According to OpenAI, enterprise AI has graduated from the sandbox and is now used in daily operations with tight workflow integration.
New data from the company shows that companies are now assigning complex, multi-step workflows to models, rather than simply requiring a text summary. These diagrams illustrate a major shift in the way organizations are implementing generative models.
OpenAI’s platform currently serves more than 800 million users each week, with a “flywheel” effect that familiarizes consumers with professional environments. The company’s latest report states that more than 1 million enterprise customers are currently using these tools, and the goal is even tighter integration going forward.
This evolution presents two realities to decision makers. Although productivity gains are tangible, the widening gap between “frontier” adopters and median firms suggests that value is highly dependent on intensity of use.
From chatbots to deep reasoning
The best metric for determining a company’s implementation maturity is task complexity, not seat count
OpenAI reports an 8x year-over-year increase in ChatGPT message volume, but a better indicator for enterprise architects is the consumption of API inference tokens, which suggests deeper integration is taking place. This number has increased nearly 320x per organization. This is evidence that companies are systematically building more intelligent models into their products to handle logic rather than basic queries.
The rise of configurable interfaces supports this view. Weekly users of Custom GPT and Custom Projects (tools that allow employees to direct models with specific organizational knowledge) have increased approximately 19x this year. Approximately 20% of all corporate messages are now processed through these customized environments, making standardization a prerequisite for professional use.
For company leaders auditing the ROI of AI seats, the data provides evidence of time savings. On average, users believe this technology saves them 40 to 60 minutes per active day. Effects vary depending on the type of job. Data science, engineering, and communications professionals report even greater savings (60-80 minutes per day on average).
Beyond efficiency, software is changing the boundaries of roles. There are certain implications for technical capabilities, especially regarding code generation.
OpenAI says enterprise users are seeing an increase in coding-related messages across all business functions. Coding queries for non-engineering, IT, and research roles have increased by an average of 36% over the past six months. Non-technical teams are using this tool to perform analysis that previously required professional developers.
Business improvements spread throughout the department. Survey data shows that 87% of IT professionals report faster problem resolution and 75% of HR professionals see an increase in employee engagement.
The widening enterprise AI capability gap
OpenAI’s data suggests a divide is emerging between organizations that simply provide access to tools and those where integration is deeply embedded in their operating models. The report identifies “frontier” workers, or workers at the 95th percentile of adoption intensity, who generate six times more messages than the median worker.
This disparity is evident at the organizational level. Frontier companies generate approximately twice as many messages per sheet as the median company and seven times as many messages for custom GPT. Larger companies aren’t just using tools more frequently; They are investing in the infrastructure and standardization needed to make AI a permanent part of their operations.
Users who tackle a variety of tasks (approximately seven types) report saving five times more time than those who limit their usage to three or four basic functions. Benefits are directly correlated to intensity of usage, suggesting that a “light touch” implementation plan may not achieve the expected ROI.
Professional services, finance, and technology sectors were early adopters and remain the biggest adopters, but other industries are doing their best to catch up. The technology sector leads with 11x year-over-year growth, followed by healthcare and manufacturing with 8x and 7x growth, respectively.
The global adoption pattern also challenges the notion that this is simply a US-centric phenomenon. International usage is rapidly increasing, with business customer growth exceeding 140% year over year in markets such as Australia, Brazil, the Netherlands, and France. Japan has also emerged as an important market with the largest number of corporate API customers outside the US.
OpenAI: Tight AI integration accelerates enterprise workflows
Case studies highlight how these tools impact key business metrics. Retailer Lowe’s implemented an employee tool in more than 1,700 stores, resulting in a 200 basis point improvement in customer satisfaction scores when employees used the system. Additionally, when online customers utilized the retailer’s AI tools, conversion rates more than doubled.
In the pharmaceutical space, Moderna used enterprise AI to speed up the creation of target product profiles (TPPs), a process that typically takes weeks of cross-functional effort. The company shortened core analysis steps from weeks to hours by automating the extraction of key facts from large packs of evidence.
Financial services company BBVA leveraged this technology to fix bottlenecks in corporate signature authority legal verification. By building a generative AI solution to handle standard legal queries, the bank automated over 9,000 queries per year, effectively freeing up the equivalent of three full-time employees for higher-value tasks.
However, moving to production-grade AI requires more than just software procurement. It requires organizational preparation. The main hurdle for many organizations is no longer the functionality of the model, but its implementation and internal structure.
Leading companies consistently enable tight system integration by turning on connectors that provide models with secure access to corporate data. However, approximately one in four companies do not take this step, limiting their models to general knowledge rather than specific organizational contexts.
Successful implementation requires executive support to set explicit mandates and encourage the codification of organizational knowledge into reusable assets.
As technology continues to evolve, organizations must adjust their approach. According to data from OpenAI, success now depends on delegating complex workflows with deep integrations and treating AI as the primary engine of revenue growth for enterprises, rather than just looking for output.
See also: AWS re:Invent 2025: Frontier AI agents replace chatbots
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