Episode Transcript
[00:00:00] Reshaping SaaS localization with automation and Risk Based Thinking By Suzanne Rose Grivel in the world of software As a service, SaaS speed is not a luxury, it's survival. New features can drop daily.
[00:00:15] Content flows from multiple teams in multiple formats, and somewhere in that stream, localization teams must instantly deliver translations that are accurate on brand and deployment ready. The challenge isn't new.
[00:00:28] What is new is how artificial intelligence, AI, and automation are rewriting the rules.
[00:00:33] They're not replacing humans. They're forcing a rethink of workflows, roles, and priorities. Interviews with SaaS localization professionals, from localization managers to content designers reveal a common thread. Success isn't about stacking tools, but about redesigning the process itself.
[00:00:52] This article presents seven best practices for optimizing localization workflows for today's SaaS landscape. When followed, they can help localization teams keep up with fast paced changes and high expectations without sacrificing quality.
[00:01:06] Recognize that governance comes first When a content designer joined a European human resources and payroll SaaS, she was surprised about the localization workflow. There's no localization team. If someone needs a translation, they post translation requests in a Slack channel called translation, and product managers do the translating themselves in phrase. We've asked for a dedicated localizer, but nothing has happened without ownership, quality faltered. In one infamous case, we used the French word collaborateurs, meaning employees, and someone translated it into English as collaborator. That triggered panic from the UK team. Her fix was not flashy AI it was governance. A Google Sheets glossary with a Chrome plugin updated quarterly so designers and developers could at least access approved terminology.
[00:01:56] Don't let localization run on ad hoc goodwill. Define ownership document standards and centralize terminology before scaling up.
[00:02:05] Automate where it hurts least.
[00:02:08] Machine translation. MT now often serves as the first step, a safety net if content must go live fast. As Teresa Tarono, localization manager at Malt, explains, we also apply MT as a first step, so there's always a fallback. If content needs to go live quickly, Translators then have 48 hours to review and edit the MT. Once confirmed, their revisions overwrite the MT content in the system.
[00:02:34] Some teams go further, using AI driven quality prediction scores to decide which segments need human review.
[00:02:40] The result? Linguists can focus on high impact work in context reviews, MT engine tuning and style guide improvements rather than blanket reviewing everything. For another senior localization specialist, MT runs through XTM Deep L for most European languages, Microsoft Translator for Asian ones. But several steps still require manual work, pre processing, post processing, tagging, quality assurance QA after review and final implementation, even developers have to create manual tickets and apply changes themselves. Best practice Let automation handle low risk, high volume segments. Save human expertise for content that shapes user trust or has legal implications.
[00:03:23] Make AI part of the plumbing At Django and in Drive, Yana Kolesnikova built pipelines that batch schedule user interface UI translations integrate crowding with GitHub and Runai assisted QA to catch issues.
[00:03:37] It became clear that we either had to hire more people or automate parts of the process to reduce repetitive effort, she said. AI now works across multiple stages Source preparation Rewrites unclear keys before translation Quality control scripts flagging inconsistencies Tone optimization Adapts voice for various markets.
[00:03:59] Best practice Integrate AI into your infrastructure rather than using it as a surface. Add on automate source preparation, QA and tone optimization so AI becomes an invisible, reliable part of your localization pipeline.
[00:04:15] Approach localization as a design feature for some teams, localization is no longer the final step it's embedded in product design from day one. At Yango, for instance, the localization team works closely with developers, designers, and product managers to ensure that every feature is designed with multilingual markets. In glossaries are integrated directly into the Computer Assisted Translation CAT tool, enabling automatic terminology checks. Annotated FIGMA screenshots provide translators with visual context, while tone and style are tested through A B experiments before release. Yango's product localization branch runs on a continuous localization model. Developers commit new strings to repositories, which are automatically batched twice a week and sent to Crowdon for translation.
[00:05:04] This batch based workflow ensures predictable delivery dates and eliminates the chaos of ad hoc microtasks. Developers know exactly when to submit strings if they want them included in the next release, and linguists receive all the context they need to work efficiently. Automation supports more than just scheduling.
[00:05:21] Custom QA scripts detect issues like mismatched variables, incorrect characters, or even the accidental use of Cyrillic letters in English text.
