The "women are falling behind with AI" narrative may be correct, but it misses the point
Who really benefits from the current push to get women excited about AI?
There’s a conversation happening right now about women “falling behind” with AI and honestly, I’m finding it really unhelpful.
High-profile voices have joined in to reinforce the message, with a mixed response. Reese Witherspoon has encouraged women to engage with AI to avoid being left behind. Her efforts have been received poorly in many quarters, particularly considering her role as a book-to-screen producer and the fact that AI is trained on copyrighted works.
At the same time, Sheryl Sandberg of “Lean In” fame has been speaking out in support of initiatives to address the growing gender gap in AI usage. (I’ve got my own set of issues with the whole “Lean In” philosophy, but that’s another article for another time.)
Right now, all the data is suggesting that women are reporting lower daily use of AI tools than men. Meanwhile, we’re also hearing speculation that women’s roles are more likely to be taken or transformed by the technology. From here, it’s logical to conclude that we have a problem that’s going to impact women and their career progression.
That assumption needs closer examination.
What no one is questioning
This narrative relies on a set of assumptions that haven’t really been questioned, being:
It treats AI adoption as inherently beneficial (to whom?).
It presents participation as necessary for relevance.
It interprets lower usage as a problem that requires correction.
Most commentary presents these positions as facts, rather than arguments.
A group with a long history of managing the consequences of poorly designed systems, such as women have in the workforce, have every right to approach a new one with caution. That response is likely to reflect their experience, rather than a deficiency.
Hesitation is not the same as being behind
Many women manage the realities of how work operates in practice. Caring responsibilities, administrative load and the ongoing negotiation between professional and personal demands shape how they assess new tools.
According to the UN, women do around 75% of unpaid care work globally, which is worth around 40% of the global economy. It’s the work that keeps everything else moving. We manage households and schedules, kids and ageing parents. And just to underline that word in the first sentence of this paragraph, in case you missed it, we don’t get paid for it.
In some cases, hesitation to adopt AI might be due to concerns around privacy, sustainability, creativity or ethics. For others, it might also reflect a clear assessment of time, effort and trade-offs.
The current narrative removes that nuance, because it reduces the issue to a single variable: adoption speed. That simplification limits the conversation.
The pressure to keep up
Currently we’re seeing pressure to “keep up with AI” aimed at a group of people who already manage significant demands.
Experimenting with new tools and integrating AI into workflows adds to existing responsibilities. Those responsibilities often include raising children, running a business, managing health and handling daily logistics. Sometimes getting through the day is enough, without adding the need to develop one’s AI skills on top of that. In that context, deprioritising AI reflects a rational choice.
Honestly, your average woman might be forgiven for wondering why AI can’t be tuned to meet us where we are, and solve some of the problems we’re currently facing. Why are we being burdened with yet another responsibility to carry, when the fundamental role of any technology is to make life better in some way?
Where is the actual benefit?
There’s also an elephant in room that we simply don’t talk about enough. We’ve been promised that AI is going to solve some of the world’s greatest challenges. So far, that doesn’t seem to be the case. In fact, I’d argue that the world is in worse shape than it was when ChatGPT arrived a few years back.
Some organisations are seeing improvements. They’re reducing costs and claim to be improving efficiency. At an individual level, the value is less clear. Many people struggle to identify consistent, tangible benefits. Learning the tools requires time, and outputs vary in quality. There are significant concerns about environmental impact and workforce implications.
When individuals carry the burden of adoption while organisations capture most of the benefit, the entire premise deserves scrutiny. Why are we doing this at all, if it only benefits the few, rather than the many?
A position based on experience
At this point I also want to note that my perspective doesn’t come from AI hatred or avoidance. I adopted AI early because it directly affected my work. Writing was one of the first functions to face disruption, so engaging with the tools made sense. I even wrote a book about it last year.
Over time, my usage has changed. I now apply it selectively. In my process, the outputs aren’t always reliable, and after a while every piece of copy starts to sound the same, no matter how much I try to tune the model. Now I’d rather spend that time tuning my brain instead.
I think it’s a useful technology that isn’t being harnessed to its full potential right now. If as a society we were to create a balance sheet of pros and cons, I feel that it would be weighted to the con side right now. That’s something that could be corrected with greater public engagement in the development and adoption, plus regulation and oversight to manage the environmental, workforce, creative and ethical issues.
The question we should be asking
The current framing doesn’t distinguish between lower usage and deliberate choice. It treats every gap as a problem that requires correction.
I’d argue that a more useful line of inquiry is to examine what’s driving the push for universal adoption, which outcomes organisations prioritise and who benefits from those outcomes.
We also need to assess whether the expectation that everyone integrates AI into their work reflects genuine need or a combination of commercial incentive and momentum.
Let’s park the conversation around “women being left behind by AI”, take a step back and have this conversation instead:
What does AI improve in a meaningful and tangible way for the broadest group of people?
Until a clear answer emerges, the focus on who is ahead and who is behind remains premature.