GCRI · Masa Depan Pekerjaan dan Keterampilan
Otomasi dan pekerjaan
Automation, robotics and AI take over some tasks, assist others and create new ones. Whether this displaces workers, raises productivity and wages or does both depends on the tasks involved, how firms deploy technology and how institutions support transitions. This page sets out how automation affects jobs, how exposure and impact are measured, and what evidence serves the policymakers, employers and unions responsible.
Automation and jobs on the record.
Automation and jobs: how the risk develops.
AI/robotics impact and job redesign. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Tasks, not whole jobs
Most occupations combine tasks that can be automated with tasks that cannot. Automation reshapes jobs more often than it eliminates them outright.
Displacement and reinstatement
Technology displaces labour from some tasks and creates new tasks that need people. Net effects depend on the balance between the two.
Productivity gains may not reach wages
If gains accrue mainly to capital owners, automation can raise output while wages stagnate for exposed workers.
Transitions are costly for individuals
Displaced workers often face long unemployment or lower pay in new jobs, especially older workers and those in declining regions.
How automation and jobs is modelled, measured and tested.
Model, asumsi, data, dan ketidakpastiannya, dalam bentuk yang akan diperiksa ilmuwan atau insinyur. Strukturnya mengikuti metode yang dipublikasikan. Nilai yang digambar bersifat ilustratif, bukan temuan.
Model sistem
The couplings between the systems in automation and jobs: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Automation exposure differs by domain.
Findings are designed for the seri Teknologi Eksponensial. The ILO’s 2023 working paper estimates exposure, which is not job loss.
- Firm adoption data.
- Worker surveys.
Apa yang diukur, dan seberapa baik.
Setiap indikator membawa satuan, frekuensi, jenis sumber di baliknya, dan label bukti yang akan dibawa suatu nilai. Tidak ada nilai yang ditampilkan: sebuah program menyediakannya berdasarkan wewenangnya sendiri.
| Indikator | Unit | Kadensi | Jenis sumber | Label |
|---|---|---|---|---|
| Employment in occupations with high automation exposure | percent of employment | per penilaian | Task-based exposure studies | ESTIMASI |
| Firms using AI or robots | percent of firms | per survey | Business surveys | DILAPORKAN |
| Displaced worker re-employment rate | percent re-employed within a year | per survey | Displaced worker surveys | DILAPORKAN |
| Wage change after displacement | percent | per survey | Linked employer-employee data | ESTIMASI |
| Labour share of income | persen PDB | tahunan | Neraca nasional | ESTIMASI |
| New job titles emerging | jumlah | tahunan | Occupational classification updates and job postings | TERAMATI |
Asal bukti.
Set data publik bernama dan catatan kelembagaan, masing-masing dengan kelas lisensinya. Sumber berdasarkan perjanjian hanya digunakan sesuai ketentuannya dan tidak pernah diterbitkan ulang.
| Sumber | Apa yang diberikannya | Kelas lisensi |
|---|---|---|
| ILO global studies on generative AI exposure | Occupational exposure estimates by country and gender | Public, ILO terms |
| OECD studies on automation and AI in the labour market | Evidence on exposure and adoption | Public, OECD terms |
| US Bureau of Labor Statistics Displaced Worker Survey | Outcomes of displaced workers | Public domain, US government |
| O*NET occupational task database | Task content of occupations used in exposure studies | Terbuka, CC BY 4.0 |
| Eurostat enterprise AI and robot use statistics | Adoption by firms | Open, Eurostat reuse policy |
Siapa yang memutuskan, dan apa yang dilayani bukti.
Future of Work and Skills prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Siapa yang memutuskan | Pertanyaannya | Bukti yang dilayani halaman ini |
|---|---|---|
| Labour ministries | Which workers and regions need transition support | Exposure profiles; displacement evidence |
| Employers and unions | How to introduce technology and share gains | Adoption and wage evidence |
| Training providers | Which skills to build for new tasks | Emerging task evidence |
Keluaran yang dihasilkan topik ini
- Task-based exposure profiles by occupation and group
- Displacement and re-employment analyses
- Evaluation designs for transition programmes
- Evidence notes for social dialogue on technology
What researchers ask about automation.
- Which tasks can current AI and robots perform reliably and at acceptable cost?
- When does technology substitute for workers and when does it complement them?
- How are productivity gains shared between firms, workers and consumers?
- How can jobs be redesigned so people keep meaningful, skilled work?
Displacement, productivity and new work.
Automation has a displacement effect, as machines take over tasks, and a productivity effect, as lower costs raise demand and employment elsewhere. Historically, new tasks and occupations have also emerged, reinstating demand for labour. The balance between these forces determines the net effect on employment and wages.
Industrial robots mainly automate physical, repetitive tasks in manufacturing and logistics. Generative AI reaches cognitive tasks such as drafting, summarising and coding. Evidence from early workplace studies suggests AI assistance can raise productivity, sometimes most for less experienced workers, but effects vary widely by task and implementation. What employers choose to automate, and how, matters as much as technical capability.
Evidence on automation and its limits.
- 01
Task exposure measures
Matching technology capabilities to occupational task descriptions, informed by kecerdasan buatan dan pembelajaran mesin and robotics and automation. Measures depend on judgements about capability that age quickly.
- 02
Robot adoption studies
Linking robot installations to local employment and wages with ekonometrika. Results differ between countries and periods.
- 03
Workplace experiments
Randomised or natural experiments in firms adopting AI, using ilmu sosial dan perilaku. Short-run effects may not reflect long-run change.
- 04
Job redesign case studies
How organisations reorganise work around new tools. Case studies illustrate possibilities rather than averages.
Cara hal ini dikaji, dan apa yang tidak dapat dinyatakan metodenya.
- 01Ilmu ekonomi dan ekonomi pembangunanAnalisis teknologi berbasis tugas · Lembaga pasar tenaga kerja · Ekonomi transisi yang adilDiterapkan di sini
- 02Kecerdasan buatan dan pembelajaran mesinEkstraksi keterampilan dari lowongan kerja · Pencocokan pekerjaan dalam layanan ketenagakerjaanDiterapkan di sini
- 03Robotika dan otomasiAdopsi robot dan lapangan kerja · Keselamatan dalam kerja manusia-robot · Perancangan ulang pekerjaan di sekitar robotDiterapkan di sini
- 04EkonometrikaDampak perpindahan dengan data tertaut · Evaluasi program pelatihan · Proyeksi tenaga kerjaDiterapkan di sini
Where this page’s references lead.
Badan dan dokumen yang menjadi acuan halaman ini. Status dan edisi berubah; setiap tautan menuju badan penerbitnya.
Ke mana halaman ini mengarah di seluruh Konsorsium.
Tanya jawab.
Will automation cause mass unemployment?
What is task-based analysis?
Who is most exposed to automation?
Is automation exposure the same as job loss?
Does AI help or hurt less experienced workers?
Can job redesign prevent displacement?
Does the platform advise firms on automation?
Kontribusikan bukti dari halaman ini.
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Bawa metode, dataset, atau pertanyaan.
Informasi dan pertanyaan sudah terbuka hari ini. Sebuah pertanyaan menerima nomor referensi dan menyebutkan langkah berikutnya; lamaran, pembayaran, alokasi komputasi, dan pendaftaran hanya dibuka ketika tim, ketentuan, dan kapasitas tinjauannya tersedia.
Dokumen hukum.
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