In the face of increasing data complexity and the demand for agile, data-driven decision-making, Cloud Business Intelligence (CBI) systems have emerged as crucial enablers of digital transformation in enterprises. However, the decision to implement CBI involves numerous uncertainties related to organizational, technological, and strategic factors. This article explores the applicability of Rough Set Theory (RST) as a tool for supporting decision-making under such uncertain conditions. RST’s capacity to process incomplete and imprecise data makes it particularly suitable for identifying critical attributes that influence the adoption of CBI. The study formulates decision rules derived from real-world data, offering insights into key implementation factors such as peer recommendations, prior project analysis, vendor offerings, the impact of the COVID-19 pandemic, public cloud usage, and existing limitations of CBI. The findings highlight the value of RST in guiding IT implementation strategies and adapting recommendation systems in response to evolving business conditions. Future research will focus on expanding the variable set and developing automated learning mechanisms for ongoing refinement of CBI-related decisions.