Smart buildings have emerged as crucial in decreasing energy consumption and minimizingenvironmental effects as the community looks for sustainable solutions for the built environment and enhanceindoor Environment Quality (IEQ). Optimizing IEQ is essential for improving occupants' lives and reducing theenvironmental footprint of buildings. Energy efficiency reduces costs, environmental impact mitigation, andsustainability. This study explores the capacity of Digital twin (DT) technology to enhance the indoor environmentquality and energy efficiency of smart buildings and set the Asset information requirements (AIR) needed for moreaccurate data to make the right decisions. This study also evaluates the potential integration of DT and artificialintelligence (AI) to improve fault detection accuracy, predictive maintenance, and overall energy efficiency inbuilding operations. A scoping review was used to search and identify the literature about DT, AI, and smartbuilding systems, interviews were conducted with specialists in the industry to obtain qualitative data, andquestionnaires were sent out to collect quantitative insights. The findings indicate that the majority of experts thinkthat the adoption of digital twins can improve energy efficiency and contribute to creating a more favorableenvironment. Moreover, they are convinced that AI can play a role in decision-making related to building assetmanagement by enabling the integration and analysis of raw data. However, several challenges exist, rangingfrom a lack of awareness about the significance of digital twins, insufficient data availability, and the need forenhancement of existing data quality to a limited familiarity with AI. Although these challenges are not overlyintricate, they require a certain amount of time to be effectively addressed and resolved.