Government Tax and Excise Officials

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 4 task statements that define Government Tax and Excise Officials (ISCO-08 3352) score an average of 0.49 on a 0–1 exposure scale — more exposed than about 88% 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 4 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Performing related administrative tasks to document findings, maintain records and report on actions taken for cases.”

Scores 0.66 on the 2025 scale. The task of performing related administrative tasks to document findings, maintain records, and report on actions taken for cases is similar to tasks that involve managing documentation and data entries, such as "Removing documentation from the teleinformatics system in case of case reopening" (adjusted score: 0.675) and "Taking over electronically, from individual organizational units, documentation of completed cases" (adjusted score: 0.68). These tasks are highly automatable due to their repetitive, structured, and rule-based nature, making them suitable for Generative AI. AI can efficiently handle data organization, retrieval, and initial processing, especially in high-income countries like Poland where technological infrastructure supports such advancements. However, human oversight is still necessary for ensuring accuracy, compliance, and handling exceptions or context-sensitive scenarios within administrative tasks. Therefore, the adjusted score of 0.675 reflects a balance that acknowledges the high automation potential with the need for minimal human intervention, similar to tasks involving document management within the provided context.

Moving fastest, 2023 → 2025

“Performing related administrative tasks to document findings, maintain records and report on actions taken for cases.”

Model capability on this task changed by +0.16 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 3352, 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.

• Tax Examiners and Collectors, and Revenue Agents

In context

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

Government Tax and Excise Officials sit at the 88th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Government Tax and Excise Officials rank in the 88th 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Performing related administrative tasks to document findings, maintain records and report on actions taken for cases.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Government Tax and Excise Officials". https://singulariki.com/gradient/3352-government-tax-and-excise-officials.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.