CaixaBank ha ampliado el acuerdo que tiene suscrito con Google Cloud, para reforzar sus capacidades tecnológicas en ámbitos clave como la inteligencia artificial (IA), la gestión de datos, la analítica
CaixaBank ha ampliado el acuerdo que tiene suscrito con Google Cloud, para reforzar sus capacidades tecnológicas en ámbitos clave como la inteligencia artificial (IA), la gestión de datos, la analítica
En contexto
- Tema: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Fuente: Revista Byte TI
- Publicado: 12/10/2026
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Why it matters
The conversation about artificial intelligence has become noisy, and that makes it harder to see what matters. What matters is rarely the model. It is which concrete process ends up cheaper, faster or more reliable, and who is accountable when the answer comes out wrong.
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 mistake I have seen repeat most is measuring nothing before starting. It gets deployed, everybody feels things improved, and when somebody asks by how much, there is no answer. Without a baseline there is no way to defend next year's budget, and that is where good projects die.
What to watch
- 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.
- Who reviews the model outputs and how often — the point at which AI becomes auditable.
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.
