Agentic AI Supporting the Source-to-Contract Phase in Procurement

Agentic artificial intelligence (AAI) changes how contracts are made and completed (Guida, C., De Santis, A., & Penco, L., Supply Chain Management: An International Journal 28, 621–638, (2023)). This activity is having a significant effect on how procurement is done. This chapter explores the automation of contract lifecycle management in strategic negotiations through AAI systems, focusing on risk assessment, compliance verification, and intelligent clause selection and modification (Cui, M., Zhang, H., & Chen, L., Artificial Intelligence and Law 28, 321–345, (2020)). Researchers and practitioners have started to use negotiation robots, smart contracts, and multi-agent systems that yield optimal outcomes while conforming to regulations (Holland, J. H., & Nof, S. Y., Computational Economics 54, 115–135, (2019)). Intelligent negotiation assistants powered by AI are a new way to speed up the process and cut costs. They use machine learning (ML), natural language processing (NLP), and market knowledge to negotiate contracts in real time (Allal-Chérif, M., Aouine, M., & El Fazziki, A., Expert Systems with Applications 185, 115598, (2021)). The theoretical framework encompasses computational contract theory, game-theoretical negotiation models, and implementation challenges, including ethical and governance considerations. Case studies of Fortune 500 organizations, including Walmart (Musani, M., Supply Chain Quarterly 17, 34–40, (2023)) and Maersk (Arunmozhi, T., Kumar, A., & Gupta, S., IEEE Transactions on Engineering Management 69, 295–309 (2022)), illustrate practical applications. Organizations that need speed, efficiency, and customer attention are finding that artificial intelligence (AI), especially generative AI (GenAI), is becoming more and more important (Kanbach, K., Kahlert, D., Schimanski, F., & Froschauer, A., MIT Sloan Management Review 65, 56–62, (2024)). This chapter looks at how AI can help make the future brighter. It emphasizes its essential attributes, such as learning, autonomy, responsiveness, and proactivity, along with its potential to enhance corporate performance (Acharya, S., Sinha, P., Kumar, R., & Vohra, A., Journal of Business Strategy 46, 12–25, (2025)). It emphasizes the scarcity of research concerning integrating diverse AAI features, such as multimodal processing, hierarchical architectures, and outsourced ML, while presenting potential applications (Mikalef, P., & Gupta, A., Information & Management 58, 103444, (2021)). This chapter examines how AAI enhances productivity and grants operators increased autonomy through task automation (Russell, S., & Norvig, P., Artificial intelligence: A modern approach. Pearson Education, (2020)). The importance of hierarchical agent architectures for system coordination is emphasized, along with the transition from assisted (“copilot”) to autonomous (“autopilot”) models (Wooldridge, M., An introduction to multiagent systems. John Wiley & Sons, (2009)). One of the most important parts is a framework for creating strategies considering the organization’s goals, the available technology, the operators’ training, and risk management (Davenport, T. H., & Ronanki, R., Harvard Business Review 96, 108–116, (2018)). The findings show that AAI greatly increases productivity, lowers costs, and encourages creativity, despite needing to remediate data reliability, privacy, security, and ethics (Floridi, L., Cowls, J., Beltrametti, C., & Pastore, R., AI & Society 33, 1145–1153, (2018)). Research focuses on case implementations specific to individual industries to enhance benefits. It is imperative to meticulously evaluate the ethical and societal implications (encompassing data privacy, data security, and labor market effects) alongside integrating AAI with emerging technologies (Dwivedi, Y. K., Hughes, L., Abbasi, I., & Aoun, F., Journal of Business Research 128, 46–56, (2021)).