Pet Groomers and Animal Care Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Pet Groomers and Animal Care Workers (ISCO-08 5164) score an average of 0.14 on a 0–1 exposure scale — more exposed than about 14% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Labelling drugs, chemicals and other pharmaceutical preparations and replenishing stock;”

Scores 0.25 on the 2025 scale. The task of labeling drugs, chemicals, and pharmaceutical preparations, along with replenishing stock, largely involves structured and repetitive processes, which generative AI can significantly assist with, particularly for documentation and compliance checks. Comparable tasks within the context, such as "Correct labeling of stored goods" (0.375) and "Applying the method of identification and labeling of drugs and narcotics" (0.3645), show that while AI can aid in maintaining consistency and accuracy in labeling, full automation is bounded by the need for human oversight to ensure compliance with regulations and handle exceptions. Tasks with physical involvement, such as handling and stocking, similarly align with the scores for maintaining order in the workspace (0.25) and monitoring warehouse inventory (0.35), reflecting the limited role AI plays due to manual requirements. Given these factors and the high digital infrastructure in a country like Poland, the adjusted score is reflective of moderate to limited automation potential, recognizing AI's role in supporting but not replacing human input in compliance-driven environments.

Moving fastest, 2023 → 2025

“Training animals to develop and maintain desired behaviours for competition, entertainment, obedience, security, riding and other activities;”

Model capability on this task changed by +0.07 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 5164, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Animal Caretakers
• Veterinary Assistants and Laboratory Animal Caretakers
• Animal Trainers

In context

Part of the 5 - Service and sales workers major group. Return to the full gradient to see how the whole group sits.

Pet Groomers and Animal Care Workers sit at the 14th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Pet Groomers and Animal Care Workers rank in the 14th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Labelling drugs, chemicals and other pharmaceutical preparations and replenishing stock;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Pet Groomers and Animal Care Workers". https://singulariki.com/gradient/5164-pet-groomers-and-animal-care-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.