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Automatisation et emplois

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.

01Registre

Automation and jobs on the record.

02La question

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.

Mécanisme

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.

Mécanisme

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.

Mécanisme

Productivity gains may not reach wages

If gains accrue mainly to capital owners, automation can raise output while wages stagnate for exposed workers.

Mécanisme

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.

03Méthodes et preuves

How automation and jobs is modelled, measured and tested.

Le modèle, ses hypothèses, ses données et son incertitude, sous la forme qu'un scientifique ou un ingénieur vérifierait. Les structures suivent des méthodes publiées. Les valeurs représentées sont illustratives, ce ne sont pas des résultats.

Modèle de système

The couplings between the systems in automation and jobs: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.

++−++TravailSociétéIntelligence artificielleCommunautésÉconomieR renforçanteB équilibrage
Causal loop diagram from the page’s systems; signs show the direction of influence. Illustrative structure for a system dynamics or network model, not estimated couplings.
Principes directeurs FAIR · GO FAIR et FORCE11W3C PROV · World Wide Web ConsortiumOrientations sur le traitement cohérent des incertitudes · GIECCEOS Analysis Ready Data · Comité sur les satellites d'observation de la Terre
05Indicateurs

Ce qui est mesuré, et avec quelle qualité.

Chaque indicateur porte son unité, sa périodicité, le type de source qui le sous-tend et l'étiquette de preuve que porterait une valeur. Aucune valeur n'est affichée : un programme les fournit sous sa propre autorité.

IndicateurUnitéFréquenceType de sourceÉtiquette
Employment in occupations with high automation exposurepercent of employmentpar évaluationTask-based exposure studiesESTIMÉ
Firms using AI or robotspercent of firmsper surveyBusiness surveysDÉCLARÉ
Displaced worker re-employment ratepercent re-employed within a yearper surveyDisplaced worker surveysDÉCLARÉ
Wage change after displacementpercentper surveyLinked employer-employee dataESTIMÉ
Labour share of incomepourcentage du PIBannuelComptes nationauxESTIMÉ
New job titles emergingnombreannuelOccupational classification updates and job postingsOBSERVÉ
06Sources

D'où proviennent les données probantes.

Jeux de données publics et registres institutionnels nommés, chacun avec sa catégorie de licence. Les sources fournies dans le cadre d'accords ne sont utilisées que selon leurs conditions et ne sont jamais republiées.

SourcesCe qu'elle apporteCatégorie de licence
ILO global studies on generative AI exposureOccupational exposure estimates by country and genderPublic, ILO terms
OECD studies on automation and AI in the labour marketEvidence on exposure and adoptionPublic, OECD terms
US Bureau of Labor Statistics Displaced Worker SurveyOutcomes of displaced workersPublic domain, US government
O*NET occupational task databaseTask content of occupations used in exposure studiesOuvert, CC BY 4.0
Eurostat enterprise AI and robot use statisticsAdoption by firmsOpen, Eurostat reuse policy
07Décisions

Qui décide, et à quoi servent les données probantes.

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.

Qui décideLa questionDonnées probantes servies par cette page
Labour ministriesWhich workers and regions need transition supportExposure profiles; displacement evidence
Employers and unionsHow to introduce technology and share gainsAdoption and wage evidence
Training providersWhich skills to build for new tasksEmerging task evidence

Résultats que produit ce thème

  • 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
08Questions essentielles

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?
09Mécanismes

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.

10Méthodes et données

Evidence on automation and its limits.

  1. 01

    Task exposure measures

    Matching technology capabilities to occupational task descriptions, informed by intelligence artificielle et apprentissage automatique and robotics and automation. Measures depend on judgements about capability that age quickly.

  2. 02

    Robot adoption studies

    Linking robot installations to local employment and wages with économétrie. Results differ between countries and periods.

  3. 03

    Workplace experiments

    Randomised or natural experiments in firms adopting AI, using sciences sociales et comportementales. Short-run effects may not reflect long-run change.

  4. 04

    Job redesign case studies

    How organisations reorganise work around new tools. Case studies illustrate possibilities rather than averages.

12Where this page’s references

Where this page’s references lead.

Les organismes et documents à l'aune desquels cette page se lit. Les statuts et éditions changent ; chaque lien mène à l'organisme émetteur.

14Questions

Questions et réponses.

Will automation cause mass unemployment?
Historically, technology has changed jobs more than it has reduced total employment, but transitions are costly for affected workers. Effects of AI are still uncertain.
What is task-based analysis?
Analysing the effect of technology on work through the specific tasks that make up occupations, rather than treating jobs as wholes.
Who is most exposed to automation?
It depends on the technology. Routine manual tasks were most exposed to robots; generative AI exposes clerical and some professional tasks more.
Is automation exposure the same as job loss?
No. Exposure means some tasks in a job could be automated. Whether jobs are lost depends on costs, demand, regulation and how employers reorganise work.
Does AI help or hurt less experienced workers?
Some early studies find the largest productivity gains for less experienced workers in specific tasks. Whether that persists and translates into pay is not yet known.
Can job redesign prevent displacement?
It can preserve and improve many jobs, especially where workers are involved in the redesign, but it cannot offset every displacement. Transition support remains necessary.
Does the platform advise firms on automation?
No. It publishes research and case studies that firms, unions and governments can use.
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