Document Type
Article
Publication Date
5-9-2026
Abstract
Artificial intelligence (AI) has significantly enhanced enterprise-scale customer relationship management (CRM) systems; however, small-scale retail businesses remain structurally excluded from these advancements due to configuration complexity, technical overhead, and limited digital capabilities. This paper introduces a Self-Configuring Agentic CRM (SC-ACRM) architecture designed to eliminate configuration barriers in micro-retail contexts. The framework operationalizes Intent-to-Schema automation, translating natural-language business intent into structured operational models and reducing configuration debt embedded in traditional metadatadriven systems. The architecture further incorporates adaptive agentic orchestration and Cognitive Infrastructure Elasticity, enabling dynamic policy adjustment under demand volatility while preserving human-supervisory governance. Using an agent-based simulation of a multi-SKU convenience store environment, the study evaluates deployment efficiency, inventory responsiveness, and managerial cognitive reallocation. The research contributes a novel sociotechnical architecture class that integrates intent interpretation, schema formalization, and supervised agentic decision support, offering a scalable pathway for inclusive AI-driven enterprise transformation.