Labor Data Readiness in the Age of AI

AI is reshaping work at a pace that most labor market information systems were not built to measure. Against this backdrop, the pressing question is not simply “who works where?” as it used to be in the past, but what people actually do, what skills they use, and how AI is changing tasks inside roles.

Today, many countries still rely on infrequent surveys, broad occupational categories, and siloed administrative datasets. That makes it harder to spot early signals of changing skills demand, target training investment, or support employers and workers as AI adoption accelerates.

Modernizing labor market data for the AI age

Our report, developed in partnership with Workday, helps governments modernize labor market data systems to better navigate AI-driven change. It establishes a global baseline across 21 countries, identifies system gaps, and sets out a practical pathway to strengthen readiness over time.

At the center is a maturity framework benchmarking countries across six dimensions of AI-ready labor market data: Forecasting readiness, Labor market granularity, Accessibility, Interoperability and integration, and Real-time responsiveness (FLAIR).

Key findings

Across the 21 countries assessed, every country performs at a frontier or near-frontier level in at least one FLAIR dimension. This suggests there is a strong foundation to build on, even if readiness is uneven across the full system.

Several findings stand out:

• Interoperability is a bright spot. 20 out of 21 countries align with international standards.
• Forecasting is not yet AI-ready. Many countries produce projections, but none have adopted AI-enabled, task-level scenario modeling.
• Task and skill visibility is the biggest gap. Only a few countries capture task or skill data through core national surveys, and only a small minority offer open, machine-readable, API-enabled microdata access.
• Near real-time monitoring is within reach. More than half of countries publish monthly labor force data that could support more timely forecasting and monitoring.

Recommendations for modernizing labor market data systems

The report sets out four priorities for action:

• Data collection: update labor force surveys and administrative data to capture tasks, skills, digital tools, and AI use.
• System foundations: build interoperable architectures, adopt shared task and skill taxonomies, and transition toward machine-readable, API-enabled platforms.
• Intelligence capabilities: strengthen forecasting, integrate high-frequency private sector data, and build more timely nowcasting pipelines.
• Governance and partnerships: clarify mandates and enable voluntary public-private partnerships that strengthen national systems over time.

Download the full report

Download the full report to explore the country benchmarking, the FLAIR maturity framework, and the step-by-step pathway for labor market data modernization in an AI-driven economy.