A AI tool has been well-received in a pilot program involving a thousand people, but it is two different things from serving thirty thousand employees. On October 22nd, OpenAI announced the launch of Uber for all employees globally with the goal of making the tool available to over thirty thousand people; previously, the pilot scale was around a thousand people. The announcement used phrases such as "launching from today" and "will be available," so it cannot be assumed that all thirty thousand employees are already using it daily, nor can it be inferred that they have all seen an increase in productivity. This difference is precisely the aspect that is most easily obscured by headlines when a company transitions from demonstration to actual operation, as mentioned in AI.
ChatGPT Work is placed within the ChatGPT Enterprise environment and can be connected to tools such as Google Drive, Gmail, Google Calendar, and Slack to assist employees in retrieving information, preparing meeting summaries, creating presentations, analyzing data, and handling cross-application workflows. OpenAI refers to GPT-6.1. Sol will serve as the default capability, with additional voice and image generation options available for teams to choose from. There are also plans to promote this with the support of an internal AI skill library, approved integrations, and specialized training; the software engineering team will continue to use Codex. Taken together, this is not just about providing employees with a chat entry point; it's about integrating models, data permissions, and work processes into corporate governance.
What lies between being available to all staff and making good use of that availability by everyone?
During the pilot phase, it is usually possible to select teams that are willing to try and for whom the tasks are suitable; however, when implementing widespread promotion, the technical proficiency, job roles, and risk preferences of actual users will vary significantly. The operations department may wish to automatically organize regional business data, the finance team needs to verify the criteria, marketing personnel need to quickly generate materials, and product managers require cross-departmental searches. The same model interface does not mean that these tasks have the same permission boundaries and acceptance standards. Typos in meeting summaries may be easy to correct, but errors in financial figures can directly affect decision-making.
Tool connectivity is the core of this promotion and also the aspect that requires the most detailed attention. If a proxy can read emails, calendars, files, and communication records simultaneously, it may be able to perform multiple tasks on behalf of employees; however, there must also be clear controls over its visibility, its ability to write on behalf of employees, and whether it can send content to third parties. The announcement mentions "approved integrations" but does not list the permission matrix for each type of connection within Uber. External readers should not equate the general capabilities of the product with the assumption that Uber has opened up all cross-application operations to all employees.
The internal skill libraries mentioned in Uber and OpenAI are worth noting. The common failures in companies are not that the models are completely incapable of providing answers, but rather that employees do not know which tasks are suitable for them, what materials should be provided, or how to review the output. If each team writes their own set of prompts, it becomes difficult to replicate the results. A skill library can fix mature processes into templates, and then training can explain when to use them and when to revert to manual work. The value of this operational mechanism needs to be observed through subsequent actual usage. Currently, the announcement does not provide complete audit data on usage rates, time savings, or accuracy rates.
External cooperation does not mean handing over all decision-making to models. The technical leader of Uber described this promotion in a statement as a tool to help employees innovate and solve problems; OpenAI emphasizes tasks that are complex and multi-step. What companies really need to establish is a distinction between “delegable” and “non-delegable” tasks: information collection, preliminary drafts of meeting materials, etc., can be done by agents first, while actions involving personnel, pricing, payment, or user-sensitive data should remain subject to approval and review. The stronger an agent’s ability to complete these tasks, the less an organization should mistakenly assume that the ultimate responsibility has been transferred to the software supplier.
What numbers are needed to prove the effectiveness of scaling?
The most direct indicator is not the number of accounts activated, but whether employees continue to use the service in appropriate scenarios. Operations teams can observe metrics such as activity rates, task completion rates, the extent of manual modifications, and the number of failures that require rollback, based on the roles of the employees. For high-value processes, it is also important to measure the entire time from when a requirement is raised until a usable result is delivered. If an employee uses a proxy to complete a task in ten minutes and then spends twenty minutes checking for errors, one should not only highlight the savings from the first ten minutes. Conversely, even if the total duration of some tasks does not decrease, improvements in data coverage or reductions in cross-team waiting times should also be recorded separately.
Security and compliance costs cannot be omitted from the calculations. Full-staff deployment means that when new employees join, leave, there are job changes, or when working across regions, data access must be adjusted accordingly. Tools that connect to corporate emails and files require audit logs, permission revocation, and monitoring of abnormal behaviors. Employees should also be aware of which customer or business information cannot be uploaded directly. ChatGPT Work acts as a proxy in the existing corporate environment but does not automatically replace Uber's own information security policies. The larger the scale, the more important it is to implement the principle of least privilege and gradual rollout (grayscale deployment).
It is also necessary to prevent the interpretation of “more than 30,000 people” mentioned in the news as some kind of achieved performance benchmark. What is described is the global employee base that can be covered, not the number of users per day. The announcement states that the two companies will share information on implementation, use cases, and measurable results in the future; this precisely indicates that these results are still to be disclosed. For other enterprises, the actions of Uber demonstrate that large organizations are willing to promote proxy workstations to a wide range of employees, but they do not provide a set of yield rates that can be copied without any adjustments.
The novelty of this promotion lies in moving from a pilot of one thousand people to the entire company, rather than AI having already completed the daily work of thirty thousand people on behalf of Uber. In the next phase, what is worth tracking is whether cross-departmental processes can operate stably, whether permission controls can withstand scaling, and whether employees have truly reduced repetitive labor. Only when these verifiable subsequent data appear can the outside world determine whether this is a large-scale account activation or whether there has been a sustained change in the organization's working methods.
Source: OpenAI and Uber Announcement on October 22, 2026 https :// openai.com / index / uber-chatgpt-work-rollout /
