Paper Number
ECIS2026-2829
Paper Type
CRP
Abstract
Data quality management (DQM) is a complex, contextual and resource-intensive process as the diversity and volume of data collected in organizations surge. While Generative artificial intelligence (GenAI) presents opportunities to support DQM tasks, for instance, to identify data defects, it is conceptualized as a simple automation activity. Instead, this study advances DQM as a human–AI collaboration and investigates how GenAI shapes DQM through new forms of delegation. Drawing on human–AI delegation literature and combining a systematic mapping of GenAI features of 209 DQM solutions with 11 DQM expert interviews, we derive a typology of four GenAI roles for DQM: Translator, Explainer, Resolver, Integrator. We discuss the governance implications for each role in the form of five theoretical propositions. The study contributes to information systems research with a socio-technical framing of GenAI-enabled DQM, conceptualizing GenAI as a cognitive collaborator rather than an autonomous decision-maker that replaces human expertise.
Recommended Citation
Roht, Karen; Nikiforova, Anastasija; and Lefebvre, Hippolyte, "A Typology For Responsible Delegation Of Data Quality Management To Generative Ai" (2026). ECIS 2026 Proceedings. 16.
https://aisel.aisnet.org/ecis2026/datasc_isresearch/datasc_isresearch/16
A Typology For Responsible Delegation Of Data Quality Management To Generative Ai
Data quality management (DQM) is a complex, contextual and resource-intensive process as the diversity and volume of data collected in organizations surge. While Generative artificial intelligence (GenAI) presents opportunities to support DQM tasks, for instance, to identify data defects, it is conceptualized as a simple automation activity. Instead, this study advances DQM as a human–AI collaboration and investigates how GenAI shapes DQM through new forms of delegation. Drawing on human–AI delegation literature and combining a systematic mapping of GenAI features of 209 DQM solutions with 11 DQM expert interviews, we derive a typology of four GenAI roles for DQM: Translator, Explainer, Resolver, Integrator. We discuss the governance implications for each role in the form of five theoretical propositions. The study contributes to information systems research with a socio-technical framing of GenAI-enabled DQM, conceptualizing GenAI as a cognitive collaborator rather than an autonomous decision-maker that replaces human expertise.