The rapidly evolving domain of artificial intelligence has given rise to generative AI technology, which, unlike traditional machine learning, is capable of learning patterns from data and generating new, meaningful outputs. These models have applications in various domains, including customer service, content creation, and personalized recommendations. Understanding the implementation of generative AI is essential for business leaders to harness its potential and drive innovation. This thesis focuses on the application of generative AI for guided buying within the context of Company X, aiming to address the challenges and potential solutions in streamlining the purchase of goods and services. The research methodology involves using elements from the grounded theory approach, utilizing a focus group discourse approach for empirical analysis. By exploring the impact of generative AI on procurement processes and an organization's orientation to guided buying, the study contributes to enhancing strategic capabilities of the organization within the competitive industrial landscape. The results indicate that there three dimensions 1) Operational Stakeholders 2) Generative AI Robustness and 3) Information Management for effective introduction of generative AI into procurement practices. The overall contribution was made to the general academic attempt to understand how to intergrate generative AI technologies into various enterprise functions, specifically within Supply Chain and Procurement.