Most localization teams struggle with finding the right context.
Translators jump between the TMS, Jira, Figma, Slack, and old docs just to understand a single string. Reviewers approve copy without ever seeing the screen it lives on. Developers spend hours every week explaining where text appears and what it must not break.
All of this context switching in localization slows releases and drives up translation rework.
On paper, it looks like “bad translation.” In reality, it’s a context deficit. The information people need to make the right call is missing, scattered, or outdated.
This is where automating context management in translation can help tremendously.
🧠 Good to know
Context helps AI stop guessing what you meant, and then helps it say it the way you would. AI needs context in two different ways:
First, to avoid getting the meaning wrong. If an engine can’t “see” what a human translator would (where the string appears, what the UI looks like, what a label is pointing to), it can produce a translation that’s incorrect. That’s why good AI workflows start with baseline context. You share descriptions, screenshots, character limits, and other in-product signals to remove any ambiguity.
Second, to make the translation a nuanced fit. Even when the meaning is technically right, AI still needs localization context to match your brand voice, audience, and regional expectations. This is where richer inputs matter. Think things like retrieving relevant product and domain knowledge (RAG) and injecting style guides, terminology, and tone rules so the output isn’t just accurate, but on-brand and consistent.
Most of what gets labelled as “bad translation” is really a context problem. Think about the situations that keep coming back in QA:
In that setup, everyone is working with partial information.
AI isn’t inherently simplistic here. It can be excellent at disambiguation and consistency, but only if the localization platform gives it the same kinds of signals humans rely on. Without them, even strong models revert to generic interpretations. The input has to contain enough evidence for it to choose the right meaning.
And then translators, just like AI, do their best to resolve ambiguity from clues. The difference is that humans bring a lot of implicit context by default. This includes product intuition, cultural knowledge, a feel for what sounds natural, and a mental model of the user journey. When the string is vague, they’ll still make a best-supported call, but it’s still a call.
That’s why AI in localization platforms works best when context is made explicit and retrievable. Screenshots and descriptions are there to anchor meaning. Product knowledge, terminology, and style guidance (often via retrieval) ensure the translation fits the brand and the user experience.
Automating context management translation means making sure that every string always carries up-to-date visual, code, and linguistic context as it moves through your workflow. This way, when designs or terminology change, that context is updated everywhere automatically, not patched by hand in scattered docs or Slack threads.
When context is always visible in the TMS, translators and AI don’t have to guess anything. This cuts rework and makes “bad translation” much less likely in the first place.
The goal is to reduce guesswork for everyone and make “the right choice” the easiest and most obvious one to make.
Context switching is both draining and expensive.
Studies on knowledge work and software development show that every time people switch tasks or tools, they pay a real productivity tax. Research summarized by the American Psychological Association suggests that task switching can cut productivity by up to 40%, and it can take 15–23 minutes to fully refocus after an interruption.
Developer-focused studies report similar patterns: frequent context changes force engineers to rebuild their mental model from scratch, leading to slower work, more errors, and earlier fatigue.
Now map this onto localization. A single string can push people through five or six tools. You’ll be looking in your TMS for the text, Jira for requirements, Figma for layout, Slack for clarification, Confluence for specs, staging for live behavior.
Reviewers repeat the same dance to see how copy behaves. Meanwhile, a steady stream of “quick questions” eats into developer time on context. This is time that should be spent shipping features, not re-explaining how flows work.
❗ Important note
The impact on productivity is bigger than a few lost minutes. Every switch increases the chance that details are missed.
Automating context management in translation is a way to shrink this tax by keeping the right information in one place, cutting the number of switches, and reducing translation rework. This makes it easier to get things right in a single pass.
Lokalise’s own research showed almost a quarter of workers waste 2.5 work weeks per year due to context switching, which is around 100 hours per person, per year.
