AI can translate, re-voice and lip-sync your marketing videos. Should you use it?

To publish live-action marketing videos across languages has always been slow and expensive. Adapting a single video for another market often means re-recording the video with native speakers. Even short social clips can become costly when repeated across multiple languages.

Generative AI tools such as HeyGen promise to change this. These programs can adjust a person´s voice, speech style, and even lip movements to match a new language. In theory, this makes global video marketing faster, cheaper, and far more scalable.

But there is a more important question: how do people actually respond to these kinds of AI-translated videos?

A recent study by Wahid et al. (2026) compared AI-translated and human-translated marketing videos in two settings: English to Indonesian, and Indonesian to English. Participants watched one version of a video and then evaluated it based on four factors: how easy it was to understand, how natural it felt, how native the accent sounded, and whether they would engage with it.

The results show a clear pattern. Across both experiments, AI-translated videos were consistently rated as less natural than human translations. Viewers could tell that something felt slightly artificial. The same applied to the accent. AI voices were perceived as less native and less convincing.

Understanding, however, was more nuanced. When translating into Indonesian, AI performed worse than humans. But when translating into English, AI actually performed better. This suggests that translation quality depends heavily on direction, likely because AI systems are trained more extensively on English.

The most surprising result is what did not change. 

Despite differences in naturality, accent, and sometimes comprehension, there was no difference in engagement. People were just as likely to like, share, or interact with AI-translated content as with human-translated content.

This creates an interesting tension. AI translation may feel slightly worse, but it still works. From a practical standpoint, that means marketers can often trade a small drop in perceived quality for significant gains in speed and cost.

AI video translation is not perfect. But it is already good enough to potentially change how global marketing works.

When Should You Use AI vs Human Video Translation?

The key insight from this research is even though the AI translation was considered by viewers as worse, it still served its purpose.

The study evaluated four dimensions: comprehension, naturality, accent neutrality, and engagement. Most marketers focus only on whether the message is understandable. But this research shows that perception and performance are not the same thing.

AI consistently scored lower on naturality and accent neutrality. People could tell that the voice was not fully human, and the accent did not feel entirely native. These are immediate, almost instinctive reactions. They happen before people consciously evaluate the content.

At the same time, these perceptual differences did not reduce engagement. This is the most important finding in the study. Even when content felt slightly artificial, people were still willing to interact with it.

This creates a practical trade-off. You can accept lower perceived quality if the outcome you care about—engagement—remains unchanged. In many marketing contexts, that is a rational decision.

The second key insight is that translation direction matters. AI performed worse when translating into Indonesian, but better when translating into English. This likely reflects how these systems are trained. English dominates most training data, which makes outputs into English more reliable than outputs into less-represented languages.

This leads to a simple but useful rule. AI translation tends to perform better when moving into dominant global languages and less reliably when moving into smaller or less-represented ones. This is not a limitation of the concept, but of current training data.

From a practical standpoint, the decision between AI and human translation depends on context.

AI is well suited for situations where speed and scale matter more than perfect delivery. This includes social media content, paid ads, and rapid testing across markets. In these cases, being able to produce and localize content quickly often outweighs small imperfections in delivery.

Human translation still matters when perception is part of the value. This includes brand campaigns, storytelling, and any situation where tone, emotion, and authenticity are central. In these contexts, subtle cues like accent and natural delivery can influence how the brand is perceived over time.

There is also a longer-term consideration. Even if engagement is unaffected in the short term, repeated exposure to slightly unnatural content may shape brand perception in ways that are harder to measure. This is especially relevant for premium or trust-sensitive brands.

The most effective approach is not choosing between AI and humans, but combining them. AI can be used to scale production and test variations quickly. Human input can then be applied where quality matters most. This hybrid approach captures the efficiency of AI without fully sacrificing perceived authenticity.

The broader implication is that AI translation changes the economics of international marketing. It lowers the cost of entering new markets and makes it possible to experiment more aggressively. Instead of carefully selecting a few markets to localize for, brands can now test many and refine based on performance.

AI video translation is not a replacement for human work. It is a new layer in the marketing stack. The advantage will go to marketers who understand where it works, where it doesn’t, and how to combine both approaches effectively.