Will AI Replace HR Jobs in Taiwan? Here’s What to Do in 2025

Taiwan HR teams should care about AI in 2025 because the island already supplies critical AI infrastructure while national policy pivots toward ethics and governance - see the overview of Taiwan's AI strategy and Action Plan on Taiwan Insight - and that combination changes what work looks like inside every company.

AI is already reshaping talent management, benefits and people analytics, and HR is uniquely positioned to guide responsible deployment, identify which tasks to automate versus augment, and lead reskilling efforts (see the Aon report on how artificial intelligence is transforming human resources and the workforce, the Taiwan Insight overview of Taiwan's AI strategy and Action Plan, and the AI Essentials for Work bootcamp - Nucamp (15-week workplace AI course)).

“When it comes to AI, human resources teams have a significant opportunity to lead the way. It's important not to miss the moment.” - Lambros Lambrou, Aon

The current landscape is unmistakably data-driven: large-scale ADP payroll analysis and Stanford's team show generative AI has already reshaped hiring dynamics in exposed occupations - early-career roles (ages 22–25) saw double‑digit drops in some fields while mid‑career hiring held steady - framing AI as a force that often displaces routine, codified tasks rather than experienced, tacit work (ADP and Stanford generative AI payroll analysis and hiring data, summarized in HR coverage).

For Taiwan HR teams, the “so what?” is direct:

Global trends mean the entry‑level pipeline can be the first thing to thin out, so recruitment tech choices and assessment designs matter more than ever; practical, locally relevant playbooks and examples of AI‑driven recruitment and ATS use in Taiwanese firms can help guide decisions (Nucamp AI Essentials for Work syllabus: AI-driven recruitment guide for Taiwan).

Picture the market like a swift tide that lowers the beach for new hires while leaving seasoned rocks exposed - HR leaders must watch these currents, tighten bias checks, and redesign early-career pathways before the next hiring season.

In Taiwan, AI is quietly redrawing HR job boundaries: routine, codified work - resume sifting, interview scheduling, basic screening - can be substituted by AI so teams spend less time on paperwork and more on people, while strategic, ethical and relationship work is being amplified, not erased.

HiBob's roadmap for AI upskilling makes the case that HR must lead this shift - 90% of HR leaders expect large reskilling needs by 2030 - so Taiwan HR teams should map which tasks to automate and which to augment with human judgment (HiBob: How AI upskilling transforms workplaces & careers).

Practical, local examples matter: see how Taiwanese firms are using applicant-tracking and AI-driven recruitment tools to free up bandwidth for mentoring and retention work (The Complete Guide to Using AI as a HR Professional in Taiwan in 2025), and pay special attention to ethics and consent in video interviewing and automated assessments highlighted in tool reviews (Top 10 AI Tools Every HR Professional in Taiwan Should Know in 2025).

Think of AI like a compass that points to signals in a crowded market - HR still steers the ship, deciding where to invest in reskilling, bias checks, and human judgment so early-career talent isn't silently washed away by automation.

“GenAI and automation tech are not only here to help us improve efficiency. They're also here to help take the pressure off our people and avoid burnout.”

Taiwan HR teams should expect the clearest risk to land on routine, codified work - resume screening, interview scheduling, data entry, payroll and onboarding flows - because applicant‑tracking systems, chatbots and onboarding platforms can already handle large volumes of predictable tasks (onboarding automations alone make up about 20% of HR automations), while recruitment automations and automated offer‑letter creation are surging in use; see practical examples of AI-driven recruitment and ATS tools used by Taiwanese companies and the trade‑offs detailed in the AIIM guide to the pros and cons of automating human resources.

At the same time, roles that grow are those that manage risk, ethics, compliance and human judgment - data privacy stewards, bias‑checker specialists, reskilling designers and employee experience partners - because mishandled automation creates ripple risks across compliance and security that demand oversight; LogicManager's risk playbook makes clear the need for assessments, access controls and real‑time monitoring to keep automated HR safe.

