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

Sammanfattning

Procurement has evolved into a strategic function and emerging technologies, such asartificial intelligence (AI) are digitizing supply chains, constituting a major digital shift in allindustries. Despite its potential, artificial intelligence is still both underutilized and underresearched within procurement. While existing research focuses on general applications ofAI in procurement, this thesis addressed the area of services procurement. By identifyingpain points across an established services procurement process and evaluating how AI canaddress these, it aimed to explore how AI could be leveraged to enhance purchasingefficiency. Additionally, it explored how change management and technology acceptancestrategies can support organizations in AI adoption.The thesis has been conducted as a case study together with the Services Purchasingorganization at the global transport- and infrastructure provider Volvo Group. Taking anexplorative approach, unstructured interviews, primarily with line managers, and semi-structured interviews with buyers were conducted. Following a thematic analysis of theinterviews, empirical findings were analyzed using theoretical frameworks developedthrough an extensive literature review. The theoretical frameworks covered AI.The empirical findings identified pain points across different stages of the servicesprocurement process, revealing both operational, organizational, and strategic challengesaffecting the efficiency of the procurement organization. Applying the theoreticalframeworks, the analysis showed how AI can support buyers and address several of the keypain points in the services procurement process. Leveraging AIs main capabilities ofautomation and smartness, the technology shows strong potential in e.g., automatingrequest-for-quotation creation and contract management, enhancing decision making, andproviding support in negotiations. Finally, trustworthiness, quality of output, job relevance,ethics, and confidentiality were identified as requirements for the further adoption of AItools. Recommended change management strategies for successful AI adoption includedamong others having local AI champions, sharing success stories, and developing a clearvision and strategy.