本研究探討企業雲端平台如何在 ChatGPT 推動企業生成式 AI 快速發展的背景下，運用資料驅動能力（data-driven capabilities）創造價值，涵蓋價值主張設計（value proposition design）與價值擷取機制（value capture mechanisms）。本研究採用多重個案研究法，以 Microsoft Azure、Amazon Web Services（AWS）與 Google Cloud Platform（GCP）為研究對象，分析期間涵蓋 2023 年至 2026 年 4 月，共蒐集 78 份公開資料作為研究證據，並依據四個構面──資料資源、資料驅動能力、商業模式設計，以及平台生態系動態──所包含的九項分析維度進行系統性分析。
研究結果顯示，三家企業雲端平台在相同的競爭環境下發展出不同的平台架構策略。Azure 透過整合 Microsoft 365 的獨特資料資產，形成「深度整合（Deep Integration）」模式；AWS 則憑藉其大規模基礎設施與最完整的第三方模型生態，採取「中立聚合（Agnostic Aggregator）」模式；Google Cloud Platform 則結合規模最大的第一方模型計畫與最積極推動開放標準互通性的策略，形成「開放標準垂直整合（Vertical Integration with Open Standards）」模式。
本研究的主要理論貢獻在於辨識出三項屬於基礎模型（foundation model）時代的平台競爭特徵：第一，平台中立性（platform neutrality）已成為一種主動的價值擷取策略；第二，資料重力（data gravity）同時扮演客戶鎖定機制與吸引模型供應商的重要力量；第三，平台研究中長期討論的「整合（integration）與中立（neutrality）」策略張力，並非僅存在單一路徑，而是至少存在三種內部一致且具可行性的策略配置。
除了上述具時代特性的發現外，本研究亦重新驗證一項歷久彌新的策略管理原則。延續資源基礎理論（Resource-Based View）與動態能力理論（Dynamic Capabilities）的觀點，本研究指出，企業競爭優勢並非源自單一資源或能力本身，而是來自資源、能力與商業模式各要素之間的協同配置與整體一致性。最後，本研究進一步提出對理論發展、管理實務及未來研究方向的意涵。

This thesis examines how enterprise cloud platform firms leverage data-driven capabilities for value creation, encompassing both value proposition design and value capture mechanisms, during the post-ChatGPT acceleration of enterprise AI adoption. Through a multiple case study of Microsoft Azure, Amazon Web Services, and Google Cloud Platform across 2023 to April 2026, the analysis draws on 78 evidence items from publicly available sources, organized along nine analytical dimensions distributed across four building blocks: data resources, data-driven capabilities, business model design, and platform ecosystem dynamics. The findings reveal that the three platforms instantiate distinct architectural responses to the same competitive environment: Azure leverages privileged Microsoft 365 data assets through a Deep Integration model; AWS deploys infrastructure scale and the broadest third-party model catalog through an Agnostic Aggregator model; and Google Cloud Platform combines the largest first-party model program with the most aggressive standards-based interoperability commitments through a Vertical Integration with Open Standards model. The principal theoretical contribution is the identification of three patterns specific to the foundation-model era: platform neutrality emerges as an active value-capture strategy, data gravity functions as both a customer lock-in mechanism and an attractor of model suppliers, and the integration-versus-neutrality tension long discussed in platform research admits at least three internally coherent resolutions. Beneath these era-specific findings, the study reaffirms an enduring strategic principle by building on the Resource-Based View and dynamic capabilities frameworks: competitive advantage arises from the alignment among resources, capabilities, and business model components rather than from the strength of individual elements. Implications for theory, practice, and future research are discussed.

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