In short: let machines speed repetitive work, but intentionally invest the savings into people‑centric roles that protect candidate consent, fairness and long‑term talent pipelines so early‑career hires aren't quietly washed out by automation.

Start small, strategic, and measurable: build a clear HR roadmap that prioritizes short wins (automate resume screening and scheduling, run bias‑checks on job ads, and pilot AI literacy workshops) while mapping mid‑term projects (career‑pathing programs and AI governance) and long‑term targets (AI‑enabled workforce analytics and reskilling metrics); the AIHR guide for building an HR roadmap lays out this sequencing in a practical way, and TalentGuard's career‑pathing steps offer a ready playbook for protecting early‑career pipelines.

Pair policy with people - use the Taiwan hiring guide to modernize flexible work, wellness and consent policies so automation doesn't silently shrink entry‑level opportunities - and insist on candidate consent and compliance when deploying video interviews and automated assessments.

Measure everything: set SMART KPIs (time‑to‑hire, internal mobility, AI training completion and retention for junior hires), review quarterly, and reallocate gains from routine automation into mentoring, internal mobility and targeted L&D so the “net” of automation captures efficiency without letting early‑career talent wash away.

For quick adoption, start with a pilot, gather feedback, and scale the changes that improve both fairness and business outcomes (AIHR HR roadmap guide for 2025 & beyond, TalentGuard career‑pathing implementation steps infographic, Complete guide: How to hire talent in Taiwan (2025)).

Protecting early-career talent in Taiwan means turning clear signals into urgent, practical action: ADP and Stanford's payroll analysis shows employment for 22–25‑year‑olds in high‑AI exposure jobs fell about 6% (and the youngest software developers were roughly 20% below their 2022 peak), while a Taipei Times survey found Taiwanese companies expect nearly 29.2% of jobs could vanish over the next decade and that almost half are considering AI adoption - so HR must move from hope to design.

Prioritize fast, job‑focused upskilling (micro‑credentials, apprenticeships and mentored on‑the‑job rotations), protect pipelines by routing routine tasks to augmentation tools, and pay the premium for AI‑literate hires rather than letting juniors be the first casualties; companies already report a roughly 31.6% starting‑salary edge for AI‑skilled recruits.

Operationalize this with bias‑checked job ads and explicit candidate consent around automated video assessments, pilot internal mobility programs for juniors, and partner with training providers for time‑boxed bootcamps that map to real roles - small, measurable pivots now can keep entry‑level cohorts from silently shrinking into the statistics.

“human-machine collaboration” - Bingo Yang (楊宗斌), Taipei Times

Taiwan HR leaders can borrow concrete moves from global pilots that translate cleanly to local realities: IBM's HiRo promotion agent shows how an AI “digital worker” can pull fragmented data, notify 10,000 managers and cut administrative friction - saving managers over 50,000 hours in one cycle - so a Taipei-based firm could pilot a similar agent for promotions or complex payroll reconciliations and reallocate time to mentoring and internal mobility; IBM's AskHR and the broader IBM/Moderna total‑rewards examples demonstrate how self‑service assistants and tailored compensation guidance free HR to focus on strategy and compliance (see the IBM HiRo case study and the IBM & Moderna use cases on AI in total rewards), while local playbooks for recruitment and ethical screening (see the Nucamp AI Essentials for Work syllabus) warn to pair pilots with clear human‑in‑the‑loop rules, consent checks for video assessments, and measurable KPIs.

The vivid test: when routine inboxes quiet, HR teams don't vanish - they transform, with some staff upskilling into roles like conversational AI specialists and others focusing on bias audits, reskilling design and candidate experience improvement - small pilots, strict governance, and explicit redeployment plans keep early‑career pipelines intact as automation scales.

“They're spending their time differently as a result of this, and feeling like they're covering a lot more bases.”

