TITLE:
AI-Driven Smart Negotiation Assistant for Procurement—An Intelligent Chatbot for Contract Negotiation Based on Market Data and AI Algorithms
AUTHORS:
Prajkta Waditwar
KEYWORDS:
AI in Procurement, Automated Negotiation, Smart Procurement Systems, Supplier Negotiation Chatbots, Machine Learning in Procurement, NLP for Contract Negotiation, Procurement Automation, Data-Driven Negotiation, AI in Supply Chain, Procurement Chatbots, Smart Procurement, Strategic Sourcing, Strategic Negotiation
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.13 No.2,
May
8,
2025
ABSTRACT: The rise of artificial intelligence (AI) in procurement has transformed how organizations engage with suppliers, optimize spending, and drive contract negotiations. Traditional procurement negotiations rely on human intuition, historical knowledge, and manual research. However, with the advancement of AI-driven Smart Negotiation Assistants, procurement teams can leverage real-time market intelligence, price benchmarks, and predictive analytics to autonomously negotiate contracts. This paper introduces an AI-powered Procurement Chatbot, capable of conducting supplier negotiations with minimal human intervention. The system utilizes machine learning (ML), natural language processing (NLP), and historical transaction data to negotiate terms, secure cost savings, and ensure compliance with procurement policies. Real-world case studies, including automated software licensing negotiations and dynamic supplier pricing adjustments, demonstrate how AI-driven negotiations can save millions in procurement costs, reduce cycle times by up to 40%, and mitigate supplier risks [1]. The paper also explores technical architecture, algorithmic models, and deployment strategies for integrating AI negotiation assistants into enterprise procurement workflows. Furthermore, it highlights regulatory and ethical considerations in AI-driven procurement, emphasizing transparency and fairness. By leveraging AI-driven negotiation chatbots, businesses can achieve autonomous, efficient, and data-driven procurement processes, ensuring better supplier relationships and long-term cost savings.