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 audio guides exist and are excellent. Human tour guides remain for those who want depth, spontaneity, and the experience of being in the presence of expertise. Premium market safe.
AI-powered audio guides (Rick Steves audio tours, Detour, Via.com) deliver high-quality scripted content at any point of interest in most major cities. These are genuinely good products. They have disrupted the low-end, standardised city tour market.
But the human tour guide's value proposition is fundamentally different: they answer questions you haven't thought of yet, they read your group's interest and adjust, they bring personal history and lived experience to a site, and they create a social experience of discovery.
The specialist guide — the expert archaeologist who leads tomb excavations in Egypt, the local historian who grew up in the neighbourhood, the naturalist who spots the bird before anyone else has seen it — delivers an experience that no audio guide can replicate. Growing tourism demand and growing premium travel market is expanding this niche.
These are the genuine threats to this profession. They are real, but they are not sufficient to overturn the fundamental analysis. Here is why.
Put the case that Tour Guide will not 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 7 core sources and 3 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 ↗Notes substantial automation risk remains, while observed labour-market effects remain mixed rather than universally destructive.
OPEN SOURCE ↗Argues advanced economies are better positioned to benefit from AI due to infrastructure, skills, and institutions.
OPEN SOURCE ↗