AI delegability varies widely between jobs and countries worldwide
The Delegation Divide: An Open Index of AI-Agent Delegability across Occupations and Countries
Artificial IntelligenceComputers and Society
Summary
Some work tasks can be partly or fully handed over to AI agents, but this varies a lot by job type and country. The authors created a detailed index to measure how much AI can take over tasks in many different jobs across 146 countries. They found richer countries tend to have more AI-delegable work, mostly because of the types of jobs available. The study also shows that how much AI is used aligns closely with how delegable jobs are, and completely handing over tasks to AI is rare.
What this means in practice
- •For workforce planners: Identify how tasks and jobs in various countries can be supported or transformed by AI to plan workforce development and training.
- •For labor market analysts: Measure and compare AI exposure and task delegability across occupations internationally for better economic and policy forecasting.
Authors
Cristian Espinal Maya, Santiago Jiménez Londoño
Abstract
35.2% of task time in world employment is at least partly delegable to AI agents in 2026, and only 2.5% can be handed over in full. Existing measures score exposure to generative AI, mostly for the United States. We build the Agent Delegability Index (ADI): two language models rate all 18,796 O*NET 30.2 task statements on a frozen, preregistered rubric of delegation levels and barriers; tasks are mapped to 433 ISCO-08 unit groups and weighted by ILOSTAT employment in 146 countries. Four findings. First, delegable employment rises with income, from 22.5% in low-income to 48.7% in high-income countries, a slope of 7.23 percentage points per log point of GDP per capita. This delegation divide is compositional: the physical barrier dominates the jobs poorer countries employ most. Second, at the occupation level ADI tracks exposure closely (r = 0.93 with Eloundou et al. and 0.89 with Felten et al.), above the preregistered bound of 0.80; ADI adds delegation levels and barriers on top of exposure, for the world, not a new ranking of occupations. Third, full delegation is rare and peaks for tasks that take a human about twelve minutes (11.8 minutes). Fourth, the preregistered adoption gap is not supported: across 101 countries, delegability and observed AI usage correlate at r = 0.86, and the gap between them is not wider in middle-income countries (p = 0.27). Model agreement is kappa = 0.77; a blind human rating of 300 tasks agrees with the model consensus at kappa = 0.85, and an agentic panel with web search at kappa = 0.87. We release the index, the atlas, the prompts and an MCP server at https://github.com/Cespial/delegation-divide.