Voice-Enabled Agentic AI for Autonomous Supply Chains: SAP Execution with Generative Interfaces

Abstract

The increasing complexity of global supply chains necessitates solutions that extend beyond traditional enterprise systems to achieve autonomy, resilience, and efficiency. This study investigates the integration of voice-enabled agentic artificial intelligence (AI) with SAP execution and generative interfaces as a pathway toward autonomous supply chain management. A prototype system was developed, combining natural language–based voice interaction, autonomous decision-making agents, and direct SAP execution modules. Using case-based experiments across manufacturing, retail, and FMCG sectors, the system was evaluated on operational efficiency, decision quality, user-centric outcomes, and resilience under disruption scenarios. Results revealed significant performance improvements, with execution times reduced by 35–45%, accuracy gains of nearly 10%, and system latency lowered by more than half. Forecast accuracy, anomaly detection, and optimization scores improved markedly, while user surveys indicated higher accessibility, reduced cognitive workload, and increased adoption intention. Resilience metrics confirmed faster recovery times and stronger tolerance to demand, logistics, and supply disruptions. Collectively, these findings highlight that the fusion of agentic AI and generative voice interfaces within SAP environments not only enhances operational reliability but also empowers users and strengthens supply chain adaptability. The study provides both theoretical contributions to AI-driven autonomy and practical guidance for organizations seeking intelligent, human-centric supply chain transformation.