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new value models based on AI. For small and medium-sized enterprises, policy priorities
should include digital management training, strategic consulting, support for data
transformation, and the development of pilot models suited to specific sectors.
5.2.2. Developing data infrastructure and AI governance frameworks
The effectiveness of AI depends heavily on data quality, system interoperability, and
the clarity of governance frameworks. It is therefore necessary to develop synchronized
data infrastructure and connectivity standards, including open data, data standardization,
safe data-sharing mechanisms, and digital platforms capable of interoperability among
firms, government agencies, and supporting organizations. At the same time, regulations
on data protection, algorithmic transparency, accountability, and responsible AI use need
to be further developed. A clear institutional framework will help reduce policy
uncertainty and build firms’ confidence in making long-term AI investments.
5.2.3. Promoting AI ecosystem linkages among enterprises, universities, and
research institutes
Policy should promote stronger linkages among enterprises, universities, research
institutes, and intermediary organizations supporting innovation. Such linkages help firms
gain access to new human resources, knowledge, and implementation capability, while
also facilitating technology transfer, application experimentation, and the development of
endogenous innovation capability. Over the long term, this is a key condition for building
a deeper AI ecosystem and reducing dependence on imported technologies or ready-
made platforms.
5.3. Discussion: alignment between management and policy
The strategic value of AI depends not only on firms’ own efforts or on isolated policy
incentives, but on the alignment between the firm’s internal capability and the supporting
institutional environment. At the enterprise level, this includes data capability, digital
human capital, innovation mechanisms, and the capacity for strategic restructuring. At
the policy level, this includes data infrastructure, appropriate institutions, mechanisms
supporting technology absorption, and networks for innovation linkages. Only when these
two levels operate compatibly can AI become a driver of innovation and sustainable
competitive capability (Haefner et al., 2021; Füller et al., 2024).
To further clarify the relationship between firms’ internal capability and the
supportive conditions of the institutional environment, the study proposes an ecosystem
model for fostering enterprise strategic AI capability, as shown in Figure 2 below.
Figure 2 shows that enterprise strategic AI capability does not arise from a single
decision to invest in technology, but from a multi-layered ecosystem in which the firm’s
internal capability is supported and guided by linked actors, implementation conditions,
and the surrounding policy–institutional environment. Unlike approaches that treat AI
primarily as a technical tool, this model emphasizes that the strategic value of AI emerges
only when technology is embedded in the interaction between internal capability and
external enabling conditions.
At the center of the model is the enterprise itself, with three core components:
strategic restructuring, internal data governance, and an innovation culture. These form
the foundation that determines whether the firm can absorb AI as an organizational
capability. Surrounding this core are linked actors such as universities, research institutes,
intermediary organizations, associations, and digital infrastructure providers, all of which
supply knowledge, human resources, technology, and implementation solutions.

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