Sales and Marketing Managers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Sales and Marketing Managers (ISCO-08 1221) score an average of 0.41 on a 0–1 exposure scale — more exposed than about 78% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Determining price lists, discount and delivery terms, sales promotion budgets, sales methods, special incentives and campaigns;”

Scores 0.59 on the 2025 scale. The task of determining price lists, discount and delivery terms, sales promotion budgets, sales methods, special incentives, and campaigns involves a combination of strategic decision-making, data analysis, and creative input, which Generative AI can significantly assist with. Similar to tasks such as "Collaboration with the sales department, substantive support in creating a commercial offer" which has an adjusted score of 0.65, this task involves elements like pricing strategies and sales promotion plans that can be efficiently supported by AI through data analysis, trend identification, and generating initial drafts for pricing models and promotional ideas. However, the task also necessitates human oversight for final decision-making, customer interaction, and the strategic alignment of sales methods with business objectives, similar to implementing sales strategies which scored 0.37 adjusted. Given its data-driven nature but significant need for human creativity and judgment in strategic aspects, my adjusted score of 0.585 reflects the balance between AI’s potential assistance and the irreplaceable human elements, accounting for AI-enhanced efficiency and supportive capability but recognizing the continued necessity for human expertise.

Moving fastest, 2023 → 2025

“Representing the enterprise or organization at sales and marketing conventions, trade exhibitions and other forums.”

Model capability on this task changed by +0.30 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 1221, 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.

• Sales Managers
• Marketing Managers

In context

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

Sales and Marketing Managers sit at the 78th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Sales and Marketing Managers rank in the 78th 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 0.12 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Determining price lists, discount and delivery terms, sales promotion budgets, sales methods, special incentives and campaigns;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Sales and Marketing Managers". https://singulariki.com/gradient/1221-sales-and-marketing-managers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.