What Work is AI Actually Doing? Uncovering the Drivers of Generative AI Adoption
Peeyush Agarwal, Harsh Agarwal, Akshat Rana

TL;DR
This study systematically analyzes how intrinsic task characteristics influence the adoption of generative AI, revealing that tasks with high creativity, complexity, and cognitive demand are most AI-driven, and classifying work into distinct archetypes.
Contribution
It introduces a comprehensive, data-driven framework linking real-world AI usage to task features and classifies work into archetypes, advancing understanding of AI's role in labor division.
Findings
High AI engagement in creative, complex, and cognitive tasks
Identification of three task archetypes: Dynamic Problem Solving, Procedural & Analytical, Standardized Tasks
5% of tasks account for 59% of AI interactions
Abstract
Purpose: The rapid integration of artificial intelligence (AI) systems like ChatGPT, Claude AI, etc., has a deep impact on how work is done. Predicting how AI will reshape work requires understanding not just its capabilities, but how it is actually being adopted. This study investigates which intrinsic task characteristics drive users' decisions to delegate work to AI systems. Methodology: This study utilizes the Anthropic Economic Index dataset of four million Claude AI interactions mapped to O*NET tasks. We systematically scored each task across seven key dimensions: Routine, Cognitive, Social Intelligence, Creativity, Domain Knowledge, Complexity, and Decision Making using 35 parameters. We then employed multivariate techniques to identify latent task archetypes and analyzed their relationship with AI usage. Findings: Tasks requiring high creativity, complexity, and cognitive…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
