Measuring GenAI and Productivity in Work and Careers

Description

Generative AI is being increasingly integrated into workplace processes and career development, reshaping how tasks are executed and decisions are made. This presents significant questions about productivity measurement, particularly as work increasingly involves collaboration between humans and AI systems rather than purely human effort.

When humans and AI systems work together to produce outputs, it becomes challenging to determine what portion of results comes from human contribution versus AI assistance. This fundamental issue means that traditional concepts of productivity may no longer adequately capture work performance in AI-augmented contexts. The special issue seeks research that reconceptualizes how productivity should be defined and measured when human-AI collaboration is involved, with attention to career implications and organizational factors that influence these dynamics.

Potential topics

  • Conceptualizing productivity in AI work and careers
  • Distinguishing between AI exposure, use, and augmentation
  • Measuring human versus AI contributions to outputs and performance
  • Changes in the distribution of performance (e.g., effects on high and lower performers)
  • Implications of GenAI for skill development, career trajectories, and employability
  • The role of HR practices, job design, and organizational context in shaping productivity related to AI
  • Methodological approaches to studying AI and work (e.g., multi-source data, longitudinal designs, behavioral, other types of data)