Enabling Real-Time Translations is a multi-step Setup process that spans license confirmation, language enablement, content surface selection, and cost validation. The workflow below covers a standard rollout from kickoff to live operation.
- Confirm license and supported languages
Verify that the org has the AI Translation feature licensed and that the languages you need to support are on the published supported-language list. From Setup, search for Translation Settings and confirm the configuration page is visible. Identify which user populations and which customer populations need which target languages. Document the scope before turning anything on; piecemeal enablement is harder to govern than a planned rollout.
- Enable target languages on user and content surfaces
From the Translation Settings page, enable each target language. Confirm that the relevant users have the right Language Locale Key on their User records (this drives which translation they see). For content surfaces (Knowledge, Chat, Case), toggle Real-Time Translations on for each surface where it should apply. Save the configuration. Run a quick verification by viewing a Knowledge article as a test user whose locale differs from the article's source language and confirming the translated version appears.
- Build the custom glossary
From the AI Translation glossary configuration, add custom dictionary entries for terms that need controlled translations. Brand names typically stay untranslated; product names follow brand-specific localization rules; industry jargon follows the org's terminology guide. Add entries for each language pair the org supports. Test by translating a sample article containing several glossary terms and confirming the engine respects the dictionary. Iterate the glossary monthly during the first three months as new edge cases surface.
- Monitor cost, quality, and adoption
After enablement, monitor the translation service consumption monthly. Compare to the estimated budget. If consumption is much higher than expected, investigate which content surface is driving the volume and consider whether caching settings can be tuned. Review the actual translation output quarterly with a multilingual reviewer to catch quality issues and glossary gaps. Track user adoption: are non-English users actually reading the translated content, or do they default to the source language anyway. The answer guides future investment in the localization program.
- Machine translation quality is good but not perfect. High-stakes content (legal, medical, financial) needs human review or at least a glossary that controls the most sensitive terms.
- Translation consumption is metered. Volume estimates from the account team are starting points; real costs can be higher if content access patterns differ from the estimate.
- Cache invalidation happens when source content is edited. Frequent edits on a popular article translate to higher translation costs because the cache misses each time.
- Real-Time Translations does not satisfy some compliance requirements for human-reviewed translations. Configure per-surface enablement to keep regulated content out of the feature.
- User Language Locale Key drives target language selection. Users with the wrong locale see translations into the wrong language, which is a common adoption complaint.