Finance Managers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Finance Managers (ISCO-08 1211) score an average of 0.37 on a 0–1 exposure scale — more exposed than about 70% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Assessing the financial situation of the enterprise or organization, preparing budgets and overseeing financial operations;”

Scores 0.43 on the 2025 scale. The task of assessing the financial situation of an enterprise, preparing budgets, and overseeing financial operations involves data analysis, strategic decision-making, and regulatory compliance—areas where Generative AI has significant but not complete potential for automation. Contextually similar tasks, such as developing budgets and preparing financial plans, received scores ranging from 0.38 to 0.48. Tasks involving strategic financial management generally reflect a lower automation potential due to the need for human judgment and strategic oversight. Generative AI can aid in analyzing financial data and generating reports, much like the tasks of preparing financial forecasts and analyzing company finances. However, the strategic interpretation of data, compliance with complex regulations, and alignment with organizational goals require substantial human involvement, limiting full automation. Thus, an adjusted score of 0.42 effectively balances AI's role in augmenting efficiency and data processing with the indispensable human expertise needed in strategic financial planning and management. This score reflects the technological capabilities in a high-income country like Poland while acknowledging the complexity and nuanced understanding required in financial management tasks.

Moving fastest, 2023 → 2025

“Consulting with the chief executive and with managers of other departments or sections”

Model capability on this task changed by +0.19 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 1211, 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.

• Financial Managers
• Treasurers and Controllers
• Investment Fund Managers

In context

Part of the 1 - Managers major group. Return to the full gradient to see how the whole group sits.

Finance Managers sit at the 70th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Finance Managers rank in the 70th 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 rose by 0.07 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Assessing the financial situation of the enterprise or organization, preparing budgets and overseeing financial operations;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Finance Managers". https://singulariki.com/gradient/1211-finance-managers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.