Life Science Technicians (excluding Medical)

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 13 task statements that define Life Science Technicians (excluding Medical) (ISCO-08 3141) score an average of 0.38 on a 0–1 exposure scale — more exposed than about 73% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 1 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

“Using computers to develop models and analyse data;”

Scores 0.71 on the 2025 scale. The task of "Using computers to develop models and analyse data" has a high potential for automation, especially in a high-income country like Poland with adept infrastructure for AI integration. Generative AI excels in data processing, model generation, and initial data analysis, similar to tasks like "Analyzing algorithms and data processing programs" (0.77) and "Creating and utilizing data structures" (0.7). These tasks leverage AI capabilities to automate repetitive or structured aspects of the work, freeing humans to focus on nuanced interpretation and strategic decision-making. However, complete automation isn't feasible due to the need for human oversight in understanding contextual nuances, hypothesis testing, and interpreting complex model outputs. The task is less reliant on human creativity and judgment compared to tasks like "Creating a concept for a game or application," which scored 0.685 due to its creative nature. Hence, an adjusted score of 0.66 reflects substantial AI assistance potential, acknowledging both the strengths and limitations of AI in handling this task while recognizing the need for human expertise in sophisticated analysis and validation.

Moving fastest, 2023 → 2025

“Participating in the research, development and manufacture of products and processes;”

Model capability on this task changed by +0.27 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 3141, 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.

• Biological Technicians

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Life Science Technicians (excluding Medical) sit at the 73rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Life Science Technicians (excluding Medical) rank in the 73rd 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 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Using computers to develop models and analyse data;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Life Science Technicians (excluding Medical)". https://singulariki.com/gradient/3141-life-science-technicians-excluding-medical.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.