The next wave of corporate growth will be driven by workers who know how to use AI. We are in the middle of Women’s History Month, and March 8 was International Women’s Day. In that context, I was surprised to learn that research from the University of Phoenix reveals a gender gap in AI use at work, with women trailing men—36% of men reported daily AI use, while only 25% of women did.
That gap matters.
Women make up 42% of the global workforce and 31.7% of senior leaders. If women are slower to adopt AI tools, it could affect everything from promotion opportunities to future leadership pipelines.
To learn more about the research and what it means, I interviewed Raghu Krishnaiah, Chief Operating Officer at University of Phoenix, and Jeanne Meister, global HR consultant and best-selling author.
Rieva Lesonsky: Research from the University of Phoenix shows a clear gender gap in AI use at work. What surprised you most about the findings—and why does this gap matter right now?
Raghu Krishnaiah & Jeanne Meister: The most surprising finding was the clear gender gap related to willingness to improve skills required to partner with AI in the workplace, with 48% of men saying they need skills to help them learn “how to work in partnership with Gen AI,” compared to only 39% of women.
There is agreement between this study and a number of research studies from Stanford University, Northeastern University and the Becker Friedman Institute for Economics at the University of Chicago which all report an AI gender gap where women are more likely to report less usage and confidence with AI and express concern about AI accuracy and bias while men are more likely to experiment with AI and describe themselves as “AI proficient.”
Lesonsky: With AI increasingly shaping how work gets done, what are the real risks if this gap isn’t addressed quickly—for women and for companies?
Krishnaiah & Meister: The future of workplace and business success will be driven by workers who know how to use AI as a tool, as a team member, and as an accelerator for re-designing their work. For women, lower and slower adoption risks widen the gender pay gap and reduce their promotion opportunities.
Recent research from Wharton professors Prasanna (Sonny) Tambe and Tiantian Yang reports that learning and working with tools, such as AI and cloud systems, has become one of the biggest drivers of pay in tech. With fewer women working in these new technologies, the pay gap is widening as men using AI and cloud tools gain more advantages in salary and career growth.
Lesonsky: Why do you think women are trailing men in daily AI usage? Is this about access, confidence, training, workplace culture—or something else?
Krishnaiah & Meister: The reasons for the AI gender gap appear to be nuanced, but we’re seeing three key reasons emerge:
1—The Imposter Syndrome. Data shows that at some point in our lives, at least 70% of us will experience imposter syndrome—a feeling which leads us to doubt our skills, talents, and abilities. Studies investigating imposter syndrome consistently demonstrate that women experience the problem more often than men. This was consistently present across a variety of industry sectors and job roles. For many women, the AI gender gap is less about access to AI tools or their ability to use them, and more about their confidence in using AI tools in the workplace.
2—The Ethics of Using AI at Work. Women consistently report higher rates of hesitation about using AI than men, with women placing greater weight on ethics, transparency, and professionalism when deciding whether to use AI at work. For example, Harvard Business School Professor Rembrand Koning’s research finds women are more concerned than men about the ethics of using AI in the workplace.
3—The Reputational Risk of Using AI. Women historically have faced extra scrutiny regarding their skills, capabilities, and technical abilities. Because of this, women perceive a higher reputational risk associated with using AI than men do, and this influences their willingness to collaborate with AI in performing their job responsibilities.
In her book, Artificial Intelligence for Business, Kamales Lardi, an expert in digital transformation in business, contends that some women fear they may be perceived as “cutting corners” or cheaters if they use AI at work. And a Harvard Business Review study validates that concern, finding that female engineers who used AI to generate code were rated 9% less competent than their male peers, despite evaluators reviewing identical outputs.
Lesonsky: Women make up 42% of the global workforce and nearly a third of senior leaders. How does increasing AI fluency among women directly impact leadership pipelines and corporate growth?
Krishnaiah & Meister: The expectation for developing AI fluency is now commonplace. Companies such as Google and Microsoft are factoring AI usage into their performance reviews by asking employees about it in performance discussions.
