Podcast: ¿Estás consiguiendo la rentabilidad que esperas de la IA?
En este podcast abordamos las mejores estrategias, equipamiento y buenas prácticas que permiten adoptar la IA de una manera más rentable, económica y sostenible en las organizaciones.
En este podcast abordamos las mejores estrategias, equipamiento y buenas prácticas que permiten adoptar la IA de una manera más rentable, económica y sostenible en las organizaciones.
En contexto
- Tema: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Fuente: Silicon España
- Publicado: 19/10/2026
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Why it matters
Every week there is an announcement that promises to change everything, and every week most organisations are still fighting the same battle: messy data, processes nobody documented, and expectations running faster than capability. This story reads better with that in the background.
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
What I see fail most is the expectation. Somebody saw a flawless demo and asked for the same thing in their area within a month, without considering the demo ran on clean data prepared for the occasion. When the real pilot shows uneven results, the unfair conclusion is that the technology does not work.
What to watch
- 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.
- Which concrete process is touched and how it is measured before and after; with no baseline there is no result, only opinion.
How I read this entry
If I had to bring this into an organisation, I would start with a boring, measurable process — reconciliation, first-line support, document review — and get it running with a metric before touching anything flashy. The credibility for big projects is bought with a small result nobody can argue with.
The original story is published in another language; the excerpt is quoted as the publisher delivers it and the commentary is written in English.
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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.
