The Global Centre for Risk and Innovation (GCRI)
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.
GCRI · In development · service level R0
Automation and jobs is the study of how machines, robots and AI change employment by taking over, assisting or creating tasks. Effects depend on the balance of displacement and new tasks, on whether productivity gains reach wages and on support for displaced workers, whose transitions are often costly.
For automation and jobs, in GCRI's spatial-intelligence reading, the map opens on population density, true-colour imagery and night lights with true colour from NASA GIBS, framed on Southeast Asia; every other NASA layer, live feed and forecast waits in the layer list.
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For automation and jobs, in GCRI's spatial-intelligence reading, the map opens on population density, true-colour imagery and night lights with true colour from NASA GIBS, framed on Southeast Asia; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
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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.
Most occupations combine tasks that can be automated with tasks that cannot. Automation reshapes jobs more often than it eliminates them outright.
Technology displaces labour from some tasks and creates new tasks that need people. Net effects depend on the balance between the two.
If gains accrue mainly to capital owners, automation can raise output while wages stagnate for exposed workers.
Displaced workers often face long unemployment or lower pay in new jobs, especially older workers and those in declining regions.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Displacement effect | The reduction in labour demand when technology takes over tasks workers performed. |
| Reinstatement effect | The increase in labour demand when technology creates new tasks for workers. |
| Augmentation | Technology that helps workers perform tasks better instead of replacing them. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Employment in occupations with high automation exposure | percent of employment | per assessment | Task-based exposure studies | ESTIMATED |
| Firms using AI or robots | percent of firms | per survey | Business surveys | REPORTED |
| Displaced worker re-employment rate | percent re-employed within a year | per survey | Displaced worker surveys | REPORTED |
| Wage change after displacement | percent | per survey | Linked employer-employee data | ESTIMATED |
| Labour share of income | percent of GDP | annual | National accounts | ESTIMATED |
| New job titles emerging | count | annual | Occupational classification updates and job postings | OBSERVED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| 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 | Open, CC BY 4.0 |
| Eurostat enterprise AI and robot use statistics | Adoption by firms | Open, Eurostat reuse policy |
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.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| 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 |
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.
Matching technology capabilities to occupational task descriptions, informed by artificial intelligence and machine learning and robotics and automation. Measures depend on judgements about capability that age quickly.
Linking robot installations to local employment and wages with econometrics. Results differ between countries and periods.
Randomised or natural experiments in firms adopting AI, using social and behavioural science. Short-run effects may not reflect long-run change.
How organizations reorganize work around new tools. Case studies illustrate possibilities rather than averages.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.