• 4 min read
Women Are Falling Behind on AI at Work
Studies show women use generative AI less than men, raising concerns that the gap could widen into a pay divide.
Image: CNET
Women are adopting AI at lower rates than men, and researchers and workplace advisers say that gap could turn into an economic one if it is not addressed soon.
The issue recently flared up after Reese Witherspoon urged her Instagram followers to learn AI with her. She drew criticism over AI’s environmental impact, data center backlash and built-in biases, but the underlying point holds: there is a growing gender divide in AI use.
A Harvard Business School meta-analysis published in April, covering 18 studies, more than 143,000 individuals and 25 countries, found that women had 22% lower odds of using generative AI than men. A Deloitte study from November 2024 found AI adoption falls with age, with the gender gap widest among people 45 and older.
Randstad reported a year ago that 71% of AI-skilled workers are men and 29% are women, a 42-point gap. Men are also more likely to be offered AI training by employers — 35% versus 27% — while women are underrepresented in generative AI skills, at 31% versus 69% for men.
As of 2025, women make up roughly a quarter of the global tech workforce and hold less than a fifth of senior leadership roles. The article also points to broader research showing women tend to perceive AI as riskier than men and are less represented in tech roles overall.
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Michael Morris, Randstad’s global head of platform and talent, told CNET that women are the largest underused talent pool in the professional workforce.
“The skills embedded in at-risk roles, including relationship management, operational coordination, ethical reasoning and stakeholder communication, are exactly what the new AI-era roles require. The connection between those two facts should be obvious.”
The “time gap” in AI learning
Experts interviewed by CNET said the problem is not just access to tools. Lakma Algewatthage, a lecturer in entrepreneurial management discipline at the Australian Institute of Business, said women often carry a disproportionate share of caregiving and household work, leaving less time to experiment with new technology.
“The time gap is a significant and often underestimated contributor to the topic. Women continue to shoulder a disproportionate share of caregiving and household responsibilities, leaving less flexible time to experiment with emerging technologies, attend training or build confidence through trial and error.”
Algewatthage said AI literacy is built through repeated practice, not one-off exposure, and that many women she works with want to understand how to use AI effectively and authentically rather than simply generating outputs.
Employers may need to make training part of the job
The article argues that employers should treat AI training as part of paid work, not an extra task left for evenings and weekends. Morris said companies making the most progress are treating AI literacy as a workplace responsibility rather than an individual one.
Potential bridge roles for women moving into AI-related work include:
- AI ethics and governance
- AI in healthcare, education and customer experience
- Product and operations roles involving AI implementation
- Data analytics focused on social impact
Elizabeth Ngonzi, an executive AI advisor, board member at the American Society for AI and adjunct assistant professor at New York University, told CNET there is urgency to close the gap before it becomes a larger pay divide.
“Employers should build AI literacy into paid work time, not treat it as an extra burden. Leaders should make access to tools, training and use cases part of workforce development, especially in functions where women are concentrated.”
Ngonzi said mothers and caregivers can start by using AI for practical tasks such as schedules, pickups and meal planning to make the technology immediately useful. But CNET also notes that not every user finds AI broadly helpful: working mother Leticia Mooney said that beyond menu planning, shopping lists and schedules, AI can get in the way of her workflow.
Enterprise Editor
Marcus follows the money. He covers enterprise software, cloud architecture, and the tectonic shifts in Big Tech strategy. He translates dense earnings calls and complex M&A activity into actionable insights about where the industry is actually heading. If a tech giant makes a silent pivot, Marcus is usually the first to notice.
via CNET
