Exploring Artificial Intelligence use in Services Procurement A case study at Volvo Group

Abstract

Procurement has evolved into a strategic function and emerging technologies, such as artificial intelligence (AI) are digitizing supply chains, constituting a major digital shift in all industries. Despite its potential, artificial intelligence is still both underutilized and under researched within procurement. While existing research focuses on general applications of AI in procurement, this thesis addressed the area of services procurement. By identifying pain points across an established services procurement process and evaluating how AI can address these, it aimed to explore how AI could be leveraged to enhance purchasing efficiency. Additionally, it explored how change management and technology acceptance strategies can support organizations in AI adoption. The thesis has been conducted as a case study together with the Services Purchasing organization at the global transport- and infrastructure provider Volvo Group. Taking an explorative approach, unstructured interviews, primarily with line managers, and semi- structured interviews with buyers were conducted. Following a thematic analysis of the interviews, empirical findings were analyzed using theoretical frameworks developed through an extensive literature review. The theoretical frameworks covered AI. The empirical findings identified pain points across different stages of the services procurement process, revealing both operational, organizational, and strategic challenges affecting the efficiency of the procurement organization. Applying the theoretical frameworks, the analysis showed how AI can support buyers and address several of the key pain points in the services procurement process. Leveraging AIs main capabilities of automation and smartness, the technology shows strong potential in e.g., automating request-for-quotation creation and contract management, enhancing decision making, and providing support in negotiations. Finally, trustworthiness, quality of output, job relevance, ethics, and confidentiality were identified as requirements for the further adoption of AI tools. Recommended change management strategies for successful AI adoption included among others having local AI champions, sharing success stories, and developing a clear vision and strategy.