[00:05:30] Integration with FIGMA allows visual assets to be pushed directly into the localization platform, linking each string to the precise UI element where it appears. For markets requiring special adaptation such as right to left languages, the team maintains design and QA guidelines covering layout, mirroring, currency formatting, and date conventions. Tone of voice is treated as a design parameter, not an afterthought. Based on user testing, the Spanish version of the app adopts a more conversational style, improving user engagement and eliminating previous complaints about stiffness.
[00:06:04] Linguists follow detailed tone and style guides and brand specific naming. Glossaries ensure consistent messaging across markets and product lines. As Kolesnikova puts it, I'd push for a culture that sees localization as part of the product design, not a final step.
[00:06:21] Treat localization as a product feature.
[00:06:24] Involve linguists from the design stage. Provide full visual context and embed glossaries into tools to ensure consistency, relevance and efficiency from the first line of code.
[00:06:35] Review by risk, not by habit.
[00:06:38] Treating every string equally is a fast track to burnout. Leading teams prioritize review based on business impact, visibility and legal stakes. Booking.com's predict model is one example, matching QA depth to defined risk. Terronio applies a similar system at Malt. Regarding quality I care deeply, but only as far as it impacts the user experience.
[00:07:01] Best quality depends on the content type. A help article doesn't need the same quality as a legal document or a product feature. We have a tiered system.
[00:07:10] Tier 1 features are either AI related, like our AI powered search, or revenue related, such as quote approval workflows. These get prioritized in our quality checks. Best practice Map your content to risk tiers. This ensures your best reviewers focus where quality really moves the needle. Start scaling before you grow.
[00:07:31] Every expert interviewed warned against waiting until growth breaks the system. Early investment in automation, quality control and integration with development pays off later.
[00:07:41] One senior localization specialist summed it up unfortunately, the localization stack is fragmented. Marketing runs a content management system, cms, integrated with translation workflows. Technical communication uses a separate CMS without integration. The gap forces manual work and slows releases.
[00:08:00] The same specialist warned that with languages split between in house staff and language service providers, internal teams might update a file while the vendor is still working from an earlier version, leading to inconsistencies.
[00:08:14] Her proof of concept boiled the essentials down to four automation, cultural adaptation, early involvement in product development, and a mindset that sees localization as strategic, not just the final step. Best practice Future proof Now design processes that can handle twice the volume you currently have without doubling the chaos.
[00:08:35] Design for internationalization early scaling Localization starts with strong internationalization designing software so it can support multiple languages and cultural formats without rework. This means separating translatable strings from code, using variables instead of hard coding text, and planning for differences in date, formats, currencies, and writing systems. Taronio saw firsthand how neglecting this step inflated work. We had 15 languages, but many were fake. For example, in Spain, some users preferred English UI but needed Spanish specific information like local contact emails or legal disclaimers.
[00:09:12] So we duplicated the English strings and created a language called English Spain just to localize 250keys manually. It looked like 15 languages, but we really had only five.
[00:09:23] By working with engineers to replace duplicated strings with dynamic variables, she deleted 10 fake languages, reducing the string count from 1.5 million to 600,000.
[00:09:34] Adding a new language now means translating new content, not duplicating effort. Best Practice Bake internationalization into development from the start. Every shortcut taken here will cost more in every future language.
[00:09:48] The takeaway AI will not solve localization, but AI automation and risk based thinking can free humans to focus on what matters user trust, legal safety, and brand voice.
[00:10:02] The seven best practices listed above provide a de facto SaaS localization playbook.
[00:10:08] Start with governance. Define ownership and standards before buying tools.
[00:10:13] Automate the repetitive focus on routing, syncing, and low risk translations.
[00:10:18] Use AI as infrastructure integrated into source preparation, QA and tone control.
[00:10:24] Embed from the start. Treat localization as part of product design.
[00:10:29] Adopt risk based QA focus review effort where it impacts users most.
[00:10:34] Prepare for scale early when growth hits, it's too late to start design for internationalization. Avoid costly retrofits when adding languages.
[00:10:44] The future is not humans versus machines.
[00:10:48] It's humans designing better systems with machines doing the heavy lifting so quality, efficiency and scalability grow together.
[00:10:56] This article was written by Suzanne Rosegroveau. She is a global Content Manager and former certified German teacher. She recently completed her master's degree in Technical Communication and Localization, TCLK at the University of Strasbourg.
[00:11:10] Originally published in Multilingual Magazine issue 246 December 2025.
