人工智慧在企業營運中的採用：策略性前因、促成機制及其對創新成果之影響

Abstract

企業雖積極投資人工智慧（AI），卻往往難以轉化為實際價值，促使策略重心從技術取得轉向組織AI能力的建構。現有文獻對AI能力的形成與創新轉化機制，特別是在制度環境快速演變的臺灣情境中，仍缺乏整合性理論探討。為了填補此缺口，本研究以資源基礎觀點（Resource-Based View, RBV）為理論基礎，建構動態分析框架，探討AI能力的前因及其與創新績效之關聯，並分析AI導向、AI倫理與制度環境的調節作用。 本研究採質性多重個案法，深度訪談臺灣電信、科技與金融產業的三家領導企業高階主管。研究結果指出：（1）組織AI能力的形成，需整合AI素養與基礎資源，並由兼具直接建構與放大效果的「跨功能導向」所驅動。（2）AI能力驅動的創新路徑循「由內而外」發展，始於低風險的內部流程創新，成熟後方擴散至產品、服務及商業模式等風險更高的領域。（3）AI能力轉化為創新績效的過程受三項關鍵因素調節：「AI導向」扮演加速器；「AI倫理」則為煞車，其限制效果隨應用風險提高而加劇；而「外部制度」是一把雙面刃，其對創新的抑制或促進效果，分別取決於法規一致性與企業規模。 理論上，本研究提出整合性的動態AI能力框架，闡明其價值創造的機制與情境調節因子，深化了RBV在數位轉型議題上的應用。實務上，本研究為管理者提供了一份可行的組織轉型藍圖，強調應優先培養組織的資源整合與轉化能力，並為應對倫理與制度挑戰提供差異化策略。

Despite significant corporate investment in artificial intelligence (AI), many firms struggle to translate these investments into tangible value. This has prompted a strategic shift from mere technology acquisition to the development of organizational AI capabilities. However, existing literature lacks an integrated theoretical framework to explain the formation of AI capabilities and their conversion into innovation, particularly within the rapidly evolving institutional context of Taiwan. To address this gap, this study draws on the Resource-Based View (RBV) to construct a dynamic analytical framework. It investigates the antecedents of AI capability, its relationship with innovation performance, and the moderating effects of AI orientation, AI ethics, and the institutional environment. This research employs a qualitative, multiple-case study methodology, conducting in-depth interviews with senior executives from three leading Taiwanese firms in the telecommunications, technology, and financial industries. The findings indicate that: (1) Developing organizational AI capability requires integrating AI literacy with foundational resources, a process driven by a cross-functional orientation that both directly builds and amplifies these capabilities. (2) The innovation trajectory driven by AI capabilities follows an “inside-out” progression, beginning with low-risk internal process innovations before expanding to higher-risk domains such as products, services, and business models as capabilities mature. (3) The conversion of AI capability into innovation performance is moderated by three key factors. “AI orientation” acts as an accelerator. “AI ethics” functions as a brake, with its constraining effect intensifying as application risks increase. The “external institutional environment” is a double-edged sword, either inhibiting or fostering innovation depending on regulatory consistency and firm size. Theoretically, this study contributes an integrated, dynamic framework of AI capability that clarifies its value-creation mechanisms and contextual moderators, extending RBV’s application to digital transformation. Practically, the findings provide managers with a viable blueprint for organizational transformation, emphasizing the need to prioritize capabilities for resource integration and conversion. The study also offers differentiated strategies for navigating ethical and institutional challenges.