Navigating the Divide: Balancing AI’s Optimal Solution with the Appropriate Solution in Chinese Construction Management

Qi Wu; Menghao Xue; Bowen Lu

doi:10.71113/JCSIS.v2i5.263

Authors

Qi Wu Department of Building Surveying, Faculty of Built Environment, University of Malaya, Kuala Lumpur, Malaysia
Menghao Xue Department of Building Surveying, Faculty of Built Environment, University of Malaya, Kuala Lumpur, Malaysia
Bowen Lu Runjia Property Services (Shanghai) Co., Ltd. Ningbo Branch, Ningbo, Zhejiang, China

DOI:

https://doi.org/10.71113/JCSIS.v2i5.263

Keywords:

Artificial Intelligence (AI), Construction Management, China, Human-AI Interaction, Adaptive Integration

Abstract

While Artificial Intelligence (AI) offers powerful tools for optimizing construction processes, its effective integration hinges on navigating the gap between algorithmic “optimality” and practical “appropriateness”. Focusing on China’s construction industry, a critical context for AI deployment, this research explores the managerial decision-making processes involved in balancing these two facets. Through a qualitative methodology involving 15 expert interviews across diverse organizational types (SOEs, private, consultancy) and thematic analysis incorporating constant comparison, this study elucidates the complex interplay between AI recommendations and human judgment. Key findings identify five interconnected themes influencing this balance: Decision Balance & Human Adjustment, Data & Technology Challenges, Human-AI Collaboration and Trust, External Constraints & Contextual Factors, and Sector-Specific Dynamics. The research highlights the proactive role of managers as “adaptive integrators” rather than passive users. Extending existing literature, this study contributes theoretically by challenging simplistic views of optimality, refining human-AI interaction concepts, and proposing an empirically grounded Adaptive Human-AI Interaction Framework that explicitly incorporates contextual modulators and managerial interpretation. The findings hold significant practical implications for developing more effective AI tools, targeted training programs, supportive organizational cultures, and nuanced policy guidelines to foster responsible and productive AI integration in construction and analogous operational fields.

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