隨著生成式 AI 與 AI Agent 技術的快速興起，行銷科技 (MarTech) 產業正面臨服務邏輯與能力結構的轉變。本研究以服務主導邏輯與動態能力理論為理論框架，從供應商視角出發，探討台灣行銷科技服務供應商在新型 AI 技術導入後，其服務邏輯、資源整合與動態能力建構的轉變。本研究採多重個案研究法，選取台灣三家分別為大、中、小型規模的行銷科技企業，透過半結構式深度訪談蒐集初級資料。
研究發現AI 的角色，已從輔助人員作業的被動工具，升級為自主協調、直接參與任務執行的操作性資源，服務邏輯也從功能導向轉變為情境整合導向；AI 導入後服務流程的重心由產品功能導入轉向需求共創，資源整合的範疇亦從企業內部擴展至外部結合客戶知識；不同規模供應商的轉型策略存在結構性差異，大型企業以技術前瞻與規模化產品擴充為核心，中型企業以垂直產業知識深耕與客戶共創，小型企業則以需求觸發與既有能力快速重組為主要應對方式，差異原因在於各自的資源條件、商業模式與市場定位；三家企業各自形成差異化的動態能力建構路徑，分別對應技術前瞻型、產業知識型與需求回應型三種運作模式。
本研究豐富了服務主導邏輯在 AI 情境下的理論，補充台灣行銷科技產業供應商角度的企業AI應用研究缺口。實務上建議 MarTech 服務供應商應以服務思維轉型為起點，依據自身規模與資源條件選擇最適合的 AI 導入路徑。

With the rapid rise of generative AI and AI Agent technologies, the Marketing Technology (MarTech) industry is facing a transformation in service and capability structure. This study adopts Service-Dominant Logic and Dynamic Capabilities as theoretical framework, taking a supplier perspective to examine how Taiwan's MarTech service providers have transformed their service logic, resource integration, and dynamic capability building following the adoption of emerging AI technologies. Employing a multiple case study approach, this research selected three Taiwanese MarTech firms of large, medium, and small scale respectively, collecting primary data through semi-structured in-depth interviews.
The findings reveal that AI's role has evolved from a passive tool assisting human operations to an operative resource capable of autonomous coordination and direct task execution, while service logic has shifted from function-oriented to context-integration-oriented. Following AI adoption, the focus of service processes has moved from product feature delivery toward need co-creation, and the scope of resource integration has expanded from internal enterprise resources to incorporating external customer knowledge. Structural differences exist in the transformation strategies of providers at different scales: large enterprises center on technological foresight and scaled product expansion; medium-sized enterprises emphasize deep vertical industry knowledge and customer co-creation; and small enterprises primarily rely on demand-triggered responses and rapid reconfiguration of existing capabilities. These differences stem from each firm's distinct resource conditions, business models, and market positioning. The three firms have each formed differentiated dynamic capability-building pathways, corresponding respectively to three operational modes: technology foresight-driven, industry knowledge-driven, and demand-responsive.
This study enriches the theoretical understanding of Service-Dominant Logic in AI contexts and addresses a research gap in supplier-side studies of enterprise AI adoption within Taiwan's MarTech industry. Practically, it is recommended that MarTech service providers begin with a service-mindset transformation, selecting the most suitable AI adoption pathway based on their scale and resource conditions.

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