How AI Is Helping Government Companies in Taiwan Cut Costs and Improve Efficiency

Taiwan's public-sector drive to cut costs and speed services is already tangible: the Ministry of Digital Affairs (MODA) uses AI to flag fraud and deepfakes - its system can inspect up to 40,000 cases a day - while open data, the MyData platform and Smart Government 2.0 help automate routine citizen services and reduce manual backlogs; local compute projects such as TAIWANIA 2 and the TAIDE language model are letting agencies run secure, Taiwan‑centric AI models that keep data local and cut vendor lock‑in.

New governance measures like the draft AI Basic Act and recent steps to grow workforce capacity (including an AI talent office launched in Taipei) aim to balance innovation with privacy and accountability.

For public‑sector teams learning to apply these tools, practical courses like the AI Essentials for Work bootcamp syllabus and details train employees to write effective prompts and integrate AI into daily workflows, and MODA's anti‑fraud platform is a good example of where those skills can immediately lower costs and free staff for complex, value‑added tasks; see how MODA's anti-fraud AI works in practice.

Attribute	Details for the AI Essentials for Work bootcamp
Description	Gain practical AI skills for any workplace; learn AI tools, prompt writing, and apply AI across business functions.
Length	15 Weeks
Courses included	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills
Cost	$3,582 early bird; $3,942 afterwards; paid in 18 monthly payments
Syllabus	AI Essentials for Work syllabus

“Early communication with stakeholders is crucial,” they say.

Taiwan's push for public‑sector AI is policy‑driven and pragmatic: after early efforts under the original Taiwan AI Action Plan (2018–2021), the government doubled down with AI Taiwan Action Plan 2.0 (2023–2026) and multi‑year funding - an Executive Yuan pledge of roughly NT$9–10 billion a year - to turn semiconductor and ICT strengths into real-world, cost‑saving services; the plan explicitly targets talent, niche AI platforms, start‑up incubation, legal clarity and industry adoption to scale solutions across health, finance and smart manufacturing (including concrete targets like training thousands of AI professionals).

To balance speed with trust, Taipei is layering governance and testing - drafting a Basic/AI Act and building evaluation frameworks so agencies can adopt models without leaking sensitive data - making policy and procurement tools part of the efficiency story, not an afterthought.

Read the government's Cabinet overview of the Action Plan and NSTC priorities for the full policy roadmap and legal context.

In the promotion of AI Taiwan Action Plan 2.0 (2023-2026), the emphasis is on the spirit of "Human-Centered AI" to promote industrial transformation and upgrading as well as well-being.

Taiwan's financing strategy for public‑sector AI mixes steady government baselines with risk‑sharing incentives to get agencies and suppliers moving: the Executive Yuan has pledged an annual NT$9–10 billion core budget to build national AI capability, while the Ministry of Digital Affairs' NT$10 billion program (with explicit 1:1 public‑private matching) channels money into talent, compute, data and commercialisation to help government companies co‑fund pilots and scale proven tools; see the government's Cabinet AI industry development plan overview and the Ministry's Ministry of Digital Affairs NT$10 billion AI investment announcement for details.

Beyond these pillars, larger initiatives - from the AI New Ten Major Construction proposals (hundreds of billions in multi‑year investment) to the “Ten Major AI Infrastructure Projects” with long‑run economic targets - create layered funding windows, matchmaking events and statutory tweaks aimed at lowering upfront costs for SMEs and encouraging joint ventures, so a municipal IT team can pilot a sovereign model with far less capital risk than building everything in‑house.

