Mind The Gap: The Growing Disparity In AI Adoption

Why, in the age of generative AI, are we still seeing gender inequalities? How can we change this before it’s too late?

Generative AI promises to revolutionise productivity and potentially narrow historical inequities. Yet, as we look at early adoption trends, a glaring reality emerges: a growing gender gap that risks leaving half the workforce behind.

The Data Speaks: Women Are Falling Behind

At LaunchLemonade, we initially saw an encouraging trend—60% of our early adopters were female. However, over the past eight months, women have churned at a higher rate compared to men. Today, women make up just 30% of our active user base, while men comprise about 40%, with the remainder being users registered by businesses whose gender data is unavailable.

Unfortunately, this trend is not isolated to LaunchLemonade—it reflects a larger, global disparity in AI adoption. Recent findings by Otis et al. (2024), compiled from 16 studies across 26 countries, reveal that women are approximately 25% less likely to adopt generative AI tools than men. Notably, even in situations where men and women had equal access to these technologies, women continued to engage less, highlighting systemic barriers that go beyond simple access.

Beyond Access: Understanding the Real Barriers

A common misconception is that access is the primary barrier. However, both our data and global trends reveal that equal access does not translate into equal adoption. Here are some deeper insights into why women might be falling behind:

Perceived Relevance

Women may perceive AI tools as less relevant to their specific needs. Studies, including those by Aldasoro et al. (2024), indicate that women often see less immediate benefit in adopting new technologies. This may be due to marketing narratives that focus on sectors or use cases that resonate more with traditionally male-dominated industries. For instance, positioning AI as a productivity enhancer for technical fields like software development can alienate users from other disciplines.

Confidence and Social Conditioning

Globally, there is a trend where women rate their technological competence lower compared to men, regardless of actual skill levels (Otis et al., 2024). Generative AI tools are often presented as intuitive and easy to use, but the underlying assumption is that users will "figure it out" themselves. Women often feel the pressure to know exactly how to use technology from the get-go, and many may not want to risk appearing 'incompetent' in front of peers. This expectation can deter individuals who feel they need more structured guidance, which, due to social conditioning around technology, disproportionately affects women.

Tech’s Longstanding Gender Gap

The barriers extend beyond AI. For decades, women have faced biases in tech—fewer mentors, less venture capital, and unequal representation in STEM fields (Goldin, 2021). Despite AI tools like ChatGPT, Midjourney, and Claude being easily accessible, these technologies enter an ecosystem still influenced by gender biases. Analytics from popular AI platforms show that women constitute only 30-42% of users (Koning et al., 2023). This discrepancy isn’t just about reluctance—it's about a system historically not designed with female users in mind.

Why It Matters More Than Ever

AI holds the potential to break barriers, allowing flexible working, enhancing productivity, and democratising skills. But if women continue to lag in adoption, the benefits of AI will skew towards men, potentially deepening economic divides rather than closing them. If we don't take action now, we risk deepening existing inequalities and missing out on the diverse innovations that come from a truly inclusive AI ecosystem. Estimates from Otis et al. (2024) suggest that if this 25% gender adoption gap persists, productivity gains from AI will disproportionately favour male users, resulting in billions of dollars in missed opportunities for women.

Rethinking Our Approach: How to Close the Gap

1. Narratives that Resonate

We need to rethink how we communicate AI's value. Current marketing often frames AI adoption around stereotypically male interests—efficiency and technical prowess. Instead, let's highlight how AI can support creative problem-solving, streamline work-life management, or assist with balancing professional and family responsibilities. By shifting the messaging, we could address what studies have found: women are more likely to engage when they see personal relevance (Kreacic & Stone, 2024).

2. Lowering the Emotional Risk of Failure

Many women report being deterred from technology when initial attempts don’t yield success. A supportive community where AI experimentation is seen as a low-risk, high-reward activity can help bridge this gap. Initiatives like women-led hackathons, peer-support groups, or showcasing more female role models actively using AI could boost confidence and comfort levels. For example, the "Women in AI" initiative has been successful in creating safe learning environments where women can explore AI tools without fear of judgement.

3. Targeted Policy and Organisational Interventions

As Otis et al. (2024) pointed out, equal access alone is insufficient. Policymakers and organisational leaders must create interventions that address specific barriers women face when adopting AI. Much like how targeted STEM initiatives have encouraged women into tech fields, similar programmes could help drive AI adoption. This could include formal training programmes tailored to diverse learning preferences, mentorship opportunities, and fostering inclusive environments where experimenting with AI tools is encouraged.

AI adoption is not just a technological challenge—it’s a societal one. The technology is still in its formative years, and how we encourage its uptake will determine whether it becomes a force for inclusion or division. It’s time to reshape the narrative around AI—one where women are not just participants but leaders. Let’s bridge the gap together, ensuring a future where AI works for all.

Generative AI can close gaps, but it requires us to actively bridge them. What are we missing if half of our workforce isn't empowered to use AI? Let's shift the narrative, adjust the systems, and make AI an empowering tool for everyone.

References

• Aldasoro, P., et al. (2024). Societal and Behavioural Barriers to AI Adoption: A Gendered Analysis. International Journal of Human-Computer Interaction.

• Goldin, C. (2021). Career and Family: Women’s Century-Long Journey toward Equity. Princeton University Press.

• Koning, L., et al. (2023). Analysing Gender Bias in the Use of AI Platforms: An Observational Study. Data and Technology Review.

• Kreacic, S. & Stone, T. (2024). Understanding Gender Perspectives in AI Adoption: Barriers and Solutions. Journal of Technology Communication.

• Otis, J., et al. (2024). Gender Differences in Technology Adoption Across 26 Countries: Implications for AI Use. Journal of Technology and Society.
  

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Cien Solon is on a mission: no one gets left behind in the time of AI. She is the co-founder of LaunchLemonade and is a thought-leader in the AI space.