Predictions 2026: The Workforce Muddles Through Ambient Disruption
Source: Forrester · 2025-11-12
It’s predictions season at Forrester! Each year, our team steps back from the noise to ask: What’s really happening in the world of work, and what’s coming next? For 2026, the signals were loud, the contradictions sharp, and the stakes high. We asked ourselves what leaders need to hear, not just what they want to hear.
We expect half of AI-attributed layoffs to be quietly reversed, with jobs returning offshore or at lower wages. The AI-washing and mirage of future AI collides with operational reality, and many firms are realizing that replacing humans with machines isn’t always cheaper, or smarter, unless they have a complete approach that accounts for the people on whom all AI success will depend.
Meanwhile, HR will face a reckoning. After years of trying to prove strategic value, HR teams may see staffing cuts of up to 50% as AI tools promise efficiency — and executives test how far they can push. Spoiler: It won’t end well unless HR leaders get serious about AI literacy and outcomes.
We also predict a deepening culture-energy chasm. There has always been a significant gap between employee and leader perception of organizational culture, and that divide will continue in 2026 as leaders envision AI-fueled success, feeding their optimistic outlook, while the workforce’s culture energy drains away in the face of continuing macroeconomic turbulence and revenue misses. “Coasting” — a type of burnout characterized by quietly easing off the accelerator — is becoming a survival strategy, and our data shows how it’s spreading.
Leaders can deftly avoid these mistakes and dead ends by examining the operational factors that contribute to high performance, directing AI to specific outcomes and setting up the human workforce for success, and using the AI opportunity to redesign work itself.
Lines of inquiry this paper opens 16
Research framings built by reading the notes related to this paper — the questions it feeds into.
How do AI-exposed occupations change in employment, wages, and skills?- Do companies consider redeployment before cutting staff for AI?
- Does organized union pressure systematically reverse premature AI-driven layoffs?
- Do employers reorganize work tasks around AI before cutting jobs?
- Why might companies choose to label layoffs as AI versus restructuring?
- Are AI layoffs concentrated in specific job categories or widespread across industries?
- Do survey expectations of job cuts eventually match observed employment data?
- Which occupations face the steepest AI-driven hiring declines right now?
- How do payroll data and employer announcements differ in measuring AI job displacement?
- Why do aggregate employment statistics miss losses in specific occupations?
- Has AI actually displaced workers in payroll data so far?
- Why do early-career workers fear AI job loss more than senior workers?
- How much do self-reported executive expectations align with actual payroll outcomes?
- Does task-level AI exposure predict which jobs will be rehired versus eliminated?
- Does AI job-loss fear match actual hiring or employment declines?
- Why do executives report no AI impact on jobs today?