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Will Women Get Left Behind in the AI Economy?

“I’m working with a woman right now who’s the global head of data and analytics at a Fortune 10 company,” said Deepali Vyas, the sector head of AI and data at talent advisory ZRG. “She owns the entire AI roadmap, but her title, by the way, is just ‘data and analytics.’ They haven’t even updated the title yet.”Such a distinction matters in a job market where employers are eager to hire anyone with AI expertise. There’s a shortage of such talent, and companies may be overlooking great hires who don’t tout their deep AI experience, as well as those with non-technical backgrounds who have the transferable skills and the willingness to learn. Many of these people are female. To stay competitive, companies need to carefully monitor the gender demographics in their AI workforce.Women occupy only about one in four AI jobs worldwide, and they’re even more underrepresented in leadership, occupying just 15% of executive AI roles.There’s a universal gap in AI use in the workplace too—as much as a 22% disparity in use among men and women, according to one study out of Harvard Business School. Authors of the report argue that the barriers are myriad across institutions, companies, and society. Some women opt out of using AI at work because they don’t want to be seen as cheating or incompetent, some have ethical concerns, and others don’t have enough exposure to AI tools in their roles to make regular use of them.The imbalance threatens women’s earning potential, job mobility, and employability—and it threatens a company’s ability to build the AI-ready workforce it needs.The Cost of the AI Gender GapArtificial intelligence—generative AI, more specifically—threatens to displace women more than it does men. Women are overrepresented in roles likely to be overtaken by AI, like bookkeeping and customer service. Unless women upskill, they risk becoming less competitive in the market. Plus, fewer women in AI roles mean that AI models can perpetuate bias by omitting gender-diverse perspectives. One study out of Johns Hopkins University found that prompts that include language commonly used by women produce less sophisticated responses from ChatGPT than ones that include language often used by men.A new report from the Harvard Kennedy School’s Shorenstein Center on Media, Politics, and Public Policy found that there is a perception gap in which men are more likely to associate AI with curiosity and excitement while women associate it with concern and anxiety—skepticism that may be holding women back.The AI gender gap exposes a critical problem. “The study didn’t find that women were less competent using AI, only that they were less confident, often thanks to a lack of support in their workplace, but also because they trust it less overall,” Nancy Gibbs, Lombard Director of the Shorenstein Center and one of the study’s principal investigators, told From Day One. “That skepticism is a valuable part of a critical debate over AI safety, guardrails, value and uses. When women’s voices are missing from the spaces where decisions are made, we have less cause to trust those decisions.”There’s also the question of the earnings gap. Workers in roles that require AI make 62% more, on average, than those in roles that don’t require these skills. If AI-centric jobs remain male-dominated, the long-standing gender pay gap can widen even further. Women aren’t the only ones paying the price. Companies that overlook female talent can fail to build a universally AI-ready workforce and fall behind their competitors. So far, companies are eager to gobble up AI-literate talent. The number of entry-level roles requiring AI skills tripled between 2025 and 2026, and LinkedIn reports that both technical and non-technical jobs requiring AI skills grew 70% in the last year, now constituting a baseline for both technical and non-technical positions.The AI Talent Companies Are OverlookingVyas noted that some women aren’t giving themselves enough credit for how proficient they already are because using AI in daily workflows has become a standard expectation in some companies, like knowing how to use an email client or Microsoft Office, so they omit it from their resume or work sample—an omission that could cost them valuable opportunities.A few years ago, many companies had clearly defined gender diversity goals, said Vyas. Today, things are moving too quickly for companies to stop and think about the demographics of their workforce. AI-literate talent is in high demand across all departments, so most employers are just looking for anyone who knows what they’re doing, regardless of gender. “It’s like, ‘Who knows this stuff? And whoever knows it, we need to be all over them,’” she said.While most AI hires in 2026 are men, women are successfully entering technical AI roles through side doors, generally with more luck within a company than from without. In fact, many of the newest female hires to AI teams are former HR employees.