Careerminds Study Reveals AI Now Top Factor Driving Layoff Decisions as 57% of HR Leaders Plan Workforce Reductions While 90% of Employees Remain Confident Jobs Are Secure

Artificial intelligence has emerged as the leading factor influencing organizational layoff decisions—ranking ahead of market conditions and industry trends—according to a national study released Tuesday by Careerminds, a global workforce solutions provider. The research reveals a striking disconnect between employer planning and employee perception, with 57% of HR leaders reporting their organizations are likely to conduct layoffs within the next year while 90% of employees express confidence their jobs will remain secure over the same period.

The Workforce Resilience in the AI Era report, based on surveys of 500 human resources leaders and 500 employees at large U.S.-based organizations conducted in November 2025, found that nearly half of HR leaders planning layoffs cite increased AI use as a major contributing factor. This marks a significant shift in how organizations frame workforce decisions, with AI adoption now surpassing traditional economic indicators as the primary driver of headcount reductions.

The study's finding that AI ranks as the top influence on layoff decisions represents a fundamental change in corporate workforce strategy. Historically, companies justified reductions primarily through market downturns, industry-specific challenges, or competitive pressures. The elevation of AI above these traditional factors suggests organizations increasingly view automation capabilities as independent justification for workforce restructuring regardless of broader economic conditions.

"This disconnect is one of the most important signals in the data," said Raymond Lee, President of Careerminds. "Organizations are actively planning for workforce changes tied to AI adoption, while many employees still view AI primarily as a productivity tool rather than a catalyst for structural change. That gap creates real risk—for both individuals and employers—leaving workers vulnerable to sudden change while organizations struggle to redeploy talent effectively."

The confidence gap identified in the research indicates employees fundamentally misunderstand AI's trajectory within their organizations or that employers have failed to communicate transformation strategies transparently. Either scenario creates conditions for workforce disruption that catches employees unprepared and organizations without clear succession or redeployment plans.

Survey data reveals that employees currently report largely positive experiences with AI in their existing roles, which may explain why concerns about job displacement have not translated into declining job security confidence. This pattern suggests workers appreciate AI's ability to automate routine tasks and enhance productivity without recognizing that these same capabilities position organizations to reduce headcount over time.

The disconnect mirrors patterns observed during previous technological transitions, where workers embraced productivity-enhancing tools without anticipating that improved efficiency would eventually reduce demand for human labor. Organizations implementing AI gradually—starting with task automation before progressing to role elimination—may inadvertently mask displacement risk until workforce reductions occur.

Lee emphasized that the positive current experiences create a false sense of security that leaves employees vulnerable. "In 2026, stability won't come from holding a static role," he noted, suggesting employees must recognize that even roles enhanced by AI today may face elimination as automation capabilities mature and organizations optimize for efficiency.

The research identified problem-solving, communication, and AI literacy as the most critical skills employees need to build in 2026 to maintain workforce relevance. This skills profile suggests organizations value human capabilities that complement rather than compete with AI systems—specifically, judgment and interpersonal abilities machines cannot easily replicate alongside technical literacy enabling effective AI collaboration.

The emphasis on AI literacy represents a notable shift from previous technology transitions, where workers could often continue performing roles without deep understanding of underlying systems. Modern AI integration appears to require workers who can prompt systems effectively, evaluate outputs critically, and determine when human judgment should override algorithmic recommendations.

Problem-solving and communication skills receiving top billing suggests organizations anticipate AI handling routine execution while humans focus on ambiguous situations requiring creativity, stakeholder management, and contextual judgment. This division of labor implies workers whose roles consist primarily of rule-based tasks face highest displacement risk, while those handling exceptions and relationship management may see AI augmentation rather than replacement.

Careerminds will host a live, data-driven webinar January 28, 2026, examining how AI and market shifts are reshaping job security, skills relevance, and workforce strategy. The session will feature Careerminds President Raymond Lee, Strategic Advisor Anjna Rughani, Chief People Officer and serial founder Steven Bianchi, and Accenture's Director of Talent Insights Nicole Coletta.

Topics will include how job security increasingly depends on skills rather than roles, why skills are expiring faster and what drives this acceleration, and how organizations can strengthen transferable skills, internal mobility, and career pathways to navigate AI-driven transformation. The webinar format suggests organizations recognize the need for practical guidance on managing workforce transitions beyond high-level strategy discussions.

The report emphasizes that organizations must pair AI adoption with investment in human talent to build resilient, adaptable workforces. This dual investment approach recognizes that technology deployment without corresponding workforce development creates instability, productivity losses, and competitive disadvantage as experienced employees exit and remaining staff lack capabilities to leverage new systems effectively.

Lee's commentary suggests successful AI integration requires transparency with employees about transformation timelines, proactive reskilling programs addressing capabilities gaps identified through workforce planning, internal mobility systems enabling lateral moves as roles evolve, and retention strategies preventing valuable employees from exiting preemptively due to uncertainty.

The research implicitly criticizes organizations planning AI-driven layoffs without corresponding programs preparing affected employees for transitions or redeploying them into evolving roles. This approach maximizes short-term cost savings while creating longer-term talent shortages and organizational knowledge loss.

For employees, the findings underscore that current job security does not guarantee future employment as AI capabilities mature. Workers in roles involving routine information processing, data entry, basic analysis, or rule-based decision-making should proactively develop skills positioning them for AI-augmented roles or entirely new positions less susceptible to automation.

The 90% confidence figure suggests most workers have not internalized this reality, creating conditions for sudden disruption when organizations execute planned reductions. Employees who wait for employers to provide training may find themselves competing for limited reskilling opportunities or facing displacement without preparation.

For employers, the disconnect represents both risk and opportunity. Organizations that transparently communicate AI strategies while investing in workforce development can capture productivity gains while retaining institutional knowledge and employee trust. Those implementing AI quietly until layoff announcements will face talent flight, engagement declines, and difficulty attracting replacements aware of the organization's approach to workforce transitions.

The Careerminds research suggests 2026 represents an inflection point where AI's labor market impact transitions from theoretical concern to operational reality, requiring both workers and organizations to move beyond experimentation toward systematic adaptation strategies.