Abstract: Against the backdrop of a workplace where AI teammates and human employees increasingly work side by side (Hillebrand et al., 2025), this research draws on team adaptation theory and develops a context-assessment-crafting-adaptation framework to systematically examine human-AI team job crafting. Specifically, by conceptualizing the theoretical meaning of human-AI team job crafting and unpacking its cross-level process of dynamic emergence, this research explores the formation mechanisms, consequences, and boundary conditions of job crafting at both the individual and collective levels in human-AI teams.
First, Study 1 explores how the meaning of job crafting is extended in Human-AI collaboration contexts. Prior research has mainly drawn on role theory to conceptualize job crafting as employees’ bottom-up physical or cognitive changes to the task and relational boundaries of their work (Wrzesniewski & Dutton, 2001), or on the job demands-resources model to define it as employees’ self-initiated changes to job demands and job resources according to their own abilities and needs (Tims & Bakker, 2010). However, as AI teammates enter teams and participate in task execution, decision-making processes, and even idea generation, the work functions, task demands, and resources available to human employees and their teams are significantly transformed (Raisch & Fomina, 2025). At the same time, the “algorithmic black box” nature of AI teammates can further complicate within-team collaboration (Jussupow et al., 2021), which has important implications for the efficiency of human-AI teamwork. Accordingly, extending the micro-level concept of individual job crafting and clarifying the conceptualization of human-AI team job crafting can enhance the theoretical understanding of job crafting in human-AI collaboration contexts.
Second, Study 2 uncovers the mechanisms underlying the emergence of employee job crafting, while Study 3 further explores the formation mechanisms of team job crafting in human-AI teams. As a key means by which employees and teams respond to environmental changes and develop competitive advantages, job crafting has been shown to have a positive impact on work engagement and job satisfaction (Tims et al., 2013). However, in human-AI collaboration contexts, it remains unclear how human employees and their teams engage in job crafting; that is, the preceding mechanisms of job crafting are still not well-understood. This gap is particularly significant considering that enhancing the efficiency of human-AI collaboration has become a central issue in artificial intelligence research (Brynjolfsson, 2022), and that human-AI teams are increasingly emerging as novel units of task execution and decision-making in organizations (Zercher et al., 2025). Examining the antecedents of human employees’ job crafting and then extending the analysis to the pathways through which human-AI team job crafting develops can thus enrich current research on job crafting.
Finally, Study 4 focuses on human-AI teams to unpack the dynamic emergence of team job crafting, along with its outcomes and boundary conditions. Team-level job crafting is not merely the aggregation of identical job-crafting efforts by individual team members; instead, it is an implicit process of collective effort carried out by the team as a whole (Tims et al., 2013). By delineating the pathway through which human employees’ job crafting evolves into human-AI team job crafting, Study 4 contributes to a more in-depth understanding of the team job-crafting process. Moreover, prior research has demonstrated that team job crafting is positively correlated with team members’ work engagement and team performance (McClelland et al., 2014; Tims et al., 2013). However, whether human-AI team job crafting similarly boosts human-AI team performance, as well as the boundary conditions under which such effects occur, remains under-explored (Siemon et al., 2025; Zercher et al., 2025). Indeed, although AI teammates may improve team operating efficiency, they may also weaken team cohesion and trust (Baird & Maruping, 2021). Accordingly, Study 4 focuses on the team-level outcomes and boundary conditions of human-AI team job crafting, providing theoretical insights that can guide job-crafting practices in human-AI teams. By shifting attention from dyadic human-AI interaction to human-AI teams as adaptive work systems, this research advances job crafting theory, extends team adaptation theory to AI-enabled team contexts, and offers a micro-level account of organizational AI transformation.

Key words: AI teammates, human-AI teams, human-AI team job crafting, human-AI team adaptation