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Indonesia's labor law is unprepared for AI-driven disruption: current statutes emphasize post-termination compensation but lack clear duties for retraining or mechanisms to share AI-driven productivity gains, prompting calls for legal reform to embed lifelong learning, social protection, and fiscal instruments such as automation taxes.

Reformasi Hukum Ketenagakerjaan di Era Artificial Intelligence: Perlindungan Pekerja, Reskilling, dan Model Kompensasi Inovatif di Indonesia
Muhammad Urifianto Ardhan, Gunardi Lie · March 29, 2026 · MANTAP Journal of Management Accounting Tax and Production
openalex descriptive n/a evidence 7/10 relevance DOI Source PDF
A normative legal analysis finds Indonesian labor law—centered around the Omnibus Law (UU Cipta Kerja)—is reactive and inadequate for AI-driven displacement, lacking explicit mandates on retraining, redistribution of AI benefits, or legal foundations for policies like UBI and automation taxes, and recommends progressive legal reforms including stronger social protection and rights to lifelong learning.

Perkembangan pesat Artificial Intelligence (AI) telah membawa perubahan mendasar dalam struktur pasar tenaga kerja, termasuk di Indonesia, dengan meningkatnya risiko penggantian pekerjaan manusia oleh teknologi otomatisasi. Fenomena ini tidak hanya menimbulkan persoalan ekonomi, tetapi juga memunculkan tantangan hukum terkait perlindungan hak pekerja, keadilan sosial, serta keberlanjutan sistem ketenagakerjaan. Penelitian ini bertujuan untuk menganalisis bagaimana hukum ketenaga kerjaan Indonesia, khususnya melalui Undang-Undang Cipta Kerja dan peraturan turunannya, mengklasifikasikan dan menjustifikasi Pemutusan Hubungan Kerja (PHK) akibat adopsi AI, serta menilai kecukupan perlindungan hukum yang tersedia bagi pekerja terdampak. Selain itu, penelitian ini juga mengkaji kebutuhan akan dasar hukum bagi penerapan model kompensasi inovatif, seperti Universal Basic Income (UBI), pajak otomasi, dan skema distribusi manfaat produktivitas AI, serta mengeksplorasi kemungkinan adaptasi kerangka hukum nasional untuk melegitimasi pendekatan tersebut. Lebih lanjut, penelitian ini menyoroti pentingnya reskilling dan upskilling dalam menghadapi disrupsi teknologi, serta mengkaji urgensi pengakuan “hak atas pengembangan keterampilan berkelanjutan” sebagai bagian integral dari perlindungan pekerja di era digital.Metode yang digunakan adalah penelitian hukum normatif dengan pendekatan perundang-undangan, konseptual, dan komparatif, yang didukung oleh analisis literatur dari jurnal nasional terindeks SINTA dan jurnal internasional bereputasi. Hasil penelitian menunjukkan bahwa kerangka hukum ketenagakerjaan Indonesia saat ini masih bersifat reaktif, dengan fokus pada kompensasi pasca PHK yang belum mampu menjawab dampak jangka panjang dari disrupsi AI. Selain itu, belum terdapat pengaturan eksplisit mengenai kewajiban pelatihan ulang maupun mekanisme distribusi manfaat teknologi secara adil. Oleh karena itu, diperlukan reformasi hukum yang lebih progresif dan adaptif, termasuk penguatan sistem jaminan sosial, pembaruan kebijakan fiskal, serta pengakuan hak atas pembelajaran sepanjang hayat. Dengan demikian, hukum diharapkan tidak hanya berfungsi sebagai alat perlindungan, tetapi juga sebagai instrumen strategis dalam mengelola transisi menuju masa depan kerja yang lebih inklusif, adil, dan berkelanjutan di era kecerdasan buatan.

Summary

Main Finding

Indonesia’s current labor-law framework (including UU Cipta Kerja and implementing regulations) is largely reactive and ill-equipped to address job displacement driven by AI. PHK caused by automation is being fitted into existing categories (efficiency, restructuring) without AI-specific safeguards. There is no explicit legal obligation for employer-provided reskilling/upskilling, nor statutory mechanisms to ensure fair distribution of AI productivity gains. The authors conclude that progressive, adaptive legal reform is required—strengthened social protection, fiscal policy updates (e.g., automation tax, UBI pilots, productivity-sharing), and formal recognition of a “right to lifelong skills development.”

