ILO Working Paper 140 (2025): Generative AI and Jobs: A Refined Global Index of Occupational Exposure
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗AI generates visual concepts. Art direction is the judgment that determines which concepts are right. That judgment remains human — for now.
Art directors set the visual direction for advertising campaigns, editorial content, branded communications, and digital experiences. Their role divides into concept generation (producing visual ideas) and creative judgment (determining which ideas are right for the brand, culture, and context).
AI generative image tools now produce concept board quality visual ideas at volume and speed. A junior art director who spent a significant share of their time producing mood boards and visual references is being displaced — AI produces these instantly.
What survives: the senior art director or creative director whose judgment determines which direction is right — who understands the cultural context, the client's authentic positioning, and how audiences will respond. This creative taste and judgment is the is moving quickly but still depends on deployment, regulation, and economics function.
However, as AI concept generation improves, the gap between AI-generated concepts and human concepts narrows. The art director's survival rests on judgment being genuinely is moving quickly but still depends on deployment, regulation, and economics — which it is today, but may not be in 15-20 years.
These are the strongest arguments for why this job might survive. We take them seriously. Below each is the counterargument that explains why they are insufficient.
Put the case that Art Director will survive AI displacement. The system responds with counterarguments from the research base. Strong arguments shift the score — up to a maximum of ±15 points. The system is not an AI. It is a structured argument engine.
This question layer is generated from the job verdict, the resistance case, the regional rollout logic, and the evidence status of this page. Use the filters to focus the discussion, or trigger a random question and work through the role from multiple angles.
Keep the framework, but add at least one sector-specific source and remove any remaining implied precision.
TIER 3 review queue with 6 core sources and 1 framework signals.
This page is grounded in task exposure research and labour-market trend reports, then translated into a reasoned occupation-level argument.
This site now treats exact timelines, total job-loss counts, and regional speed as interpretive estimates unless a cited source states them directly. The argument on this page should be read as a structured forecast, not a guaranteed future.
These impact figures are site estimates for comparison and should not be read as official labour-market counts.
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗Finds clerical work is the most highly exposed occupational group and that augmentation is often more likely than full occupation automation.
OPEN SOURCE ↗Shows AI exposure is highest in many white-collar cognitive occupations, while manual occupations tend to have lower exposure.
OPEN SOURCE ↗Advanced economies are more exposed to AI because they have more cognitive-intensive jobs; infrastructure and skills limit adoption elsewhere.
OPEN SOURCE ↗Large-employer survey showing clerical roles among the fastest-declining and care, education, software and green-transition jobs among growth areas.
OPEN SOURCE ↗Argues advanced economies are better positioned to benefit from AI due to infrastructure, skills, and institutions.
OPEN SOURCE ↗