
Most AI localization focuses on text… but visuals are where it gets interesting
Been experimenting with AI-driven localization workflows recently, and something stood out.
Most AI tools today handle:
- Text translation
- Subtitles
- Voice
- UI strings
But when it comes to visuals, things get messy.
Images are still treated as static assets, even though they often contain:
- Headlines
- Feature callouts
- Product benefits
- Embedded context
So you end up with:
👉 Perfectly translated copy
👉 But visuals still in the original language
Which creates a weird disconnect.
What’s interesting is how different approaches are emerging:
1. Rebuild approach
Generate new creatives per market using AI → high quality, but time/effort heavy
2. Template approach
Swap headline layers → fast, but limited depth
3. Adaptation approach (what I’ve been testing)
Treat images as translatable — adapt text inside visuals while keeping layout intact
The third one is still evolving, but it feels like a middle ground between speed and quality.
I’ve been experimenting with this using tools like Translate.photo — still early, but interesting to see how much it reduces manual work for multi-language creatives.
Curious what others here think:
- Do you see visual localization becoming a bigger AI use case?
- Or will full creative generation replace this entirely?