Program	Funding / Goal
Executive Yuan Cabinet AI budget announcement	NT$9–10 billion per year (core budget)
Ministry of Digital Affairs NT$10 billion AI investment plan announcement	NT$10 billion (multi‑year; 1:1 public‑private matches)
AI New Ten Major Construction	NT$190–200 billion initial reference (multi‑year)
Ten Major AI Infrastructure Projects	Target: generate ~NT$15 trillion economic value by 2040

Taiwan is stacking the plumbing that will let government companies move from piloting models to running production-grade, sovereign AI: public‑private partnerships and a multi‑hundred‑billion‑NT$ buildout in national compute aim to lift Taiwan's domestic computing power and give agencies safe, local options for training and serving models, while the AI supercomputing push (including an “AI factory” announced at Computex using NVLink Fusion) and sector bets on silicon photonics, quantum and robotics connect chips to cloud and edge use cases.

These plans - summarised in the island's AI New Ten Major Construction plan and the Ten Major AI Infrastructure Projects - pair large seed funds and private co‑investment with regional data governance work so ministries can host sovereign models without exposing sensitive data; see the government's Cabinet AI industry plan and the TechSoda analysis of the NT$190–200 billion roadmap for details.

Initiative	Key figure / note
TechSoda analysis: Taiwan NT$190–200 billion AI New Ten Major Construction plan	Initial reference NT$190–200 billion; builds sovereign AI & compute
Taipei Times: Ten Major AI Infrastructure Projects (Taiwan)	Target: generate ~NT$15 trillion economic value by 2040; focus on silicon photonics, quantum, robotics
Computex 2025 “AI factory” NVLink Fusion announcement (SmartOSC report)	“AI factory” powered by NVLink Fusion to give local researchers and agencies high-end compute

“overtake on the curve,” - Jiann‑Chyuan Wang

Lowering the bar for SMEs to sell to government starts with money and simple processes, and Taiwan is stacking both: nearly 91% of local SMEs have already begun digital transformation but cite capital and talent as the biggest hurdles, so the government's Taiwan Cloud Marketplace (Tcloud) - which lists almost 800 cloud tools and has processed over 50,000 applications, with subsidies and forms that “can take as little as 10 minutes to complete” - helps small suppliers adopt cloud services and meet public‑sector technical requirements quickly (Taiwan Cloud Marketplace (Tcloud) subsidies and uptake report).

At the same time fiscal levers make pilots and procurement less risky: R&D and investment tax credits under the Statute for Industrial Innovation let qualifying firms claim R&D credits (up to 15% of qualified R&D expenses, with special SME rules) and new investment tax credits now explicitly cover smart machinery, 5G, cybersecurity and AI expenditures (creditable at up to 5% of eligible spend in certain periods), which lets public buyers source capable local vendors without forcing them to front large capital outlays (Taiwan corporate tax credits for R&D and AI incentives – PwC).

The combined effect - fast cloud onboarding plus targeted tax credits and guarantee schemes - turns procurement from a blocker into a toolkit for scaling SME suppliers, so a municipal vendor can go from prototype to paid contract without draining a year's working capital.

“What you can measure, you can manage of,”

Public–private partnerships are the engine turning Taiwan's chip prowess into practical AI capacity for government companies: programs that stitch together firms, cloud providers and research institutes let agencies tap world‑class silicon design tools and localised compute without building fabs - Microsoft and TSMC's Joint Innovation Lab, for example, integrates cloud and EDA workflows so designers can burst to tens of thousands of cores on Azure and shorten design cycles, a capability that translates directly into faster, cheaper AI deployments for the public sector (Microsoft and TSMC Joint Innovation Lab for silicon design on Azure).

That industrial glue - backed by policy and investment - leverages TSMC's semiconductor leadership and the broader industry ecosystem to lower vendor lock‑in, speed time‑to‑market, and keep sensitive workloads onshore, reinforcing the island's broader AI‑semiconductor synergy and giving municipal and national agencies practical, lower‑cost routes to sovereign models and edge deployments (Taiwan AI and semiconductor synergy: leading global innovation); the upshot is simple and concrete: hardware and cloud partners make what used to take years possible in months, turning chip advantage into measurable speed and savings for public services.