While AI fluency is becoming crucial for women at any stage in their careers, this is creating an environment where the stakes are especially high for women over 40. These women are often at the height of their careers—and the consequences can be high if women opt out of developing AI fluency. A Harvard Business School study titled Navigating the Jagged Technological Frontier found that less-experienced workers who used ChatGPT matched or even outperformed more seasoned workers who did not use ChatGPT.
Lesonsky: Many women already use AI in their personal lives, even if they don’t label it that way. How can employers better connect everyday AI use to workplace confidence and career advancement?
Krishnaiah & Meister: Women are nearly three times more likely than men to hold jobs that generative AI can automate, such as clerical and routine cognitive roles. Leaders should be aware of this potential disparity in their own workplaces and identify opportunities to develop AI fluency that support personal and career development growth. Active listening with women employees is critical to understanding how they are already using these tools and then identifying how to bridge personal use to workplace opportunities.
Lesonsky: What role should employers play in closing the AI gender gap—especially businesses that don’t have massive training budgets or in-house AI teams?
Krishnaiah & Meister: Leadership approach and learning opportunities are key to closing the AI gap. Organizations will achieve the most with AI if they create the right conditions for success. This means training employees how to safely use AI tools and applications, sharing clear policies and guidelines for working with AI, and setting organizational goals and expectations around AI. Workers who are most confident using AI are those who work at organizations where AI usage is expected and where leaders role-model their own use.
Access to training for all employees is crucial in closing the AI gender gap. Research from the Association for Talent Development finds more than 70% of employees are very or extremely interested in learning how to use AI, but less than one-third of organizations provide such training. At the same time, companies must go beyond AI literacy training and host AI hackathons—events where employees work together to create new AI products. These initiatives provide all employees with “AI sandbox time” to test AI tools and take risks without fear of making mistakes. This is especially important for female employees who report having less confidence in partnering with AI at work.
Lesonsky: For women who feel behind or intimidated by AI, what’s the smartest place to start building skills that matter on the job today?
Krishnaiah & Meister: There are several ways to engage women in developing AI fluency, and these go well beyond formal training programs. For example, companies can provide access to Slack channels where employees share their best practices using AI. This can encourage women to start experimenting with AI in their jobs.
Another option is for companies to launch mentoring and coaching programs where women who are mature in their usage of AI coach and mentor women who are getting started using AI on the job. This provides women with both a network and role models for using AI at work. Finally, there are several community organizations that encourage women to develop AI fluency, including She Is AI and Women Defining AI.
Both offer AI training, resources, and a supportive community composed of thousands of women learning AI.
Lesonsky: Looking ahead, what gives you the most optimism about women’s role in the next wave of AI-driven growth—and what needs to happen in 2026 to make sure progress sticks?
Krishnaiah & Meister: We are optimistic that leaders will provide focused access to AI training to ensure equitable adoption and use of generative AI. Three strategies that leaders can adopt to drive equity in AI fluency include:
- Communicate that developing AI fluency is a baseline expectation for all roles at all levels in the organization. Then, embed AI training into leadership development programs and link performance management to AI usage.
- Measure and audit AI training participation by gender and role. Also, monitor AI stretch assignments and promotions by gender as well, so leaders can track women’s participation in AI training over time.
- Provide role-based AI training to avoid one-size-fits-all AI training. Role-specific training is critical to long-term adoption and creates AI learning pathways for both technical and non-technical job roles.
Lesonsky: Are there differences in AI usage by women workers in businesses owned by women vs. those owned by men, or by age (young owners vs. older)?
Krishnaiah & Meister: AI adoption is driven more by job role than by age. In roles where AI is embedded into the workflow, such as engineering or finance, AI fluency is expected in the job, and workers are more likely to experiment with AI regardless of their age.
But overall, younger workers under the age of 35 are generally more likely to experiment with AI than workers over the age of 50, who are more likely to express skepticism and uncertainty about how to use AI in their jobs.
Rieva Lesonsky is the founder of Small Business Currents, a content company focusing on small businesses and entrepreneurship. You can find her on Twitter @Rieva, Bluesky @Rieva.bsky.social, and LinkedIn. Or email her at Rieva@SmallBusinessCurrents.com.
Photo courtesy Getty Images for Unsplash+
