隨著生成式人工智慧與大型語言模型快速發展，企業顧客互動服務正由規則式聊天機器人，逐步轉向具備語意理解、動態生成與任務執行能力之AI Agent。對平台型SaaS服務業者而言，此一變化不僅涉及組織能力的轉變與產品功能的升級，也涉及商業模式的轉變。因此，本研究從動態能力觀點出發，探討平台型SaaS服務業者如何由聊天機器人服務轉向具AI Agent特徵之Omni AI服務，並分析其商業模式的變化。
本研究採取單一個案研究法，以O公司做為研究對象，透過半結構式深度訪談蒐集初級資料，並輔以官方網站、產品資料、新聞稿與產業文章等次級資料進行交叉驗證。研究架構上，本論文以動態能力之感知、捕捉與轉化能力，探討個案公司之轉型過程；並以價值主張、客戶區隔、關鍵資源、關鍵活動、客戶關係與合作夥伴網絡等商業模式六變項，分析及比較聊天機器人階段與Omni AI階段之商業模式差異。
本研究所得到的主要結論如下：
一、平台型SaaS服務業者的AI轉型，本質上並非僅是技術採用，而是由技術機會、客戶需求與商務模式再定位共同構成的轉型過程。
二、AI Agent使SaaS平台的價值邏輯，由流程效率提升轉向任務能力補強。
三、AI Agent的商業化不會普遍均勻發生，而是會優先落地於痛點明確、風險可控且成效可衡量的任務場景。
四、平台型SaaS服務業者在AI Agent時代的競爭優勢，來自AI技術與既有平台資產的互補整合，而非單一AI模型能力。
五、平台型SaaS服務業者在導入AI Agent服務後，其商業模式將由單純服務交付，轉向更深層的客戶工作流程嵌入；但此一嵌入效果需透過信任建立、風險降低與客戶教育才能實現。
本論文研究最後提出學術與管理意涵，並提供後續研究建議。

With the rapid development of generative artificial intelligence and large language models, enterprise-customer interaction services are gradually shifting from rule-based chatbots toward AI Agents with semantic understanding, dynamic content generation, and task execution capabilities. For platform-based SaaS providers, this transition is not merely a product function upgrade, but also involves business model transformation. Therefore, from the perspective of dynamic capabilities, this study explores how platform-based SaaS providers transform from chatbot services to Omni AI services with AI Agent characteristics, and further analyzes the changes in their business models.
This study adopts a single-case study method, selecting Company O as the research subject. Primary data were collected through semi-structured in-depth interviews, supplemented by secondary data including the company's official website, product materials, press releases, and industry articles for cross-validation. In terms of research framework, this study applies the dynamic capabilities perspective, including sensing, seizing, and transforming capabilities, to analyze the transformation process of the case company. In addition, this study compares the business model differences between the chatbot stage and the Omni AI stage through six dimensions: value proposition, customer segments, key resources, key activities, customer relationships, and partner network.
The main conclusions of this study are as follows:
1. The AI transformation of platform-based SaaS providers is not essentially a single form of technology adoption, but a transformation process jointly shaped by technological opportunities, customer needs, and business model repositioning.
2. AI Agents shift the value logic of SaaS platforms from improving process efficiency to strengthening task execution capabilities.
3. The commercialization of AI Agents does not occur evenly across all application contexts, but first takes place in task scenarios where pain points are clear, risks are controllable, and performance outcomes are measurable.
4. The competitive advantage of platform-based SaaS providers in the AI Agent era lies in the complementary integration of AI technologies and existing platform assets, rather than in the capability of a single AI model.
5. After the introduction of AI Agent services, the business model of platform-based SaaS providers shifts from mere service delivery toward deeper embedding in customers' workflows; however, such embedding can only be realized through trust building, risk reduction, and customer education.
Finally, this thesis presents academic and managerial implications and provides suggestions for future research.

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