AI Marketing & MarTech
AI Video and Podcasts: Two Real Enterprise Cases
When I explain what AI-generated content can really do, I avoid demos and start from two projects I built first-hand at Xister Reply: multilingual video dubbing with AI for Enel and the AI-generated podcast service for Toyota and Intesa Sanpaolo. They are two real enterprise cases, with real constraints, and the reason they worked is the same in both: we did not take people out of the process, we took the mechanical part out of their hands.
The Enel case: dubbing video in multiple languages
The starting problem was simple to describe and expensive to solve: corporate and training videos to adapt into multiple languages, with the traditional process running through a recording studio, professional voice actors and review cycles for each language. Every additional language multiplied time and cost almost linearly.
We built a pipeline where AI handles the repetitive part, transcription, translation, voice and lip-sync, while people remain responsible for the linguistic review and the tone check before publication. The result was a 75% reduction in localisation work, without giving up the quality a brand like Enel requires on content aimed at different markets. I have devoted a separate article to how to cut multilingual localisation costs with AI.
The Toyota and Intesa Sanpaolo case: podcasts at cadence
In the AI-generated podcast service the constraint was not the production cost itself, but the cadence. A corporate podcast that has to ship regularly needs a script, a voice consistent with the brand, and editing that previously required a dedicated audio studio for each episode. With AI we could generate the audio base at a volume a traditional studio would not have sustained on the same budget.
The project only worked because we kept a human step on three points: writing or reviewing the script, the consistency of the voice over time, and the final listen before publication. A podcast generated entirely without supervision, for two brands like Toyota and Intesa Sanpaolo, would have been a risk neither client would have accepted.
What separates a case that holds from a demo
The difference between these two projects and the many generative-AI demos that never reach production is not the technology, but the workflow design. In both cases we had decided in advance who is responsible for the final output, what human checkpoint exists before publication, and what happens when the AI gets it wrong. They are the same three questions I ask before taking any AI content process into production.
The thread linking Enel, Toyota and Intesa Sanpaolo is not the tool used, but the discipline with which we introduced it. How I apply this approach across the whole landscape of AI in marketing is in the guide to AI for marketing.
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