New
ECIS 2026 (2026) AI Processed Human Approved
A Typology For Responsible Delegation Of Data Quality Management To Generative AI
This study investigates how Generative AI (GenAI) reshapes Data Quality Management (DQM) into a collaborative human-AI process through evolving delegation patterns. Adopting a mixed-method approach, the authors conduct a systematic mapping of 209 DQM solutions alongside semi-structured interviews with 11 domain experts to conceptualize GenAI roles and their governance implications.
Problem
Traditional rule-based data quality management is resource-intensive, rigid, and struggles to scale alongside growing enterprise data complexity. Furthermore, integrating GenAI without strategic human oversight introduces severe organizational risks, such as model hallucinations, data corruption, privacy breaches, and unclear accountability boundaries.
Outcome
- Formulated a four-role typology for GenAI in DQM: Translator (natural language to rule logic), Explainer (narrative summaries of anomalies and metadata), Resolver (data correction suggestions), and Integrator (cross-system schema alignment).
- Revealed that current GenAI adoption in commercial DQM tools remains nascent (~17%), functioning largely as an interpretive co-pilot rather than an autonomous decision-maker.
- Proposed five theoretical governance guidelines highlighting task decomposability appraisal, mandatory human validation for high-impact actions, reverse delegation protocols, explainability standards, and retained human accountability.
- Revealed that current GenAI adoption in commercial DQM tools remains nascent (~17%), functioning largely as an interpretive co-pilot rather than an autonomous decision-maker.
- Proposed five theoretical governance guidelines highlighting task decomposability appraisal, mandatory human validation for high-impact actions, reverse delegation protocols, explainability standards, and retained human accountability.
What it means for you
- CIO / IT Executive: On Monday morning, initiate a review of your organization's current DQM tool landscape to identify which, if any, are actively experimenting with or have integrated GenAI capabilities, and schedule a follow-up meeting with the IT Manager to discuss initial findings and potential pilot opportunities based on the 'Translator' role.
- IT Manager: On Monday morning, dedicate an hour to review the existing data quality rulebooks and documentation within your team's purview, noting down specific areas where natural language descriptions of data quality issues or desired rules are prevalent, to prepare for a discussion with the CIO/IT Executive about the potential application of the 'Translator' GenAI role.
- Business Strategist: On Monday morning, identify one critical business process that is currently hampered by data quality issues and then, using the 'Explainer' GenAI role as a conceptual framework, draft three potential narrative-style questions you would ask an AI about the root causes and impact of these data quality problems to present to stakeholders.
- Researcher: On Monday morning, begin by outlining a research proposal to investigate the effectiveness and potential biases of GenAI 'Resolver' roles in correcting common data anomalies within a specific industry domain, drawing upon the governance guidelines related to mandatory human validation for high-impact actions.
- Policymaker: On Monday morning, begin drafting a preliminary policy document that outlines the principle of 'retained human accountability' for any data quality decisions made or influenced by GenAI within the organization, focusing on the need for clear audit trails and oversight for 'Resolver' and 'Integrator' roles.
Transcript
Host: Welcome to A.I.S. Insights — powered by Living Knowledge. I'm Anna Ivy Summers. Today, we're diving into a fascinating study titled "A Typology For Responsible Delegation Of Data Quality Management To Generative AI." Joining me is our lead analyst, Alex Ian Sutherland. Alex, data quality sounds like one of those silent enterprise systems—nobody notices it until it breaks. What critical problem is this study tackling?
Expert: You nailed it, Anna. High-quality data is the foundation for everything from operational analytics to training trustworthy AI models. But industry surveys show that roughly seventy-seven percent of data professionals rate their organization's data quality as average at best. Traditional rule-based data management is rigid, resource-intensive, and fails to scale as data volumes explode. While Generative AI offers powerful ways to help, simply turning an AI loose on your enterprise data introduces severe risks, including hallucinations, privacy breaches, and silent data corruption.
Host: So full automation without guardrails is a dangerous trap. How did the researchers investigate how organizations can strike the right balance?
Expert: The authors adopted a mixed-method approach. First, they conducted a systematic mapping of two hundred and nine commercial data quality software solutions to see how Generative AI is currently being integrated into real-world tools. Then, they conducted in-depth interviews with eleven data quality experts to evaluate human-AI collaboration, delegation mechanisms, and governance needs.
Host: What did they learn from looking at those commercial software tools? Are most platforms already fully automated?
Expert: Not at all. The study revealed that current Generative AI adoption in commercial data quality tools remains quite nascent, appearing in only about seventeen percent of the analyzed solutions. More importantly, where GenAI is used, it operates almost entirely as an assistive co-pilot rather than an autonomous decision-maker.
Host: That's a crucial distinction for business leaders to understand. The study organizes these generative capabilities into a four-role typology. Can you break those four roles down for us?
Expert: Absolutely. The first role is the Translator. Here, GenAI converts natural language prompts from business users into executable technical code or validation rules, lowering technical barriers for non-programmers. The second role is the Explainer. Instead of generating code, it summarizes complex data profiles, lineage, or anomalies into clear, natural language descriptions so humans can interpret issues quickly.
Host: That covers rule creation and explanation. What about actually cleaning or integrating the data?
