Augmenting Retail Intelligence: A Human–AI Framework for Operational Decision-Making and Digital Governance
Keywords:
Human-in-the-Loop, Large Language Models, Retail Operations, Explainable AI, Decision AugmentationAbstract
Structural shifts in retail have brought artificial intelligence from peripheral experimentation into the operational core of commercial enterprises worldwide. Yet accumulated deployment experience has surfaced a consistent finding: systems configured for maximal automation frequently underdeliver relative to those that distribute decision authority deliberately between algorithmic processes and human practitioners. The gap between technical capability and organizational utility, it turns out, is bridged less by model sophistication than by interaction architecture — the degree to which AI-generated outputs are made accessible, interpretable, and genuinely actionable for the people who must apply them. This article examines that gap directly, constructing a structured three-layer framework for Human-in-the-Loop retail intelligence and tracing its application across merchandising, demand planning, pricing strategy, inventory coordination, and e-commerce infrastructure operations. Large language models receive focused attention as the interface mechanism through which operational practitioners engage with machine-generated insight without requiring analytical or technical specialization. Governance structures, explainability requirements, and ethical conditions are treated as integral components of the collaboration model rather than supplementary considerations. The article concludes that augmented intelligence — specifically, the deliberate structuring of complementary human and machine contributions — constitutes the configuration at which retail AI systems generate their most durable and organizationally meaningful value.
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