## 6 Conclusions  
The advancement of digital twin technology has gained significant momentum in recent years, driven by a growing body of academic research and strategic investments from industry leaders aimed at its development and widespread adoption. In this context, this study aimed to explore the benefits and challenges associated with implementing AI-driven digital twins within the Saudi construction industry. This was achieved through a survey study, with the data collected from 106 industry professionals. The findings underscore the transformative potential of digital twins and AI-driven DTs within Saudi Arabia's construction sector, aligning with global research and practice. The study identified several key benefits of adopting digital twins in the construction industry, including improved documentation and communication, enhanced facilities management and operations, and the real-time monitoring and control of construction processes. Furthermore, the study revealed that integrating AI-driven digital twins provides significant benefits such as enhanced analytical capabilities, supports the modeling of complex real-world systems, and facilitates strategic decision making. Despite these advantages, several significant challenges persist. These include the absence of standardized frameworks for integrating AI-driven DTs in construction projects, limited awareness of AI capabilities, logistical constraints, inadequate IT infrastructure, and the complexity of AI algorithms. Furthermore, the correspondence analysis (CA) demonstrated that the research quantifies the prominence of socio-cultural and technical barriers, such as resistance rooted in institutional inertia and skills gaps that hinder adoption despite practitioners recog  
Figure 8: Future research directions. nizing the benefits of DTs. Notably, the research highlights the primary advantages and obstacles associated with DT adoption in Saudi Arabia's construction projects. Based on these insights, integrating AI-based DTs presents substantial additional benefits for Saudi projects. The study's conclusions align with the existing literature, demonstrating that AI integration enables intelligent, real-time functionalities, including decision-support mechanisms, \"what-if\" scenario simulations, and predictive maintenance. Consequently, such capabilities significantly improve cost efficiency and project timelines, ultimately leading to more favorable project outcomes. Simultaneously, the study identifies critical barriers to DT implementation, such as the shortage of skilled professionals, the absence of national guidelines and standardized codes of practice, resistance to transitioning towards new systems, and the high costs associated with implementation. Beyond identifying these challenges, the study extends its scope to explore potential strategies for optimizing the implementation of AI-based DTs. The findings suggest that, while AI applications exhibit immense potential to enhance process efficiency and intelligence, their successful adoption is fundamentally contingent upon three interdependent factors: human, environmental, and technological characteristics. This underscores the necessity of a comprehensive approach that holistically addresses these dimensions to fully unlock AI-based DTs' transformative benefits. To accelerate the integration of AI-based DTs in Saudi Arabia's construction sector, stakeholders must prioritize institutional capacity building through targeted training programs, policy standardization to ensure data interoperability and cybersecurity, and the establishment of public-private partnerships. Additionally, cultural change management is essential, necessitating awareness campaigns and leadership advocacy to mitigate resistance and foster acceptance of these emerging technologies. By implementing these strategies, the construction sector can effectively align with Saudi Arabia's Vision 2030, leveraging AI-based DTs to drive digital transformation.