Less effort, less meaning? Authorship ambiguity and impostor-like experiences in GenAI-mediated knowledge work

Purpose This study examines how knowledge workers make sense of GenAI-enabled co-production, with particular attention to authorship ambiguity and its implications for competence attribution, self-evaluation and meaningful work. Design/methodology/approach The study uses a qualitative approach based on in-depth interviews with 32 knowledge workers who work directly with GenAI tools, combining inductive coding with abductive interpretation. We used the Gioia methodology to analyze participants' reported experiences and interpretations of human–AI collaboration, with sensemaking informing the interpretation of emergent themes and aggregate dimensions. Findings The findings identify authorship ambiguity as a central interpretive challenge in GenAI-mediated knowledge work. When GenAI contributes to ideation, analysis, drafting, coding or other knowledge-intensive outputs, workers may find it difficult to distinguish their own contribution from that of the technology. Participants responded to this ambiguity differently. Some reconstructed their expertise around judgment, validation and contextualization and experienced GenAI as extending their professional capability. Others struggled to internalize successful outputs as evidence of their own competence and reported impostor-like experiences and reduced meaningfulness. The findings suggest that experiences of GenAI-enabled co-production depend on whether workers interpret and reconstruct their contribution. Practical implications Managers in comparable knowledge-intensive settings may consider making human judgment visible, clarifying legitimate uses of GenAI and recognizing contributions beyond output speed and quality. Originality/value This study identifies authorship ambiguity as a central interpretive challenge in GenAI-mediated knowledge work. It shows how uncertainty regarding the respective contributions of the worker and GenAI can complicate competence attribution and initiate sensemaking about professional contribution. By explaining how workers' interpretations of authorship ambiguity are associated with divergent identity and meaning outcomes, including enhanced capability, impostor-like experiences and changing meaningfulness, the study extends research on human–AI collaboration beyond its predominant focus on performance and productivity.