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

TL;DR

This paper develops a detailed ontology of work activities to analyze and predict AI application distribution, revealing significant market concentration in specific activities and providing a framework for understanding AI's current and future roles in work.

Contribution

It introduces a comprehensive, reorganized ontology of work activities and applies it to classify AI applications and robotic systems, enabling systematic analysis of AI deployment across work activities.

Findings

AI market value is highly concentrated, with 1.6% of activities accounting for over 60% of value.

Most AI market value (72%) is in information-based activities, especially creating information.

AI is predominantly used in information transfer and interactive activities, comprising 48% of market value.

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.