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System 1 - Activity Drivers: The first system focuses on activating and expanding AI
application demand in both the business and government sectors. In terms of innovation
economics, this is a key step because technology only creates value when it is absorbed
and integrated into operational processes. For the business sector, NAIS 2.0 particularly
emphasizes SMEs as a foundation for productivity diffusion. The GenAI Navigator program
was launched to help businesses identify specific business problems that can apply AI,
thereby reducing information search costs and experimentation risks. Additionally, the
Enterprise Compute Initiative (ECI), with a budget of up to SGD 150 million, helps
businesses access advanced computing infrastructure and cloud services (Smart Nation
Singapore, 2023). These policies directly address two core barriers to AI adoption: high
deployment costs and a lack of internal technical capacity, thereby fostering an
environment conducive to endogenous growth through technological diffusion. When
experimentation costs decrease, the probability of successful innovation increases,
thereby promoting the process of technology diffusion in the economy. For the public
sector, Singapore pursues the “government as lead adopter” model. State agencies apply
AI to administrative management, population data analysis, and personalization of public
services (Smart Nation Singapore, 2023). This mechanism has two important impacts: (i)
improving public sector efficiency; and (ii) creating an initial market for domestic AI
solutions, reducing commercialization risks for technology enterprises.
System 2 - People & Communities: A prominent feature of NAIS 2.0 is the emphasis
on human capacity as a prerequisite for AI to create sustainable value. Singapore
implements universal literacy programs such as AI for Everyone (AI4E) to raise basic AI
understanding for the public and the workforce (IMDA, 2018; Smart Nation Singapore,
2023). This approach aligns with OECD (2019) recommendations that AI development
must be linked to enhancing human capacity and ensuring social inclusion. Without an
appropriate training strategy, AI could increase skill inequality and cause labor market
polarization. In addition to training, Singapore builds open dialogue spaces like “Lorong
AI”, fostering exchange between businesses, developers, and regulators. This contributes
to forming a “responsible innovation culture” - a crucial factor in the AI ecosystem.
System 3 - Infrastructure & Environment: AI, especially generative AI, requires large-
scale computing power and data infrastructure. Singapore has implemented the Green
Data Centre Roadmap to expand data center capacity while ensuring energy sustainability
standards (IMDA, 2024a). Linking digital growth with green goals reflects a long-term
approach, mitigating environmental risks during the AI expansion process. Equally
important is the layer of governance and digital trust. Singapore developed the Model AI
Governance Framework (Personal Data Protection Commission, 2020) and the AI Verify tool
to evaluate and verify AI systems according to principles of transparency, fairness, and
accountability (IMDA, 2022). Recently, the Project Moonshot initiative supports testing
large language models (LLMs), reflecting policy adaptation to the rapid development of
generative AI (IMDA, 2024b). Theoretically, these verification mechanisms reduce “trust
costs” in the digital economy, aligning with the trustworthy AI governance framework.
When businesses can prove their AI systems comply with ethical and safety principles,
social acceptance increases, thereby facilitating large-scale application expansion (OECD,
2019).
It can be seen that NAIS 2.0 is not just a technological strategy but a growth
ecosystem design. The three systems of NAIS 2.0 correspond to the three necessary
conditions for AI to create sustainable growth: (1) Strong application demand; (2)

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