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fragmentation rather than foster integration. Finally, as AI becomes increasingly
widespread, advantage no longer stems from owning the technology itself, but from the
ability to organize, integrate, and exploit it more effectively than competitors.
Therefore, only firms capable of undertaking AI-driven strategic restructuring can
transform technology into sustainable competitive capability.
4.6. General discussion
Three main observations can be drawn from the above analysis. First, AI should not
be approached as a stand-alone technology, but as an organizational capability capable of
influencing enterprise strategy. Second, strategic restructuring is the mediating
mechanism that determines whether AI creates value; AI realizes value only when firms
use it to reconfigure resources, redesign operations, and adjust business models. Third,
sustainable competitive capability in the AI era is not the result of a one-time
transformation, but of a continuous process of learning and innovation.
Accordingly, the strategic question for firms is not only where to apply AI, but how
to reorganize the enterprise so that AI becomes a foundation for adaptation, innovation,
and long-term development.
5. Managerial and policy implications
5.1. Managerial implications for enterprises
5.1.1. Moving AI from a tool to a strategic capability
Firms should approach AI not as an isolated technical solution, but as an
organizational capability with strategic influence. This requires AI to be integrated into
the strategic restructuring process, including resource allocation, operating model design,
and business model adjustment. AI generates strategic value only when it is tied to
decisions that reshape core capabilities and the firm’s value-creation structure.
5.1.2. Building foundations in data, human resources, and leadership capability
Effective AI deployment requires firms to develop data governance capability,
digital human capital, and leadership capability simultaneously. This includes
standardizing and integrating internal data, improving the ability of managers at different
levels to use and interpret technology, and strengthening leadership capability in
identifying strategic opportunities, managing organizational change, and orchestrating
cross-functional innovation. These are foundational conditions for shifting from
experience-based management to data-driven management.
5.1.3. Establishing continuous innovation mechanisms and AI risk governance
Firms need to establish continuous innovation mechanisms based on
experimentation, data feedback, and process improvement, rather than treating AI as a
one-off technological investment. At the same time, AI deployment must be accompanied
by appropriate risk governance mechanisms, including model quality control, monitoring
of algorithmic bias, data security assurance, and maintenance of accountability in AI-
supported decisions. Such an approach helps firms balance innovation and control during
the transformation process.
5.2. Policy implications
5.2.1. Improving institutional frameworks to support AI absorption and
transformation
AI policy should be designed in connection with innovation, digital transformation,
and enterprise development, rather than as an isolated technology program. The policy
focus should not only be on promoting AI adoption, but also on enhancing firms’
absorptive capability, supporting process restructuring, and enabling firms to develop

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