The state of enterprise AI

The most widely deployed GPTs either codify institutional knowledge into reusable assistants or automate workflows through integrations with internal systems. Some organizations have built a culture of developing and sharing Custom GPTs at scale. For example, BBVA regularly uses more than 4,000 GPTs, indicating that AI-driven workflows are increasingly implemented as persistent tools embedded in daily operations

Seventy-five percent of surveyed workers report that using AI at work has improved either the speed or quality of their output. On average, ChatGPT Enterprise users attribute 40–60 minutes of time saved per active day to their use of AI, with data science, engineering, and communications workers saving more than average (60–80 minutes per day). Time saved per message varies by function: accounting and finance users report the largest benefits followed by analytics, communications, and engineering.

AI is not only accelerating existing work; it is also expanding the tasks and skills workers can perform. Several studies find that AI has an equalizing effect, disproportionately aiding lower performing workers.1 Consistent with these findings, 75% of workers report being able to complete tasks they previously could not perform, including programming support and code review, spreadsheet analysis and automation, technical tool development and troubleshooting, and custom GPT or agent design.

At the individual worker level, impact increases as workers deepen their use of AI. Across a large sample of workers, time saved is correlated with the use of more advanced ChatGPT features, including Deep Research, GPT-5 Thinking, and Image Generation. Workers consuming the most intelligence (as measured by credits used2) report higher time savings. Workers who save more than 10 hours per week are not just using more intelligence, they are also using multiple models, engaging with more tools, and using AI across a wider range of tasks.

But usage is diversifying: customer service and content generation now represent approximately 20% of API activity, and non-technology firm API use has grown 5x year-over-year. Taken together, this pattern suggests adoption is expanding beyond technology-led product embedding toward a broader set of operational and workflow deployments across industries.

Even among active ChatGPT Enterprise users, many have not tried some of the most capable tools. Of monthly active users, 19% have never used data analysis, 14% have never used reasoning, and 12% have never used search. Among daily active users, those shares drop to 3%, 1%, and 1%, respectively.