AI is changing everyday tasks in U.S. workplaces. Women sit in many exposed roles, but clear upskilling choices can protect options.
Anna Radulovski
Founder and Global CEO, WomenTech Network; Co-founder, Chief in Tech Capital
On a sunny afternoon in Oklahoma City, Tabby Toney opened an email that ended her job. She had worked as a software engineer. The layoff pushed her into welding school. “Software engineering has changed so much,” she later wrote. “Now the internet is full of AI-assisted programming.” Her story was reported by Business Insider in 2025.
Her experience is no longer rare. In the past year alone, U.S. layoffs were more than one million, the highest annual total in years. Companies cut roles as automation expanded and budgets tightened. The technology sector alone accounted for about 150,000 job losses, with reductions across cloud services, support teams, and development functions.
The burden has not been shared evenly. Research from WomenTech Network shows women are 1.6 times more likely than men to be laid off. One reason is structural. Women are more likely to hold roles that sit lower in the hierarchy, where automation hits first. They are also overrepresented in functions often targeted early during restructuring.
Global labor data points in the same direction. Analysis by the World Economic Forum, using LinkedIn workforce data, finds women are more likely to work in jobs disrupted by AI and less likely to work in jobs where AI increases productivity and pay.
This gap explains why advice to “just upskill” often falls flat.
Restructuring the inequality gap
WomenTech Network’s 2025 Barriers to Leadership survey shows the limits of skills alone. Seventy percent of respondents said promotion processes are unfair. Fifty-eight percent reported unequal access to networking and sponsorship. Even when women gain new capabilities, they often lack pathways to apply them.
So upskilling cannot mean learning alone at night and hoping it pays off. For women, upskilling only works when it connects to access, visibility, and proof.
What does work is a phased approach that fits real constraints. The goal is not to become an AI expert overnight. The goal is to move deliberately, step by step, from using AI tools to owning outcomes they affect.
- Zero to two weeks. AI literacy. Low cost. Start with skills tied directly to your current role. Focus on prompting, output verification, privacy basics, and simple workflow automation. Time commitment: Two to five hours a week. Proof point: one improved workflow, documented in a short explanation you can share
- One to three months. Transferable skills. Add an adjacent skill that travels across roles and industries. Strong options include data analytics, project management, cybersecurity fundamentals, or customer operations quality. Time commitment: Five to 10 hours a week. Proof point: a small portfolio or documented project that shows applied work
- Six to 12 months. Credential or pivot. When deeper change is needed, choose earn-while-you-learn paths when possible. Apprenticeships, community college certificates, and employer-funded programs reduce risk. Time commitment: 10 to 15 hours a week. Proof point: a capstone project and a skills transcript that hiring managers can verify
Change equals new value-adds
The constraints are real. U.S. Bureau of Labor Statistics time-use data shows women spend significantly more hours than men on unpaid household and caregiving work. That leaves less time for training that is unpaid or speculative. At the same time, promotion gaps, often called the “broken rung,” make it harder to turn new skills into new roles, as documented by Lean In and McKinsey.
This is not only an individual problem. Employers shape whether upskilling leads anywhere.
Paid learning time matters. Clear internal mobility paths matter. Skills-based hiring matters. Sponsorship matters. Companies that invest in these areas reduce attrition and avoid wasting trained talent. Labor groups are already pressing for worker input and training commitments as AI reshapes jobs across sectors.
The most common question heard today is, “Is AI going to replace my job?” That misses the point. The better question is simpler and more useful: Which tasks are changing, and what new value can I own next? Ownership, not fear, determines who benefits from the next wave of work.