“Nurturing ecosystem collaboration has been the core of TSMC Open Innovation Platform® (OIP), and this Joint Innovation Lab with Microsoft is one big step forward elevating cross-industry partnership to the next level.” - Dr. Cliff Hou, TSMC

Taiwan is turning talent and reskilling into a direct cost‑reduction lever for public agencies: the Executive Yuan's jumpstart program and related pledges aim to train hundreds of thousands - by 2028 the plan targets professional training for roughly 450,000 people (and to attract 120,000 foreign professionals) - so government organisations can hire or redeploy staff who already understand AI workflows rather than paying expensive consultants; see the Executive Yuan overview for details.

Shorter cycles of practical training are already underway: a NT$50 million government tranche funds a fast‑track scheme to develop 152 skilled professionals in its first phase and scale toward training 200,000 AI practitioners over four years, with trainees supported by stipends (NT$20,000/month during study and NT$30,000/month during internships) and employer internships that link graduates straight into vendor or agency roles.

These national moves sit alongside the AI New Ten Major Construction pledge to pour sustained resources into human capital (a dedicated talent investment line appears in the plan), creating pipelines that let municipal IT teams replace routine hires with multi‑skilled staff who can maintain chatbots, run sovereign models and cut outsourcing fees - so labor savings show up quickly in reduced contractor bills and faster service delivery.

“AI will be ubiquitous in the future.”

Regulatory sandboxes and legal flexibility are vital gears in Taiwan's governance toolkit, giving agencies a controlled‑risk way to field new AI and fintech services - the FinTech Development and Innovation and Experiment Act (the Sandbox Act) lets applicants test under FSC approval with certain licensing requirements relaxed, and AI test fields are now being promoted to accelerate safe public‑sector experimentation; see the Sandbox Act summary on Lee and Li law.asia summary of Taiwan's FinTech Sandbox Act and a practical guide to AI sandboxes and test fields from Nucamp AI Essentials for Work syllabus - Complete Guide to Using AI in Government.

At the same time, independent reviews warn the sandbox has been too cautious in practice - committees meant to rule in 60 days often slowed startups down, and restrictive conditions limited scaling - so policymakers are wrestling with how to pair experimental freedom with consumer protection; Kapronasia's reassessment of Taiwan's sandbox shows why faster, clearer review paths matter if sandboxes are to deliver tangible, cost‑saving public‑sector AI.

“the legislation would greatly increase the flexibility of financial-oriented regulations that have been limiting innovation, while ‘breaking down barriers between sectors, so that tech and financial firms can work together seamlessly.'” - Karen Yu

Taiwan's sectoral rollouts show how AI can shave costs while boosting service speed: municipal projects in Yilan are using IoT‑based intelligent disaster prevention to automate alerts and coordinate responses - turning scattered sensors into an early‑warning fabric that reduces manual monitoring burdens (Research on Technology Governance of IoT Smart City in Yilan); similarly, routine citizen interactions are being offloaded to AI chatbots so public hotlines handle first‑tier inquiries automatically and human agents focus on complex cases (AI chatbots for public hotlines in Taiwan).

To make these deployments safe and procurement‑friendly, ministries can adapt ready‑made prompts to draft a risk‑tiering framework that simplifies compliance checks under the emerging AI Basic Act, lowering legal and rollout friction for suppliers and agencies alike (AI risk‑tiering framework for government procurement).

The result is concrete: fewer manual hours, faster response times and cheaper pilots that scale into enduring, on‑island efficiencies.

Taiwan's macro picture now shows tangible, measurable payoffs from AI‑led demand: real GDP jumped 8.01% year‑on‑year in 2025Q2 and the statistics agency has raised the full‑year growth forecast to 4.45% for 2025 - driven largely by a roughly 35% surge in exports and strong private investment tied to AI and data‑centre buildouts - so AI infrastructure and chip exports are no longer niche inputs but material drivers of national growth (see the DGBAS preliminary estimate).

