Generative artificial intelligence (GenAI) is rapidly being integrated into work processes, organizational and HR systems, and careers (Shao et al., 2025; Marr, 2023; Rabenu and Baruch, 2025). Emerging research suggests that GenAI is reshaping task execution, decision-making, and knowledge work, with implications for productivity across multiple levels (Nyberg et al., 2025). To date, however, much of the literature has focused on adoption and broad impact, with less attention to how work is reconfigured in practice and how productivity should be understood in AI contexts.
A key development is the increasing prevalence of human and AI collaboration in which outputs are produced together through interactions between individuals and AI systems. This implies, to some degree, augmented integration, or a form of collaboration in which humans and AI systems jointly complete tasks (Raisch and Krakowski, 2021). However, such integration creates a fundamental challenge: when work is augmented by GenAI, it becomes difficult to attribute outcomes to human effort versus AI contributions. As a result, long-standing assumptions about productivity (e.g., how it is defined, measured, and compared) require reconsideration.
List of Topic Areas
- 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)
Submissions Information
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Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
Journal Information: Scopus Journal Q1, H-Index 104
Key Deadlines
Opening date for manuscripts submissions: 31/08/2026
Closing date for manuscripts submission: 31/01/2027
Email for submissions: Tony Fang, Canada, tfang@mun.ca
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