Pharmacists

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 13 task statements that define Pharmacists (ISCO-08 2262) score an average of 0.33 on a 0–1 exposure scale — more exposed than about 62% 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 Minimal 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 13 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Providing information and advice to prescribers and clients regarding drug interactions, incompatibility and contraindications, side effects, dosage and proper medication storage;”

Scores 0.50 on the 2025 scale. The task of providing information and advice regarding drug interactions, side effects, dosage, and proper medication storage involves substantial expertise in pharmacology and a significant level of personalized attention to patient-specific factors, which are beyond the full automation capabilities of current generative AI technologies. Comparable tasks, such as selecting antihypertensive drugs considering renal insufficiency (0.364) and managing medical documentation (0.386), also require comprehensive understanding and professional judgment, indicating partial AI-assisted potential but with substantial human involvement. Although generative AI can support by analyzing data and offering preliminary advice based on medical databases, its inability to fully comprehend and interact with complex human health nuances limits its automation potential. Given the task's reliance on human expertise and the need for nuanced interpretation, the adjusted score of 0.355 reflects the current limitations of AI in achieving full automation while acknowledging its supportive role in providing information in a high-tech environment like Poland.

Moving fastest, 2023 → 2025

“Supervising and coordinating the work of pharmacy technicians, pharmacy interns and pharmacy sales assistants;”

Model capability on this task changed by +0.24 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 2262, 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.

• Pharmacists

In context

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

Pharmacists sit at the 62nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Pharmacists rank in the 62nd 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Providing information and advice to prescribers and clients regarding drug interactions, incompatibility and contraindications, side effects, dosage and proper medication storage;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Pharmacists". https://singulariki.com/gradient/2262-pharmacists.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.