Mind the (Gender) Gap: AI Edition

When I reviewed the recent global study results showing that women are less likely to use AI than men, I wasn’t surprised—and I’ll explain why shortly.

The new paper by Otis, Cranney, Delecourt, and Koning, titled Global Evidence on Gender Gaps and Generative AI (Berkeley Haas, Stanford University, Harvard Business School), was no small endeavor. Its methodology spanned 16 studies, surveying 100,000 individuals across 26 countries. This edition of Confluence does a great job outlining the highlights, and provides a link to the full paper for those interested in diving deeper.

Although the study didn’t pinpoint causation, it’s likely that thought leaders will soon speculate on some of the more apparent factors contributing to the gender gap in AI adoption, including:

• Perception of Complexity: AI can seem technical and complex. If women perceive themselves as less tech-savvy—a stereotype reinforced by society—they may feel less confident using AI tools.

• Representation in STEM: Historically, fewer women have been in STEM fields, impacting comfort and familiarity with AI-driven technology, which is often linked to those domains.

• Bias and Trust Issues: AI systems are sometimes viewed as biased, especially when they reinforce gender stereotypes. Women may feel that AI systems aren’t designed with their perspectives in mind, reducing trust and adoption.

• Work-Life Balance Concerns: AI is often marketed as a productivity tool. While men might view it as a way to improve work-life balance, women juggling multiple roles may see it as another tool that adds to their workload.

• Marketing and Product Design: AI products are often developed and marketed with male-dominated industries in mind (like finance or gaming). When these products don’t address areas where women are more likely to engage (such as healthcare or education), women may see less reason to try them.

• Privacy Concerns: Studies suggest that women generally have greater concerns about online privacy, and since AI often involves data usage, this worry could deter them from using AI-driven products or services.

While these factors may play a role in the gender gap in AI adoption, my nearly two decades of partnering with female leaders as an executive coach and consultant bring me to a different conclusion.

The reason I wasn’t surprised by the study’s findings is that women who feel pressured to prove their competence may see AI as a crutch that could undermine their perceived abilities. This is especially relevant in professional environments where women often feel they must work harder than their male counterparts to demonstrate equal competence. For this reason, some women may hesitate to use AI—or even to admit they use it.

DALL-E Prompt: Create a colorful depiction of an exasperated woman feeling the pressures to prove herself.

Here’s how this dynamic might play out:

1. Self-Reliance and Competence: Women may worry that using AI tools could suggest they’re not fully capable on their own, especially in fields with high standards for proving oneself. Relying on AI could be misinterpreted as lacking expertise, particularly in male-dominated or high-stakes environments.

2. Perceived Value of Effort: Women who feel the need to over-perform to be viewed as equally competent may fear that using AI to simplify tasks could lead to their efforts being undervalued. There’s a perception that work done with AI isn’t “real” work or is somehow less impressive.

3. Fear of Undermining Skill Development: Many women are aware of implicit bias in the workplace and might worry that AI could hinder their skill development, reinforcing biases about women being less technically capable.

4. Reluctance to Appear Dependent: There’s often an expectation that women should be self-sufficient. Using AI might feel like a threat to that image. Women may want to be seen as independently capable and not reliant on AI, especially when under pressure to demonstrate self-sufficiency.

Overcoming these barriers may require shifting workplace culture and narratives around AI to emphasize that AI doesn’t replace skill or expertise but rather complements and enhances it. Normalizing AI as a tool that everyone uses—not as a “shortcut” but as a professional resource—could reduce stigma and boost adoption across the board.

Now, to address the critics of my opinion. Some might argue that I’m “planting” the idea that AI could be seen as a crutch for women. To those with this view, I offer a gentle reminder: women don’t need my help finding things to worry about—they’ve been navigating double standards and glass ceilings just fine without my “suggestions.” But thanks for your concern.

What Can Leaders Do About The AI Gender Gap?

If I’m right and women are concerned about using AI due to its impact on their perceived credibility, here are some actionable steps leaders can take to help women feel more comfortable leveraging AI in the workplace. As a bonus, these steps will also mitigate the majority of the more apparent contributing factors listed at the top of this article:

1. Normalize AI Use: Emphasize that AI is a tool for everyone, not a shortcut or replacement for skill and expertise. Highlight how it can complement existing strengths and improve efficiency, rather than diminish personal capabilities.

2. Encourage Skill-Building Opportunities: Offer accessible training programs focused on AI and its practical applications. This could include workshops, online courses, or peer-led sessions that make AI less intimidating for all employees.

3. Promote Women as AI Champions: Spotlight female role models in the organization who use AI effectively. Invite women leaders or tech-savvy team members to share their experiences and successes with AI, inspiring others to see it as an empowering tool.

4. Create Safe Learning Environments: Foster a culture where asking questions and learning at one’s own pace is encouraged. Avoid judgment or assumptions about skill level based on gender, and provide ongoing support to build confidence in AI usage.

5. Challenge Stereotypes Openly: Address stereotypes directly by openly discussing how misconceptions about technology and gender can hold back progress. Encourage dialogue that reinforces the idea that everyone can develop technical skills, regardless of background.

6. Reframe AI as a Growth Tool: Position AI as a means of professional development rather than just a productivity booster. Help employees understand how AI can enhance their skill sets, making them more competitive and innovative in their roles.

7. Offer Mentorship and Support Networks: Establish mentorship or support groups specifically for women interested in learning about AI. Having a network of allies and mentors can create a sense of belonging and reduce feelings of isolation in traditionally male-dominated areas.

8. Make AI Applications Relatable: Demonstrate AI’s relevance to various aspects of the business, including fields where women are often more represented, like HR, marketing, or healthcare. Show real-life examples of how AI can solve problems and add value across diverse functions.

9. Be Transparent About AI’s Limitations: Discuss the limitations and potential biases in AI openly. This transparency can build trust, making employees feel more comfortable using AI tools without fearing negative repercussions or compromising their values.

10. Solicit Feedback and Improve: Regularly ask for feedback on AI initiatives and adjust approaches based on employee experiences. Tailoring training and support based on real input helps create a more inclusive, responsive workplace culture.

The goal is to create an environment where women feel empowered and encouraged to explore the benefits of AI, making it a natural and rewarding part of their work.

Why is all of this important? The authors of the paper lay out the answer perfectly:

This disparity has the potential to be significant. As generative AI systems are still in their formative stages, the under-representation of women in their early use and testing risks shaping tools that fail to meet the needs of half the population (Koning, Samila, and Ferguson, 2021; Cao, Koning, and Nanda, 2023a). Biases in user data — similar to those that have previously led to racial disparities in AI performance — could result in AI systems that reinforce gendered stereotypes and overlook tasks more often performed by women (Koenecke et al., 2020; Guilbeault et al., 2024). Ensuring that AI tools are designed inclusively will be crucial for unlocking their full potential to enhance productivity and reduce inequality. Given recent estimates that AI has the potential to increase US economic output and worker productivity levels by nearly 20% over the next decade (Baily, Brynjolfsson, and Korinek, 2023), and that women make up just under 50% of the US workforce, a persistent 25% usage gap could result in hundreds-of-billions of dollars of lost productivity and output gains in the US alone.