That export‑heavy expansion lifts forecasts for exports (up more than 20% in 2025) and has pushed indicators like manufacturing and industrial orders to multi‑month highs, yet risks remain: weak domestic consumption and looming U.S. tariff uncertainty could temper momentum, making headline GDP gains fragile without continued policy support and export market clarity (AmCham's September brief and FocusTaiwan coverage outline these balances).

The upshot for government companies is clear - AI investments are already showing up in national accounts, but careful risk management is essential to lock short‑term gains into long‑run productivity.

Indicator	Value / Note
Q2 2025 real GDP (YoY)	8.01% (DGBAS preliminary)
2025 real GDP forecast	4.45% (DGBAS)
2025 exports projection	~23.7–24.0% (DGBAS / AmCham)
2026 real GDP forecast	2.81% (DGBAS)
CPI 2025	1.76% (DGBAS)

“The core of AI hegemony lies in Taiwan.” - Jensen Huang

Practical beginners' steps for Taiwan government teams are deliberately small, governed and test-driven: map each use case to the Draft AI Product and System Evaluation Guidelines' risk tiers and start with minimal‑risk pilots (for example, clearly labelled AI chatbots for first‑tier hotline queries), use the Executive Yuan's

guidance‑before‑legislation

approach to document testing and traceability, and reel up complexity only after basic controls pass review; the Guidelines spell out four risk levels and require transparency measures so citizens know when they're talking to a machine, and high‑risk systems must be checked against ten quality criteria - so bake explainability, privacy (PDPA) and logging into every pilot from day one (see the STLI overview of Taiwan's AI governance and the Draft AI Product and System Evaluation Guidelines).

Pair technical pilots with a simple legal and procurement checklist - capture vendor due diligence, third‑party supervision and contractual liability allocations per industry guidance - before scaling (Lee and Li's practice guide is a handy reference).

Finally, use ready templates and a risk‑tiering prompt to speed assessments and keep procurement predictable: start small, prove measurable labour savings, then iterate into sovereign models or sandboxes as confidence and controls mature (see our Nucamp risk‑tiering prompt for a starter template).

Risk level	Beginner action
Unacceptable	Do not deploy; redesign to remove discriminatory or manipulative features
High	Formal evaluation, third‑party testing, ten‑criteria checks (safety, fairness, explainability)
Limited	Pilot with transparency obligations (label AI outputs) and documented opt‑outs
Minimal	Fast pilots (chatbots, spam filters) with basic logging and review

Taiwan's path forward is practical: finish the draft AI Basic Act while keeping the “guidance‑before‑legislation” spirit, align risk tiers with international frameworks and focus agency pilots on minimal‑risk, high‑payoff use cases - think clearly labelled chatbots for first‑tier hotline queries that free staff for complex, empathy‑led work - before moving to higher‑risk systems that need testing, certification and traceability; see the Ministry of Science & Technology / STLI overview for the Action Plan's pillars and MODA's evaluation work.

Pair these staged pilots with on‑island compute and evaluation capacity (TAIWANIA 2 and TAIDE), tighter procurement templates and sandboxed test fields so municipalities can scale sovereign models without exposing sensitive data (legal context and risk pointers are usefully summarised by Lee & Li).

A simple rollout checklist helps: map use cases to risk tiers, run short, measurable pilots, track labour‑hour savings and upskill in parallel - practical training like the AI Essentials for Work bootcamp accelerates prompt skills and operational adoption so teams can translate policy into cheaper, faster public services.

Attribute	AI Essentials for Work
Description	Gain practical AI skills for any workplace; learn AI tools, prompt writing, and apply AI across business functions.
Length	15 Weeks
Courses	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills
Cost	$3,582 early bird; $3,942 afterwards; paid in 18 monthly payments
Syllabus / Register	AI Essentials for Work bootcamp syllabus

“Early communication with stakeholders is crucial,”