The AI Gender Gap Is Real — And Women Leaders Can't Afford to Wait

Something has been bothering me about the AI conversation. Not the technology itself, but who is actually using it.

Because in meeting after meeting, I see the same pattern the people experimenting, testing, and integrating AI into their work are not the same people waiting to “understand it better” before they start.

That gap is starting to matter. And this is where it becomes a problem.

Because AI doesn’t reward caution. It rewards usage.

In many cases, the women leaders I know — incredibly capable, strategic executives — are holding back. Curious, yes. But waiting to see where this technology is heading before diving in.

This isn’t just a perception. The data shows the same pattern.

A large meta-analysis from Harvard Business School examined 18 studies covering more than 140,000 people across industries and countries. The finding was remarkably consistent:

Women had 22% lower odds of using generative AI than men. 

That might not sound dramatic at first. 

But over the course of my career in marketing, I’ve seen several waves of technological change, from digital transformation to data-driven marketing and automation. And the pattern is always the same: the people who experiment early usually end up defining how the technology is applied. 

It’s not a question of capability. That assumption is outdated. But in moments like this, experimentation matters. The people who start exploring new tools early often end up shaping how they’re used across organizations. 

Why hesitation around AI is understandable 

In conversations with other women executives, I often hear a similar reaction to AI. 

Not resistance. More like thoughtful caution.  And the research reflects the same pattern. 

In the Harvard Business School study on generative AI adoption, researchers found that the gender gap persisted even when men and women had equal access to AI tools. In some experiments, participants were explicitly told they could use AI to complete a task.

Even then, women were significantly less likely to do so. 

That raises an uncomfortable question. Why is this happening? 

According to the study, women may be more concerned about the reputational risks of relying on AI-generated answers. If using AI is perceived as cutting corners or “cheating,” the potential cost can feel higher. 

As economist Rembrand Koning explains, women often face greater penalties when their expertise is questioned. That can make the decision to rely on AI tools more complicated. 

Someone might think: even if the answer is correct, will people assume I didn’t know it myself? 

Seen through that lens, hesitation makes sense. But it also creates a problem. Because the people who experiment early usually end up shaping how the technology gets used. And if women stay cautious while others move first, the influence gap will follow the usage gap. 

But the research also highlights something important. 

The challenge isn’t capability. The challenge is creating an environment where women feel safe enough to experiment with these tools early. Otherwise, the gap will only grow. 

Why the AI gender gap matters 

People who adopt productivity-enhancing tools tend to move faster than those who don’t. If women experiment less with generative AI, they risk falling behind on a capability that will shape how work gets done. Over time that affects who moves faster, who makes decisions with better information, and ultimately who has influence. 

But it doesn’t stop there. 

As Koning points out, productivity gaps at the individual level can translate into lost growth at the organizational and economic level. If a large share of the workforce isn’t fully benefiting from AI tools, companies, and the economy as a whole, lose potential productivity gains. 

Lastly, generative AI systems improve through interaction with users: the prompts people write, the workflows they build, and the problems they ask AI to solve. If fewer women are actively shaping these interactions, the technology risks evolving in ways that overlook women’s perspectives and experiences. 

AI as a leadership capability 

Generative AI is often framed as a productivity tool. But for leaders, the value may lie somewhere else. 

It changes how we work with information. 

For example, you can use AI to summarize research before a board meeting, explore strategic scenarios, or pressure-test the assumptions behind a major decision. 

That doesn’t replace judgment, but it can sharpen it. And that’s why AI literacy is quickly becoming a leadership capability.  If some leaders experiment extensively while others remain observers, the difference in familiarity and confidence will grow quickly. And familiarity tends to translate into influence. 

The people who understand how a technology works usually have a stronger voice in how it gets implemented. 

Three ways women leaders can engage with AI today 

The good news is we’re still early in this transition. No one has this fully figured out yet. 

From what I’ve seen, the leaders getting the most value from AI aren’t necessarily the most technical ones. They’re simply started experimenting earlier. 

A few ways to start: 

• Use AI in the work you already do 

  AI doesn’t require a complete reinvention of your workflow. In fact, it works best when applied to tasks already on your agenda: preparing for a board meeting, reviewing a report, drafting communication, or exploring strategic scenarios.  Used this way, AI becomes less about technology and more about supporting thinking. 

• Treat AI as a thinking partner 

  One shift that helps is seeing AI as more than a productivity tool. It can also act as a sparring partner.  You can use it to challenge assumptions, explore alternative perspectives, or refine an argument before presenting it to others. Often the real value appears after the first response, when follow-up questions push the thinking further. 

• Make your experimentation visible 

  Leaders set the tone for how new tools get used.  When you openly share how you’re using AI, what works and what doesn’t, it lowers the barrier for others to experiment. In many organizations, AI adoption spreads through peer learning rather than formal training. 

  
  

I’ll also say something that might sound slightly uncomfortable. 

At some point we have to stop framing this only as a structural issue. AI tools are widely available right now. No one needs permission to start experimenting.  This is one of those moments where leaning in actually matters. 

A moment worth paying attention to 

Every major technological shift creates a short window where the rules aren’t written yet. AI is still in that window.

You don’t need permission. You don’t need a full strategy. You just need to start.

One use case. One workflow. One hour of experimentation.

Because this moment isn’t just about learning a new tool. It’s about deciding who participates in shaping how that tool transforms work. And the leaders who shape how AI is used in their organisations won’t be the ones who understood it best.  It will be the ones who started earliest.

That includes more women leaders stepping in, not just as users of AI, but as voices influencing how it’s applied, governed, and integrated into organisations.

Because the question is no longer whether AI will shape the future of work.  The real question is who will shape how it’s used.

About the author 

Frida Ahrenby is Chief Marketing Officer at Rillion, a platform for AI-powered accounts payable and payment automation. She regularly shares perspectives on leadership, learnings from building high-performing marketing teams, and the changing role of AI in modern organizations.  She is also a Founding member of Wednesday Women Executive Membership and a Wednesday Women Partner.