A few months ago, a group of friends and I were talking about our period tracking apps – which ones we used, which ones we trusted. One of the women had recently deleted hers because she had discovered it was selling her data. Intimate health information – cycle patterns, fertility signals, reproductive intentions – being shared for commercial gain. She felt violated and rightly so.
Something really struck me: that app was built and owned by men. I’m not saying that their choice to sell her data was malicious. I’m saying that if women had been in the room when those product decisions were made, things would likely have been thought about differently. The men building that app would never have to feel the stakes of that data being misused – subpoenaed, sold to insurers, used to infer health status. It wasn’t on their radar because it didn’t involve their bodies.
It was a reminder that commitments to honour diversity that are made on company websites don’t actually change what gets shipped and used in the real world. Diverse perspectives tend to be sidelined until the room where product decisions are actually made is filled with the people who are actually affected by them.
I work as an Assistant Director in energy transition policy for the Australian government, specifically on what we call just transitions – ensuring that when a major economic system shifts, the communities most at risk don’t simply absorb the disruption while everyone else captures the benefit. I’ve watched this play out in regional coal mining communities facing decarbonisation – workers whose skills and livelihoods are bound up in disrupted industries, navigating a change designed by people whose lives look nothing like theirs. The AI transition is surfacing the same issues, but this time women are the ones being sidelined.
41% of all high-income work for women is exposed to AI disruption, compared to 28% of men’s jobs – and the replacement risk for female-dominated roles is three times higher (ILO-NASK Global Index, May 2025). We are underrepresented in the teams building AI systems – and those systems amplify human biases rather than correcting for them, routing outputs toward assumptions that were never examined because the people who made them never had to live inside them. The productivity gains from AI will accrue broadly. The disruption will land unevenly. My work in energy transition policy has made one thing clear to me: communities who don’t participate in designing systematic change end up bearing the cost. The systems shaping our hiring outcomes and medical diagnoses weren’t designed with malice. They were designed with assumptions. Assumptions made in rooms without women will encode the absence of women into future infrastructure.
But here is what I know from watching this up close: when women do engage with AI – even at the most basic level, asking for help drafting an email, using it to research a decision, letting it handle something that’s been sitting on their to-do list for weeks – something shifts because the mystery dissolves. And women, who are running households, managing careers, carrying the mental load for their families and absorbing the invisible work that keeps everything moving, have more to gain from this than almost anyone. Not because they’re a demographic to target, but because they are structurally the most time-poor people in the economy. AI, at its most basic, is a tool for reclaiming time. Every woman who learns to use it is a woman who gets some of that time back.
Discussions around AI tend to skew towards framing it as technical, intimidating and frankly, “tech bro” culture. The conversation is dominated by voices that don’t look like most of the people whose lives it will reshape, pushing women into thinking that AI isn’t for them. The messenger matters. A woman talking about how she uses AI changes the frame – not because she’s explaining it differently, but because she’s making something visible to women who assumed it wasn’t available to them.
There is one more angle that I don’t think gets talked about enough. Women are the people shaping how the next generation first encounters AI. As mothers, as carers, as the ones children turn to when they’re curious or confused, women need AI fluency not just for themselves – but to be able to guide what comes after them. A mother who understands what AI can and can’t do is better positioned to keep her kids safe, to ask the right questions, to be something other than a bystander in that conversation. AI literacy for women is AI literacy for the next generation.
Because of this, in my own time, I’ve been building an AI consulting practice for female-led small businesses. The idea is still taking shape but the premise is simple: every woman who learns to direct her own AI adoption is one more woman who gets to shape this technological transition rather than be handed someone else’s version of it. I’m building access ramps because waiting to be invited into the room is not something I’m willing to do.
The question of who gets to write the next chapter of the economy is not a future question. It is being answered right now. I’m teaching myself to build with AI because the room needs different builders in it.