Inside ChatGPT Enterprise: Usage Grew 7x, Skewed to Big Firms and Junior Staff

OpenAI researchers, working with academics from Columbia and Wharton, linked ChatGPT Enterprise account records to usage logs, worker job titles, message-level task classifications, and public-company financials through March 2026 to build one of the first large-scale, telemetry-based pictures of workplace AI adoption. The six-month-horizon sample alone covers more than 1,500 organizations and over 17 million messages. Unlike the surveys that dominate this field, the study measures what employees actually do rather than what they report doing, and it frames enterprise AI as a general-purpose technology that firms are still learning to absorb.

Four findings stand out. Growth is steep and driven from both ends: aggregate output tokens rose roughly sevenfold between June 2025 and March 2026, and nearly fourfold even within a fixed cohort of earlier adopters, meaning about half the growth came from existing customers using the tool more intensively rather than from new sign-ups. Adoption among U.S. public companies is concentrated in larger, more valuable firms that already spend heavily on R&D and SG&A, suggesting early enterprise AI tracks with prior investment in intangible and organizational capital. Within adopting firms, use spans every function and seniority level but varies sharply in intensity — engineering and technical staff make up about 11% of weekly active users and executives roughly 9%, yet analysts, marketing/communications workers, and especially early-career employees send disproportionately more messages per person.

The task mix reinforces the general-purpose reading. More than half of active users do documentation or technical writing, nearly half do technical or coding work, and large shares use the tool for drafting messages, research, topic overviews, planning, data analysis, and legal or financial tasks — with a similar core of high-volume categories recurring across industries. The authors caution these are preliminary working-paper results, but the through-line is that enterprise AI is broad yet uneven: dispersed individual experimentation that has not yet consolidated into the complementary organizational changes that typically determine whether a general-purpose technology pays off.