Top 5 Jobs in Government That Are Most at Risk from AI in Taiwan - And How to Adapt

Taiwan's government has placed AI at the center of national renewal: the Ministry of Digital Affairs is rolling out initiatives that prioritize computing power, data and talent, while the cross‑agency “AI New Ten Major Construction” plan (a roughly NT$200 billion proposal) aims to build sovereign infrastructure and spur industry adoption nationwide - part of a push to grow AI output toward NT$1 trillion and train hundreds of thousands of specialists.

The Executive Yuan is similarly prioritizing public‑sector AI adoption to modernize services and share data governance best practices. With vivid projects like Foxconn's planned supercomputer and an aging, 23.4‑million population driving urgency, public servants face fast‑moving change; upskilling programs such as the AI Essentials for Work bootcamp can help clerical and frontline staff learn practical prompt writing and AI tools for day‑to‑day government roles.

“AI can help us develop new solutions more quickly and efficiently, becoming another key engine for economic growth,” - Vice President Bi‑khim Hsiao

Methodology: this analysis started with primary government guidance and high‑quality legal summaries to keep the findings tightly grounded in Taiwan's own policy direction - chiefly the Executive Yuan's draft “Guidelines for the Use of Generative AI” and related NSTC and Ministry of Digital Affairs statements - and then triangulated those rules against independent legal commentary and international trackers to understand practical limits for public‑sector roles.

Key sources reviewed include the Executive Yuan release on the Cabinet's approval of draft guidelines (Executive Yuan draft Guidelines for the Use of Generative AI (Taiwan government release)), a detailed legal brief summarising those Guidelines and compliance implications (Lee, Tsai & Partners legal brief on Taiwan generative AI guidelines), and a global regulatory tracker to place Taiwan's approach in international context (White & Case AI Watch global regulatory tracker for Taiwan).

The methodology prioritized explicit, government‑issued limits (for example: bans on using generative AI for classified documents, disclosure and procurement rules, and the requirement that human handlers make final judgments) and mapped those constraints onto job tasks to assess where routine, repetitive duties are most exposed - a pragmatic approach that focuses on where policy forbids automation, where it demands transparency and data governance, and where upskilling (e.g., prompt literacy and human oversight) buys the greatest risk reduction.

“The Guidelines recognize that the use of generative AI contributes to improved administrative efficiency.”

Administrative clerks and clerical officers are among the most exposed in Taiwan's public sector because their work is dominated by repetitive, high‑volume tasks - data entry, scanning and indexing paper forms, routine filing, simple verification and report generation - that are precisely what optical character recognition (OCR), robotic process automation (RPA) and template‑driven workflows can absorb; see a practical list of these duties in a “Data Entry Administrator” job breakdown (Data Entry Administrator job responsibilities and duties) and the related “Office Automation Clerk” role that explicitly calls out automation tools and document management as core tasks (Office Automation Clerk job description and automation tasks).

With Taiwan's drive to modernize services and adopt AI across agencies, routine clerical pipelines become obvious targets for efficiency gains, but the Executive Yuan's guidance on generative AI and public‑sector use means automation must preserve human oversight and transparency - so the real risk is not just job loss but deskilling unless staff are reskilled to manage exceptions, validate outputs and design safe prompts; imagine a filing cabinet that once groaned under paper now replaced by a system that only surfaces “exceptions” for human review, a vivid shift that makes clear where training in prompt literacy and automation governance will matter most (see government use cases and prompts for public services in Taiwan's AI planning Taiwan government AI prompts and public service use cases).

Frontline customer‑service and call‑centre staff in Taiwan are especially exposed because their jobs balance high volumes, emotional labour and split‑second judgment - precisely the areas where chatbots promise scale but can fail spectacularly; the Tessa case shows a generative system can produce “potentially harmful answers” when used as a substitute for trained humans (The Conversation: Why replacing frontline workers with AI can be a bad idea).

In emergency dispatch and public‑health hotlines, AI can boost triage, routing and transcription (reducing delays for ambulances or surfacing key background noise), yet regulators and practitioners warn these are high‑risk applications that must keep humans making final decisions (EENA: AI in public safety - emergency services future and risks).

Research on crisis‑line workers also shows mixed feelings: AI can ease routine admin and quality assurance, but hidden automation, informal use by staff, or opaque data practices erode trust and deter help‑seeking - imagine callers hanging up because they suspect a machine, not a person, is listening.

Practical adaptation for Taiwan means focused upskilling in prompt literacy and human‑in‑the‑loop workflows, clear disclosure and strict privacy rules - see concrete government use cases and prompts for public services in Taiwan's planning to guide safe pilots (Taiwan government AI prompts and use cases for public services (Top 10)).

“Ultimately, the chatbot generated what have been described as potentially harmful answers to some users' questions.”

