The increasing demand for sustainable infrastructure, energy efficiency, and intelligent resource management has accelerated the transformation of conventional buildings into smart, adaptive, and digitally integrated environments. Smart buildings equipped with artificial intelligence (AI), advanced monitoring systems, renewable energy integration, and automated control mechanisms represent a significant opportunity for improving energy performance throughout the building lifecycle. However, the successful implementation of AI-driven energy optimization requires not only technological advancement but also effective construction management strategies that integrate planning, design coordination, implementation control, and operational decision-making.

This research examines AI-driven energy performance enhancement in smart buildings from a construction management perspective. The study adopts a conceptual research methodology based on a systematic synthesis of existing literature related to knowledge management, artificial intelligence applications, energy optimization, renewable energy systems, energy storage, building materials, and sustainable transformation strategies. The research develops an integrated framework connecting AI-based energy management technologies with construction project management principles to improve building performance, reduce energy consumption, and support long-term sustainability objectives.

The findings indicate that AI technologies can significantly enhance building energy performance by enabling predictive analytics, adaptive control, demand forecasting, fault detection, and optimized energy scheduling. However, technological capability alone does not ensure successful implementation. Construction management practices including stakeholder coordination, data management planning, lifecycle assessment, risk management, and technology integration strategies play a critical role in achieving effective outcomes. Knowledge management principles further strengthen decision-making by enabling systematic collection, interpretation, and application of operational information.

The study also identifies challenges associated with AI implementation, including high initial investment costs, data quality requirements, cybersecurity risks, interoperability limitations, and the need for skilled professionals capable of managing interdisciplinary systems. The integration of renewable energy, energy storage technologies, and advanced thermal management approaches provides additional opportunities for improving building resilience and reducing environmental impacts.

The research contributes an engineering and construction management-oriented perspective by demonstrating that AI-driven smart buildings should be treated as complex socio-technical systems requiring coordinated technological and managerial approaches. The proposed framework provides guidance for construction professionals, facility managers, and decision-makers seeking to implement intelligent energy solutions. Future smart building development will depend on effective integration between artificial intelligence, sustainable construction practices, and strategic management approaches.