Why Women Workers Are Facing the Biggest AI Risk - and What HR Should Do Now
New research from the Brookings Institution and the Centre for the Governance of AI puts a spotlight on clerical and administrative jobs as highly exposed to AI. Of the roughly 6 million workers who would have the hardest time adapting to AI-related job loss, 86% are women. That makes this an HR priority, not a tech story.
The job isn't to wait and see. It's to redesign work, create skill pathways, and keep equity front and center.
What the data says
Roles with the steepest mix of high AI exposure and low ability to adapt are largely female-majority positions:
- Secretaries and administrative assistants (96% female)
- Court and municipal clerks (85% female)
- Payroll and timekeeping clerks (89% female)
Tech has been chipping away at these tasks for years. AI is the next wave. The good news: about 70% of workers in AI-exposed roles could pivot into comparable-paying jobs if displaced. Those transitions tend to land in fields with broader skill mixes (think marketing, finance, or science) where roles like marketing manager, financial analyst, and web designer show high AI exposure too, but more room to adapt.
Source: Brookings Institution
What this means for HR
- Expect task shifts before headcount cuts. Job redesign is the first lever.
- Risk is concentrated in female-majority roles. Treat this as a gender equity issue.
- Mobility is possible, but only if you build structured pathways and pay people to learn.
90-day HR playbook
- Weeks 1-2: Task-level audit - Break target roles into tasks. Tag each as automate, augment, or retain. Flag adjacent roles that reuse 50%+ of the skills.
- Weeks 3-4: Skills inventory - Survey employees, pull HRIS data, and ask managers. Map current skills to target roles. Prioritize data literacy, project coordination, HR tech, and basic AI use.
- Weeks 5-6: Learning sprints - Give 2-4 paid hours per week to learn. Offer micro-credentials and short courses. Tie each course to a real internal project or shadowing opportunity.
- Weeks 7-8: Pilot redeployment - Create 60-90 day rotations, job shares, and stretch assignments. Example: move payroll clerks into HRIS reporting or compliance ops.
- Weeks 9-12: Update roles and scale - Refresh job descriptions, career ladders, and competencies. Stand up an internal talent pool for at-risk employees.
Skill tracks that convert admin experience into mobility
- Data literacy and reporting - Spreadsheets, dashboards, basic SQL, data quality checks.
- Project coordination - Scoping, timelines, stakeholder updates, risk logs, light Agile.
- AI basics and on-the-job use - How large models work, writing effective prompts, review practices, audit trails.
- Business software and automation - HRIS, CRM, e-sign, forms, simple workflow automation.
- People operations - Policy, scheduling, compliance, HR analytics, employee communications.
Career pivots don't have to be expensive. Coursera, LinkedIn Learning, and Google Career Certificates offer low-cost paths, and many public libraries provide free access. Community colleges often run accelerated programs built for working adults.
Design internal pathways for impacted roles
- Admin assistant → project coordinator, operations analyst, recruiting coordinator
- Payroll/timekeeping clerk → HRIS analyst, workforce scheduling analyst, compliance coordinator
- Court/municipal clerk → records management, customer operations, procurement support
Choose bridges where current skills overlap heavily with the target role, the pay is comparable, and the learning gap is clear and bite-sized.
Policy moves to protect equity
- Guarantee paid learning time and manager coverage so people can actually participate.
- Offer exam vouchers or stipends for short credentials. Make schedules flexible for caregivers.
- Use transparent selection criteria for pilots. Track participation and outcomes by gender and pay band.
- Adopt a "retrain and redeploy first" stance before considering layoffs tied to AI.
Responsible AI rollout HR should lead
- Form a rollout team with IT, Legal, and reps from impacted roles. Get their feedback early.
- Set rules: data privacy, human review for high-stakes tasks, and bias testing on AI-assisted workflows.
- Train managers to review AI outputs and coach teams on good use and quality checks.
- Document changes to tasks and skills so job architecture and pay stay honest.
Practical use cases in HR (with human review)
- Drafting job posts and interview questions, then editing for accuracy and tone.
- Summarizing interview notes and flagging follow-ups.
- Creating first-draft SOPs, onboarding checklists, and internal FAQs.
- Building simple dashboards from HRIS exports to spot trends.
Metrics to track
- Participation rate of at-risk employees in upskilling
- Internal mobility rate and time-to-placement from at-risk roles
- Time-to-competency on target skills
- Pay changes pre/post redeployment (watch for drops)
- Quality and error rates on AI-assisted work
Give people a head start
If your company is rolling out AI tools, invite at-risk employees to join the implementation team. The person who understands both the tool and the human process is hard to replace.
Need structured paths your team can start this week? Browse curated learning by role or skill here: Courses by Job and Courses by Skill.
Reality check
Ben May, director of global macro research at Oxford Economics, noted there's little evidence right now of firms replacing significant numbers of workers with AI. He's "skeptical that firms can quickly and seamlessly substitute workers with AI even in sectors where the potential for AI disruption is greatest." That gives HR a window - not forever, but enough - to reskill and redeploy with intention.
Act now. Build skill paths, redesign work, and move people into roles where they can grow alongside the tech.
Related source: Oxford Economics
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