Live market intelligence from 661 active vacancies
Based on 661 AI-enriched vacancies.
AIOE measures generative AI augmentation potential per role (Eloundou et al., 2023). Counts shown as number of enriched jobs (% of all active vacancies).
Distribution of automation risk bands across employer types. Public sector = jobs classified as government/public authority. Private / Agency = direct hire and agency-placed roles.
Based on 661 AI-enriched jobs. Click any skill to filter live vacancies.
Most frequently required knowledge domains across active IOM vacancies, derived from our canonical knowledge taxonomy.
Classification of individual tasks across AI-enriched vacancies: routine (automatable), augmented (AI-assisted human work), and human (resistant to automation). Based on O*NET task decomposition and GPT-4o analysis.
Total tasks analysed: 5,347 across 661 enriched vacancies. Human and augmented tasks represent the automation-resilient portion of IOM's workforce profile.
Technologies flagged as emerging / high-demand by O*NET across AI-enriched IOM vacancies.
Average AI exposure (AIOE) and automation risk across SOC2020 major occupational groups, derived from 661 AI-classified IOM vacancies.
Each axis = a SOC major group. Outer = higher exposure. Blue = AI exposure (AIOE), pink = automation risk.
Each dot = one enriched vacancy. Hover for job details. Dashed lines at 33 % and 66 % risk. Colour = employer type.
Top 8 occupational categories. Orange = observed adoption, blue = unexploited potential.
SOC sub-major groups. X = AIOE, Y = risk, size = IOM vacancies.
Bubble size = number of IOM vacancies in that occupational group. Based on 661 enriched jobs.
SOC sub-major groups, public sector employers only.
Bubble size = number of IOM vacancies in that occupational group. Based on 71 enriched jobs.
SOC sub-major groups, private employers and recruitment agencies.
Bubble size = number of IOM vacancies in that occupational group. Based on 586 enriched jobs.
Comparing theoretical AI capability with observed adoption rates across occupational categories, and the resulting deployment opportunity gaps.
Top 8 categories. Stacked bars: orange = current observed AI adoption; blue = unexploited AI potential (capability − observed). Hover to see IOM vacancy count. Based on Eloundou et al. (GPTs are GPTs, 2023).
Difference between theoretical AI capability and observed adoption (capability − observed). Larger = more untapped AI potential. Dashed line at 50%. Averaged across research categories per SOC group.
Top 8 categories by vacancy count, mapped from SOC2020 major groups. SOC groups spanning multiple categories have their count distributed evenly.
Job counts by Standard Occupational Classification, based on 661 AI-classified vacancies.
This site incorporates information from O*NET Web Services by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). O*NET® is a trademark of USDOL/ETA. Occupational data (tasks, skills, knowledge, abilities, interests, career clusters, Bright Outlook designations, and hot technologies) is sourced from O*NET Online and used to enrich IOM job listings with AI-powered career intelligence.
