AI summaryⓘ
The authors studied how companies use ChatGPT Enterprise by linking its usage data with job roles, tasks, and company financials through March 2026. They found that many companies, especially larger U.S. public firms with high R&D spending, have quickly adopted this AI tool and that usage is growing both by new adopters and increased use by current ones. The AI is used across a variety of job functions and seniority levels, with newer employees using it the most. ChatGPT is applied to many knowledge work tasks like writing, technical work, communication, and summarizing information. Overall, the authors highlight that companies vary in how fast and broadly they adopt AI and are still figuring out how to use it best in their workflows.
ChatGPT EnterpriseGenerative AIAI adoptionKnowledge workOrganizational workflowsR&D intensitySG&A expensesTask classificationEmployee rolesEnterprise AI usage
Authors
Aaron Chatterji, David Holtz, Neel Rakholia, Prasanna Tambe, Gawesha Weeratunga
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.