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2. Theoretical foundation and research analytical framework
2.1. AI as a general-purpose technology and foundational capability
In the economics of innovation, AI is regarded as a general-purpose technology
because of its broad applicability, continuous improvement potential, and capacity to
generate complementary innovations across multiple sectors (Bresnahan & Trajtenberg,
1995). Beyond its role in automation, AI expands capabilities for analysis, forecasting, and
decision support, thereby directly affecting firms’ governance structures and competitive
logic.
At the organizational level, the value of AI does not depend solely on ownership of
the technology, but on the ability to internalize it as organizational AI capability. This
capability encompasses not only data infrastructure and algorithms, but also the
integration of technological platforms, digital human capital, and mechanisms for
translating analytical outputs into strategic decisions. According to Russell and Norvig
(2021), AI refers to systems capable of perceiving, learning, and acting to achieve
specified goals. Accordingly, without integration into managerial processes, AI remains a
set of fragmented applications; when deeply embedded in the organization, however, AI
can become a strategic asset that enables firms to reconfigure resources and adapt more
flexibly to competitive environments (Aghion et al., 2019).
In sum, the strategic value of AI lies not in the technology itself, but in the firm’s
ability to combine AI with organizational learning in order to build a foundational
capability for long-term innovation and competition.
2.2. Dynamic capabilities theory
Dynamic capabilities theory provides a critical foundation for explaining the
strategic role of AI in firms. According to Teece et al. (1997), dynamic capabilities are the
firm’s ability to integrate, build, and reconfigure resources in order to adapt to changing
environments. At their core, dynamic capabilities are manifested in three key processes:
sensing opportunities and risks, seizing opportunities through decision-making and
resource allocation, and transforming or reconfiguring the organization to sustain long-
term adaptation.
In the digital context, AI may be viewed as an intermediate capability that supports
all three of these processes (Warner & Wäger, 2019). First, AI helps firms sense more
effectively by analyzing large-scale, real-time data to identify market signals, demand
trends, and potential risks. Second, AI supports seizing opportunities by improving
forecasting quality, enhancing decision-making, and optimizing resource allocation. Third,
AI facilitates reconfiguration by supporting process redesign, data integration, and more
flexible modes of organizing operations.
From this perspective, organizational AI capability is not simply the deployment of
specific tools such as machine learning or natural language processing, but the ability to
select, combine, and exploit these technologies in alignment with the firm’s strategic
objectives (Russell & Norvig, 2021). Thus, AI is not an end result in itself, but a driver that
strengthens dynamic capabilities and lays the foundation for strategic restructuring and
continuous innovation.
2.3. AI-driven strategic restructuring
In this study, strategic restructuring is understood not merely as adjusting an
organizational chart, but as a process through which the firm repositions itself by
reconfiguring resources, redesigning operating models, and adjusting business models
(Johnson, 1996). Under the influence of AI, this process becomes faster and deeper as

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