Veterinarians

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Veterinarians (ISCO-08 2250) score an average of 0.14 on a 0–1 exposure scale — more exposed than about 15% 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

“Advising clients on health, nutrition and feeding, hygiene, breeding and care of animals;”

Scores 0.22 on the 2025 scale. The task of advising clients on health, nutrition, feeding, hygiene, breeding, and care of animals requires a combination of theoretical knowledge and practical, hands-on experience. Generative AI can support this task by providing general guidelines, nutritional plans, and hygiene protocols based on existing data. However, the nuanced understanding of specific animal needs, real-time observation, and tailored advice based on individual circumstances remain crucially human-driven elements. Similar tasks, such as "Providing advice on proper socialization and upbringing of young domestic animals" and "Preventing and combating diseases," have low adjusted scores reflecting their dependence on human judgment and interaction. These tasks share the necessity for hands-on care and adaptability to rapidly changing conditions. Given AI's role as an augmentative tool rather than a replacement in this domain and considering the high access to technology in high-income countries like Poland, the adjusted score reflects the potential for AI to aid in general advice but not replace expert judgment and practical skills.

Moving fastest, 2023 → 2025

“Determining the presence and nature of abnormal conditions by physical examination, laboratory testing and through diagnostic imaging techniques including radiography and ultrasound;”

Model capability on this task changed by +0.11 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 2250, 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.

• Veterinarians

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Veterinarians sit at the 15th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Veterinarians rank in the 15th 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 0.04 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Advising clients on health, nutrition and feeding, hygiene, breeding and care of animals;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Veterinarians". https://singulariki.com/gradient/2250-veterinarians.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.