Junior legal officers and paralegals are acutely exposed because their day‑to‑day tasks - drafting memos and contracts, bulk review of court decisions, e‑filing and preparing sentencing summaries - are precisely the inputs for generative models and legal‑data tools that Taiwan's courts and agencies are already experimenting with; the Judicial Yuan's sentencing system and government pilots show how fast AI can surface patterns, but they also underline limits around explainability and oversight (see Lee and Li's practical overview of Taiwan's AI law A general introduction to Artificial Intelligence Law in Taiwan).

That creates three concrete hazards for junior lawyers: invisible IP and training‑data risk when AI drafts documents (TIPO guidance cautions about copyright and training data), loss of craft as routine research is automated, and exposure to liability if an AI‑produced recommendation lacks traceability or harbours bias - issues flagged across Taiwan practice guides and global trackers (Artificial Intelligence 2025 - Taiwan).

The practical fix is targeted reskilling: prompt literacy, record‑keeping of model inputs/outputs, and strict human‑in‑the‑loop checks so a polished AI draft never becomes an unchallengeable source of risk; imagine a flawless brief that can't tell you why it picked its precedents - that's where training and governance must step in.

AI cannot be considered a natural or legal person, so it cannot be considered an author (or co-author) of a work.

Routine data analysts and reporting officers are especially exposed because the island's push for cloud, AI and real‑time dashboards turns repetitive ETL, template reports and trend‑monitoring into automatable pipelines: Taiwan's smart‑city projects and air‑quality sensor networks already feed live dashboards, while government scorecards and public visualizations create standard outputs that models can replicate or refresh on demand (see coverage of AI, cloud and IoT trends in Taiwan and how sensors feed dashboards AI, cloud and IoT trends in Taiwan and the role of state dashboards in data‑driven governance The Rise of Data‑Driven Governance: How State Dashboards Lead the Way).

The civic‑tech movement that made open data ubiquitous - g0v's hackathons, public platforms and PDIS experiments - further lowers the barrier for automated reporting by standardizing datasets and APIs (g0v civic tech and Open Data).

So what matters is less the disappearance of whole jobs than the shift in work: routine tables and weekly reports become background plumbing, while human judgment must concentrate on anomalies, policy interpretation and the civic questions that raw numbers can't answer - a change that makes prompt literacy, data governance and anomaly‑investigation skills the most valuable next steps for affected staff.

Permit and licensing processors and benefits case officers are at particular risk because their day‑to‑day work - checking eligibility, validating documents and applying fixed policy rules - maps neatly onto automation: template matching, rules engines and generative assistants can draft decisions or prefill determinations, but Taiwan's strict personal‑data regime and sectoral guidance turn those efficiencies into legal minefields.

The Personal Data Protection Act treats health, financial and biometric details as “sensitive” and demands careful purpose‑limitation and informed consent, so automated drafting or batch approvals can create PDPA exposure and audit headaches unless every model input, output and consent link is logged (see the ICLG Digital Health chapter on Taiwan's PDPA and health data rules).

Intellectual‑property and training‑data issues add another layer - TIPO's stance on AI and copyright means agencies must avoid untraceable model outputs that could implicate third‑party works (see Lee & Li's overview of Taiwan's AI governance).

Practically, the fix is operational: keep humans in the loop for final judgments, build traceable approval trails, train staff in prompt literacy and exception handling, and pilot conservative, auditable automation so a conveyor‑belt of routine files becomes a smart triage system rather than an unaccountable black box.

For concrete legal framing and government guidance, consult the Executive Yuan and sectoral practice guides on AI deployment.

AI cannot be considered a natural or legal person, so it cannot be considered an author (or co-author) of a work.

Conclusion: Taiwan's path is clear - a principles‑led, risk‑based AI regime (from the Taiwan AI Action Plan 2.0 to the draft AI Basic Act) and active Executive Yuan guidance create both guardrails and opportunities, so workforce planning must be equally strategic: map high‑risk tasks, pilot conservative, auditable automation in sandboxes, and channel displaced activity into roles that require judgement, anomaly investigation and oversight.

Practical next steps for agencies include targeted retraining (human‑in‑the‑loop skills, prompt literacy and traceability), coordinated upskilling across ministries, and fast feedback loops with civic participation platforms so public concerns shape deployments; these measures reflect Taiwan's

guidance‑before‑legislation

stance and calls for stakeholder engagement in the draft Basic Act (see Taiwan's AI governance overview).

Start small with measurable pilots that keep humans as final decision‑makers, scale proven, auditable workflows, and invest in talent pipelines so clerks become

exception hunters

and call‑centre staff become AI‑enabled quality controllers rather than replaced operators - a practical, rights‑aware transition the Executive Yuan is already supporting through civil‑servant AI literacy drives.

For teams ready to act now, consider practical workplace training such as the AI Essentials for Work bootcamp to build prompt and oversight skills that align with Taiwan's evolving rules and procurement expectations.