## 6 Conclusions
In terms of the theoretical implications, this study makes a significant contribution to theoretical knowledge by providing empirical evidence from 106 Saudi professionals on their awareness and perceptions of the implementation of AI-driven DTs. It identifies context-specific benefits unique to Saudi Arabia's digital transformation environment. These findings show the construction sector's readiness to adopt and leverage AI-driven DT technologies. Additionally, the research highlights key knowledge gaps and barriers that obstruct the complete optimization of AI-driven DTs, thereby enriching academic discourse. By highlighting areas that require further research investment, this study provides a foundation for future investigations to address these obstacles and unlock the full transformative potential of AI-based DTs. In terms of practical implications, the CA findings identified significant benefits and challenges in adopting such technologies, which led to the formulation of actionable insights and strategies for decision makers, policymakers, and industrial developers. These include:  
* Initiating targeted and structured training programs to enhance the technical and managerial competencies required for the implementation of AI-based DTs;
* Developing robust data standardization protocols to ensure consistency, interoperability, and reliability across digital systems;
* Establishing incentive and reward mechanisms to motivate organizational engagement and encourage the adoption of AI-DT technologies;
* Enhancing IT infrastructure to support the scalability and operational reliability of DT applications across diverse construction environments;
* Advancing policy development and standardization by introducing data interoperability frameworks, IoT integration policies, and comprehensive cybersecurity measures to enable the safe and scalable deployment of AI-DTs;Fostering public-private partnerships to serve as incubators for pilot projects that validate AI-DT solutions in real-world settings, thereby generating practical evidence of their impact and reducing resistance to technological change. Integrating these methods into existing construction codes, regulatory frameworks, and national digital transformation strategies will enhance clarity, increase stakeholder confidence, and help institutionalize AI-driven digital twins across project lifecycles. In conclusion, the study offers a dual contribution: it extends theoretical knowledge through context-specific empirical evidence and provides a strategic framework to transform these insights into operational practices and national standards. Doing so equips policymakers, developers, and industry leaders with practical tools to accelerate the integration of AI-based digital twins, thus aligning with the goals of Saudi Arabia's Vision 2030 and paving the way for a more intelligent, efficient, and sustainable construction industry. Contrary to expectations, the findings reveal that resistance to change remains a significant barrier for professionals adopting new systems. This resistance is often attributed to a lack of awareness regarding both the benefits and challenges essential for effectively adopting emerging technologies. Although participants demonstrated a reasonable level of understanding concerning AI-based technologies, they still perceive resistance to change as a critical obstacle. Beyond the scope of this study, several underlying factors may contribute to this resistance, including fear of failure, insufficient technical skills and knowledge, dependence on conventional practices, inadequate institutional support, cultural and behavioral influences, and the time and effort required for adaptation. Therefore, addressing these challenges is imperative to facilitating wider acceptance, enhancing implementation efforts, and ultimately unlocking the full transformative potential of AI-driven digital twins in the construction industry.