企業如何成功導入M365 Copilot：變革管理與使用者採納策略

Abstract

隨著生成式人工智慧（Generative AI）技術快速發展，組織正面臨從技術試點邁向規模化價值的關鍵轉型期。本研究旨在探討企業導入「通用型生成式AI」之成功關鍵因素與策略，並以 Microsoft 365 Copilot 為核心案例進行剖析。研究背景源於當前企業在 AI AI轉型中，普遍遭遇技術限制、使用者採納落差及組織變革僵化等議題，導致計畫常面臨「試點癱瘓」困境。研究首先彙整生成式AI特性、關鍵成功因素，以及變革管理理論（Kotter 八步驟）與使用者採納理論（TAM、UTAUT），作為分析架構之基礎。 本研究採質性研究方法，選取早期導入M365 Copilot之代表性企業（如Dow公司）進行案例分析。研究結果顯示，企業主要面臨使用者採納落差、變革管理不足及資料治理疑慮等挑戰。分析指出，導入成效取決於組織如何針對「幻覺風險」、成本門檻與資料權限等特性，建立領導支持與願景對齊機制。員工對AI的知覺有用性與易用性顯著影響採納意願，需透過系統化變革管理、實作引導與信任建立，方能有效提升使用率。 基於個案分析與理論驗證，本研究提出3D-SAF整合框架，並歸納四階段導入旅程：(1) 準備階段：聚焦數據品質與權限治理，建構技術信任底座；(2) 上線與參與階段：透過推廣大使產生社會影響，降低員工努力期望；(3) 影響力擴展階段：將應用深化至業務流程，驗證損益貢獻與短期勝利；(4) 擴展與最佳化階段：建立人機協作持續回饋機制，並評估自主型 Agent 網絡之演進。本研究所提策略框架旨在為企業推動通用型生成式AI時，提供系統化之實務參考。

With the rapid advancement of Generative Artificial Intelligence (Generative AI), organizations are currently in a critical transition phase from technical piloting to achieving scalable value. This research aims to explore the key success factors and implementation strategies for enterprises adopting "Generalized Generative AI," using Microsoft 365 Copilot as the core case study. The study is motivated by the common challenges faced by enterprises during AI AI transformation, such as technical constraints, user adoption gaps, and organizational inertia, which often lead to a state of "Pilot Paralysis." This research first synthesizes the characteristics of Generative AI, key success factors, and theoretical foundations—including Kotter’s Eight-Step Process for Leading Change and user adoption models (TAM and UTAUT)—to establish the analytical framework. Adopting a qualitative research method, this study conducts case analyses of pioneer enterprises that implemented M365 Copilot, such as Dow Inc. The findings indicate that enterprises primarily face challenges related to user adoption gaps, insufficient change management, and concerns over data governance. The analysis reveals that implementation effectiveness depends on how an organization establishes leadership support and vision alignment mechanisms to address tool characteristics such as "hallucination risks," cost barriers, and data permission complexities. Since users' perceived usefulness and ease of use significantly influence their adoption intentions, systematic change management, hands-on guidance, and the establishment of trust are essential to effectively increasing usage rates. Based on case analysis and theoretical validation, this study proposes the 3D-SAF Integration Framework and identifies a four-stage implementation journey: (1) Prepare Stage: focusing on data quality and permission governance to build a technology trust foundation; (2) Launch & Engage Stage: leveraging champions to create social influence and reduce users' effort expectancy; (3) Drive Impact Stage: deepening applications within business processes to validate profit-and-loss contributions and secure short-term wins; and (4) Scale & Optimize Stage: establishing continuous feedback mechanisms for human-AI collaboration and evaluating the evolution of autonomous agent networks. The strategic framework proposed in this study aims to provide a systematic practical reference for enterprises promoting Generalized Generative AI.