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 ↗Funeral directing is the administration of human mortality and the support of grief. It is irreducibly human. No society has yet found this service appropriate to automate.
Funeral directors arrange and conduct funeral services, prepare bodies, manage death registration, advise grieving families, and serve as the human institutional presence at the moment of death and during its immediate aftermath. This service is irreducibly human.
The bereaved family making funeral arrangements is in acute distress. The trust they extend to a funeral director is profound — they are placing the care of their loved one and the management of their grief in human hands. No AI system could be trusted with this role by a grieving family.
Practically, funeral directors perform physical preparation of bodies, manage the legal and administrative requirements of death registration, and coordinate the clergy, celebrant, cemetery, and crematorium services that constitute a funeral. These physical and administrative functions require human presence and professional accountability.
The funeral industry is growing: ageing populations across all developed nations mean that death rates, and therefore funeral service demand, are increasing.
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 Funeral Director 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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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 ↗Argues advanced economies are better positioned to benefit from AI due to infrastructure, skills, and institutions.
OPEN SOURCE ↗