Organizations are currently navigating a systemic shift from traditional business operations toward 'Agentic Business Transformation' (ABT), integrating autonomous AI agents into core organizational structures.

組織目前正經歷一場系統性轉型，從傳統業務營運向「代理式業務轉型」（ABT）邁進，將自主 AI 代理整合至核心組織結構中。

The current corporate landscape exhibits a significant divergence between strategic ambition and operational capacity. While a substantial majority of organizations aspire to achieve agentic capabilities within a three-year horizon, a corresponding majority report that existing infrastructures are insufficient to support such a transition. This deficiency is attributed to the tendency of firms to overlay AI agents upon legacy human-centric operating models rather than redesigning the fundamental architecture of work. The conceptual framework of Agentic Business Transformation (ABT), as proposed by Ema and HFS Research, posits that true value is realized only when AI agents function as connective tissue across the enterprise, facilitating the execution of complex workflows with minimal human intervention.

目前的企業環境顯示，策略雄心與營運能力之間存在顯著分歧。雖然絕大多數組織渴望在三年內實現代理能力，但同樣比例的組織報告指出，現有基礎設施不足以支持此類轉型。這種不足歸因於企業傾向將 AI 代理疊加在舊有以人為中心的營運模式上，而非重新設計工作的基本架構。由 Ema 和 HFS Research 提出的代理式業務轉型（ABT）概念框架認為，只有當 AI 代理在整個企業中扮演連接組織的角色，在極少人力干預的情況下促進複雜工作流的執行時，才能實現真正的價值。

This transformation is predicated upon three primary pillars: the technology stack, the workforce, and success metrics. The technological requirement involves a shift from application-centric workflows to systems capable of supporting machine-speed operations across multiple datasets. Concurrently, workforce structures are undergoing a reconfiguration; the traditional industrial hierarchy is being challenged by the capacity of AI agents to coordinate tasks independently. This necessitates a shift in managerial responsibilities toward the oversight of hybrid teams, focusing on trust and explainability. Furthermore, the emergence of specialized roles, such as 'AI workflows' or 'AI business automation engineers,' indicates a professional pivot toward 'AI Ops,' where the primary objective is the identification and implementation of AI-driven optimizations across diverse departments.

這一轉型基於三個主要支柱：技術棧、人力資源與成功指標。技術要求涉及從以應用程式為中心的工作流，轉向能夠支持跨多個數據集、以機器速度運行操作的系統。與此同時，人力結構正經歷重新配置；傳統的工業層級制度正受到 AI 代理獨立協調任務能力的挑戰。這使得管理職責必須轉向監督混合團隊，並側重於信任與可解釋性。此外，如「AI 工作流」或「AI 業務自動化工程師」等專業角色的出現，顯示出專業重心正轉向「AI Ops」，其主要目標是在不同部門中識別並實施 AI 驅動的優化。

Finally, the transition requires a fundamental recalibration of performance evaluation. Traditional output-based metrics—such as volume of interactions—are deemed obsolete in an agentic environment. Instead, a shift toward outcome-based metrics is required to accurately measure return on investment. Evidence suggests that prioritizing high-value outcomes over low-complexity tool metrics can significantly enhance the measured efficacy of AI deployments. This systemic evolution is further supported by professional development initiatives, such as those at NTUC LearningHub, which emphasize the synthesis of technical AI proficiency with enduring human cognitive skills, including critical thinking and professional judgment, to ensure operational resilience.

最後，這一過渡需要對績效評估進行根本性的重新校準。在代理環境中，傳統基於產出的指標（例如互動量）被認為已過時。相反，需要轉向基於結果的指標，才能準確衡量投資報酬率。證據顯示，優先考慮高價值結果而非低複雜度的工具指標，能顯著提升 AI 部署的衡量成效。這種系統性演進 further 得到了專業發展計畫的支持，例如 NTUC LearningHub 的計畫，其強調將 AI 技術熟練度與持久的人類認知技能（包括批判性思考與專業判斷）相結合，以確保營運韌性。

The integration of agentic AI necessitates a comprehensive redesign of technology, personnel, and metrics to bridge the gap between organizational intent and execution.

整合代理式 AI 需要對技術、人員與指標進行全面重新設計，以彌合組織意圖與執行之間的差距。