Agentic AI for Sustainability and Compliance in Procurement

Bernardo Nicoletti
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Bernardo Nicoletti: Temple University, Fox School of Business

Chapter Chapter 8 in Agentic AI for Procurement, 2026, pp 139-160 from Springer

Abstract: Abstract Traditional auditing techniques are no longer adequate as organizations become more conscious of their environmental, social, and governance (ESG) obligations within intricate global supply networks. A significant shift is represented by agentic artificial intelligence (AAI), which goes beyond basic compliance checks to offer advanced, real-time sustainability monitoring and proactive management. To enforce policies in line with ESG objectives, foresee risks, and evaluate impact across the procurement cycle, artificial intelligence (AI) solutions combine data, analytics, and legal interpretations. The foundation of this intelligence is sustainability ontologies and enhanced life cycle assessment (LCA) concepts, which allow AAI to analyze intricate connections between procurement choices and their effects on society and the environment. AAIs analyze dynamic legislation across various countries using predictive analytics, machine learning (ML), and natural language processing (NLP) to ensure regulatory compliance. This change in approach moves management from reactive violation identification to proactive prevention. Regarding functionality, AAIs can track intricate carbon footprints, assess resource and water usage, include the circular economy concepts, and monitor labor laws and human rights. They carry out ongoing, risk-based audits to effectively evaluate evidence and employ technologies like blockchain and Internet of Things (IoT) sensors for end-to-end traceability. By facilitating independent policy enforcement, AAI helps green procurement initiatives succeed and propels notable, quantifiable advancements in corporate sustainability goals.

Keywords: Procurement; Sustainability; Compliance; Agentic AI; Circular economy; ESG; Life cycle assessment; Internet of things (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-23024-9_8

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DOI: 10.1007/978-3-032-23024-9_8

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