With women adopting AI tools at lower rates than men, understanding employee concerns and actively working to improve AI trust and literacy can help reduce the polarity.
By Maggie Mancini
From streamlining and automating tedious, repetitive tasks to reimagining entire roles, procedures, and processes, AI has transformed the way people work. Yet, despite American workers leading the charge to adopt AI at work, research from Harvard Business School suggests that corporate adoption of AI tools is not even among men and women. The study, conducted earlier this year, finds that women are adopting AI tools at a 25% lower rate than men, on average.
“We’re entering a moment where AI is becoming as fundamental to work as the internet or email once were, and the decisions we make today will determine whether this technology expands equality or quietly erodes it,” says Nirit Peled-Muntz, chief people officer at HiBob. “AI has the power to widen gaps or close them.”
AI as an opportunity to enhance productivity, democratize access to information, and empower every employee to grow, she says. When used thoughtfully, AI amplifies people by strengthening their ability to lead, act, and make data-driven decisions with confidence.
Yet, there are a few reasons why women are adopting AI tools at significantly lower rates than men, including ethical hesitation, she explains. Many women wonder whether they’ll be judged for using AI and are concerned that adopting automation tools at work might be perceived as cheating or diminishing the value of their work.
In addition, working in an environment where psychological safety is not prioritized is also a reason for these low adoption rates. “Women often operate under heightened scrutiny. Trying a new technology in public, especially once still shrouded in uncertainty, can feel risky,” Peled-Muntz says.
Increasing female representation in tech leadership provides an opportunity to bridge these gaps. “When women see other women confidently leading and experimenting with AI, it creates visibility, inspiration, and belief that they too can step into these roles,” she says. “This is why it’s so impactful for women at all levels, especially in senior leadership, to actively use, explore, and model AI adoption.”
The gender gap is not about capability, but about context, culture, and safety—all of which organizations have the power to shape, Peled-Muntz says.
Improving Trust in AI
Even as women adopt AI tools at work, research from Deloitte finds their feelings of trust toward the technology are significantly lower than men’s, and their feelings of uncertainty remain higher. For Peled-Muntz, this means it’s important for organizations to build trust and encourage experimentation from the top-down.
“Create psychological safety first,” she says. “People need to understand how AI will be used, and equally, how it won’t be used. When ambiguity disappears, trust grows. Invest heavily in training and learning. Most employees worldwide learn AI alone, but that approach widens gaps. At HiBob, we chose instead to build structured, ongoing training for all employees. Democratizing access replaces intimidation with confidence.”
She explains that it’s important to lead by example—especially through female leadership. It’s important to use it daily, openly share how it accelerates work, and where leaders are still learning. This can break down barriers and communicate to female employees that AI is for them, too.
“Make learning visible and judgment-free,” she adds. “Encourage a pilot mindset where everyone tests, learns, and improves. I remember an employee telling me that they were afraid to try AI at first, but once she saw leaders using it openly and without judgment, she realized she could, too. This moment is exactly why culture matters and is likely the case for women across organizations.”
As AI adoption becomes a more important factor in employee productivity, unequal adoption leads to unequal outcomes, Peled-Muntz says. This can show up in a handful of ways: career mobility, engagement and belonging, and productivity and innovation.
Employees who use AI progress faster. If women adopt it less, they risk being overlooked for promotions and other opportunities, she says. The risk here is especially great, as there is already a significant gender gap when it comes to pay and promotions. It’s important, then, to empower women to adopt AI and become AI literate to stay ahead in the promotion cycle.
“Feeling left out of a major shift in the way work happens can erode confidence and connection to the organization,” she adds. “This is why it’s critical to make sure AI learning is part of the cultural DNA of an organization.”
AI boost efficiency and can enhance creativity—but this is only true for those who are harnessing its benefits effectively, Peled-Muntz explains. When adoption is uneven, organizations can lose out on potential talent and innovation capacity. It’s important to address these issues early before they cause a divide in the workplace.
Improving Adoption Across the Business
To close the AI adoption gap, HR and business leaders must take a few steps, Peled-Muntz explains.
- Build inclusive, role-based AI training. By making AI relevant to real work, showing how it supports talent discussions, how it improves customer interactions, and how it helps analyze trends, training becomes less intimidating and more actionable.
- Normalize experimentation. Create a pilot culture where learning is encouraged, missteps are accepted, and curiosity is celebrated. When possible, set aside designated time for employees to “play” with AI so they know it’s part of their job to learn it, and not something they push down on their to do lists.
- Develop women-led AI communities and champions. Peer-based learning reduces fear, and women supporting women accelerates adoption dramatically.
- Openly address ethical concerns. Talk about attrition, plagiarism fears, and when disclosure is needed. Having clear-cut rules reduces the fear of being judged.
- Model behavior from the top. Leaders should show curiosity, vulnerability, and transparency. When senior leaders say, “I’m still learning this,” others feel safe to try without judgment.
- Measure sentiment, adoption, and impact. Measure, listen, and adapt as needed for your specific teams and organization overall.
Before rolling out AI, companies should ask themselves if they have cultivated a psychologically safe environment, if the business model incorporates responsible AI use from the top-down, if access to AI tools are equitable and inclusive, if the business is listening to women’s concerns as they integrate work with AI, and whether employees know how AI should be used at work.
“These questions will shape whether AI becomes a divider or a unifier,” she says. “Women’s hesitation around AI should not be dismissed. It should guide us to build frameworks that are more transparent, more ethical, and more human. Our own experience shows what’s possible when trust and learning come first. When we design environments where everyone feels confident to experiment and grow, AI becomes a powerful force for closing gaps, not widening them.”
