Government AI Adoption Framework: From Policy to Deployment (2026)

1. Policy Context: Three Years from Action Plan to Legislation

Government AI adoption in Taiwan is undergoing a fundamental shift — from tentative experimentation to legislatively mandated transformation. Three landmark policy milestones are driving this change:

1.1 Taiwan AI Action Plan 2.0 (2023-2026)

In 2023, the Executive Yuan approved the "Taiwan AI Action Plan 2.0"^[1], a four-year national strategy organized around five pillars: talent development, technology cultivation and industry growth, operational environment enhancement, international engagement, and social impact management. The 2025 science and technology budget reached NT$15.748 billion, a 29.9% increase over the prior year, with a 2023 execution rate of 99.5%^[4].

For public-sector IT leaders, this signals two things. First, 2026 is the plan's final year, and government agencies face budget utilization pressure — AI-related tenders are expected to increase. Second, the successor program beyond 2027 has yet to be defined, meaning current resource allocations may shift. Now is the optimal window for launching AI initiatives.

1.2 Generative AI Reference Guidelines (October 2023)

On October 3, 2023, the Executive Yuan issued the "Reference Guidelines for Use of Generative AI by Executive Yuan and Subordinate Agencies"^[2] — a 10-article framework governing how civil servants may use generative AI tools. Core provisions include:

• Classified, personal, or non-public information must never be entered into generative AI tools
• All AI-generated content must undergo human review before being used in official proceedings
• Each agency must establish usage policies and training programs
• Agencies are encouraged to actively experiment with AI within manageable risk parameters — blanket prohibitions are explicitly discouraged

The significance of these guidelines cannot be overstated: they created a formal permission space for public-sector AI use. Prior to their release, most government agencies took a conservative stance, reluctant to adopt AI without explicit authorization. The guidelines effectively served as the Executive Yuan's signal to the entire civil service that experimentation could begin.

1.3 The AI Basic Act (Enacted December 2025)

The most consequential milestone arrived when Taiwan's legislature passed the AI Basic Act on December 23, 2025^[3]. This 20-article law elevates AI governance from administrative guidance to statutory obligation:

Central Authority: The National Science and Technology Council (NSTC) is designated as the central competent authority, with municipal and county governments serving as local authorities.

Seven Governance Principles: Sustainable development and well-being, human autonomy, privacy protection and data governance, cybersecurity and safety, transparency and explainability, fairness and non-discrimination, and accountability.

National AI Strategy Committee: Chaired by the Premier, with participation from academia, industry, agency heads, and local government leaders.

Government Obligations: Adequate budget allocation, regulatory sandboxes, talent mobility programs, labor protections, and workforce retraining.

For public-sector leaders, the AI Basic Act sends an unambiguous message: government AI adoption is no longer merely encouraged — it is a legislated national mandate. Agency CIOs should position AI adoption as a core annual priority, not an optional experiment.

2. International Benchmarking: Where Taiwan Stands on the Global Government AI Map

Understanding the opportunities and challenges facing Taiwan's public sector requires placing its AI readiness in an international context.

2.1 The European Union: Regulation-Driven Governance

The EU AI Act (Regulation (EU) 2024/1689), which entered into force in August 2024, will become fully applicable on August 2, 2026^[7]. The most relevant provision for public agencies is its "high-risk AI" classification — any AI system used for public-service eligibility assessments, credit scoring, law-enforcement biometrics, or immigration border control is classified as high-risk and subject to stringent transparency, safety, and human oversight requirements.

For Taiwan, the implication is this: if an agency's operations involve interactions with EU citizens (such as immigration processing or international trade facilitation), or if its AI systems process EU-originating data, the EU AI Act's extraterritorial provisions may apply.

2.2 South Korea: Asia-Pacific's AI Legislation Pioneer

South Korea passed its AI Framework Act in January 2025, making it the first comprehensive AI law in the Asia-Pacific region. The act takes full effect on January 22, 2026^[12]. Even more notable is Korea's investment trajectory — the 2026 AI budget stands at KRW 10.1 trillion (approximately US$6.7 billion), triple the KRW 3.3 trillion allocated in 2025, with a stated goal of reaching 95% public-sector AI adoption by 2030.

2.3 Singapore: A Model for Strategy-to-Execution

Singapore released its "National AI Strategy 2.0" in December 2023^[11], structured around 3 systems, 10 enablers, and 15 action items, complemented by a S$120 million AI Adoption Fund. Singapore's distinctive advantage lies in its dual offering: the "Model AI Governance Framework" (a governance blueprint) paired with "AI Verify" (a testing toolkit), providing government agencies with a clear compliance pathway from policy to practice.

2.4 Taiwan's Position and Gaps

In the Oxford Insights "Government AI Readiness Index," Taiwan ranked 19th globally in 2023^[13] — a solid position, but still trailing Singapore (top 7) and South Korea (top 5). Deloitte's "Government Trends 2025" report reveals an even more sobering data point: across government agencies worldwide, only 1% of leaders report that more than 60% of their civil servants can access generative AI, and AI proficiency in the public sector remains far below that of other industries^[9].

