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.
Abstract (translated)
人工智能（AI）有望深刻改变工作的执行与组织方式，但我们目前仍缺乏深入框架来理解AI的应用场景。本文提供了一个全面的工作活动本体论，可帮助系统性地分析和预测AI的使用。为此，我们解构并重组了美国劳工部广泛使用的O*NET职业数据库中约2万项活动。接着，我们利用该框架对13,275项AI软件应用及全球2,080万台机器人系统进行了分类。最后，我们基于这两类AI的数据，生成了图形化展示，呈现当今全球所有AI系统预估数量与市场价值如何分布于其辅助完成的各类工作活动中。我们发现AI市场价值在活动间分布极不均衡：前1.6%的活动占据了超过60%的市场价值。大部分市场价值（72%）集中于基于信息的活动，尤其是信息创造（36%），而仅12%用于体力活动。交互式活动涵盖基于信息和体力两类活动，占AI市场价值的48%，其中26%涉及信息传递。这些结果可视为对所有不同工作活动AI适用性的粗略预测，甚至细化到极低层级。因此，我们认为这一系统性框架有助于在详细层面预测当今AI系统能否应用于特定工作，以及未来AI能力可能如何改变这一格局。
URL
https://arxiv.org/abs/2603.20619
