Where can AI be used? Insights from a deep ontology of work activities

arXiv cs.AI / 3/24/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisModels & Research

Key Points

  • 本研究は、AIが「どの仕事の活動に使えるか」を体系的に分析・予測するための、仕事活動の包括的オントロジー(約2万件の活動を再編)を提示している。
  • O*NETの活動分類を基盤に、13,275件のAIソフトウェアアプリケーション記述と、世界の2,080万台規模のロボットシステムの実態データを用いて、AIの用途を活動単位で分類している。
  • AI市場価値は活動間で極めて偏っており、上位1.6%の活動だけで市場価値の60%以上を占める一方、物理的活動への配分は12%にとどまると示している。
  • 情報ベースの活動(特に情報の作成)と、情報と物理の両方を含むインタラクティブ活動(情報の伝達を含む)が大きな比率を占めることを明らかにしている。
  • このフレームワークにより、現在のAIシステムが適用できる領域/できない領域を細かな粒度で推定し、将来のAI能力が活動分布をどう変え得るかを粗い予測として提示している。

Abstract

Artificial intelligence (AI) is poised to profoundly reshape how work is executed and organized, but we do not yet have deep frameworks for understanding where AI can be used. Here we provide a comprehensive ontology of work activities that can help systematically analyze and predict uses of AI. To do this, we disaggregate and then substantially reorganize the approximately 20K activities in the US Department of Labor's widely used O*NET occupational database. Next, we use this framework to classify descriptions of 13,275 AI software applications and a worldwide tally of 20.8 million robotic systems. Finally, we use the data about both these kinds of AI to generate graphical displays of how the estimated units and market values of all worldwide AI systems used today are distributed across the work activities that these systems help perform. We find a highly uneven distribution of AI market value across activities, with the top 1.6% of activities accounting for over 60% of AI market value. Most of the market value is used in information-based activities (72%), especially creating information (36%), and only 12% is used in physical activities. Interactive activities include both information-based and physical activities and account for 48% of AI market value, much of which (26%) involves transferring information. These results can be viewed as rough predictions of the AI applicability for all the different work activities down to very low levels of detail. Thus, we believe this systematic framework can help predict at a detailed level where today's AI systems can and cannot be used and how future AI capabilities may change this.