---
id: "lib-2026-07-00-skills-in-the-ai-age"
type: "library"
title: "Skills in the AI age"
lang: "EN"
org: "OECD"
source_type: "Paper"
date: "2026-07-01"
week: "2026-W27"
url: "https://nosov.ai/library/lib-2026-07-00-skills-in-the-ai-age/"
original_url: "https://www.oecd.org/content/dam/oecd/en/publications/reports/2026/07/skills-in-the-ai-age_e8d8c1e6/972bd15e-en.pdf"
tier: "B"
tier_name: "Useful, limited"
tier_reason: "A short OECD AI Paper (No. 60) by a single staff author that compiles existing OECD and partner evidence on AI and skills with country policy examples; it adds no new data or analysis."
summary_basis: "full-text"
original_minutes: "40"
tags: ["skills", "enablement"]
---

# Skills in the AI age

Tier B (Useful, limited): A short OECD AI Paper (No. 60) by a single staff author that compiles existing OECD and partner evidence on AI and skills with country policy examples; it adds no new data or analysis.

Summary based on the full text.

## In short

"Skills in the AI age" is OECD Artificial Intelligence Paper No. 60, written by Elif Köksal-Oudot under the OECD-Korea Digital Society Initiative and declassified by the Digital Policy Committee on 27 March 2026. It summarises how AI adoption changes jobs and skill demand and what governments can do about it. Its core message: exposure to AI is highest in high-skill white-collar jobs, while automation risk is highest in routine low- and middle-skill work, so skill policy has to address both groups.

## Key numbers

- 7% to 20%: share of firms in OECD countries using AI, 2021 to 2025
- about 1%: share of the workforce with advanced AI skills
- 72%: vacancies in high-AI-exposure occupations demanding at least one management skill
- about a quarter: OECD workers exposed to generative AI (20%+ of tasks amenable) in 2022-2024

## What it reports

- AI use among firms in OECD countries rose from around 7% to 20% between 2021 and 2025, with larger firms and start-ups ahead and SMEs held back by cost, infrastructure and skills shortages.
- About a quarter of OECD workers were exposed to generative AI in 2022-2024, defined as at least 20% of their tasks amenable to AI assistance.
- Managers, professionals and engineers are the most exposed but the least likely to be automated; routine manual and cognitive roles carry the highest automation risk.
- Workers with advanced AI skills are about 1% of the workforce, a share that has almost tripled in under a decade.
- Across ten OECD countries (Lightcast data), one-third of 2021-2022 vacancies were in high-AI-exposure occupations; 72% of those demand at least one management skill and 67% a business-process skill.
- Women account for only 25% of AI researchers and 11% of sole-authored AI publications.
- Policy examples include Estonia's AI Leap (over 20,000 high-school students and 3,000 teachers in phase one; EUR 3.2 million rising to EUR 6 million), Korea's AID 30+ and Germany's CVET funding, which covers up to 100% of wage costs for some training.

## How they got it

A desk synthesis of OECD sources (ICT usage surveys, PIAAC, AI Policy Observatory live data on talent flows) and partner studies such as Green (2024) on Lightcast vacancies, combined with country policy case boxes. No new empirical estimation.

## Limits

- Secondary evidence only; vacancy figures are from 2021-2022, before generative AI tools spread widely in workplaces.
- Exposure measures describe what AI could do, not what employers deploy.
- Written for governments; employer actions appear only through public training subsidies.

## Why it matters for companies

The most exposed roles in a traditional company are its managers, analysts and professionals, and vacancy data shows that management and business-process skills still dominate what employers ask for in those roles. The 1% figure for advanced AI skills means most firms will build capability through broad AI literacy and redesigned roles rather than hiring specialists. Public co-funding schemes for continuing training, such as Germany's, can lower the cost of reskilling programmes. SMEs and suppliers in a company's value chain are the likeliest to lag on adoption.

Original: https://www.oecd.org/content/dam/oecd/en/publications/reports/2026/07/skills-in-the-ai-age_e8d8c1e6/972bd15e-en.pdf