Why Women Are Left Behind in AI—and How to Fix It

Recent research highlights a persistent gender gap in generative AI (GenAI) adoption, with women less likely to use these tools than men. This disparity has significant implications for productivity, pay equity, and access to opportunities in an AI era. Here’s a closer look at the challenges and actionable strategies to bridge the gap.

The Challenges

1. The Pressure to "Get It Right"

Imagine walking a tightrope with the world watching. For many women and minorities, every misstep at work feels amplified by societal expectations. This phenomenon, rooted in stereotype threat and psychological safety, discourages these individuals from "playing" with tools like AI. The experimentation essential for discovering AI’s value often feels like a risky indulgence rather than a professional necessity.

2. Time Constraints

Time is the ultimate luxury, and for many women, it’s in short supply. Women perform 2.5 times more unpaid household and caregiving work than men globally, according to a 2023 UN report. At work, they often shoulder additional "invisible" labor—emotional support, mentorship, and coordination tasks. This labor drains energy and time, making the trial-and-error required to unlock AI’s potential a near-impossible task.

3. Male-Dominated Fields of Experimentation

AI experimentation emerged first in coding and engineering—fields historically dominated by men. For women in other sectors, the message is often implicit but clear: "This isn’t for roles like yours." These stereotypes discourage adoption, perpetuating the cycle of underrepresentation.

The Solutions

Closing the AI adoption gender gap requires more than awareness; it demands action. By addressing systemic barriers and reframing how AI is introduced, organizations can foster a culture of experimentation and inclusion.

1. Simplify Success with AI

No one has time to master complex tools before seeing results. That’s why simplifying AI interactions is essential. MIT Sloan research shows that simple, user-friendly interfaces drive AI adoption among busy professionals. For example, at ApplyAI we teach a "flow engineering" approach that allows employees to start with simple, question-based prompts like, "How can you help me with [task]?" This approach reduces the intimidation factor and quickly demonstrates AI’s value—we regularly see people go from “never use” to “use 5x day” after just one hour of training on flow engineering.

2. Shift the Mindset Around AI

Frame AI as a "co-pilot," not a "replacement." Stanford research shows this perspective boosts engagement, particularly among AI novices. Working with our clients, we’ve seen that drawing parallels between new skills like prompt engineering and existing skills like delegation can help non-technical employees feel more confident. Leaders can further demystify AI by encouraging personal, low-stakes use cases—like planning vacations or drafting emails—which build comfort and ease workplace adoption.

3. Training Should Address the Human Side of AI

AI training should develop both technical proficiency and the human skills needed for adoption. Hands-on workshops demystify tools while fostering adaptability and confidence. Collaborative learning, which improves skill retention by up to 40%, should explore not just how to use AI but also when and why. This dual focus on practical skills and strategic thinking helps employees understand the types of AI use that are safe and rewarding, which further drives adoption.

4. Reward Learning Along With Results

What if the journey mattered as much as the destination? Many women-dominated roles involve extracting and sharing insights; critical skills for spreading AI knowledge. Organizations can harness these skills by valuing the learning process. Create spaces—team meetings, forums, or Slack channels—to share AI-driven discoveries and reward participation. Celebrate attempts, even imperfect ones, to normalize experimentation and foster collective growth.

5. Carve Out Time for Experimentation

If time is money, then experimentation is an investment. McKinsey reports that companies dedicating time for skill development see a 35% higher rate of AI tool adoption. Leaders should establish "AI Exploration Hours" or do AI experimentation as part of team interactions. This relieves the performance pressure of wondering if it’s “okay” to be tinkering with AI, which dramatically increases participation and engagement.

The Path Forward

Closing the AI gender gap isn’t just a matter of fairness—it’s about unlocking untapped potential across your workforce. Simplify access, reward curiosity, and create an environment where experimentation is the norm, not the exception.

Leaders, it’s time to take action. Identify barriers, implement solutions, and track progress. The future of work demands it—and so does your team’s potential.