Next-Gen Data Analytics and Intelligent Automation
Cognitive Procurement in SAP Ariba: Leveraging Large Language Models for Intelligent Sourcing and Cyber Risk Alerts in the Life Sciences Industry
Authors: Manoj Somisetty
Publishing Date: 24-12-2025
ISBN: 978-81-975670-6-3
Abstract
This document outlines a cognitive procurement framework designed specifically for the life sciences sector by incorporating Large Language Models (LLMs) BERT and GPT-4 into SAP Ariba. It fully automates intelligent sourcing, contract clause review, and cybersecurity risk assessment. The framework’s architecture contains three critical elements: (1) an LLM-enabled classification engine for clause extraction and compliance tagging for contract clause excerpting, (2) a Cyber Alert Engine for passive anomaly-based supplier communication and metadata anomaly detection, and (3) an SAP Ariba integration layer for embedding LLM workflows where relevant, providing AI-based context-aware commentary on event, risk, and contract workflows. In a GxP environment, a controlled pilot investigation was performed using synthetic procurement datasets spanning 120 contracts and 200 RFPs. The system also performed RFPs and Redlining. Significant RFP analysis time reduction (75%) and major compliance error reduction (87.5%) alongside reduction of both workload and time on manual redlining (72%) were observed. The results showcase the framework’s ability to optimize procurement processes, compliance, and risk management in regulated industries. Multilingual document processing and audit trail design using blockchain forensics will be investigated in the following development phase.
Keywords
SAP Ariba • Cognitive Procurement • Large Language Models • Life Sciences • Cyber Risk Alerts • Intelligent Sourcing • Generative AI • GxP Compliance
Cite as
Manoj Somisetty, "Cognitive Procurement in SAP Ariba: Leveraging Large Language Models for Intelligent Sourcing and Cyber Risk Alerts in the Life Sciences Industry", In: Kusum Kumari Bharti and Noor Firdoos Jahan (eds), Next-Gen Data Analytics and Intelligent Automation, SCRS, India, 2025, pp. 38-47. https://doi.org/10.56155/978-81-975670-6-3-4