本研究聚焦於建築工程工地品質管理情境，探討人工智慧（AI）導入後，對品質缺失預防、查驗效率提升、改善追蹤落實與制度化管理能力形成之影響。傳統工地品質管理普遍面臨資料分散、查驗標準不一致、缺失改善追蹤中斷及跨部門責任界面模糊等問題，致使品質管理成效常受限於個人經驗與人工判斷，難以形成可持續、可複製之管理模式。
基於此，本研究採質性研究取向，明確以某甲級營造公司為主要個案，並結合半結構式深度訪談與個案企業資料分析方式。研究資料包含工地主任、品管工程師、監造代表、專案經理與資訊導入主管等受訪者觀點，並輔以缺失回報次數、返工率、缺失改善週期、查驗工時與系統使用率等導入前後數據。若部分數據涉及多個專案或協力廠商，均以相同指標口徑彙整後進行比較，並於研究設計中說明其匿名處理與資料來源。
研究結果顯示，工地品質管理之核心痛點較可能源於資料斷裂、流程不一致與責任界面模糊；在AI導入與相關管理配套同步推動後，個案公司之品質管理指標呈現改善趨勢。AI較可能藉由影像辨識、自動比對、缺失分類、風險預警與追蹤提醒等機制，促進資訊透明化、查驗一致化與回應速度提升。然而，本研究亦納入AI導入失敗或成效不彰之反面案例，指出當資料品質不足、現場填報不確實、主管未以系統資料作為管理依據，或制度配套未同步建立時，AI可能僅形成額外行政負擔，而未能轉化為穩定的品質管理績效。
本研究據此提出五項較具條件性與可檢證性的分析命題，並進一步彙整為建築工程導入AI於工地品質管理之研究架構與實務推動建議。研究結果可供營造公司、建築開發商、監造單位及工程管理資訊系統開發者作為品質管理數位轉型之參考，亦可補充既有文獻對建築工程品質管理、AI導入限制與制度化能力形成之理解。

This study focuses on construction-site quality management and examines how the adoption of artificial intelligence (AI) affects defect prevention, inspection efficiency, corrective-action tracking, and the formation of institutionalized management capability.
Using a qualitative approach, the study combines semi-structured interviews with case-company data analysis. Interviewees include site managers, quality engineers, supervisors, project managers, and digital-implementation managers. These qualitative findings are triangulated with indicators before and after AI adoption, including numbers of defect reports, rework rate, corrective-action cycle time, inspection labor hours, and system usage rate.
The findings show that the core pain points in site quality management stem from data discontinuity, inconsistent processes, and unclear responsibility interfaces. In the case company, quality-management indicators showed improvement after AI adoption was implemented together with managerial arrangements, process standardization, and user-behavior requirements. AI appears to influence performance mainly through mechanisms such as information transparency, inspection consistency, and faster response, rather than as an isolated technological cause. Its effectiveness depends not only on technology, but also on data quality, system use, managerial support, and institutionalized governance.

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