Agentic AI-Driven Transformation: From Traditional Practices to Intelligent Automation in Procurement

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

Chapter Chapter 2 in Agentic AI for Procurement, 2026, pp 37-54 from Springer

Abstract: Abstract Procurement has changed due to artificial intelligence (AI) and machine learning (ML), which have altered how businesses handle their resources and supplier networks. This thorough investigation, which looks at the transition from conventional procurement methods to systems driven by Agentic AI (AAI), demonstrates a notable improvement in cost reduction, operational effectiveness, and strategic decision-making ability. AAI-driven procurement systems have produced impressive outcomes for organizations, such as an average 37% decrease in processing time, a 42% improvement in partner selection, and a 15–20% reduction in overall procurement expenses. Some polls claim that AI applications in procurement include partner identification and evaluation, demand forecasting, contract management, risk mitigation, and autonomous operations (Bruno, Z., Global Journal of Management and Business Research 24, 1, (2024)). Real-world AAI applications in the manufacturing, retail, and IT industries show their revolutionary potential. Businesses cite increased productivity, cost savings, and operational efficiency. Combining artificial intelligence (AI) with cutting-edge technologies like blockchain and smart contracts promises increased transparency, security, and automated execution capabilities, despite implementation obstacles relating to data protection (Tikkinen-Piri, C., Rohunen, A., & Markkula, J., Computer Law & Security Review 34, 134–153, (2018)), legacy system integration, and inexperience. A well-rounded strategy considering commercial objectives and social and environmental responsibilities requires combining AAI skills with ethical and ecological standards.

Keywords: intelligent automation; strategy; procurement; artificial intelligence (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_2

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DOI: 10.1007/978-3-032-23024-9_2

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