隨著⼈⼯智慧（AI）技術的快速發展，企業導⼊ AI 已成為數位轉型的核⼼議題。然⽽，⼤型企業在導⼊過程中往往⾯臨組織慣性、成員認知衝擊與⾓⾊轉型等多重挑戰，現有⽂獻對於這些挑戰如何在微觀組織場域中運作，以及外部協作機制如何跨層次促進調適，仍缺乏整合性的實證探討。
本研究以⼀家全球科技企業與外部技術機構協助⾦融機構導⼊知識管理 AI系統的四個⽉專案為場域，採⽤質性個案研究法，透過三位關鍵⾓⾊的半結構式深度訪談，從組織、個⼈與外部協作三個層次探討慣性挑戰與調適機制。
研究發現：第⼀，組織慣性同時展現在制度⾯與認知⾯，專案團隊分別以平⾏⼯作流程與跨層次三段式路徑加以克服。第⼆，員⼯積極擁抱 AI 卻伴隨過⾼期待，驅動技術團隊主動將隨機性降⾄零，最終交付⼀套⾼度客製化的知識管理⼯具。第三，AI 導⼊觸發的並⾮去中介化，⽽是再中介化，四種新興⾓⾊在⼈機磨合中⾃發浮現。第四，外部顧問的關鍵⾓⾊在於提供制度正當性⽽⾮技術⽅案，但知識移轉存在顯性可達⽽隱性受限的結構性邊界。

As artificial intelligence (AI) technologies advance rapidly, AI adoption has become a central issue in enterprise digital transformation. However, large organizations frequently encounter organizational inertia, cognitive disruption among employees, and role transformation challenges during the adoption process. Existing literature lacks integrative empirical investigations into how these challenges operate at the micro-organizational level and how external collaboration mechanisms facilitate cross-level adaptation.
This study examines a four-month project in which a global technology firm and an external AI consulting institution assisted a state-owned financial institution in implementing a knowledge management AI system. Employing a qualitative single-case study design, the research conducted semi-structured in-depth interviews with three key participants and analyzed the inertia challenges and adaptation mechanisms across organizational, individual, and external collaboration levels.
The findings are as follows. First, organizational inertia manifests simultaneously at the institutional level—an institutional time lag caused by a compliance-first culture—and the cognitive level—a collective cognitive deadlock stemming from inflated expectations of AI capabilities. The project team overcame these through parallel workflows and a cross-level three-stage adaptation pathway. Second, employees' enthusiastic embrace of AI was accompanied by unrealistically high expectations, creating what this study terms an embrace gap, which drove the technical team to proactively reduce AI randomness to zero, ultimately delivering a highly customized knowledge management tool. Third, AI adoption triggered not disintermediation but rather re-intermediation, as four emergent roles spontaneously arose through human-machine interaction. Fourth, the critical role of external consultants lay in providing institutional legitimacy rather than technical solutions, although knowledge transfer effectiveness exhibited a structural ceiling where explicit knowledge could be transferred within the project cycle while tacit knowledge remained difficult to embed.

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