Artificial Intelligence and Autonomous Systems
The Global Centre for Risk and Innovation (GCRI)
AI changes which tasks people do, how work is organized, which skills are valued and how information spreads. Effects differ by occupation, country, gender and age, and depend on how firms and governments choose to deploy it. This page sets out how AI affects work and society, how exposure and impacts are measured, and what evidence serves the labour ministries, employers, unions and educators involved.
GCRI · In development · service level R0
AI and jobs is the study of how artificial intelligence changes the tasks people do, how work is organized and which skills are valued. Tasks change before whole jobs do, exposure differs by occupation and gender, and outcomes depend on how employers and governments choose to deploy it.
Population density, night-time lights, air quality, heat and floods, framed on East Asia; each layer is a dated NASA product.
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People and exposure
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Natural Earth
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Population density, night-time lights, air quality, heat and floods, framed on East Asia.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
Work, education, public trust and inclusion. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
AI automates or assists specific tasks within occupations. Jobs are reorganized around remaining tasks before they disappear or new ones form.
Clerical and some professional tasks are highly exposed to generative AI, while manual and interpersonal work is less exposed. Women are over-represented in some highly exposed clerical jobs.
Workers need new skills to use AI well, and those displaced need retraining. Training systems often respond more slowly than technology adoption.
Generated content, recommendation systems and synthetic media change how people find and judge information, with effects on trust and public debate.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Task-based approach | Analysing technology's effect on work through the specific tasks within occupations. |
| Augmentation | Use of AI to assist workers in tasks rather than replace them. |
| Occupational exposure | The share of an occupation's tasks that AI could perform or assist. |
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 highly exposed to AI | percent of employment | per assessment | Exposure studies using occupational data | ESTIMATED |
| Firms using AI | percent of firms | annual | Business surveys | REPORTED |
| Workers using AI tools at work | percent of workers | per survey | Labour force and worker surveys | REPORTED |
| AI-related job postings | share of postings | monthly | Online job posting data | OBSERVED |
| Participation in AI skills training | percent of workforce | annual | Training surveys | REPORTED |
| Wage changes in exposed occupations | percent change | annual | Wage statistics | OFFICIAL |
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 studies on generative AI exposure of occupations | Occupational exposure estimates by country and gender | Public, ILO terms |
| OECD Employment Outlook and AI in work studies | Evidence on AI adoption and labour effects | Public, OECD terms |
| Eurostat ICT usage in enterprises survey | Share of enterprises using AI | Open, Eurostat reuse policy |
| Online job posting data such as Lightcast summaries | Demand for AI skills | Commercial; public summaries (to check) |
| Stanford AI Index | Compiled indicators on AI adoption, investment and labour | Public, licence to check |
Artificial Intelligence and Autonomous Systems 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 sectors need transition support | Exposure analyses; adoption tracking |
| Employers and unions | How to introduce AI at work and share its gains | Adoption and wage evidence |
| Education and training bodies | Which skills to teach and to whom | Skills demand evidence |
Jobs are bundles of tasks. AI tends to automate or assist specific tasks, such as drafting, summarising, coding or classifying images, which changes the content of jobs rather than simply removing them. Whether that raises productivity and wages or displaces workers depends on how organizations redesign work and how gains are shared.
In education, AI can personalise support but also undermine assessment and the learning that comes from effort. Trust depends on experience: visible errors, unexplained decisions and synthetic content erode it, while accountable use can build it. People with limited digital access, skills or language support risk exclusion.
Mapping AI capabilities onto occupational task data. Exposure is not the same as displacement.
Studying workplaces and classrooms that adopt AI, using social and behavioural science. Early studies may not generalize.
Measuring how people use and perceive AI. Attitudes change quickly and vary by context.
Linking what systems can actually do, from AI evaluation and assurance engineering, to claims about social effects.
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.