overall content lifecycle.

Automated systems can monitor a CMS for new or updated content. As soon as changes are detected, the system can trigger a localization workflow without requiring manual input.

Once the translations are complete, the CMS automatically publishes localized content, ensuring that updates to websites, apps, or digital platforms are reflected simultaneously across all supported languages. Some CMS platforms even allow content to be tagged for different regions, cultures, or languages.

Automated quality assurance

Automated checks find potential issues in the localized content like spelling and formatting issues, inconsistent terminology, broken placeholders and text that’s too long, so manual review can focus on meaning and tone.

Some QA tools can even flag content that may not be appropriate or relevant for the target market. Localization teams can set specific rules to flag inappropriate content such as idiomatic expressions or localized terms that may not be suitable for certain markets. Certain QA tools also offer compliance checks to ensure that translations comply with regional legal and regulatory standards.

In-context translation

In-context translation tools allow translators to see how the translations will appear in the final product through screenshots or live previews. This is great because is allows them to account for space limitations, UI constraints, and design elements.

Code repository and CI/CD integration

Connecting your localization platform to GitHub, GitLab, Bitbucket or Azure Repos turns localization into part of every release: new strings go out for translation and finished translations come back automatically. This is continuous localization.

Challenges to plan for

Also take into account the following:

• Quality: machine and AI translation still miss nuance and cultural context. Use a hybrid approach: automation for the first pass, people for review.
• Integration: several systems need to work together and trigger each other reliably. Prefer native integrations over custom scripts where you can.
• Consistency: translation memory and glossaries need maintenance as your product and brand evolve.
• Upfront effort: it takes time to set up automation. For small teams, start with the steps that repeat most often.

Automated systems, especially MT tools, often struggle with nuances, idiomatic expressions, and culturally specific content. To address this challenge, many businesses turn to a hybrid approach where machine translation is used for the first pass, and human linguists review and fine-tune the output.

Localization automation often requires integrating multiple systems and product development tools. You need to make sure that these systems work together smoothly. Each platform must be configured to communicate efficiently, detect updates in source content, and trigger appropriate localization workflows.

Another significant challenge in localization is maintaining terminology consistency and ensuring that translations adhere to brand voice across multiple languages. Translation memories, integrated into TMS platforms, help maintain consistency, but you need to manage and update these databases so that new terms and phrases are aligned with current branding and market-specific adaptations.

Without a doubt, localization automation can save costs in the long run by reducing reliance on manual processes. Nonetheless, for smaller companies, the initial investment in tools, platforms, and system integration can be significant. If this is your case, evaluate whether the potential savings and efficiency gains justify the upfront expenses.

Localization workflows in POEditor

POEditor lets you build the workflow above in one place, and automate most of it with workflows. A translation automation workflow starts with a trigger and runs a series of steps:

• Triggers: new terms are added, a language is complete or proofread, the reference language is updated, a schedule, or a manual start.
• Steps: AI or machine translation, translation memory, QA checks, AI quality evaluation and fix, tagging or flagging strings, notifications, project statistics, and importing from or exporting to your Git repository.

For example, when new strings arrive from GitHub, translate them with AI, score their quality, flag the weak ones for review, and notify your translators.

Around workflows, you have integrations with code repositories, Figma, Slack and Microsoft Teams, the API and webhooks, translation memory, glossaries and QA checks. See all automation features.

Frequently asked questions

What are the steps of a localization workflow?

Planning, content preparation, translation, editing and QA, deployment, and monitoring.

What is localization automation?

Using tools to run the repetitive parts of the workflow automatically: pulling new strings, translating them, checking quality, and sending translations back to your product.

Can the whole localization workflow be automated?

Most of it, yes. Human judgment is still needed for planning, review of important content, and cultural decisions.

What features should I look for in a translation workflow automation tool?

Triggers that start work automatically (new strings, a completed language), AI and machine translation, translation memory, QA checks and quality scores, notifications, integrations with your code repository, and control over which steps need human review.

What is the difference between a localization workflow and continuous localization?

A workflow is the set of steps; continuous localization runs them continuously, as part of every release, instead of in large batches.