Let’s say you have a localization team of 5 people who regularly get dragged into context switching:
So, that’s around 500 hours per year lost just to rebuilding context Now add a simple cost assumption. If the average fully loaded cost (salary + overhead) is $80,000/year, that’s roughly $38/hour.
500 hours x $38 = $19,000 per year
So, for one small localization team, you’re easily burning around 500 hours and ~$19K every year on context switching alone. This time could otherwise go into shipping features, improving content, or helping reduce translation rework.
Getting back up to speed is expensive. At typical SaaS salary levels, that’s tens of thousands of dollars a year spent on people reopening tabs and reconstructing context instead of shipping features and reducing translation rework.
To get context right, it helps to think in three streams. Your TMS should bring all three together instead of forcing people to chase them across tools.
This is everything that shows where the text lives: screenshots, UI previews, Figma frames, live in-product views. When translators and reviewers see the screen, they instantly understand whether a string is a label, a CTA, a tooltip, or helper text, how it wraps, and what surrounds it.
💡 Pro tip
Strong visual context in TMS unlocks the real in-context editing benefits. It’s how you get to fewer layout issues, fewer “what is this referring to?” questions, and more first-time-right decisions without leaving the translation view.
This tells you how the text behaves and what it’s connected to: key names and namespaces, file paths, platform (web, iOS, Android), variables, plural forms, feature flags, character limits, and other constraints.
When this code and structural context is visible in the TMS, translators and AI can avoid breaking logic, mistranslating variables, or picking a tone that doesn’t fit the component or platform.
This is the layer that keeps everything consistent. It includes everything from glossaries, style guides, translation memory, domain notes (e.g., legal, medical, fintech), to any rules about tone, formality, or sensitive terminology.
When this linguistic and brand context is tightly integrated into the translation workflow, the same concept is named the same way in product, docs, and marketing. Likewise, AI suggestions stay much closer to how your brand actually speaks.
📚 Further reading
Continuous localization only really works when these three streams are kept in sync and surfaced directly in the TMS, not scattered across design files, tickets, and internal docs. Learn more about other context pillars and why they matter.
Once you see how much time you’re losing to scattered context, the next question is: What do we actually do differently?
Here’s a simple workflow you can run, without changing your entire stack overnight.
Start by picking one high-impact journey such as onboarding, checkout, billing, upgrade, wherever mistakes and questions hurt the most. Then list all the strings and related content for that flow:
For each string, ask two questions:
You’re not fixing anything yet. The goal is just to expose the hidden context switching in localization for that one flow, and see how often people leave the TMS to get basic answers.
Before you automate anything, you need a shared definition of “enough context”. Create a short, repeatable template that anyone creating strings can follow. For example:
This will give you enough info to create a “context checklist” for PMs, designers, and writers. The rule is to fill this in whenever you create a new string. That way, “good context” stops being a nice idea and becomes part of how content is written from day one.
Next, move from “context scattered everywhere” to “context lives where translation happens”. You can use Lokalise to:
In this way, your TMS becomes the single source of truth for context.
Once the basics are in place, you can stop pushing screenshots and specs around by hand. Connect tools so context moves automatically:
Design → TMS
Use the Figma–Lokalise integration or automated screenshot sync, so new or updated designs bring their visual context with them.
Dev → TMS
Use branch-based workflows or CI/CD hooks to pull new keys (and their metadata) into Lokalise as part of your normal release process.
Docs / help center → TMS
Set up content integrations or structured exports for help articles and knowledge base content, so they arrive with the right tags and relationships.
Then you can configure Lokalise workflows so that:
Automation here means fewer manual uploads, fewer “did you see that screenshot?” messages, and a lot less back-and-forth to clarify basics.
To prove this matters, you need to show what changed. For the flow you started with, track a small set of KPIs before and after you automate context:
Even simple counts or rough logs will show a trend. If questions drop, reopens go down, and developers get fewer pings, you’ve got proof that better context is buying back focus and time.
