This study examines the influence of Artificial Intelligence (AI) in conjunction with BIM to improve the efficiency of project management in the field of construction projects. There are many issues within the construction industry globally, such as high cost overruns that amount to 80%, and time overruns averaging 20% for megaprojects over $500 million. Although BIM is a very strong tool for collaboration and data management, its capabilities for predicting problems are very limited. In contrast, AI technologies can provide pattern recognition, predictions, and automation, all of which are useful additions to the capabilities provided by BIM. A multiple case study method is applied: Crossrail/Elizabeth Line (UK) using predictive analytics for schedule risk; Google Bay View Campus (USA) using generative design for MEP optimization; and Melbourne Metro Tunnel (Australia) using computer vision for progress and safety monitoring. Findings show documented efficiency gains: 70% faster risk identification (Crossrail), 10-15% MEP cost reduction (Google), and 80-90% faster progress reporting (Melbourne Metro). Cross-case analysis reveals three critical success factors: BIM maturity as a prerequisite, clear problem definition before AI selection, and investment in data quality and change management. The paper concludes with a phased implementation framework for practitioners and recommendations for future research.