Designing Work That Doesn't Break People

Abstract

Hackman and Oldham's Job Characteristics Model identifies five core job dimensions — skill variety, task identity, task significance, autonomy, and feedback — that predict work motivation, satisfaction, and performance. As AI reshapes job content, organizations face a design challenge: ensuring that AI-augmented work preserves and enhances these core characteristics rather than eliminating them. This paper translates work design theory into design requirements for AI workforce systems.

Key Findings

• Jobs that preserve skill variety, autonomy, and feedback after AI integration produce higher motivation and lower burnout than AI-simplified roles.

• Task significance — the sense that work matters — is one of the most important preservable qualities in AI-augmented roles.

• Feedback loops that include AI-generated insights improve performance when they enhance rather than replace manager feedback.

• Autonomy preservation in AI-augmented jobs requires explicit design decisions about when humans lead and when AI assists.

• Organizations that measure job characteristic quality alongside productivity KPIs make better AI implementation decisions.

How this connects to RUDY