Uber rolls out ChatGPT Work to all 30,000 employees

Uber is expanding its OpenAI partnership by giving ChatGPT Work to its full global workforce of about 30,000 people, according to TechCrunch. The move matters because Uber is taking the tool from a controlled pilot into broad daily use across a large company, with non-engineering teams playing a major role.

From pilot to companywide access

OpenAI introduced ChatGPT Work in July as an enterprise-focused AI agent for corporate teams, according to TechCrunch. Uber first tested it with 1,000 employees earlier this year and is now making it available companywide.

For readers tracking workplace AI adoption, the important shift is not just the size of the rollout. It is that Uber is treating ChatGPT as a general work platform rather than a tool reserved for software teams.

Uber told TechCrunch that OpenAI worked closely with the company on integrating the chatbot. The company did not say how much it is spending on the broader deployment.

Non-engineers drove much of the early use

Uber’s initial trial included employees from multiple departments, including finance, operations and product. Sumanth Sukumar, Uber’s VP of engineering, told TechCrunch that about 80% of the early adopters were not engineers.

That detail is useful for smaller companies deciding where to begin with AI assistants. The obvious first use case is often coding or technical support, but Uber’s pilot suggests that broad business functions can generate meaningful demand too.

According to Sukumar, early users applied ChatGPT Work to several kinds of tasks: analyzing operational data to spot patterns and form recommendations, localizing training content for different markets and languages, and creating personalized interactive dashboards for daily work management.

For creators, marketers, freelancers and small businesses, those examples map to familiar problems. Teams often need to translate or adapt internal materials, summarize messy operational data, and create lightweight reporting views without waiting for a dedicated analyst or developer. Uber’s use cases show how a large organization is testing those patterns at scale, though the company has not yet defined one fixed way employees should use the tool.

Uber is leaving room for teams to experiment

Uber has not finalized a narrow set of approved workflows for ChatGPT Work. Sukumar told TechCrunch that the company’s approach is to make AI tools broadly available while allowing individual teams to explore what works for them.

One example from earlier this year was Uber’s use of what it called AI pods. The company paired 30 AI engineers with teams across Uber and gave them two weeks to automate important parts of their jobs, according to Sukumar.

For smaller organizations, the practical lesson is a rollout sequence rather than a specific tool mandate:

• Start with a cross-functional pilot, not only a technical team.
• Look for repeatable tasks where employees already know the pain points.
• Pair domain experts with people who understand how to build or configure AI workflows.
• Measure the workflows that emerge before standardizing them.

Uber says it will evaluate how employees use ChatGPT Work, what kinds of workflows they build, and whether the technology helps them work more effectively. That measurement-first posture is important because enterprise AI deployments can otherwise become a collection of disconnected experiments.

A marker for the enterprise AI race

TechCrunch frames the rollout as part of OpenAI’s push to grow its enterprise business while competing with Anthropic. OpenAI was first widely known as a consumer AI company, but business customers have become a major focus for the ChatGPT maker, according to the report.

For buyers, this is another sign that workplace AI is moving from individual subscriptions toward organization-wide deployments. Teams comparing assistants from OpenAI, Anthropic and Google can read our ChatGPT vs Claude vs Google Gemini buying guide for broader context on how these tools fit different work styles.

The open question is not whether employees will try the technology; Uber is putting it in front of everyone. The question is which workflows become durable enough to change how teams operate, and how companies decide when experimentation has turned into measurable productivity.