AI Marketing & MarTech
AI Multilingual Localization: Cutting Costs
Multilingual localisation is one of the places where AI makes a measurable difference on cost, because it is repetitive, high-volume and well-specified work: exactly the kind of task where AI makes almost free what used to cost time and people. I implemented multilingual video dubbing with AI for Enel, cutting localisation work by 75%. In this article I explain how a project like this works and, above all, where AI really makes a difference and where it does not.
Why traditional localisation costs so much
In the classic process, adapting a video into a new language means transcribing, translating, hiring voice actors, booking a recording studio, syncing the audio and running review cycles, for each language. Every additional language multiplies time and cost almost linearly, which makes reaching different markets with the same content expensive. It is a bottleneck that slows international expansion more than people admit.
Where AI cuts the cost (and where it must not touch)
The pipeline we built for Enel automates the mechanical part: speech transcription, translation, voice generation and lip-sync. This is where the 75% cost reduction comes from, because it is the part of the work that previously absorbed time, studio and manual repetition. What AI does not touch is control: the linguistic review and the tone check stay with people, before publication.
What it takes for a project like this to hold
An AI localisation project does not fail because of the model, but because of the process design. For it to hold up in production you need three things: clean source data (the original scripts and materials well structured), a clear human checkpoint before publication, and an explicit criterion for what is acceptable and what must be redone. Without these, even the technically best pipeline produces output no one trusts enough to publish.
When it actually pays off
AI localisation pays off when the volume is high and recurring, not for the one-off single video. The return grows with the number of languages and the frequency of production, because that is where the traditional cost accumulates. The Enel dubbing is one of the two enterprise cases of AI-generated content I most often bring up, alongside the podcast service for Toyota and Intesa Sanpaolo: I have told both in the article on the enterprise cases of AI video and podcasts. The full picture of how I apply AI to marketing is in the guide to AI for marketing.
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