The Complete Guide to Using AI as a HR Professional in Taiwan in 2025

Taiwan HR teams should treat 2025 as the year to turn AI experiments into measurable value: Mercer's Global Talent Trends warns that redesigning work for a “machine-augmented” workforce and human‑centric productivity are top priorities, and practical wins include personalized onboarding, skills-based hiring, and outcome-focused workflows.

Local HR leaders can harness conversational assistants that support Mandarin and Traditional Chinese to automate screening and scheduling at scale while preserving candidate experience (conversational hiring assistant), and closing the skills gap will mean structured reskilling programs - start with applied courses like the AI Essentials for Work syllabus.

Treat governance, consent and human oversight as non‑negotiable, prioritize human skills that AI can't replicate, and measure impact in productivity, retention and fairness rather than novelty.

“HR is tasked with cultivating continued innovation while maintaining a healthy work culture in a climate where opportunities are high, yet budgets are tight.” - Kate Bravery, Senior Partner, Mercer

Taiwan's new AI Basic Act is best read as a principle-first framework that aims to shepherd innovation while codifying ethics: the National Science and Technology Council's July 2024 draft lays out core principles - sustainability, human autonomy, privacy and data governance, security, transparency, fairness and accountability - and asks ministries to build a risk‑based regime rather than a one‑size regulatory ban; the draft also explicitly tasks the Ministry of Digital Affairs (MODA) with developing an AI risk classification and subordinate rules that sector regulators will apply, while encouraging regulatory sandboxes and public‑private cooperation (see the NSTC public consultation summary).

Critics from civil society - notably the Taiwan Association for Human Rights and the Judicial Reform Foundation - warn the draft is short on concrete enforcement mechanics and could, via broad clauses like Article 5, create conflicts with the PDPA unless the law clarifies how existing data and privacy rules will prevail; others urge clearer accountability rules, explicit prohibitions for the riskiest uses, and stronger procedural safeguards for government AI. For HR teams this means preparing now for required transparency, stronger data‑minimization and human‑oversight clauses in procurement and vendor contracts as the bill moves through the legislature; follow the draft and committee timeline closely for implementation details and sectoral guidance from MODA and industry regulators.

“Taiwan's AI industry must deploy ahead - not just regulate, but actively promote R&D and innovation. If authorities focus only on control and red lines, Taiwan will always lag behind in the AI wave.” - Legislator Ko

Taiwan's AI strategy in 2025 reads like a national industrial playbook: an ambitious, government‑led push to turn the island into an “AI Island” by pairing big public investment with cross‑agency programs that target talent, computing capacity and trustworthy AI practices.

The National Development Council's “Five Trusted Industry Sectors” ties AI to core industries and backs new funds and infrastructure, while the much‑publicised “AI New Ten Major Construction” proposes an NT$200 billion‑scale investment to build sovereign computing, platform software exports and R&D in areas such as silicon photonics, quantum and robotics (Taiwan AI "New Ten Major Construction" NT$200 billion plan), and the NDC's plan maps the sectoral priorities and a dedicated NT$10 billion seed fund for AI and digital industries.

The Ministry of Digital Affairs and NSTC are creating evaluation services and labelling frameworks - MODA's AI product evaluation effort and the government's encouragement of regulatory sandboxes aim to make adoption safe and auditable (MODA trustworthy AI product evaluation and labelling initiative).

For HR teams the headline is clear: national policy now treats skill pipelines, public‑private training and evaluation standards as strategic infrastructure, so reskilling, talent pipelines and vendor due diligence will be core to any HR AI roadmap.

“Without proper AI regulations, Taiwan risks chaotic applications and hindered industrial development; citizens could be ‘running naked in the AI wave'.” - KMT legislator Ko Ru‑chun

Practical HR use-cases in Taiwan in 2025 are already pragmatic rather than hypothetical: AI can automate screening and scheduling with Mandarin and Traditional Chinese support (think a conversational hiring assistant that preserves candidate experience), speed routine performance analytics, and power personalised L&D pathways that plug into national training pipelines - while also demanding tighter vendor checks and explainability in contracts.

Recruitment gains are the clearest win: automated short‑listing and interview logistics free HR to focus on human judgement and cultural fit, and agentic AI/“copilot” tools showcased at AI Expo Taiwan promise to turn knowledge bases into 24/7 decision support for hiring, learning and internal mobility.

