Early-stage decision-making in building construction is often constrained by fragmented communication and the weak integration of design and cost management processes. These limitations reduce cost precision and compromise the reliability of life-cycle cost (LCC) assessments. This research aims to enhance cost predictability and decision quality during early design phases by combining Artificial Intelligence (AI), Building Information Modeling (BIM), and LCC analysis into a structured decision-support artifact. Grounded in value-based decision principles such as Choosing by Advantages (CBA) and Target Costing (TC), the study promotes a systematic evaluation of design alternatives according to their life-cycle and financial impacts. AI-assisted cost interpretation within BIM environments enables designers to assess construction and long-term costs dynamically during modeling. Following the Constructive Research Approach (CRA), the study evolves through literature synthesis, artifact development, and validation in real-world case studies across Brazil and Canada. The expected results include improved cost accuracy, enhanced collaboration, and more sustainable project outcomes—demonstrating the potential of AI- and BIM-driven decision-support systems to optimize early-stage design and lifecycle performance.