Uma pesquisa da TeamViewer aponta para uma desconexão dentro das empresas em relação ao uso da inteligência artificial, segundo comunicado da companhia. O estudo, realizado entre abril e maio de 2026 em nove mercados, incluindo o Brasil, mostra que profissionais ligados às operações impulsionadas por IA desejam mais su…
Uma pesquisa da TeamViewer aponta para uma desconexão dentro das empresas em relação ao uso da inteligência artificial, segundo comunicado da companhia. O estudo, realizado entre abril e maio de 2026 em nove mercados, incluindo o Brasil, mostra que profissionais ligados às operações impulsionadas por IA desejam mais su…
Em contexto
- Tema: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Fonte: CISO Advisor
- Publicado: 10/10/2026
Continuar lendo na fonte original →
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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 always separate two things that get mixed up: the demo and the operation. A demo needs to work once with someone watching. An operation needs to work a thousand times with nobody watching, with odd cases, dirty data, at two in the morning. The gap between the two is where the budget goes.
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
- 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.
- Cost per query at real volume, not pilot volume: the variable that produces the most surprises.
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.
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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.
