How generative AI search tools are used in sourcing

Sammanfattning

This study explores how generative AI search tools are used in early-stage supplier identification and shortlisting within business-to-business sourcing. The purpose of the study was to understand how sourcing professionals use and experience generative AI tools during supplier search activities. It also examines how these tools influence supplier visibility, information evaluation, and early-stage sourcing decisions. To understand this, the study explores factors such as perceived usefulness, trustworthiness, supplier visibility, and digital information structures, in sourcing environments. The study was conducted through a qualitative research method, where eleven professionals from different industries were interviewed through semi-structured interviews. By operationalizing theoretical concepts, the study was able to explore how sourcing professionals use generative AI during supplier search and evaluation processes. Through an analysis of both theoretical and empirical material, the study identified both advantages and limitations of using generative AI in sourcing activities. The study then concludes that generative AI tools are mainly used as supportive research and information tools rather than as final decision-makers. The main advantages identified were improved efficiency, reduced research time, faster market overviews, and structured summaries of supplier information. Generative AI is described as particularly useful during the earliest stages of supplier search when identifying supplier categories, comparing alternatives, and gathering market research. At the same time, limitations were also identified, including hallucinations, outdated information, lack of transparency, and difficulties understanding how AI systems prioritize supplier information.