💡 Pro tip
Use insights from the above-mentioned measurements to decide which flow to tackle next. Then, you can run the same audit and gradually extend automated context management across your product. Each step will help you reduce translation rework and cut the invisible tax of developer time on context.
Even when translation teams agree that context is a problem, automation often stalls on the same set of obstacles. The good news is that most of them are fixable with a few clear decisions and the right setup in Lokalise.
Here’s a simple map of what usually gets in the way and what to do about it.
| Barrier | How it shows up | Automated solution |
| Context is scattered across tools | Translators open five tabs (TMS, Jira, Figma, Slack, Confluence) to understand one string. Screenshots and specs live in tickets and chats, not where translation happens. | Centralize visual context in TMS. Attach screenshots and previews to keys, and connect Jira/Figma/GitHub to Lokalise so strings arrive with their context instead of being chased manually. |
| No shared definition of “good context” | Some keys are over-documented, most have nothing. Everyone has a different idea of what counts as “enough info” for translation. | Standardize a lightweight context template (screen, user type, purpose, constraints) and make it part of the string creation process for PMs, designers, and writers. |
| AI feels risky | Teams don’t trust AI suggestions, so they redo everything by hand or avoid AI entirely. | Give Lokalise the richest possible context (visual, structural, linguistic) and let its AI handle the bulk of the work. Use workflows and review rules to decide which content can be auto-approved and which should always go through human review. |
| Developers are the context bottleneck | Engineers answer “where is this?” and “can it wrap?” all day instead of shipping features. | Make context self-serve with in-context editing and live previews so translators see what devs see. Reduce the need for ad-hoc questions by exposing constraints directly in the TMS. |
| Rework is invisible | Extra review rounds, reopened strings, and hotfixes are treated as “just how we work,” so nothing changes. | Track questions, reopens, and delays as explicit KPIs tied to your context automation initiative. Use this data to show the cost of rework and prioritize where to improve context next. |
These barriers are usually symptoms of the same root issue. Context wasn’t designed into the workflow. Once you make it standard, central, and measurable, automation becomes much easier to justify (and much easier to scale).
📚 Further reading
If you still think AI translation isn’t “there yet”, you’re missing out. Learn how to give AI translation tools more context so that you can get publish-ready translations for all your digital assets up to 10x faster.
When context is automated, localization moves in step with product development instead of lagging behind it.
Translators and reviewers work from full context inside the TMS. Everything from screenshots, UI previews, key metadata, to linguistic guidance lives next to every string. Product teams localize in parallel with development, so by the time a feature is ready to ship, localized versions are ready too.
As a result, context switching in localization drops, translation rework shrinks, and developer time on context goes into real edge cases. Automating context management eliminates the need to re-explain how a flow works.
If this was useful, we’d invite you to explore how Lokalise can bring visual, code, and linguistic context into one place.
Start by signing up for a free 14-day trial and you’ll see that automated context management can feel like a natural part of your translation workflow.
Author
Mia has 13+ years of experience in content & growth marketing in B2B SaaS. During her career, she has carried out brand awareness campaigns, led product launches and industry-specific campaigns, and conducted and documented demand generation experiments. She spent years working in the localization and translation industry.
In 2021 & 2024, Mia was selected as one of the judges for the INMA Global Media Awards thanks to her experience in native advertising. She also works as a mentor on GrowthMentor, a learning platform that gathers the world's top 3% of startup and marketing mentors.
Earning a Master's Degree in Comparative Literature helped Mia understand stories and humans better, think unconventionally, and become a really good, one-of-a-kind marketer. In her free time, she loves studying art, reading, travelling, and writing. She is currently finding her way in the EdTech industry.
Mia’s work has been published on Adweek, Forbes, The Next Web, What's New in Publishing, Publishing Executive, State of Digital Publishing, Instrumentl, Netokracija, Lokalise, Pleo.io, and other websites.
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