Bossed Up

What does have to do with adopting ? Research has shown that women are more reluctant to embrace AI than men, and it’s causing a whole new kind of workforce gap. Now, a new study from Lean In provides some solid evidence of why one reason, the belief that AI is or will be perceived as cheating, is grounded in some concerningly legitimate gender discrepancies.

In this episode of the Bossed Up , I take a closer look at this research. We’ll get into the glaring difference in how credit and blame are assigned to men versus women, the problem with linking competence and AI use, and the internal factors that reinforce our reluctance to integrate AI into our workflows. And, of course, we’ll talk through some ways you can start calling out the bias and fostering change, so that women can become a larger part of the conversation around this domineering technology trend. Tune in now: https://www.bossedup.org/podcast/episode546

AI isn’t impacting everyone equally.

And yet…we keep talking about it like it is.

Women are facing what I call a double disadvantage in the AI economy:

1️⃣ Our jobs are more exposed to automation and disruption
2️⃣ The new opportunities being created are disproportionately going to men

Here’s what the data shows:

• Women’s jobs are nearly 3x more likely to be at high risk of AI automation
• Women make up just 22% of the global AI workforce
• And even when access is equal, women are still 25% less likely to adopt AI tools

That last one matters.

Because while women are asking important questions about ethics, bias, and accountability...

Men are developing new AI skillsets and capturing productivity gains.

And in a moment like this, productivity = power.

So we’re stuck in a tension:

👉 Question the system
👉 While also learning to operate within it

Because opting out doesn’t slow the AI revolution down.
It just determines who benefits from it.

I break all of this down in today's new episode of the Bossed Up podcast: https://www.bossedup.org/podcast/episode540

But I’m more curious about your take: when it comes to AI, are you leaning in, pushing back, or trying to do both at once?