Although the construction industry is rapidly embracing digital technologies, small and medium-sized enterprises (SMEs) continue to encounter substantial barriers when integrating sophisticated tools into their cost management practices. While larger companies benefit significantly from Business Intelligence (BI) systems, SMEs frequently lack sufficient financial and technical capabilities to successfully implement these solutions. To bridge this gap, the current research introduces a standardized BI data model specifically designed for the operational and financial realities characteristic of SMEs in construction. This model incorporates real-time data tracking, predictive analytics, and detailed cost assessments within a scalable, user-friendly BI architecture. Through enhanced visibility of project costs and informed decision-making capabilities, SMEs can mitigate inefficiencies inherent in traditional manual systems. The study adds to existing scholarship by providing a structured BI approach explicitly suited to the construction SMEs, an area largely overlooked in prior research. Additionally, the practical value of this study lies in presenting an adaptable framework that facilitates SMEs’ transition toward transparent, automated, and data-centric financial operations. Results from a pilot study conducted in Bulgaria confirm notable improvements, including better adherence to budgets, a significant decrease in manual inaccuracies, and heightened financial control. By effectively bridging advanced BI technology and SMEs’ limited digital expertise, this research demonstrates the transformative impact of BI tools on cost efficiency and competitiveness within construction SMEs. The study concludes by suggesting avenues for future investigations, particularly emphasizing the integration of Artificial Intelligence (AI) and Internet of Things (IoT) to further enhance BI-driven financial strategies.