The chess analogy is the best explanation I've seen for why a 10-minute training video does nothing. Knowing the rules and playing a real game against a real opponent aren't remotely the same skill, and most corporate AI training stops at "here's how the bishop moves." Question two is the hard one honestly, since nobody actually wants to ask their team what they say to each other after the all-hands ends.
Elaine, the workaround doesn't have to be a better question.
Pull a handful of things a team already shipped with AI recently and check whether anyone verified the output before it went out, or just trusted it looked right. That gives you the real mindset without anyone having to self-report it, same gap Joel's five questions are getting at, just measured a different way.
Thanks for this. The "training without judgment" gap tracks with something similar in hiring, a different domain than your DataCamp citation. A survey of 80 enterprise hiring leaders found over 90% have deployed AI in talent acquisition, but fewer than 5% report transformational outcomes. When asked what's actually holding results back, leaders ranked change management and governance above technology quality, both around 55%, well ahead of the tools themselves.
That's a useful corroboration of your judgment-gap frame: the people running these deployments day to day are naming the human layer as the bottleneck, not the model. It also fits your chess analogy neatly. Teaching the rules produces the 90% deployment rate; developing judgment is what the missing 85% would need to actually win with it.
Good judgment develops through making meaningful decisions and experiencing the outcomes that follow. That’s why traditional courses alone rarely build strong judgment. Instead, give leaders real-world decisions to make, let AI serve as one source of input, and then evaluate the reasoning behind their choices.
Judgement is built by making calls that carry consequences. That is why a course rarely produces it. Put leaders on real decisions where AI is one input, then review what they chose.
Joel, excelente ponto. Esse dado dos líderes é exatamente o que tenho acompanhado aqui no Brasil.
O tradicionalismo não foi preparado para o automatismo e essa lacuna está além do julgamento. Trata-se de eliminação. Não é só a precariedade no treinamento de IA. A falha está na lógica habitual que já não faz sentido nessa era tecnológica.
Suas 5 perguntas deveriam ser obrigatórias antes de qualquer compra de IA. Esse seu artigo vai ao encontro do que publico amanhã.
The chess analogy is the best explanation I've seen for why a 10-minute training video does nothing. Knowing the rules and playing a real game against a real opponent aren't remotely the same skill, and most corporate AI training stops at "here's how the bishop moves." Question two is the hard one honestly, since nobody actually wants to ask their team what they say to each other after the all-hands ends.
Yes exactly! It stops before it can be useful
Elaine, the workaround doesn't have to be a better question.
Pull a handful of things a team already shipped with AI recently and check whether anyone verified the output before it went out, or just trusted it looked right. That gives you the real mindset without anyone having to self-report it, same gap Joel's five questions are getting at, just measured a different way.
Thanks for this. The "training without judgment" gap tracks with something similar in hiring, a different domain than your DataCamp citation. A survey of 80 enterprise hiring leaders found over 90% have deployed AI in talent acquisition, but fewer than 5% report transformational outcomes. When asked what's actually holding results back, leaders ranked change management and governance above technology quality, both around 55%, well ahead of the tools themselves.
That's a useful corroboration of your judgment-gap frame: the people running these deployments day to day are naming the human layer as the bottleneck, not the model. It also fits your chess analogy neatly. Teaching the rules produces the 90% deployment rate; developing judgment is what the missing 85% would need to actually win with it.
Thanks for sharing!
Oof! I'll never stop thinking about tools increasing confidence without increasing correctness. More reason why we need systems and processes.
Oh higher confidence with lower correctness is dangerous, well said
Good judgment develops through making meaningful decisions and experiencing the outcomes that follow. That’s why traditional courses alone rarely build strong judgment. Instead, give leaders real-world decisions to make, let AI serve as one source of input, and then evaluate the reasoning behind their choices.
Judgement is built by making calls that carry consequences. That is why a course rarely produces it. Put leaders on real decisions where AI is one input, then review what they chose.
Joel, excelente ponto. Esse dado dos líderes é exatamente o que tenho acompanhado aqui no Brasil.
O tradicionalismo não foi preparado para o automatismo e essa lacuna está além do julgamento. Trata-se de eliminação. Não é só a precariedade no treinamento de IA. A falha está na lógica habitual que já não faz sentido nessa era tecnológica.
Suas 5 perguntas deveriam ser obrigatórias antes de qualquer compra de IA. Esse seu artigo vai ao encontro do que publico amanhã.