讓 AI 落地 Make AI Land

模型能力每年躍進,企業落地進度長期停滯。 Models keep getting smarter. Landing doesn't move.

Klarna 投入 4000 萬美元與 OpenAI 共建 AI 客服,一年後回退至人機混合架構;麥當勞與 IBM 合作的 AI 點單系統,於三年後終止。模型供應商止步於 API 交付,傳統顧問公司缺乏適配的方法工具,企業實施環節長期處於空白。 Klarna spent $40M on OpenAI's customer-service AI. One year later, they walked it back. McDonald's pulled IBM's AI ordering after three years. Model companies stop at the API. Consultancies don't have the tools.

Wayne InsightSpring 將 AI Implementation Science 作為交付核心。這套源自醫學界、累積 20 年實證的學科體系,是目前少有的能系統應對 AI 實施鴻溝的成熟方法論。 We bring AI Implementation Science to the enterprise—a 20-year discipline from medicine, now applied to AI.

每個項目從五維診斷開始:創新本身、外部環境、內部環境、人、實施過程。 Every project starts with a Five-Dimension Diagnostic: Innovation Itself, External Context, Internal Context, People, Implementation Process.

該模型基於 CFIR 框架改造而成。原始 CFIR 在醫學界沿用 20 年,我們在此基礎上引入三個 AI 特有參數:迭代速率、監管漂移頻率、角色重疊度。 A CFIR adaptation. Twenty years of medical implementation framework, with three AI-specific parameters: iteration velocity, regulatory drift, role overlap.

診斷階段完成後再進入方案設計,上線後以 RE-AIM 框架支撐持續優化。 Diagnose first. Decide what to build after. Maintain with RE-AIM post-launch.

AI 實施鴻溝一個 2030 年之前必將誕生的領域 The AI Implementation Gap A Discipline That Will Emerge Before 2030

Roland 對未來五年產業方向的核心判斷。報告完整呈現 CFIR、NPT、RE-AIM 等醫學實施科學框架在 AI 場景下的適配路徑。 Roland's five-year career bet, articulated. A systematic translation of CFIR / NPT / RE-AIM from medicine to AI.

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