Agentic AI and the operating model shift : Redesigning enterprise procurement for the AI-native era

The swift emergence of agentic artificial intelligence (agentic AI) signifies not merely a new trend in automation but a fundamental transformation in the design, governance and execution of work. Unlike preceding waves of machine learning and generative AI (GenAI), which predominantly offered recommendations or generated content, agentic AI introduces software agents capable of perceiving context, making decisions, executing tasks across various systems and learning over time. Procurement serves as an exemplary domain for demonstrating this paradigm shift. It operates at the confluence of finance, supply chain, legal, risk, environmental, social and governance (ESG) and business stakeholders, managing complex, rules-intensive, high-value decisions. This paper posits that agentic AI will profoundly overhaul the procurement operating model, transitioning it from function-centric workflows to an AI-native hub that orchestrates autonomous agents, human experts and interconnected data. It outlines the practical aspects of agentic AI, explains why procurement is central to its adoption, and describes how capabilities such as autonomous sourcing, negotiation, onboarding and compliance monitoring reshape processes, roles and decision-making rights. Furthermore, it proposes an operating model blueprint for AI-native procurement, encompassing governance, risk management and policy systems. It examines the skills, behaviours and organisational structures necessary for effective human–AI collaboration. Ultimately, it offers a pragmatic roadmap for chief information officers (CIOs) and chief procurement officers (CPOs) and considers implications for other enterprise functions. This framework enables the design of procurement organisations that leverage the value of agentic AI while maintaining control, resilience and trust.