ILO Working Paper 140 (2025): Generative AI and Jobs: A Refined Global Index of Occupational Exposure
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗Upholstery is skilled craft in fabric and furniture. It is experiencing a revival driven by sustainability and heritage. It cannot be automated.
Upholsterers cover furniture with fabric, leather, and padding — restoring antique chairs, creating bespoke sofas, and upholstering headboards and window seats. This is skilled craft work requiring knowledge of materials, pattern matching, staple and nail work, and the ability to stretch and shape fabric over three-dimensional furniture forms.
Every piece of furniture is different: the specific shape of an antique chair seat, the compound curves of a Chesterfield sofa back, the custom dimensions of a window seat — all require the craftsperson to adapt their technique to the specific object.
Factory upholstery for mass-market furniture uses automated machinery for standardised processes. But the restoration of antique upholstery, the creation of bespoke furniture, and the repair of existing pieces — the majority of upholstery work — is entirely hand craft.
Sustainability-driven furniture restoration (repair rather than replace), heritage furniture conservation, and interior design trends favouring unique pieces are driving demand for skilled upholsterers. The profession is experiencing a genuine craft revival.
These are the genuine threats to this profession. They are real, but they are not sufficient to overturn the fundamental analysis. Here is why.
Put the case that Upholsterer / Soft Furnishings Maker will not survive AI displacement. The system responds with counterarguments from the research base. Strong arguments shift the score — up to a maximum of ±15 points. The system is not an AI. It is a structured argument engine.
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Safe to present as a framework-level forecast, provided the page remains labelled as interpretive and source-grounded rather than certain.
TIER 3 review queue with 7 core sources and 3 framework signals.
This page is grounded in task exposure research and labour-market trend reports, then translated into a reasoned occupation-level argument.
This site now treats exact timelines, total job-loss counts, and regional speed as interpretive estimates unless a cited source states them directly. The argument on this page should be read as a structured forecast, not a guaranteed future.
These impact figures are site estimates for comparison and should not be read as official labour-market counts.
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗Finds clerical work is the most highly exposed occupational group and that augmentation is often more likely than full occupation automation.
OPEN SOURCE ↗Shows AI exposure is highest in many white-collar cognitive occupations, while manual occupations tend to have lower exposure.
OPEN SOURCE ↗Advanced economies are more exposed to AI because they have more cognitive-intensive jobs; infrastructure and skills limit adoption elsewhere.
OPEN SOURCE ↗Large-employer survey showing clerical roles among the fastest-declining and care, education, software and green-transition jobs among growth areas.
OPEN SOURCE ↗Notes substantial automation risk remains, while observed labour-market effects remain mixed rather than universally destructive.
OPEN SOURCE ↗Argues advanced economies are better positioned to benefit from AI due to infrastructure, skills, and institutions.
OPEN SOURCE ↗