Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.
ILO researchers explain why exposure is estimated from the tasks inside occupations rather than from dramatic job headlines. The 2023 study models potential automation and augmentation, then emphasizes job quality, gender, access, worker voice, redeployment, and the gap between technical possibility and adoption.
Plan for this page
Watch actively with preparation, signposts, and a comprehension check.
You will leave with
How a task-based global exposure study was built, why task exposure is not observed job loss, and how workplace choices shape automation, augmentation, and job quality.
Time
57 min
Before you begin
Workers, managers, educators, and policymakers willing to engage with a long research presentation and its caveats.
Do this now
Read the terms and attention points before loading the video.
Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.How a task-based global exposure study was built, why task exposure is not observed job loss, and how workplace choices shape automation, augmentation, and job quality.
00:00 · Why study generative AI and work
03:00 · A global task-based snapshot
07:00 · ISCO tasks and model-assisted scoring
10:00 · Where exposure clusters
15:00 · Global estimates and gender patterns
What it teaches
How a task-based global exposure study was built, why task exposure is not observed job loss, and how workplace choices shape automation, augmentation, and job quality.
Workers, managers, educators, and policymakers willing to engage with a long research presentation and its caveats.
Pay attention to
Track the unit of analysis: task, occupation, worker, organization, country, or realized employment outcome.
Listen for the presenters’ repeated description of estimates as upper bounds and directional signals.
Notice how worker participation, gender, digital access, and organizational design alter possible outcomes.
A job is a bundle of tasks; exposure of some tasks does not by itself mean the occupation disappears.
Technical capability, cost, infrastructure, skills, demand, organizational choice, regulation, and worker voice mediate adoption and outcomes.
Augmentation can improve productivity or intensify work and reduce employment; job quality is an outcome to design and measure, not an automatic benefit.
Active check
What does a high task-exposure estimate establish?
Caveats
The presentation reports a 2023 snapshot using GPT-4-like capabilities and occupational task descriptions; capabilities, jobs, adoption, and evidence have changed since then.
The estimates are upper bounds and directional. They do not include adoption costs, infrastructure, labor prices, worker or employer choice, new tasks and jobs, demand responses, or all forms of AI and robotics.
The model helped score and cluster tasks; the researchers describe human review and consistency checks, but synthetic scores still carry methodological and bias uncertainty.
A June 2026 ILO review finds uneven productivity gains and limited large-scale displacement so far; it should accompany, not retroactively replace, the webinar.
Accessibility
Only auto-generated English captions and transcript were confirmed; they contain errors in names, acronyms, and some figures and are not independently corrected captions. No manual French track was confirmed. Slides and charts are important, and the official paper provides the clearest accessible record of methods and numbers.
Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.International Labour Organization
ILO Working Paper 96, published 21 August 2023, by Paweł Gmyrek, Janine Berg, and David Bescond. It documents the task-level method and caveats behind the webinar.
The impact of GenAI on jobs, productivity and work organization: a review of the empirical evidenceInternational Labour Organization
June 2026 evidence update. It reports real but uneven productivity effects, limited large-scale displacement so far, and continuing concerns about inequality, autonomy, and job quality.
inAi