Expert: That brings us to the third role, the Resolver. In this capacity, GenAI suggests or executes data corrections, like fixing formatting issues or deduplicating records, operating under bounded autonomy. Finally, the fourth role is the Integrator, where GenAI assists in mapping and reconciling disparate schemas and data assets across distributed enterprise systems using semantic context.
Host: Having those four clear roles—Translator, Explainer, Resolver, and Integrator—really clarifies where AI can fit into data workflows. But how should companies govern these AI roles so they don't corrupt critical systems?
Expert: That is the core contribution of the study. The authors outline five theoretical governance guidelines. First, organizations must appraise task decomposability before delegating, ensuring tasks are semantically stable. Second, mandatory human validation must be enforced for high-impact or sensitive tasks, especially when using the Resolver or Integrator roles.
Host: What happens when the AI isn't confident in its own suggestion?
Expert: That leads to the third principle, which the study calls reverse delegation protocols. When the AI detects uncertainty or low confidence, it must hand control back to human experts to supply context. Fourth, organizations must enforce strict explainability standards so AI recommendations are auditable and grounded in evidence. And fifth, human data stewards must always retain ultimate accountability for data quality and logic, regardless of AI involvement.
Host: So the big business takeaway here is that Generative AI is a powerful cognitive collaborator that can scale data quality operations, but it should augment human expertise, not replace it.
Expert: Exactly, Anna. GenAI increases efficiency, accessibility, and speed in data management, but human judgment remains essential for maintaining intrinsic data accuracy, domain context, and accountability.
Host: That provides a very clear roadmap for enterprise data leaders navigating the AI landscape. Alex, thank you for breaking down this study for us today. And thank you to our listeners for tuning in to A.I.S. Insights — powered by Living Knowledge. Until next time!
Expert: You nailed it, Anna. High-quality data is the foundation for everything from operational analytics to training trustworthy AI models. But industry surveys show that roughly seventy-seven percent of data professionals rate their organization's data quality as average at best. Traditional rule-based data management is rigid, resource-intensive, and fails to scale as data volumes explode. While Generative AI offers powerful ways to help, simply turning an AI loose on your enterprise data introduces severe risks, including hallucinations, privacy breaches, and silent data corruption.
Host: So full automation without guardrails is a dangerous trap. How did the researchers investigate how organizations can strike the right balance?
Expert: The authors adopted a mixed-method approach. First, they conducted a systematic mapping of two hundred and nine commercial data quality software solutions to see how Generative AI is currently being integrated into real-world tools. Then, they conducted in-depth interviews with eleven data quality experts to evaluate human-AI collaboration, delegation mechanisms, and governance needs.
Host: What did they learn from looking at those commercial software tools? Are most platforms already fully automated?
Expert: Not at all. The study revealed that current Generative AI adoption in commercial data quality tools remains quite nascent, appearing in only about seventeen percent of the analyzed solutions. More importantly, where GenAI is used, it operates almost entirely as an assistive co-pilot rather than an autonomous decision-maker.
Host: That's a crucial distinction for business leaders to understand. The study organizes these generative capabilities into a four-role typology. Can you break those four roles down for us?
Expert: Absolutely. The first role is the Translator. Here, GenAI converts natural language prompts from business users into executable technical code or validation rules, lowering technical barriers for non-programmers. The second role is the Explainer. Instead of generating code, it summarizes complex data profiles, lineage, or anomalies into clear, natural language descriptions so humans can interpret issues quickly.
Host: That covers rule creation and explanation. What about actually cleaning or integrating the data?
Expert: That brings us to the third role, the Resolver. In this capacity, GenAI suggests or executes data corrections, like fixing formatting issues or deduplicating records, operating under bounded autonomy. Finally, the fourth role is the Integrator, where GenAI assists in mapping and reconciling disparate schemas and data assets across distributed enterprise systems using semantic context.
Host: Having those four clear roles—Translator, Explainer, Resolver, and Integrator—really clarifies where AI can fit into data workflows. But how should companies govern these AI roles so they don't corrupt critical systems?
Expert: That is the core contribution of the study. The authors outline five theoretical governance guidelines. First, organizations must appraise task decomposability before delegating, ensuring tasks are semantically stable. Second, mandatory human validation must be enforced for high-impact or sensitive tasks, especially when using the Resolver or Integrator roles.
Host: What happens when the AI isn't confident in its own suggestion?
Expert: That leads to the third principle, which the study calls reverse delegation protocols. When the AI detects uncertainty or low confidence, it must hand control back to human experts to supply context. Fourth, organizations must enforce strict explainability standards so AI recommendations are auditable and grounded in evidence. And fifth, human data stewards must always retain ultimate accountability for data quality and logic, regardless of AI involvement.
Host: So the big business takeaway here is that Generative AI is a powerful cognitive collaborator that can scale data quality operations, but it should augment human expertise, not replace it.
Expert: Exactly, Anna. GenAI increases efficiency, accessibility, and speed in data management, but human judgment remains essential for maintaining intrinsic data accuracy, domain context, and accountability.
Host: That provides a very clear roadmap for enterprise data leaders navigating the AI landscape. Alex, thank you for breaking down this study for us today. And thank you to our listeners for tuning in to A.I.S. Insights — powered by Living Knowledge. Until next time!