Laboratória abre inscrições para curso gratuito de IA para mulheres

O programa Ativa sua Carreira, da Laboratória, está com inscrições abertas para a terceira edição. Voltado exclusivamente a mulheres com 18 anos ou mais de qualquer parte do Brasil e que sentem que ficaram desatualizadas diante das transformações rápidas do mercado de trabalho.

O programa Ativa sua Carreira, da Laboratória, está com inscrições abertas para a terceira edição. Voltado exclusivamente a mulheres com 18 anos ou mais de qualquer parte do Brasil e que sentem que ficaram desatualizadas diante das transformações rápidas do mercado de trabalho.

Em contexto

• Tema: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
• Fonte: IT Forum
• Publicado: 26/08/2026

Continuar lendo na fonte original →

Trecho publicado automaticamente pelo radar do site. O texto completo pertence ao veículo e está vinculado acima.

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.

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

• 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.