Bridging the gender gap in AI usage: A path to equity

Closing the gender gap in AI usage requires equitable access to practical learning, clear workplace policies, visible manager support and opportunities to practice with real tasks. HR and L&D leaders can help women build confidence with generative AI while addressing the trust, privacy and cultural barriers that shape adoption.

Artificial intelligence is changing how work gets done, but employees are not adopting it at the same rate. Building an AI-first culture requires organizations to understand those differences and design learning that reaches people across roles, levels and backgrounds.

What does the gender gap in AI adoption look like?

A 2024 Bank for International Settlements working paper based on a representative survey of U.S. household heads found that 50% of men and 37% of women had used generative AI during the previous 12 months. Self-assessed knowledge of generative AI explained nearly three-quarters of the measured gap.

More recent evidence suggests the gap has narrowed while remaining persistent. A May 2026 Harvard Business School working paper pooled comparable sources covering 318,924 respondents or observed users and found sample-size-weighted adoption rates of 47.8% for men and 39.3% for women.

The reasons for the gender gap in AI usage are multifaceted:

• Knowledge and familiarity: Employees who have had fewer opportunities to learn how generative AI works may have less confidence identifying useful applications or evaluating outputs.
• Trust and privacy concerns: Research has found gender differences in concerns about data privacy, provider trust and the potential consequences of AI misuse.
• Workplace signals: Ambiguous policies, uneven access to training and inconsistent manager encouragement can shape whether employees feel supported in experimenting with AI.

Most cited datasets use binary gender categories, so employers should also consider whether AI learning and support reach nonbinary and gender-diverse employees. A broader view can help People teams identify barriers that aggregate survey data may overlook.

The potential consequences extend beyond tool usage. McKinsey estimates that activities accounting for up to 30% of current U.S. work hours could be automated by 2030 in its modeled scenario, which makes equitable access to AI fluency and practice increasingly important.

How can employers close the gender gap in AI usage?

HR, People and L&D leaders can address the disparity through coordinated learning, policy and culture changes. The goal is to give employees clear permission, practical skills and repeated opportunities to apply AI responsibly.

1. How can organizations build digital literacy?

Practical education can help employees understand where AI is useful, how to prompt effectively and how to review outputs critically. Role-relevant examples and guided practice make learning easier to apply in daily work.

• Use Electives' live expert-led Private Classes and AI simulations to give teams guided instruction, practice and feedback.
• Create a structured learning pathway that connects foundational AI fluency with role-specific applications and ongoing professional development.
• Pair learning with real work scenarios so employees can build confidence through experience.
• Use the principles for creating an AI-proficient workforce to support experimentation, collaboration and continuous skill development.

2. What creates an inclusive environment for AI adoption?

Employees need clear signals that responsible AI use is supported. Deloitte reported that 61% of women generative AI users said their employer actively encouraged use, compared with 83% of men, while 49% of women users reported company investment in training compared with 79% of men.

• Publish clear guidance on approved tools, data handling, acceptable use and human review expectations.
• Equip managers to discuss AI regularly, invite questions and encourage practice consistently across their teams.
• Develop a diverse group of AI champions who can model effective use and share examples from different roles.
• Include ethical AI use in learning and communications so employees can experiment within responsible boundaries.

3. How can employers reshape workplace norms?

Workplace norms influence who feels comfortable trying unfamiliar technology and discussing mistakes. Leaders can reduce uncertainty by recognizing thoughtful experimentation, making learning visible and applying expectations consistently.

• Treat early mistakes as learning opportunities when employees follow responsible-use policies and review processes.
• Highlight employees from varied roles and backgrounds who have improved a workflow or solved a problem with AI.
• Include bias, equity and responsible experimentation in regular learning initiatives.
• Review participation, confidence and application data across employee groups while protecting individual privacy.

Why does inclusive AI adoption matter?

AI adoption affects who gains experience with emerging workflows and who contributes to redesigning how work gets done. If access to learning and practice remains uneven, existing differences in skills, visibility and opportunity may grow.

For companies pursuing enterprise AI adoption, broad participation is essential to lasting behavior change. HR and L&D leaders can support that participation by combining practical learning, clear expectations, manager reinforcement and measurement.

AI can support a more equitable future when organizations address knowledge gaps, build trust and create inclusive conditions for practice. The future of AI belongs to everyone, and every employee should have a meaningful opportunity to help shape it.