Rapid urbanization and surging vehicle ownership in Chinese cities have intensified pressure on conventional parking infrastructure, leading to chronic congestion, inefficiency, and environmental harm. While smart parking technologies, anchored in the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), offer compelling solutions, the organizational and operational conditions required for successful adoption remain poorly understood. This paper presents the diagnostic phase of an action research study conducted at Shenzhen Ai Ke Technology, a company actively transitioning from traditional parking facility installation to AI- and cloud-enabled smart parking services. Through semi-structured interviews with ten members of top management, supplemented by fishbone diagram analysis, root cause analysis, and a prioritization matrix, the study identifies the principal barriers impeding this transition. Findings reveal that outdated infrastructure and continued reliance on manual processes constitute the most critical root causes of operational inefficiency, ranked ahead of secondary challenges, including high initial investment costs, workforce skill gaps, regulatory complexity, and limited market acceptance. Grounded in the Resource-Based View (RBV) and Change Management theory, the study proposes a sequenced intervention strategy in which cloud computing integration precedes AI deployment, establishing a reliable, centralized data foundation before higher-order intelligence functions are layered on. This sequencing not only reflects sound technical logic but also constitutes a manageable organizational change pathway for small and medium enterprises (SMEs). The study contributes an evidence-based diagnostic framework and a theoretically grounded roadmap for firms navigating the organizational challenges of smart parking transformation within China's rapidly evolving smart city ecosystem.
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