Where can AI be used? Insights from a deep ontology of work activities
🤖 AI Summary
This study addresses the absence of a systematic framework for delineating the applicability boundaries of artificial intelligence (AI) in workplace activities. It constructs, for the first time, a fine-grained ontology of work activities, structurally reorganizing approximately 20,000 tasks from the O*NET database. Leveraging this ontology, the research classifies 13,275 AI software applications and 20.8 million robotic systems, enabling cross-domain data integration and visual analytics. The findings reveal that the current market value of AI is highly concentrated in just 1.6% of all work activities, with 72% of these focused on information processing and only 12% involving physical operations. This provides a quantitative foundation and structural insight into the scope and limitations of contemporary AI deployment in the labor domain.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
artificial intelligence
work activities
AI applicability
ontology
occupational tasks
Innovation
Methods, ideas, or system contributions that make the work stand out.
work activity ontology
AI applicability prediction
O*NET reorganization
AI market value distribution
systematic AI deployment analysis
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