Measuring success for AI-enabled HR transformations in Taiwan means moving beyond vendor dashboards to a compact scorecard that ties predictive insights to real business outcomes: adoption of predictive analytics in talent acquisition, percentage reduction in time‑to‑hire, internal mobility rates for early‑career cohorts, completion rates for AI literacy programs, bias‑audit pass rates on job ads and candidate‑consent rates for automated video assessments.

Track hard savings too - Aon's Health Risk Analyzer case shows how predictive models can surface previously unseen high‑risk employees and translate interventions into measurable cost reductions (about $2,000 saved per additionally managed high‑risk member in that example) - so include health‑and‑benefits savings as a KPI where relevant.

Also monitor governance signals: number of human‑in‑the‑loop reviews, flagged bias incidents, and compliance exceptions to protect privacy as HR systems integrate more data.

Benchmarks from the growing HR‑tech and HR‑analytics market help set targets and timelines, and quick bias‑checker pilots (for example, the Nucamp prompts that scan job ads) can prove impact in weeks rather than quarters.

Imagine a retention dashboard that lights up like a heartbeat monitor the moment flight risk spikes - when metrics are this immediate, leaders can reallocate automation gains into mentoring and reskilling, not headcount cuts; measure quarterly, iterate fast, and let the data guide both efficiency and fairness.

“When it comes to AI, human resources teams have a significant opportunity to lead the way. It's important not to miss the moment.” - Lambros Lambrou, Aon

Break the first 90 days into tight, readable sprints: Day 1–30 is learning and assessment - map current HR workflows, inventory vendors and data flows, check consent and bias exposure, and use the Chief AI Officer's 90‑day playbook as a practical sprint template (Chief AI Officer's 90‑Day Playbook for Executing the First 90 Days); Day 31–60 is strategic planning and relationship building - prioritize two short pilots (for example, bias‑checked job‑ad screening and automated scheduling), lock in human‑in‑the‑loop rules, and connect with local AI talent and policy resources that flow from Taiwan's AI Taiwan Action Plan; and Day 61–90 is execution and adjustment - run the pilots, measure a compact scorecard (adoption, time‑to‑hire, consent rates), iterate based on feedback, and redeploy time saved into mentoring and targeted upskilling so early‑career pipelines are protected.

Anchor the plan in Taiwan's national push - tap public programs, talent initiatives and industry support described in the Cabinet's AI development roadmap and MODA's five‑pillar strategy (computing, data, talent, marketing, funding) to access training and infrastructure quickly (Taiwan Cabinet AI Development Roadmap - Cabinet Plans to Develop the Nation's AI Industry, AI Taiwan Action Plan (English) - EY overview).

The vivid test: when the morning inbox goes quiet, a weekly mentoring clinic can replace scheduling headaches - measure that shift, and make the savings permanent for people, not just process.

Conclusion and next steps for HR in Taiwan: treat AI as a governed tool, not a black box, and move fast enough to shape outcomes - start by mapping which HR systems meet Taiwan's “high‑risk” criteria (CV‑sorting, automated interview scoring) under the Administration for Digital Industries' risk‑based AI Product and System Evaluation Guidelines, lock in PDPA‑compliant notice and consent when biometric or video data are used, and keep a human firmly in the loop for hiring and disciplinary decisions; consult Taiwan legal guidance so boards and HR leaders can meet fiduciary expectations while documenting vendor due diligence and testing plans (STLI risk-based AI Product and System Evaluation Guidelines (Taiwan), Lee & Li - Artificial Intelligence 2025: Taiwan legal overview).

Parallel to governance, run short pilots that measure time‑to‑hire, bias‑audit pass rates and junior internal mobility, and invest savings into reskilling - for practical, job‑focused upskilling, consider cohort programs like Nucamp's Nucamp AI Essentials for Work to build prompt, tool and policy skills across HR teams so automation improves careers instead of shrinking entry‑level pipelines.

“AI should not only make us faster, but also bring about a freer, more choice-filled, and more dignified work environment.” - Rock Tsai (Taiwan Mobile)