Generative AI as a cognitive collaborator in Data Quality Management: roles, delegation patterns, and responsible adoption

As part of the “Generative AI as a cognitive collaborator in Data Quality Management: roles, delegation patterns, and responsible adoption" (anonymized), we investigate the emerging GenAI role in DQM practices. Accordingly, we ask How does GenAI shapes DQM through processes of human–AI delegation, and what implications does this have for AI governance? To answer this question, we follow a mixed-method approach combining a systematic review and feature-level analysis of 209 DQM solutions, identifying patterns of GenAI adoption in 15 of them, and interviews with DQ experts, thereby confronting the findings from the systematic study with the reality of practitioners and organizations. Drawing on these insights and the Framework of Delegation to and from Agentic IS Artifacts proposed by Baird & Maruping (2021), which explains how humans and intelligent systems co-construct work processes through cycles of delegated and reciprocal action, we develop and validate a typology of GenAI roles in DQ, namely GenAI as Translator, Explainer, Resolver, and Integrator. Materials accompanying this study, namely (1) SLR overview, (1.1) list of selected academic articles and DQ solutions idenitfied in them, (1.2) list of non-academic sources and DQ solutions identified in them, (2) GenAI-empowered functionalities of identified GenAI-enabled DQ tools and derived typology, (4) interview protocol, (5) expanded version of typology of GenAI in DQM profiles, delegation mechanisms, agent attributes, task characteristics, outcomes and failures, are provided in respective files. Link to the paper will be added upon its acceptance.