Agentic AI in Procure-to-Pay: Opportunities, Challenges, and a Roadmap for Autonomous Procurement Systems

Abstract

The procure-to-pay cycle remains critical yet burdened by inefficiencies, manual interventions, and persistent exceptions that traditional automation struggles to address. Agentic AI, autonomous, goal-oriented systems capable of planning, reasoning, coordinating multiple agents, and adapting to changing conditions, represent a transformative evolution beyond rules-based automation and robotic process automation. This work presents a modular architecture for embedding AI agents within P2P workflows and identifies concrete applications, including intelligent invoice matching, anomaly detection, contract compliance monitoring, autonomous negotiation, payment optimization, and supplier risk assessment. Benefits span reduced cycle times, lower operational costs, enhanced controls, and strategic reallocation of procurement talent toward value-adding activities. However, deployment confronts significant challenges: data quality requirements, explainability and trust deficits, human-agent handoff complexity, governance frameworks, model drift, organizational resistance, and security vulnerabilities. A phased adoption roadmap progressing from pilot projects to full autonomy provides a practical implementation pathway. AI agents should be positioned not as replacements but as intelligent collaborators that augment human judgment, enabling procurement organizations to achieve operational excellence while maintaining strategic oversight and accountability.