For performance and monitoring, the legal guide for Taiwan flags privacy and PDPA constraints plus the need for traceability and fairness checks in any monitoring or evaluation system (Taiwan AI legal and governance guidance (Chambers Practice Guides)), so HR playbooks should bake in data minimisation, consent steps and audit trails.

Upskilling at scale is supported by government programs: a newly funded training phase includes NT$50 million to accelerate AI-ready talent and a multi‑phase curriculum with industry internships - an HR team that partners with these programs can shorten onboarding into AI roles and reduce vendor dependence (Taiwan NT$50 million AI-ready talent training initiative (DIG.Watch)).

Practical caution: adoption in many firms still lags, so pilot small, prove outcomes for retention or time‑to‑competency, then scale with governance guardrails and clear contractual liability clauses.

For hands‑on HR benefits, start with conversational screening, AI copilots for learning pathways, and automated compliance checks to balance productivity gains with employee rights (Conversational hiring assistant tools for HR in Taiwan (resource)).

“The tech is ready. Businesses that ignore AI today will soon be at a competitive disadvantage - just like companies without websites in the early 2000s.” - Alex Yeh, AI Expo Taiwan 2025

HR teams must treat data protection as survival‑level risk management: Taiwan's PDPA explicitly treats items like name, passport number, fingerprints and health records as personal or “sensitive” data, so even a single misplaced passport number or biometric file can trigger breach notices, civil damages and steep administrative or criminal penalties - recent guidance flags fines up to NT$15 million and possible imprisonment for serious violations (Taiwan PDPA definitions and sanctions - DLA Piper).

Practical implications for HR are concrete: give clear privacy notices at first collection, limit hiring and monitoring data to what's necessary, and treat employment contracts and written consent clauses as a key legal basis for routine monitoring and attendance checks where permitted (ICLG guide to Taiwan data protection laws and regulations).

Cross‑border transfers need careful review (with special restrictions historically on transfers to mainland China) and sector rules may impose 72‑hour reporting to regulators for material breaches, so vendor due diligence, airtight data‑processing clauses and security plans should be non‑negotiable.

For a ready internal playbook, follow examples like corporate employee privacy declarations that map purposes, retention and remediation steps so HR can balance talent automation with PDPA compliance and clear contractual liability for suppliers (Veolia Taiwan personal data protection employee policy).

Governance for HR teams in Taiwan should be practical, paper‑trail focused and risk‑aware: publish a clear AI usage policy that links hiring and monitoring practices to PDPA consent, data‑minimisation and retention rules, require vendor contracts to include testing histories and liability clauses, and build audit logs and traceability so automated decisions can be reconstructed “like a signed ledger” when questions arise; these steps reflect the Draft AI Act's emphasis on transparency, traceability and human oversight and mirror the board-level fiduciary advice in Taiwan's AI governance guidance (see the Taiwan AI governance practice guide - Lee & Li / Chambers for detailed recommendations) (Taiwan AI governance practice guide - Lee & Li / Chambers).

Operationally, HR should embed repeated model validation and employee‑facing notices into procurement checklists, pilot systems in sandboxes, and use government evaluation services where available - MODA's AI product and system evaluation framework and the national evaluation centre are designed to help organisations test interpretability and safety before wide rollout (overview of MODA AI product and system evaluation framework) (MODA AI product and system evaluation framework overview).

Start small, document everything (purpose, data sources, outcomes and remediation steps) and align board reporting to these controls so oversight is auditable, defensible and scalable as Taiwan's legal framework evolves.

“Early communication with stakeholders is crucial.”

When buying AI tools or outsourcing HR functions in Taiwan, contracts must be the line of defence: start with a clear statement of lawful purpose, minimum necessary data categories (remember PDPA defines sensitive items like medical records and passport numbers), and explicit instructions that the vendor will only process data as commissioned; require technical and organisational security measures and the right to audit so records and logs can be reconstructed if something goes wrong.

Build breach obligations into the SOW - timelines (some sectors expect material breach reporting within 72 hours), detailed notification content and remediation plans - and cap exposure but keep indemnities for wilful or negligent PDPA violations (administrative fines can reach into the millions and serious breaches carry criminal penalties); see the DLA Piper Taiwan PDPA overview and the ICLG Taiwan data protection chapter for specifics.

