隨著數位化與人工智慧技術的快速發展，企業營運模式正經歷深度變革，其中業務部門作為企業面對市場與顧客的第一線，其智慧轉型已成為企業提升反應力與競爭優勢的關鍵課題。傳統以人力經驗與直覺判斷為主的業務作業模式，難以因應當前高變動、客製化的市場需求，需導入資料驅動、智慧決策與流程自動化等技術機制，建構更具彈性與效率的營運架構。
本研究聚焦於部門層級智慧轉型之方法論構建與應用實證，提出一套以 PDCA 循環為核心，整合流程建模（IDEF0）、問題診斷（KPI 成效分析、魚骨圖與 Pareto 分析）與智慧模組設計的系統化架構。研究以某科技製造業業務部門為案例，針對其現行流程活動所面臨之三大問題類型：流程遲滯、決策誤差與跨部門協作斷點，進行結構化診斷與智慧化設計。
於轉型設計層面，本研究提出涵蓋十三項核心業務活動的智慧流程設計，導入 NLP、RPA、資料預測、智慧客服與知識圖譜等技術，建構模組化、自動化與可擴展的作業支援架構；並進一步設計主從式智慧代理人系統，透過知識型、規則型與分析型子代理人模擬常見業務查詢任務，驗證智慧化系統在任務分派、回應生成與使用介面整合等層面之實作可行性與對應邏輯。導入實作亦獲得部門使用者正面回饋，顯示其於減少人力負擔、提升回應速度與決策輔助等層面具顯著效果。
本研究亦關注智慧轉型推動過程中的導入策略與組織回應，強調應以部門為單位，先行進行小規模試點應用，逐步驗證並調整設計架構，進而擴展至其他部門，形塑可持續演進的智慧作業藍圖。研究結果不僅提供一套具體可行的轉型方法論，亦可作為企業未來進行 AI 導入與流程重構的參考依據，特別適用於追求部門自主轉型與快速應變的情境。
本研究建議企業在進行智慧轉型時，應從小規模的試點項目開始，逐步積累經驗，再擴大至全公司的變革。且建立跨部門的協作機制以及長期追蹤進度機制，以確保轉型成效的長期可持續性。

This study explores a systematic methodology for the intelligent transformation of sales departments in the context of enterprise digitalization. Traditional sales workflows are often fragmented, reliant on manual operations, and lack real-time decision-making capabilities. To address these limitations, this research proposes a department-level transformation framework integrating IDEF0-based functional modeling, KPI-driven problem diagnosis, and AI-embedded system architecture.
The methodology emphasizes process automation, decision intelligence, and agent-based collaboration as three pillars of transformation. Anchored in the PDCA cycle, the framework provides a structured and iterative pathway for continuous improvement. It also incorporates tools such as fishbone diagrams and Pareto analysis to facilitate the diagnosis of process bottlenecks and performance gaps, enabling a fact-based understanding of operational challenges. The proposed methodology addresses not only technical implementation, but also organizational readiness, process transparency, and knowledge codification—ensuring a balanced approach to transformation.
A real-world case study in a high-tech manufacturing firm was conducted, in which thirteen business activities—ranging from customer engagement to post-sales support—were redesigned using intelligent tools such as NLP-based customer service, demand forecasting with LSTM, and decision tree-based negotiation support. These redesigned activities were supported by a hierarchical intelligent agent architecture composed of knowledge-based, rule-based, and analytical sub-agents, each assigned to simulate and automate specific sales tasks. The modular nature of the agent system allows flexible deployment and task adaptation across different sales contexts and organizational layers.
Implementation outcomes include improved workflow continuity, faster response times, reduced manual workload, and enhanced decision accuracy. The agent system also demonstrated potential for reusability, transparency in task delegation, and integration with CRM platforms, facilitating better knowledge management and customer experience. User feedback from internal stakeholders indicated high usability, improved decision support, and increased confidence in handling complex sales scenarios.
This research contributes academically by formalizing a modular, repeatable transformation approach specifically tailored for department-level application, bridging the methodological gap between enterprise-wide digital transformation strategies and practical department-level implementation. Practically, it offers actionable and scalable guidelines for embedding AI technologies into core sales operations, emphasizing low-risk pilot-based deployment strategies and long-term adaptability. Furthermore, the framework’s extensibility enables future incorporation of generative AI technologies and cross-departmental collaborative intelligence.
The findings not only address current inefficiencies but also illustrate how AI-driven systems can reconfigure sales execution logic and reshape organizational behavior toward a more agile, data-centric operating model. The study provides a validated pathway for enterprises seeking to initiate smart transformation from within operational departments, setting the stage for enterprise-wide AI adoption.

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