Managing Directors and Chief Executives

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 11 task statements that define Managing Directors and Chief Executives (ISCO-08 1120) score an average of 0.38 on a 0–1 exposure scale — more exposed than about 71% 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 11 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Determining objectives, strategies, policies and programmes for the enterprise or organization;”

Scores 0.47 on the 2025 scale. The task of determining objectives, strategies, policies, and programmes for an organization involves complex decision-making, strategic planning, and contextual understanding, akin to the tasks of developing company strategies, overseeing management, and coordinating development plans as seen across semantically similar tasks. From the context provided, tasks such as developing strategic action plans for an advertising agency or directing business development plans had automation scores adjusted to around 0.42-0.46, reflecting the role of human expertise in nuanced decision-making despite AI assistance in data processing and drafting strategic documents. Generative AI can assist in analyzing data, performing scenario modeling, and drafting documents. However, its current limitations in providing strategic insights, understanding organizational context, and handling ambiguous human factors mean that the core strategic nature of this task requires human oversight and expertise. Thus, considering the capabilities of AI and the need for human input in a nuanced task like this, the adjusted score of 0.48 acknowledges the supportive yet non-autonomous role of AI with recognition of human leadership essentials.

Moving fastest, 2023 → 2025

“Selecting or approving the selection of senior staff;”

Model capability on this task changed by +0.22 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 1120, 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.

• General and Operations Managers
• Chief Executives
• Chief Sustainability Officers

In context

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

Managing Directors and Chief Executives sit at the 71st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Managing Directors and Chief Executives rank in the 71st 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 0.08 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Determining objectives, strategies, policies and programmes for the enterprise or organization;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Managing Directors and Chief Executives". https://singulariki.com/gradient/1120-managing-directors-and-chief-executives.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.