“HR has been a little bit on the leading edge of this because of people analytics,” Vyas said. “They have a lot of data to work with, so a woman that has led data modernization or analytics in a complex product, like HRIS systems, can bring relevant capabilities even without the AI title.”But persistent misconceptions about the HR department, which has a lingering reputation for being non-technical, prevent some companies from considering resumes from the HR function for AI roles. “The best HR leaders that I know now are so freaking technical,” Vyas said.In a similar vein, at global software engineering company DataArt, some of the freshest technical talent has come from an unlikely place: the English classroom.Maryna Melnik, who leads learning and development, has seen employees who spent years teaching English retrain as business analysts and quality assurance engineers. Some had spent 15 years in education before deciding they wanted a new career.Stereotypes of who has the capabilities and who doesn’t are long-standing barriers to advancement for women entering traditionally male industries, but “you can’t define an AI leader as a copy of the person who held that power for you,” said Vyas. “A search can become really narrow when it starts with a really familiar resume—certain companies, certain schools, certain titles.” Women are winning where they’re given the opportunity to demonstrate their transferable skills and curiosity, and they must be allowed to climb up. Opening the door isn’t enough. The Alan Turing Institute found that even within the AI and data science fields found that “women are more likely than men to occupy a job associated with less status and pay in the data and AI talent pool, usually within analytics, data preparation and exploration, rather than the more prestigious jobs in engineering and machine learning.”One way to broaden that search is to evaluate candidates on what they can do, rather than where they’ve worked or which titles they’ve held. Adam Vassar, senior director of talent science & learning design at CodeSignal, which develops skills assessments and training programs, said such evaluations can give candidates opportunities to demonstrate capabilities that might not be evident on their resumes. “Hopefully it helps level the playing field in some way by giving any candidate a chance to showcase what they can do.”Internal mobility often requires some kind of skill mapping, and that’s how many women at DataArt have made the move from non-technical roles into technical ones. The company built skill matrices for all roles that include descriptions of the requisite AI skills and advice on how to develop them. “So if a junior HR team member wants to level up, they can check the skill matrix to learn what’s expected for more technical roles or ones on the production side of the business. Our HR colleagues were the first ones and the best,” said Melnik. “And then others followed.”DataArt is prioritizing investment in AI-native engineers because that’s the motor for their profit center, “but we deliberately didn’t stop there,” Melnik noted. “We invest in AI capability for our corporate functions at the same intensity as we invest for our engineers, and because those functions are predominantly women, that decision is what we can translate into equal opportunity.”Making AI Skills Pay OffBut even an objective assessment can reinforce disparities if women have fewer opportunities to acquire in-demand skills. “It’s one thing to evaluate, ‘Do you have it or not?’ But is that a result of some systematic lack of access to training?” Vassar said. “And then all we’re doing is keeping score on a larger problem.”AI could solve its own problem, at least in part. AI lets companies democratize learning by replacing expensive human instructors with on-demand-anywhere virtual modules, which means everyone, not just men already in tech roles, can develop new skills just by opting to do so. In fact, some experts advocate for truly universal access. Beth Simone Noveck, director of the Burnes Center for Social Change at Northeastern University, argued in a recent New York Times opinion piece  that governments should train their entire workforce on AI use, so transformational is its widespread use.But companies must also have a culture that rewards those skills regardless of gender. Handing out AI tokens and free training modules doesn’t necessarily open new doors, nor does it reward learners for their new skills. Skill competency should be a documented criteria for promotions and pay raises, says Vyas. “If I save the company money, does that hit my bottom line? Do I get kudos for that in terms of compensation?”As AI reshapes the very nature of work, employers will determine whether existing disparities persist or begin to narrow. “Companies that are making decisions right now about who gets trained, who gets hired, who gets to lead AI, those choices are going to influence careers for the next number of years,” Vyas said. “Women need meaningful access to the work and the rewards and the decisions shaping the field.”Emily McCrary-Ruiz-Esparza is an independent journalist and From Day One contributing editor who writes about business and the world of work. Her work has appeared in the Economist, the BBC, The Washington Post, Inc., and Business Insider, among others. She is the recipient of a Virginia Press Association award for business and financial journalism. She is the host of How to Be Anything, the podcast about people with unusual jobs.(Photo by Dragos Condrea/iStock)