A survey of 933 U.S. business leaders found that 60% agree most white-collar jobs will be fully automated by AI within 12 to 18 months. For HR leaders, that number should command attention for a reason that goes beyond the possibility of layoffs. It shows how quickly senior executives may translate technological predictions into expectations about workforce size, hiring, skills, and organizational design.
The same survey found that 42% of business leaders say AI is already shrinking their workforce. Nearly one-quarter say they are actively eliminating roles because AI can perform the work, while another 18% say they are consolidating roles or reducing backfills.
Those numbers signal genuine workforce pressure and an HR challenge. Leadership may believe AI can eliminate large shares of work before the organization has redesigned jobs, trained managers, established governance, or determined who will perform work AI cannot reliably handle.
In my recent Wise Decision Maker Show interview with Julia Toothacre, Chief Career Strategist at ResumeTemplates, she described the 60% figure as partly aspirational. Leaders can turn sweeping AI predictions into assumed business targets, leaving HR to translate those expectations into workforce strategy.
HR Should Treat Automation Forecasts as Workforce Signals
HR leaders should resist treating predictions about AI capabilities as ready-made workforce plans. The gap between technical capability and organizational execution remains enormous.
McKinsey’s 2025 global AI survey found that 88% of respondents report regular AI use in at least one business function. Yet nearly two-thirds say their organizations have not begun scaling AI across the enterprise. Only about one-third report reaching the scaling stage.
That gap matters for workforce planning. An AI system may perform a task impressively while the surrounding job still requires judgment, context, communication, accountability, institutional knowledge, and coordination.
HR needs to push leadership conversations from jobs to tasks. Instead of asking, “Can AI replace this employee?” ask which activities AI can automate, accelerate, require human review, or should remain primarily human.
Real usage patterns support that approach. Anthropic’s Economic Index found more augmentation than automation, with 57% of analyzed Claude interactions classified as collaboration that enhanced human capabilities and 43% classified as automation. AI use also remained concentrated in subsets of occupational tasks rather than extending across most tasks within most occupations.
When AI can perform 30% or 40% of a position’s repetitive work, eliminating the position may destroy institutional knowledge while leaving remaining work without an owner. Keeping the role unchanged wastes productivity. Job redesign offers a third option.
Redesign Jobs Before Reducing Headcount
HR should lead a systematic review of how work changes when employees gain access to AI.
Start with work employees already perform. Ask teams which recurring tasks consume substantial time, involve moving information between systems, require repetitive synthesis or predictable communication, and keep employees stuck in administration instead of higher-value work.
Then identify what employees should do with the capacity AI creates.
A recruiter who saves hours preparing candidate summaries can spend more time interviewing and advising hiring managers. An HR business partner who automates routine analysis can focus on difficult employee issues. An L&D professional who accelerates training drafts can spend more time testing whether employees learn and apply the material.
Hours saved are only a starting point for measuring AI productivity. HR should examine whether employees use freed capacity productively, quality improves, error rates change, managers decide faster, and employees develop more valuable capabilities.
Reskilling must extend beyond prompt writing. Employees need to decide when AI fits a task, provide context, evaluate output, identify errors, protect sensitive information, and know when human judgment should override automation.
Managers must also learn to redesign roles, set expectations for AI use, evaluate performance in AI-enabled work, and distinguish real productivity gains from superficial output volume.
Workforce consequences will vary by role. Some positions will shrink, others expand, and many will change without disappearing. HR needs a framework for these outcomes instead of isolated staffing assumptions by department.
Build an AI Workforce Strategy Around Skills and Trust
The skills challenge already extends far beyond technical employees. The World Economic Forum’s Future of Jobs Report 2025 found that employers expect 39% of workers’ existing skills to change or become outdated by 2030. If the global workforce consisted of 100 people, employers estimate that 59 would need training. Twenty-nine could be upskilled in their existing roles, while another 19 could be reskilled and redeployed elsewhere.
Sixty-three percent of employers identify skills gaps as a major barrier to business transformation, while 85% plan to prioritize workforce upskilling.
The organization needs to identify which skills become more valuable as AI absorbs routine cognitive work, including AI literacy, analytical thinking, judgment, communication, leadership, collaboration, quality control, curiosity, and continuous learning.
HR should connect those skills to talent systems. Job descriptions should reflect changing work. Recruiting should assess appropriate AI use, performance management should reward process improvement and capability development, learning should focus on actual workflows, and internal mobility should move employees from declining tasks toward growing areas.
HR also has to manage the psychological side of the transition. Employees who hear executives predict widespread white-collar job losses will wonder whether participating in AI initiatives means helping automate themselves out of work. That fear can produce resistance, superficial compliance, or reluctance to share process knowledge.
Leaders need to explain how workforce decisions will be made, how employees can become more valuable, how the organization will support reskilling, and where human accountability remains essential. Employees should also help identify AI use cases because they know where broken processes, repetitive tasks, and quality risks live.
In any AI workforce strategy, HR should require evidence that a redesigned workflow functions reliably in practice before theoretical automation capacity becomes an eliminated position.
The 60% figure matters because executive expectations will influence workforce planning long before anyone knows whether the 12-to-18-month prediction proves accurate.
HR leaders should prepare for substantial disruption without anchoring their strategy to a dramatic forecast. Their job is to determine how work changes, which skills gain value, where employees can move, how managers should redesign roles, and where automation genuinely justifies changing headcount.
Organizations that handle this transition will redesign work, train people for what remains, redeploy talent where possible, and base workforce decisions on demonstrated operational reality rather than predictions.
That creates a much more useful question for HR than whether AI will automate white-collar work by 2028: How quickly can we redesign our workforce so that our people and our AI systems become more valuable together?
Adapted from: “The Psychology of AI Adoption at Work: From Resistance to Results” (Georgetown University Press, 2026).
Dr. Gleb Tsipursky, a behavioral scientist called the “Office Whisperer” by The New York Times, helps tech-forward leaders stop overpaying for AI while boosting engagement and innovation. He serves as the CEO of the AI consultancy Disaster Avoidance Experts, and wrote eight books, including “The Psychology of AI Adoption at Work: From Resistance to Results” (Georgetown University Press, 2026).