Valerie Ehrlich, PhD

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I’ve been thinking a lot about the "efficiency trap" in AI adoption.

We’re so focused on how much time these tools can save us that we’re forgetting to ask what we’re actually doing with the time we get back.

Lately, I’ve been experimenting with using AI not to do things *faster*, but to do them *deeper*. I’ve been using LLMs to pressure-test my own assumptions before I head into strategy sessions with clients.

It’s not about the output; it’s about the friction.

By asking the AI to "play the skeptic" or "identify the systemic gaps" in my logic, I’m arriving at insights that I simply wouldn't have found if I was just looking for a quick summary.

But (and this is the important part...), this only works if we have the "human infrastructure" in place to actually listen to what the tool is telling us.

❇️ Cultures that value dissent and questioning,

❇️ The psychological safety to be "wrong" in front of a bot, and

❇️ The time to actually reflect on the feedback.

Without these, we’re just building faster ways to make the same old mistakes.

I’m curious—how are you using AI to slow down and think, rather than just speed up and produce? I’d love to hear your "slow AI" stories.

#aiforgood #systemschange

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