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 ↗The funeral celebrant holds the grief of a family and creates the ceremony that says goodbye to a person. This is the most profoundly human of all professional roles. Demand is growing.
Funeral celebrants conduct non-religious funeral ceremonies — meeting with bereaved families, listening to the story of the person who has died, writing a tribute that captures their life, and conducting the ceremony that gives the family a meaningful farewell.
This profession exists because families want a human being who has taken time to understand their loved one to stand before them and give voice to who that person was. The celebrant's role is to hold the grief of the room and create a ceremony that is both dignified and personal.
AI could, technically, generate a eulogy from information provided. Families do not want this. The bereaved want a human professional who has listened to them, who understood their loved one, and who will stand with them in the most difficult moment of their lives.
This is the most protected of all service professions from AI displacement — not because AI cannot generate words, but because what families are paying for is human presence, human care, and human witness to the significance of a life. Demand is growing as secular funerals replace religious ceremonies.
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 Celebrant 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 ↗