Babel advierte de que la batalla se producirá "entre sistemas de inteligencia artificial capaces de aprender y evolucionar más rápido que el adversario", unos para delinquir y otros para proteger a las empresas e internautas.
Babel advierte de que la batalla se producirá "entre sistemas de inteligencia artificial capaces de aprender y evolucionar más rápido que el adversario", unos para delinquir y otros para proteger a las empresas e internautas.
En contexto
- Tema: Estrategia y Gobierno de TI — Decisiones de portafolio, costo total, gobierno y marcos de referencia.
- Fuente: Silicon España
- Publicado: 26/08/2026
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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.
I measure AI maturity with an uncomfortable question: can anyone explain, in one line, what decision the model makes and who answers if it gets it wrong? Where that is clear, the project moves. Where it is not, it stays in permanent pilot and gets cancelled with the excuse that the technology was not ready.
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
- 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.
Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