Include sub‑processor controls, approval rights for cross‑border transfers (Article 21 restrictions apply and some competent authorities limit transfers to certain jurisdictions), data subject request support, retention/deletion rules and proof of secure deletion, and require vendor cooperation for audits and DSR fulfilment.

For cloud vendors, map responsibilities to the provider's shared‑security model and insist on region selection, encryption and third‑party attestations; for guidance see AWS data privacy and compliance resources for Taiwan.

Treat procurement as a privacy risk process: score vendors on PDPA alignment, breach history, labelling/certification and contractual enforceability before scaling any HR automation.

Agentic AI is poised to reshape workforce design in Taiwan by turning discrete HR tasks into autonomous, goal‑driven workflows - think agents that continuously source talent, match internal skills to openings, and even manage preboarding and offers while humans focus on coaching and culture; local firms already using AI for compliance and better work‑life policies suggest a practical path for Taiwan HR to adopt agents without losing employee trust (AIHR article on AI use in Taiwanese employee retention and compliance).

Start by decomposing jobs into units of work, then assign where agents should augment, automate, or own tasks so roles evolve from execution to oversight, model maintenance and ethical governance - this human‑at‑the‑helm approach reduces risk and preserves judgment.

Proven safeguards include human‑in‑the‑loop controls, routine audits and explainability checks drawn from global practice (see practical guidance in the HRCI primer on agentic AI in HR and practical governance), and pilot projects that measure retention, fairness and time‑to‑competency before scaling.

For pragmatic use‑cases and governance templates that translate to measurable outcomes in hiring, L&D and workforce planning, review vendor case studies and implementation playbooks such as Gloat and Beamery case studies on agentic AI use-cases in HR; imagine a trusted “digital teammate” that quietly handles repetitive admin overnight while HR designs the next talent strategy by morning.

“Predictive AI shows us what might happen, generative AI creates something new, but agentic AI takes the next step: acting on our behalf. For HR, that means reimagining the work we do every day - shifting time from repetitive processes to higher-value human interactions.” - Andre Allen, HRCI

Becoming an AI‑savvy HR pro in Taiwan in 2025 means pairing practical, hands‑on skills with a clear understanding of the island's policy and evaluation ecosystem: learn core concepts (data minimisation, explainability, human‑in‑the‑loop controls and PDPA constraints) and couple them with model validation, vendor due‑diligence and simple data‑engineering habits so automated hiring or L&D pilots are auditable:

like a signed ledger.

Start with short, focused courses in Taipei that cover AI strategy and governance, add a recognised credential such as AIHR Artificial Intelligence for HR certificate to prove applied capability, and tap government resources - MODA's and NSTC's evaluation and talent programmes are explicitly designed to help organisations test and label trusted systems - so pilots can move to scale with confidence (Chambers Taiwan AI legal and governance guide, AIHR Artificial Intelligence for HR certificate, BMC Taipei short AI strategy and governance courses).

Practically, schedule micro‑projects (screening assistant, L&D copilot, compliance checker), measure retention or time‑to‑competency, and document outcomes and risks before scaling - this blend of training, certification and tightly scoped pilots is the fastest path from curiosity to measurable HR impact.

Conclusion: make 2025 the year Taiwan HR moves from experimentation to disciplined adoption: first, take an AI inventory and map data flows so every screening, monitoring or L&D use-case is tied to a lawful PDPA basis and minimised to the necessary fields; second, classify each system by risk (follow MODA/NSTC guidance and the Draft AI Act's risk-based approach) and pilot only low‑to‑medium risk systems with human‑in‑the‑loop controls and clear rollback plans; third, bake vendor clauses and audit rights into contracts, require testing histories and traceability, and use government evaluation services where available to validate safety and explainability (see the Taiwan AI legal and governance handbook at Chambers for practical obligations); fourth, measure pilots with business KPIs - time‑to‑hire, retention, time‑to‑competency - and technical KPIs - bias tests, drift checks and immutable audit logs (treat logs like a cryptographically timestamped ledger); and finally, upskill HR operators so oversight, model validation and vendor due diligence are repeatable (consider a practical program like the Nucamp AI Essentials for Work syllabus to build applied capability).

Keep board reporting tight, document every decision, and scale only after demonstrable fairness, privacy and operational gains - this checklist converts regulatory caution into competitive advantage in Taiwan's fast‑moving AI landscape.

“Don't settle for generic claims about “responsible AI.” Ask for evidence.”