How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Contact organizations to explain services and facilities offered.74
- Represent company at trade association meetings to promote products.74
- Coordinate activities of departments, such as sales, graphic arts, media, finance, and research.73
- Train and direct workers engaged in developing and producing advertisements.73
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Plan and execute advertising policies and strategies for organizations.01
- Assemble and communicate with a strong, diverse coalition of organizations or public figures, securing their cooperation, support, and action, to further campaign goals.02
- Direct, motivate, and monitor the mobilization of a campaign team to advance campaign goals.03
- Coordinate activities of departments, such as sales, graphic arts, media, finance, and research.04
- Manage sales team, including setting goals, providing incentives, and evaluating employee performance.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Advertising Sales Agents
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Related work
Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
