The news: US workers are nearly evenly divided on whether AI will change their day-to-day roles and employment statuses, per Workera’s 2026 AI Workforce Preview report, but many are already changing their behavior in anticipation.

• 76% of workers plan to learn new AI skills in 2026—4 in 10 of those will apply those skills in their current position while 36% are boosting their AI knowledge specifically to stay competitive for future job opportunities.
• 39% expect AI to affect their employment, either by causing them to change positions within their company (29%) or costing them their jobs (10%).

What it means: AI’s perceived impact on jobs is already reshaping worker behavior, even before its effects are fully felt.

• Anticipation of future changes is driving behavior more than lived change, setting up tension between expectations and reality.
• Workers are preparing for AI-driven job changes more out of fear than firsthand benefit or experience, reshaping behavior and skills investment ahead of tangible results.

Looking ahead: AI adoption may not be as widespread at enterprises as the “AI-first” mantra implies—19% say AI isn’t even needed in their current role. Some companies could be implementing AI only within specific departments, such as asset generation for marketing teams or code-generation for development teams, leaving some employees out of the AI push.

This could leave workers uncertain about what expectations for AI productivity are in place or whether they should upskill now to avoid falling behind later, causing some teams to work more efficiently than others.

Recommendations for marketers: Marketing leaders should facilitate open conversations about how AI applications could reshape teams, avoid penalizing employees for moderate productivity gains as adoption experiments grow, and select work-sponsored tools that best fit employees’ actual tasks rather than choosing based on hype or brand name.

AI may add new expectations around learning, experimentation, and role evolution before it consistently reduces workloads, making adaptability more important than short-term efficiency gains.

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