AI-Augmented Data Stewardship: Evaluating a Supervised GenAI Workflow and Diagnosing Master Data Inconsistencies in the FMCG context - Fournier, Arnaud (My University, 2026)
Multinational enterprises in the Fast-Moving Consumer Goods (FMCG) sector increasingly manage product master data across heterogeneous enterprise systems. Structural differences across these fragmented environments produce incompatible records, creating a manual reconciliation burden that is slow and difficult to scale. Recurring inconsistencies persist because the underlying organizational and process conditions remain structurally unchanged. This study investigates whether a supervised Generative AI (GenAI)-assisted workflow improves reconciliation efficiency and representational consistency over manual processes, and examines the root factors causing recurring data errors. Task-Technology Fit (TTF) and data quality research and frameworks provide the interpretive lens. Using an embedded mixed-method single-case design, the quantitative component applies a quasi-experimental comparison of two workflow conditions across a dataset of product records from a real operational harmonization project. The qualitative component draws on eight semi-structured stakeholder interviews analyzed through thematic analysis. The findings reveal that the supervised GenAI workflow substantially reduced total processing time and improved structured output quality, while achieving near-equivalent attribute-level correctness to manual methods. Human review remained central to the design, with the majority of model outputs accepted without modification. The qualitative component identified four upstream themes, covering governance fragmentation, manual transfer dependencies, data entry disconnection from downstream consequences, and structural inheritance from legacy system decisions, that explain why similar inconsistencies reoccur regardless of downstream efficiency gains. Taken together, the study demonstrates that supervised GenAI augmentation improves the efficiency and structural consistency of stewardship work, but operates entirely at the correction layer. Addressing recurrence requires upstream governance intervention that the workflow alone cannot provide. The study extends TTF to a performance-diagnostic application in governance-constrained enterprise stewardship and offers transferable insights for FMCG organizations operating in comparable multi-system environments.
Keywords: master data management; data stewardship; generative AI; Task-Technology Fit; human-in-the-loop; data quality; prompt engineering; FMCG; information quality; data governance