In preparing to create this report, Omdia by Informa TechTarget conducted a comprehensive online survey fielded between Aug. 13, 2025, and Sept. 17, 2025. All respondents represent organizations with 500 or more employees. About 34% of respondents represent organizations with more than 5,000 employees; 49% represent orgs with 1,000–4,999 employees; and 17% come from companies of 500–999 workers.
The intent of this report, and the research that underpins it, is to better understand the experiences enterprise organizations are having with generative AI technologies. In many regards, this research is a continuation of 2025’s research and report and it spans considerations like LLM use cases, benefits, challenges, ROI, expectations for the future and more. Additionally, it breaks new ground as organizations turn to agentic AI solutions to further automate business processes. Many of these insights can only be gleaned by surveying organizations with real gen AI experience in production. As such, our survey targeted organizations currently using gen AI to augment and execute business processes in production.
However, the research allows us to make observations about how broadly gen AI has been adopted by enterprises to date. Of the 3,479 respondents who started the survey, 59% reported their organization is already using gen AI across many business use cases (39%) or for a few initial use cases (20%). Further, just 2% of respondents reported their organization has no plans or interest in adopting solutions.
These findings show remarkable consistency with the data collected a year ago indicating that gen AI has, to date, resisted the typical boom-bust-recovery adoption cycle endemic to new enterprise technologies. We believe there are two dynamics at play to bolster adoption: users’ integration of gen AI into daily digital experiences and a significant, demonstrable enterprise impact being achieved across multiple use cases. Together, gen AI’s widespread impact on users’ personal lives and the impacts it has had to date on enterprise workers’ efficiency and productivity appear to be helping it resist usage and investment pullbacks typically seen when hype outstrips reality.
The 2,050 adopters who completed the survey were drawn from IT and cybersecurity (49%), software development (16%), data operations (9%) and other lines of business (for example, marketing, customer support, manufacturing, 25%). To qualify, respondents must have reported that they would be influential in their organization’s future AI purchases. A range of seniorities are represented, from C-level executives to senior individual contributors. Additionally, the research includes responses from across the globe, including the United States (41%), Canada (7%), the United Kingdom (7%), France (7%), Germany (7%), Australia and New Zealand (7%), Japan (7%), Singapore (7%) and India (7%). The margin of error for this sample size is +/- 2 percentage points at the 95% confidence level. The totals presented in figures and tables throughout this report may not add up to 100% due to rounding.