Defining the AI Workforce

2026-04-24 · Source: Center for Security and Emerging Technology · Field: Business & Management — Human Resources & Workforce Development, Artificial Intelligence & Machine Learning, Research Methodology & Innovation · Depth: Intermediate, quick

Summary

Defining the AI workforce is critical for accurate talent measurement, policy formulation, and workforce strategy design, yet current definitions vary widely. This analysis evaluates existing approaches, highlighting their strengths and weaknesses in capturing diverse AI-related work. It introduces CSET's new, narrower definition specifically for "AI development jobs," aiming to provide a more policy-relevant framework. The blog post explores how different definitions impact the understanding of AI talent pools and the identification of skill gaps, emphasizing the need for clarity in this rapidly evolving sector.

Key takeaway

For policymakers and researchers tasked with assessing AI talent shortages or designing workforce strategies, you should critically evaluate the scope of your AI workforce definition. Adopting a precise, policy-relevant definition, such as CSET's "AI development jobs," will lead to more accurate measurements and targeted interventions, avoiding the pitfalls of overly broad or ambiguous classifications.

Key insights

Clear AI workforce definitions are crucial for effective policy and talent measurement.

Principles

• Definitions shape policy outcomes.
• Specificity aids policy relevance.

Topics

• AI Workforce Definition
• AI Talent Measurement
• Workforce Strategy
• CSET Definition
• AI Development Jobs

Best for: Research Scientist, Policy Maker, HR Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Center for Security and Emerging Technology.