Key Points

  • Classification of AI-driven layoffs:
    • PHK due to AI is likely to be classified under existing grounds: “efficiency” or “restructuring” (cited articles in UU Ketenagakerjaan / UU Cipta Kerja and PP 35/2021).
    • Existing categories are ambiguous for AI: firms adopting AI for competitiveness (not distress) can still claim “efficiency,” creating a legal gap that may disadvantage workers.
  • Inadequacy of compensation and protections:
    • Current statutory remedies (pesangon, termination benefits) are one-off and do not account for long-term displacement, lost career trajectories, or re-employment difficulties when occupations vanish.
    • No explicit legal duty for employers to provide reskilling/upskilling or placement support tied to AI-induced displacement.
    • Jaminan Kehilangan Pekerjaan (JKP) exists but is generic and not tailored to AI-driven transition needs.
  • Legal and policy instruments considered:
    • Innovative compensation and redistribution mechanisms discussed: Universal Basic Income (UBI), automation taxes, AI productivity-sharing schemes, and mandated benefit-sharing.
    • The paper examines legal bases needed to accommodate/legitimize such schemes within Indonesian law.
  • Rights framing:
    • Authors argue for recognizing a legal “right to continuous skills development” as a component of worker protection in the digital era.
  • Recommendations overview:
    • Progressive legal reform: clarify PHK grounds vis-à-vis AI; mandate employer or state roles in reskilling; embed lifelong learning rights.
    • Strengthen social security and active labor-market policies.
    • Explore fiscal reforms (automation tax) and redistribution models (UBI, productivity sharing), with legal instruments to operationalize them.
    • Increase algorithmic transparency and accountability in workplace AI deployment.
  • Comparative and normative lens:
    • The paper uses comparative examples from other jurisdictions to identify transferable lessons and best practices, highlighting gaps in Indonesian regulation.

Data & Methods

  • Research design: normative (yuridis) legal research.
  • Approaches:
    • Statute approach: systematic review of Indonesian primary legislation (e.g., UUD 1945, UU No.13/2003 on Manpower, UU No.11/2020 Cipta Kerja, and PP No.35/2021 on PHK) and implementing regulations.
    • Conceptual approach: doctrinal analysis of legal concepts (efficiency, restructuring, worker rights, compensation, reskilling).
    • Comparative approach: review of policies/regulatory responses in other jurisdictions (EU/advanced economies) for lessons.
  • Sources:
    • Primary legal texts, court decisions, and regulatory materials.
    • Secondary literature: national (SINTA-indexed) and international peer-reviewed articles; reports from ILO, World Economic Forum, McKinsey, and other relevant institutions.
    • Tertiary references (legal dictionaries, encyclopedias) for conceptual framing.
  • Analysis: qualitative, descriptive, interpretive, and evaluative—identifying legal gaps and prescribing prescriptive recommendations.

Implications for AI Economics

  • Labor market dynamics and welfare:
    • Legal uncertainty around AI-driven PHK can exacerbate transitional unemployment, skill mismatch, and long-term scarring effects—raising social welfare costs beyond immediate severance payouts.
    • One-off compensation is insufficient where whole occupations shrink; active investment in reskilling/upskilling is economically efficient to preserve human capital and facilitate sectoral reallocation.
  • Incentives for firms and technology adoption:
    • If legal frameworks impose clear reskilling obligations or benefit-sharing requirements, firms’ adoption calculus will change—potentially slowing some automation but encouraging complementary investment in human capital and higher-value tasks.
    • Regulatory clarity reduces litigation risk and can stabilize firm expectations, encouraging productive AI adoption rather than opportunistic workforce displacement.
  • Redistribution and fiscal policy:
    • Automation taxes or productivity-sharing levies could internalize some social costs of displacement, fund retraining programs, or finance UBI pilots—but design matters: poorly designed taxes may distort investment, encourage offshoring, or be evaded.
    • UBI and broad cash transfers can provide consumption smoothing during transitions but are fiscally costly; targeted retraining and wage insurance may yield higher social returns per rupiah spent in many contexts.
  • Human capital and inequality:
    • Legal recognition of lifelong learning rights and funded reskilling programs can mitigate widening inequality by improving access to in-demand skills; without it, AI adoption risks concentrating gains among capital owners and high-skill workers.
  • Policy sequencing and complementary measures:
    • Economically optimal policy mixes will likely combine: clearer legal rules on PHK; employer co-responsibility for retraining (possibly subsidized); active labor-market policies; targeted income support; and pilot redistribution mechanisms (automation levies, participatory profit-sharing).
    • Algorithmic transparency and accountability in workplace AI reduce informational asymmetries, enabling better labor-market matching and more equitable automation outcomes.
  • Research and evaluation needs:
    • Policymakers should pilot and rigorously evaluate interventions (e.g., employer training mandates, automation taxes, UBI pilots, productivity-sharing schemes) to measure impacts on employment, firm behavior, innovation, and public finances before scale-up.

Overall, the paper highlights that legal design is a key determinant of the economic distributional impacts of AI: law can either smooth transitions and promote inclusive growth or leave workers exposed to durable dislocation and inequality. Policymakers should pair legal clarity with economically efficient, evidence-based instruments to manage the AI transition.

