Publicidad en ChatGPT, un canal con potencial sin benchmarks consolidados
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En contexto
- Tema: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Fuente: Silicon España
- Publicado: 07/10/2026
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Why it matters
Adopting AI in a company is less like buying software and more like hiring someone. You have to teach it the context, review its work at the start, and define what it can sign off alone and what it cannot. The stories worth reading are the ones showing somebody solving that part, not the announcement part.
The first thing I look at is not model capability but the data feeding it. Almost every project I have seen fail did not fail on the algorithm. It failed because the data lived in three systems with three different definitions and nobody wanted to be the one to fix that.
What usually goes wrong
The point where it usually breaks is the handover to operations. The team that built the pilot knows how to read the results; the team receiving it does not. Without training and written criteria, people start accepting everything the model says or ignoring it entirely, and both are equally expensive.
What to watch
- Cost per query at real volume, not pilot volume: the variable that produces the most surprises.
- Where the data ends up and under what contract, especially when customer information is involved.
- Whether the organisation can change provider without rebuilding everything, which is the proof it did not get locked in.
How I read this entry
My practical advice is to fix on day one what happens when the model is wrong: who reviews, how often, and at what point it gets switched off. It sounds defensive, but it is exactly what lets you be aggressive later, because the risk stopped being an unknown.
The original story is published in another language; the excerpt is quoted as the publisher delivers it and the commentary is written in English.
Comments
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.