Authors :
Vijay Mittal
Volume/Issue :
Volume 11 - 2026, Issue 9 - September
Google Scholar :
https://tinyurl.com/3f44s66a
DOI :
https://doi.org/10.38124/ijisrt/26sep571
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Procurement is moving beyond transaction automation toward a new operating paradigm in which data,
enterprise applications, artificial intelligence, governance, human judgment, and measurable value must function as an
integrated system. Yet many organizations approach AI-enabled procurement through disconnected technology pilots,
isolated process automation, or platform-led implementation programs. These approaches can produce local efficiency while
leaving unresolved questions of enterprise readiness, capability maturity, architectural coherence, governance, and value
realization.
Keywords :
Intelligent Procurement; Artificial Intelligence; Procurement Transformation; Enterprise Architecture; AI Readiness; Maturity Model; SAP S/4HANA; SAP Ariba; SAP Business AI; Responsible AI; Digital Procurement; Agentic AI.
References :
- Bhattacharya, S., Govindan, K., Ghosh Dastidar, S., & Sharma, P. (2024). Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda. Transportation Research Part E: Logistics and Transportation Review, 184, 103455. https://doi.org/10.1016/j.tre.2024.103455
- International Organization for Standardization. (2023). ISO/IEC 42001:2023 Information technology — Artificial intelligence — Management system. ISO.
- Mittal, V. (2026). Architecting Intelligent Procurement: Designing AI-Driven Enterprise Procurement with SAP Business AI, SAP Ariba, SAP S/4HANA, SAP BTP, and Clean Core. Manuscript submitted for publication.
- NIST. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.600-1
- Shahzadi, G., John, A., Jia, F., & collaborators. (2024). AI adoption in supply chain management: A systematic literature review. Journal of Manufacturing Technology Management, 35(6), 1125–1150. https://doi.org/10.1108/JMTM-09-2023-0431
- Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
- Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. (2024). Computers in Industry, 162, 104132. https://doi.org/10.1016/j.compind.2024.104132
- Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice. (2024). Journal of Purchasing and Supply Management. https://doi.org/10.1016/j.pursup.2024.100896
- Assessing transformative procurement maturity: A general framework using a fuzzy model. (2025). Journal of Open Innovation: Technology, Market, and Complexity, 11(3), 100622. https://doi.org/10.1016/j.joitmc.2025.100622
Procurement is moving beyond transaction automation toward a new operating paradigm in which data,
enterprise applications, artificial intelligence, governance, human judgment, and measurable value must function as an
integrated system. Yet many organizations approach AI-enabled procurement through disconnected technology pilots,
isolated process automation, or platform-led implementation programs. These approaches can produce local efficiency while
leaving unresolved questions of enterprise readiness, capability maturity, architectural coherence, governance, and value
realization.
Keywords :
Intelligent Procurement; Artificial Intelligence; Procurement Transformation; Enterprise Architecture; AI Readiness; Maturity Model; SAP S/4HANA; SAP Ariba; SAP Business AI; Responsible AI; Digital Procurement; Agentic AI.