Assessment

Paper Typedescriptive Evidence Strengthn/a — The study is a normative legal analysis grounded in statutory interpretation, conceptual argument, comparative law, and literature review rather than empirical causal inference or quantitative measurement, so it does not produce causal evidence. Methods Rigormedium — Uses standard normative legal methods (legislative analysis of UU Cipta Kerja and implementing regulations), complemented by conceptual and comparative review and literature synthesis; rigorous for doctrinal aims but lacks primary empirical data, stakeholder interviews, or quantitative validation that would strengthen policy claims. SampleLegal and policy texts (Indonesian labor law, notably Undang-Undang Cipta Kerja and implementing regulations), comparative statutes/regulatory examples from other jurisdictions, and secondary literature drawn from indexed Indonesian (SINTA) journals and international peer-reviewed journals; no original survey or administrative microdata used. Themesgovernance labor_markets skills_training inequality adoption GeneralizabilityCountry-specific focus on Indonesian statutory and regulatory framework limits direct applicability to other legal systems., Normative legal conclusions may not reflect real-world employer behavior, labor-market heterogeneity, or enforcement capacity., Literature-based approach depends on selection of sources (SINTA and selected international journals), which may omit non-academic or practitioner perspectives., Absence of empirical measurement of AI adoption or labor outcomes reduces ability to generalize to different sectors, firm sizes, or skill groups., Temporal limitations: legal and regulatory environments evolve rapidly, so conclusions may become outdated as laws or policies change.

Claims (11)

ClaimDirectionConfidenceOutcomeDetails
Perkembangan pesat Artificial Intelligence (AI) telah membawa perubahan mendasar dalam struktur pasar tenaga kerja di Indonesia dengan meningkatnya risiko penggantian pekerjaan manusia oleh teknologi otomatisasi. Job Displacement negative high risiko penggantian pekerjaan oleh automasi (job displacement risk)
0.18
Fenomena adopsi AI menimbulkan tantangan hukum terkait perlindungan hak pekerja, keadilan sosial, dan keberlanjutan sistem ketenagakerjaan. Governance And Regulation negative high kebutuhan perlindungan hukum untuk hak pekerja dan keadilan sosial
0.18
Penelitian ini bertujuan menganalisis bagaimana Undang-Undang Cipta Kerja dan peraturan turunannya mengklasifikasikan dan menjustifikasi Pemutusan Hubungan Kerja (PHK) akibat adopsi AI. Governance And Regulation null_result high klasifikasi dan justifikasi PHK dalam kerangka UU Cipta Kerja
0.3
Penelitian menilai kecukupan perlindungan hukum yang tersedia bagi pekerja terdampak PHK akibat adopsi AI. Social Protection null_result high kecukupan perlindungan hukum bagi pekerja terdampak AI
0.3
Belum terdapat pengaturan eksplisit mengenai kewajiban pelatihan ulang (retraining) maupun mekanisme distribusi manfaat teknologi secara adil dalam kerangka hukum ketenagakerjaan Indonesia saat ini. Governance And Regulation negative high kekosongan regulasi terkait kewajiban pelatihan ulang dan mekanisme distribusi manfaat teknologi
0.18
Kerangka hukum ketenagakerjaan Indonesia saat ini bersifat reaktif, dengan fokus pada kompensasi pasca-PHK yang belum mampu menjawab dampak jangka panjang disrupsi AI. Social Protection negative high orientasi kebijakan hukum (reaktif vs proaktif) dan kecukupan penanganan dampak jangka panjang
0.18
Diperlukan dasar hukum bagi penerapan model kompensasi inovatif seperti Universal Basic Income (UBI), pajak otomasi, dan skema distribusi manfaat produktivitas AI. Governance And Regulation positive high kebutuhan dasar hukum untuk mekanisme kompensasi inovatif (UBI, pajak otomasi, distribusi manfaat)
0.03
Diperlukan reformasi hukum yang lebih progresif dan adaptif, termasuk penguatan sistem jaminan sosial dan pembaruan kebijakan fiskal untuk menangani dampak AI. Governance And Regulation positive high kebutuhan reformasi hukum (jaminan sosial dan kebijakan fiskal)
0.03
Pengakuan 'hak atas pengembangan keterampilan berkelanjutan' (right to lifelong learning) penting dan perlu dimasukkan sebagai bagian integral dari perlindungan pekerja di era digital. Skill Acquisition positive high pengakuan hak atas pembelajaran berkelanjutan untuk pekerja
0.03
Hukum diharapkan tidak hanya berfungsi sebagai alat perlindungan, tetapi juga sebagai instrumen strategis dalam mengelola transisi menuju masa depan kerja yang lebih inklusif, adil, dan berkelanjutan di era kecerdasan buatan. Governance And Regulation positive high peran hukum sebagai instrumen pengelolaan transisi tenaga kerja
0.03
Metode penelitian yang digunakan adalah penelitian hukum normatif dengan pendekatan perundang-undangan, konseptual, dan komparatif, didukung oleh analisis literatur dari jurnal nasional terindeks SINTA dan jurnal internasional bereputasi. Other null_result high metodologi penelitian (penelitian hukum normatif dan tinjauan literatur)
0.3

Notes