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3.3. Reliability and limitations of the study
The reliability of the study is strengthened through the use of diverse sources and
cross-comparison among foundational theoretical frameworks, particularly Teece et al.
(1997) and Barney (1991), together with contemporary studies on AI and strategic
transformation. Combining academic literature, international reports, and policy
documents reduces dependence on any single line of argument and strengthens the
linkage between theoretical foundations and practical context. The literature was also
selected according to criteria of relevance, academic reliability, and practical reference
value, thereby ensuring consistency in the analytical process.
However, as a theoretical and conceptual study, this article does not empirically test
the relationships proposed in the model. Therefore, the relationships presented here
should be regarded as theoretical propositions, providing a basis for future research to
develop measurement scales, collect primary data, and test them through empirical
methods.
4. Analysis and discussion
4.1. From fragmented AI applications to the formation of organizational AI
capability
A common misconception in management practice is to equate AI adoption with
organizational AI capability. In reality, deploying AI in isolated activities such as customer
service, sales analysis, or content automation merely reflects the use of technology and is
not sufficient to constitute organizational AI capability. This capability emerges only when
the firm is able to integrate data, digital infrastructure, analytical personnel, and
governance mechanisms so that AI outputs become inputs for strategic decision-making.
The value of AI, therefore, lies not in the algorithm itself, but in the firm’s
technology absorption capability. When data is fragmented, the technology function is
separated from business functions, or leaders lack the ability to interpret analytical
outputs, AI tends to remain confined to localized technical applications. By contrast, when
firms build unified data ecosystems, internal analytical capability, and cross-functional
coordination mechanisms, AI begins to shift from being a tool to becoming an
organizational capability.
From a dynamic capabilities perspective, this represents a shift from possessing
technology to internalizing technology. AI becomes a strategic resource only when firms
invest not only in the technology itself, but also in the ability to use, interpret, and
integrate it into decision-making processes. This is the condition under which AI can
influence strategic restructuring rather than merely producing localized efficiency gains.
4.2. AI and enterprise strategic restructuring
From a value chain perspective, AI enables firms to reconsider how value-creating
activities are organized, thereby adjusting operating models and strengthening strategic
coordination among the firm’s different functions (Porter, 1985).
Once internalized as an organizational capability, AI begins to affect the strategic
level of the firm. This impact is reflected not only in more accurate forecasting or faster
data processing, but also in changing how firms identify opportunities, allocate resources,
and design operating models. This constitutes the core content of AI-driven strategic
restructuring.
AI first changes the logic of resource allocation as data, analytical capability, digital
platforms, and organizational knowledge become increasingly important resources in the
digital business environment. At the same time, AI drives a shift from experience-based

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