Uber Puts All 30,000 Employees on ChatGPT Work

Uber is deploying ChatGPT Work to its entire global workforce of roughly 30,000 people, the company told TechCrunch on Thursday as it announced an expanded partnership with OpenAI. The rollout turns one of the world's largest ride-share operators into one of the biggest named customers yet for OpenAI's enterprise product.

The move matters because of who used it first. ChatGPT Work debuted in July as an enterprise-focused AI agent that corporate teams could deploy across their workflows. Uber piloted it earlier this year with just 1,000 employees, drawn from departments including finance, operations, and product. A majority of those early adopters — 80%, according to Sumanth Sukumar, Uber's VP of engineering — were not engineers.

That's the tell. When four out of five pilot users come from non-technical functions, AI adoption stops being a developer story and becomes an operations story.

What were those non-engineers actually doing?

Sukumar didn't describe the pilot in vague productivity terms. He gave TechCrunch a concrete picture of the tasks: "They used it for a wide range of tasks: analyzing complex data from our operations to identify trends and develop recommendations; localizing training materials for different markets and languages; and building personalized interactive dashboards to manage their daily work."

Three patterns sit inside that quote. Data analysis turned into recommendations rather than raw reports. Training materials got localized for multiple markets and languages at once. And employees built their own interactive dashboards — work that previously would have gone to someone with engineering skills or a ticket in an IT queue.

In other words, the pilot targeted the unglamorous middle of a global company's workload: analysis, translation, and internal tooling.

How is Uber deciding what comes next?

Here's the unusual part. Uber hasn't dictated how its 30,000 employees should use the tool. "Our approach is to make powerful AI tools available across Uber while giving teams room to experiment," Sukumar said.

The company has already run one structured experiment to learn what sticks. Earlier this year, Uber paired 30 AI engineers with teams across the company and gave them two weeks to automate key parts of their job. Sukumar called these squads "AI pods," and said they "showed us how much opportunity there is when you combine technical expertise with a deep understanding of a particular job."

Thirty engineers, two weeks, one mandate: automate your own workflow. That's a cheap discovery model other large employers can copy.

What does the money question look like?

Uber didn't disclose what it's spending on the rollout. Sukumar said the company will instead track usage and let outcomes drive the strategy. "We'll look at how employees are using the technology, the kinds of workflows they develop and how it helps them work more effectively," he said. "We'll evaluate those outcomes and use what we learn to guide how we deploy AI across the business."

So the deal is effectively measure-first, scale-later. Uber buys seats for everyone, watches what workflows emerge, and uses that evidence to steer broader AI deployment across the business. Uber also said OpenAI worked closely with the company on integrating the chatbot.

Why this matters for OpenAI

The deal lands at a pointed moment in the enterprise AI race. OpenAI has moved quickly to scale up its enterprise business as it competes with its chief competitor, Anthropic. Once known largely as a consumer brand, the ChatGPT creator has swiftly gained a loyal following among business customers, according to the report, while other AI companies have struggled to keep up.

A 30,000-seat commitment from a Fortune-scale operator is exactly the kind of reference customer that enterprise sales teams build pipelines around. And the composition of Uber's pilot — 80% non-engineers — gives OpenAI a data point its rivals can't easily match: proof that its enterprise agent found traction outside the engineering org first.

What does this change for practitioners? It signals that the next wave of AI deployments won't be scoped by IT departments but discovered by finance, operations, and product teams themselves — and that companies like Uber are now willing to buy seats for everyone before they know exactly what those seats will do.