This research paper identifies the disciplinary differences of stakeholders and its effects on working cross-functional in the context of machine learning. This study specifically focused on 1) how stakeholders with disciplinary differences interpret a search system, and 2) how the multi-disciplines should be managed in an organization. This was studied through 12 interviews with stakeholders from design disciplines, product management, data science and machine learning engineering, followed by a focus group with a participant from each of the different disciplines. The findings were analyzed through a thematic analysis and institutional logics and concluded that the different logics had a high impact on the stakeholders’ understanding of the search system. The research also concluded that bridging the gap between the multi-disciplinary stakeholders are of high importance in context of machine learning.