Valuers and Loss Assessors

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Valuers and Loss Assessors (ISCO-08 3315) score an average of 0.45 on a 0–1 exposure scale — more exposed than about 83% 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 2 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Preparing reports of value, outlining the estimation factors and methods used.”

Scores 0.68 on the 2025 scale. The task of "Preparing reports of value, outlining the estimation factors and methods used" involves both qualitative and quantitative data analysis, creating structured reports, and ensuring compliance with client-specific guidelines. Similar tasks such as "Developing reports encompassing analyses in terms of quality, cost, and delivery time" and "Preparing reports and expert opinions in the scope of the aforementioned tasks" have received adjusted scores of 0.635 and 0.65, respectively, indicating potential for automation in data processing and preliminary drafting. Generative AI can significantly enhance efficiency by automating data handling, generating initial report drafts, and identifying estimation factors. However, as seen in the context, tasks that require contextual interpretation, like "Preparing reports in accordance with client guidelines," require human insight to align outputs with strategic objectives. Given the high-income setting of Poland, where digital tools are widely accessible, Generative AI's role in automating structured and data-driven aspects is substantial. Therefore, an adjusted score of 0.67 reflects the significant yet not complete role of AI in automating this task, acknowledging the crucial need for human oversight in qualitative analysis and strategic decision-making.

Moving fastest, 2023 → 2025

“Assessing the extent of damage or loss and liabilities of insurance companies and underwriters for losses covered by insurance policies;”

Model capability on this task changed by +0.20 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 3315, 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.

• Claims Adjusters, Examiners, and Investigators
• Insurance Appraisers, Auto Damage

In context

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

Valuers and Loss Assessors sit at the 83rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Valuers and Loss Assessors rank in the 83rd 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 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Preparing reports of value, outlining the estimation factors and methods used.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Valuers and Loss Assessors". https://singulariki.com/gradient/3315-valuers-and-loss-assessors.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.