Insurance Representatives

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Insurance Representatives (ISCO-08 3321) score an average of 0.53 on a 0–1 exposure scale — more exposed than about 90% 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 3 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Assisting clients to determine the type and level of coverage required, calculating premiums and establishing method of payment;”

Scores 0.64 on the 2025 scale. The task of assisting clients to determine the type and level of coverage required, calculating premiums, and establishing method of payment involves significant interaction and understanding of client needs, similar to other insurance-related tasks. Tasks such as "Developing and presenting insurance offers to the client" (automation score: 0.69) and "Providing expert advice on the most beneficial types and conditions of insurance for the client" (automation score: 0.64) are comparable, as they all include personalizing advice based on data and meeting client-specific requirements. Generative AI can automate data analysis, calculation, and generate initial offers or templates, thus enhancing efficiency, particularly in standardized aspects like providing information and calculating premiums. However, human expertise remains crucial for understanding complex client situations, ensuring compliance, and building trust through nuanced communication. Given the focus on automation potential and the nature of AI's role in assisting but not replacing the expert judgment and client interaction in high-stakes financial and risk assessment environments, an adjusted score of 0.63 reflects a balance between AI's strengths in processing and the essential human elements of this task. This score takes into account the capabilities of AI against complete automation constraints in a context where Poland's advanced digital infrastructure supports effective technology integration.

Moving fastest, 2023 → 2025

“Negotiating and placing reinsurance contracts;”

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 3321, 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.

• Insurance Sales Agents
• First-Line Supervisors of Non-Retail Sales Workers
• Insurance Underwriters

In context

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

Insurance Representatives sit at the 90th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Insurance Representatives rank in the 90th 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 0.07 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Assisting clients to determine the type and level of coverage required, calculating premiums and establishing method of payment;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Insurance Representatives". https://singulariki.com/gradient/3321-insurance-representatives.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.