本論文深入探討了人工智慧（AI）在營運管理中的戰略實施，並利用人工智慧成熟度曲線（AIMC）來評估 AI 需求和問題複雜度。研究採用麥肯錫 7S 框架，將組織成熟度劃分為 AI-新進 (AI-new)、AI-啟用 (AI-enabled)、AI-豐富運用經驗(AI-experienced)和AI-進階運用能力(AI-advanced)等不同階段。

通過詳細的案例研究和專家訪談，本研究提供了一個系統性 AI 採行路線圖，強調 AI 計劃必須與組織的策略、結構、系統、人員、技能、風格和共同價值觀相配合。研究結果顯示，擁有一套 AI 策略框架在提升組織韌性和運營效率方面發揮著關鍵作用，並結合現實生活中的應用案例，如機器人流程自動化（RPA）解決方案、SAP Ariba 的實施和區塊鏈技術等等，展示了 AI 與結構化方法相結合的轉型潛力。

本研究旨在為企業提供切實可行的指導，幫助其在充滿變化的市場環境中有效應對 AI 整合挑戰，促進可持續發展並保持競爭優勢。AIMC 模型作為一個策略工具，為組織提供了通過不同 AI 採用階段的指引，從而提升決策能力、精簡流程並優化營運管理，此即為了強調，通過結構化的能力驅動方法，AI 能夠在企業的運營和策略層面帶來顯著的變革效益。

This thesis examines the strategic implementation of Artificial Intelligence (AI) in operational management, using the AI Maturity Curve (AIMC) to assess AI demand and problem complexity. Leveraging the McKinsey 7S framework, the research categorizes organizational maturity into stages: AI-new, AI-enabled, AI-experienced, and AI-advanced.

Through case studies and expert interviews, the study provides a detailed roadmap for AI adoption, emphasizing alignment with organizational capabilities. Findings highlight the importance of tailored AI strategies in enhancing organizational resilience and efficiency. Real-world applications, such as RPA solutions, SAP Ariba implementation, and blockchain technology, demonstrate AI's transformative potential when integrated with a structured approach.

This research offers practical guidance for enterprises to navigate AI integration, fostering sustainable growth and competitive advantage. The AIMC model serves as a strategic tool to guide organizations through AI adoption phases, enhancing decision-making, streamlining processes, and optimizing operational management.

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