Taiwan's advantages are real: it now possesses a legal framework (the AI Basic Act), substantial policy resources (the multibillion-dollar AI Action Plan 2.0 budget), and a growing technology foundation (the TAIDE sovereign language model and moda's AI application platforms). The bottleneck is not resources — it is execution. The challenge is converting these assets into actual AI capabilities within individual agencies.

3. The Current State of Government AI in Taiwan: Progress and Persistent Bottlenecks

3.1 The Ministry of Digital Affairs' Achievements

The Ministry of Digital Affairs (moda) has established an initial foundation for public-sector AI adoption^[5][6]:

Government AI Service Hub: Over 20 government AI services are now live, spanning five categories including national tax customer service, judicial affairs chatbots, trademark image search, AI-assisted tariff classification, and food safety assistants^[6].

TAIWAN AI RAP Platform: A government-dedicated high-performance AI application development platform that provides secure, controlled compute and model resources.

TryAI Platform: A government AI application sandbox that integrates over 10 commercial and open-source large language models, enabling agencies to test AI solutions in a controlled environment.

AI Bot Marketplace: Over 20 reusable AI bots available for direct deployment by agencies, reducing redundant development costs across government.

2025 National CIO Conference: Hosted by moda in November 2025, the conference drew approximately 200 IT leaders from central ministries, agencies, and municipal governments. Twelve vendors demonstrated AI products across three categories: document processing, specialized applications, and intelligent customer service^[5].

3.2 Four Critical Bottlenecks

Despite these foundational achievements, a significant gap remains between available policy resources and on-the-ground AI capabilities across agencies:

Bottleneck 1: Acute AI Talent Shortage — The public sector's AI expertise lags significantly behind private industry. An HBR survey shows that 39.1% of enterprises are already scaling AI into production, but government agencies remain far below this threshold^[14]. Most agency IT staff are focused on systems maintenance, lacking the skills for AI project planning and management.

Bottleneck 2: Structural Procurement Constraints — Government procurement regulations are designed for well-specified hardware or software license purchases. AI projects — with their iterative development cycles, uncertain outcomes, and need for ongoing refinement — fundamentally conflict with the traditional "fixed specification + lowest bid" procurement model. Furthermore, under Article 34 of the Government Procurement Act (tender document confidentiality), civil servants are prohibited from entering non-public tender documents into AI tools — limiting AI use within the procurement process itself^[2].

Bottleneck 3: Security Concerns Breeding Conservatism — Public agencies handle highly sensitive data: citizen personal records, tax information, household registration, and national health insurance data. Without well-established AI security assessment standards, most agencies default to a risk-averse posture of inaction.

Bottleneck 4: Cross-Agency Integration Barriers — Taiwan's government IT systems have historically been built independently by each agency, resulting in extensive data silos. AI's greatest value often comes from cross-system data integration, but the current architecture makes inter-agency data sharing extremely difficult.

4. A Practical Framework: Five Stages of Government AI Adoption

Drawing on international best practices and the specific conditions of Taiwan's public sector, we propose a five-stage adoption pathway:

Stage 1: Needs Assessment and Use Case Identification (1-2 Months)

The first step in AI adoption is not selecting a technology — it is identifying pain points. We recommend screening use cases across three dimensions:

Quantifiable Impact: Which business processes consume the most staff time? Document routing, citizen complaint classification, and application pre-screening — high-volume, repetitive tasks like these make the best starting points for AI adoption.

Data Availability: Does the process already have digitized historical data? AI models require training data or reference examples. If a process is still paper-based, digitization must precede AI implementation.

Risk Controllability: Initial projects should avoid processes where errors carry serious consequences (such as administrative rulings or tax assessments). Start with use cases that position AI as a decision support tool rather than a decision-making replacement.

Stage 2: Security Assessment and Regulatory Compliance (1-2 Months)

Once use cases are selected, a thorough security assessment of the AI system is essential. We recommend aligning with the four core functions of the NIST AI Risk Management Framework (AI RMF 1.0)^[8]:

Govern: Establish an organizational governance structure for AI use — who approves AI projects, who monitors AI output quality, and what the escalation path is when issues arise.

Map: Identify the AI system's role within agency operations — its position in the decision chain, the scope of data it processes, and the stakeholders it may affect.

Measure: Define performance and risk metrics for the AI system — accuracy rates, false-positive rates, bias detection, and performance degradation monitoring.

Manage: Build ongoing risk management mechanisms — periodic reviews, model update strategies, and incident response procedures.

Simultaneously, ensure compliance with the seven governance principles of the AI Basic Act^[3] and the 10 provisions of the Executive Yuan's Generative AI Reference Guidelines^[2].

Stage 3: Proof of Concept (PoC) (2-3 Months)

Validate the AI solution's feasibility in a controlled environment. The goal of a PoC is not to prove that AI is impressive — it is to answer three questions:

Is it technically feasible? On real-world data, does the AI model achieve the predetermined accuracy threshold (typically 85% or higher)?

Can it integrate into existing workflows? Can AI outputs be seamlessly embedded into current business processes rather than creating additional workload?

