How Organizations Use AI: Evidence from ChatGPT

2026-08-12

AI Summary

This study investigates the adoption patterns of generative AI systems, such as ChatGPT, within enterprises and their impact on organizational workflows. Leveraging a dataset comprising tens of millions of anonymized message logs and firm-level data from over one thousand publicly listed companies, the research integrates employee roles, task taxonomies, and financial metrics. Employing privacy-preserving record linkage and large-scale log analysis, it offers the first systematic characterization of generative AI usage across functions and hierarchical levels. The findings reveal that adoption occurs more rapidly in larger firms with higher R&D intensity, with predominant use in knowledge-intensive tasks—including writing, technical development, communication, and information synthesis. Early-career employees exhibit significantly higher usage intensity, and their engagement deepens progressively over time.

Abstract

We study how organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026. These linked data enable a privacy-preserving analysis of adoption, worker roles, and message-level tasks at scale: for instance, the worker-level sample we analyze at the six-month adoption horizon includes over 1,500 organizations and over 17 million messages. We document four facts about enterprise AI adoption and use. First, ChatGPT Enterprise usage has grown rapidly due to a combination of new firm adoption and growing intensity among existing adopters. Second, U.S.-based public company adoption is concentrated among larger, more valuable, and more R&D- and SG&A-intensive firms. Third, active use within adopting firms spans job functions and seniority levels, with especially high usage intensity among early-career workers. Fourth, ChatGPT Enterprise usage encompasses a broad range of knowledge work tasks, including writing, technical work, communication, and information synthesis. In aggregate, these results suggest that firms differ widely in the speed, breadth and purpose of their enterprise AI adoption, and that they are still actively learning how to integrate AI into organizational workflows.

Problem

• enterprise AI adoption
• generative AI
• organizational use
• ChatGPT
• knowledge work tasks

Innovation

• generative AI
• enterprise adoption
• message-level analysis
• organizational workflows
• privacy-preserving data