企業流程優化的關鍵成功因素-以I公司為例

Abstract

本研究旨在探討企業於推動流程優化過程中，成功的關鍵因素為何，人工智慧（AI）能力是否能有效助力流程績效提升，以及不同流程角色對於成熟度與實際效益之影響。研究架構延伸自Hammer（2007）所提出之PEMM模型，並建構AI-PEMM架構，結合問卷資料與訪談質性分析，針對I公司六大流程進行評估。 本研究嘗試以五個流程資料進行初步分析，建構出『效益 = 50.11 × 成熟度 − 177.51』模型，結果顯示成熟度與流程效益之間呈現正向關係，但因樣本數有限，尚待更多資料驗證其穩健性。質性探討歸納出企業流程優化成功的「3+1」關鍵要素，分別為AI治理、流程負責人、流程標準化，以及需審慎關注的AI接受度；其中「AI整合、AI治理與AI轉型領導力」三者構成驅動高效績效之「黃金三角」。 角色觀點分析顯示，流程設計者在流程優化中扮演關鍵推進者，其價值不僅在於熟稔細節、具備跨部門協作歷練，更關鍵在於能夠橋接部門間認知落差、主動發掘改善機會，並具備推動數位與AI專案之能力。唯有設計者能跳脫既有流程框架，從技術整合與資訊流重塑的角度切入，方能促成流程實質變革。另一方面，流程負責人需具備整合視野與決策引導能力，其top-down行動不僅能破除組織穀倉，更是促進團隊共識與資源集中之驅動點。訪談詞頻分析亦指出，流程負責人對負向詞彙出現頻率高於正向，反映其對基礎治理與橫向協作之敏感度與高標準。 本研究提出AI-PEMM模型，實務上則歸納「強化溝通、治理優先、角色激勵、效益追蹤」之AI導入流程優化四步驟，並強調資料治理與欄位標準化為推動AI應用落地的基礎工程。綜合量化與質性觀察，提供企業導入AI與推進流程優化之策略方針。

This research investigates the key success factors influencing enterprise process optimization, the extent to which artificial intelligence (AI) capabilities contribute to performance enhancement, and the varying impacts of different process roles on maturity and actual outcomes. Building upon Hammer’s (2007) Process and Enterprise Maturity Model (PEMM), this study proposes an extended AI-PEMM framework, integrating structured survey data with in-depth qualitative interviews to assess six core processes within a case organization (Company I). A preliminary empirical model—Benefit = 50.11 × Maturity − 177.51—suggests a positive correlation between process maturity and optimization benefits. However, given the limited sample size, this result remains indicative and requires further validation. On the qualitative side, the analysis identifies a “3+1” critical success structure comprising AI governance, process ownership, process standardization, and the closely linked but potentially constraining factor of AI acceptance. Among them, AI integration, AI governance, and AI transformation leadership are conceptualized as the “Golden Triangle” driving high-impact performance outcomes. Role-based analysis reveals that process designers play a pivotal role as transformation agents. Their impact is not limited to operational familiarity or cross-functional experience, but more critically, in their ability to reconcile interdepartmental perspectives, proactively identify improvement opportunities, and lead digital and AI-enabled initiatives. The capability to transcend legacy constraints and reframe processes through system integration and information flow redesign is essential to realizing tangible transformation. In parallel, process owners must exhibit strategic vision and decisive top-down leadership to dismantle organizational silos, consolidate team consensus, and align internal resources. Interview-based text analysis further shows that process owners frequently express concerns using negative sentiment, reflecting a high sensitivity to governance, risk, and horizontal coordination challenges. Practically, this study presents the AI-PEMM framework and proposes a four-phase roadmap for AI-driven process optimization: (1) strengthening internal communication, (2) prioritizing governance, (3) activating and aligning roles, and (4) systematically tracking optimization benefits. Furthermore, it emphasizes that data governance and field-level standardization constitute the essential foundation for AI deployment. Synthesizing both quantitative indicators and qualitative insights, this research contributes actionable recommendations for enterprises aiming to scale AI adoption and enhance process performance in a structured and sustainable manner.