Speed. Scalability. Simplicity. That’s what every content team wants when it comes to multilingual subtitles, especially when deadlines are tight and budgets are tighter.
So when one client chose to use machine-generated subtitles to streamline costs for two well-known TV series, the logic made sense. The source files had been quickly generated — fast, automated, and budget-friendly. But by the time those subtitles reached our team, the picture looked very different.
The Hidden Cost of the “Faster Option”
The subtitles for two well-known TV series had been machine translated into Slovak, directly from English source files without any human post-editing. What we received was, technically, a “ready” file.
But:
- Sentences were mistranslated or made no sense in context
- Subtitle timing was inconsistent or distracting
- Character speech was hard to follow, especially in fast-paced dialogue
- Visual alignment was off
- Grammar and typography errors were everywhere
Even for experienced post-editors, one episode took 4 to 6 hours to correct and that didn’t even include full retiming. And the deeper issue? Translators were being paid per minute of content, not per hour of work — a mismatch that quickly made the project unfeasible for several freelancers involved.
One translator even stepped away from the project entirely, choosing to finish only the first series. It wasn’t about unwillingness; it was about practicality. The math simply didn’t add up.
Meeting the Challenge Without Blame
We knew the client wasn’t at fault. Like many production teams, they were looking for a way to do more with less. The intention was clear: automate the repeatable tasks, then finalize.
The problem wasn’t the goal. It was the gap between input and output expectations.
So instead of pushing back, we restructured our approach:
- Adjusted post-editing workflows for high-complexity language pairs (like English→Slovak)
- Flagged quality thresholds to avoid last-minute panic at delivery stage
- Communicated openly with our freelance team to set realistic expectations and support
The result: a project that could be rescued, stabilized, and delivered with quality intact even under complex conditions.
A Subtle but Crucial Lesson
Many teams experimenting with AI-generated subtitles are running into this same issue:
What looks like a quick win turns into a slow, quiet bottleneck, especially when human review isn’t built into the workflow.
That doesn’t mean you shouldn’t use machine translation. It just means you should know when and how to use it effectively.