Will users accept it? Are front-line civil servants willing to use the system? Can they understand its outputs? What is their level of trust in AI-generated results?

We recommend using moda's TryAI Platform for initial validation, which can significantly reduce PoC infrastructure costs.

Stage 4: Tender Specification and Procurement (2-4 Months)

After a successful PoC, the project enters the formal procurement phase. Drafting tender specifications for government AI projects is the most error-prone step in the entire process — because AI projects differ fundamentally from traditional IT procurements.

Avoid "Fixed Specification + Lowest Bid": AI project outcomes are inherently uncertain. Agencies should use "most advantageous tender" or "reasonable price" evaluation methods, prioritizing technical capability over price alone.

Include Iterative Development Clauses: AI models require continuous tuning and optimization. Tender specifications should mandate at least three months of "co-tuning" between the vendor and the agency, rather than a single handoff delivery.

Define Data and Model Ownership Clearly: AI tenders must explicitly address who owns the training data, who retains the model weights, and whether the vendor may use agency data to train models for other clients. We recommend that all public-sector data and model weights remain the property of the government agency.

Require Public-Sector Experience: Government AI projects have unique characteristics — stringent security requirements, data sensitivity, and multi-layered approval processes — that not every AI vendor can handle. Evaluation criteria should weight "demonstrated public-sector implementation experience" accordingly.

Stage 5: Production Launch and Ongoing Operations (Continuous)

Going live is not the finish line — it marks the beginning of the AI system's lifecycle:

Performance Monitoring: Establish continuous quality monitoring for AI outputs. Model performance degrades over time (model drift), requiring periodic reassessment and recalibration.

Feedback Loops: Create accessible channels for front-line users to report AI errors, and feed this data back into continuous model improvement.

Scale-Out Planning: Successful deployments should be systematically replicated across other agencies — moda's AI Bot Marketplace is specifically designed as a sharing mechanism for this purpose.

5. International Success Stories and Lessons for Taiwan

Deloitte's report^[9] highlights several international cases that offer valuable reference points:

U.S. Department of the Treasury: Deployed AI to detect and recover improper payments, recovering US$4 billion in 2024 — more than five times the amount recovered in 2023. This demonstrates AI's transformative potential in government financial oversight.

City of Buenos Aires: Launched an AI chatbot that handled 58 million interactions by the end of 2022, substantially reducing the burden on in-person service counters.

Australian Federal Government: In pilot programs, civil servants using AI tools saved an average of one hour per day on administrative tasks.

State of New Jersey, U.S.: After deploying AI, resident response times improved by 35%, and customer service case resolution rates increased by 50%.

McKinsey's broader estimates reinforce the macro picture: generative AI could create US$2.6 trillion to US$4.4 trillion in annual global economic value, with 60-70% of work activities theoretically automatable^[10]. For the public sector, the greatest value lies not in replacing civil servants, but in freeing their time to focus on higher-value judgment and service delivery.

6. How to Select a Government AI Service Provider

The evaluation criteria for public-sector AI vendors differ significantly from those used in the private sector:

Security and Compliance Capabilities: Does the vendor hold ISO 27001 / 27701 certifications? Can it handle data at government-classified security levels? Can the solution be deployed on-premise to ensure data never leaves the agency's perimeter?

Public-Sector Track Record: Has the vendor delivered AI projects for other government agencies? Does it understand government procurement processes, multi-layered approval workflows, and project closeout procedures?

Technological Autonomy: Can the AI model operate within the agency's own infrastructure? Does it depend on a specific cloud platform? If the vendor discontinues service, can the agency maintain operations independently? In line with moda's promotion of the TAIDE sovereign language model, prioritize vendors that support open-source models and on-premise deployment.

Ongoing Service Capability: AI systems are not one-time deliverables. Does the vendor have the capacity for continuous model tuning, performance monitoring, and technical support?

Talent Development Support: The best vendors do more than deliver systems — they help agencies build internal AI capabilities through user training, technology transfer, and comprehensive knowledge documentation.

7. Conclusion: The Legal Framework Is in Place — Now Is the Time to Execute

Taiwan's public-sector AI adoption environment in 2026 has never been more favorable: the AI Basic Act provides the legal framework^[3]; the AI Action Plan 2.0 provides policy resources^[1]; the Generative AI Reference Guidelines provide operational guardrails^[2]; and moda's AI application platforms provide the technological infrastructure^[6].

Internationally, the pressure to act is mounting. South Korea is accelerating with a threefold budget increase, targeting 95% public-sector AI adoption by 2030^[12]. Singapore leads with its combined governance framework and testing toolkit^[11]. The EU AI Act becomes fully applicable in August 2026^[7]. Taiwan can no longer afford a wait-and-see approach to this global wave of public-sector AI transformation.

Meta Intelligence brings deep AI technical capability, hands-on public-sector project experience, and thorough understanding of the regulatory landscape. We provide end-to-end services — from needs assessment and security evaluation through PoC validation, tender specification, and production deployment. Whether you are a CIO driving your agency's AI transformation, a procurement officer planning an AI tender, or a decision-maker evaluating AI policy — we deliver the strategic and operational support to take you from vision to implementation.