Uber has officially rolled out ChatGPT Work across its entire global workforce of about 30,000 employees, taking its earlier pilot program company-wide. Initially tested this year with around 1,000 workers, the enterprise AI tool will now become available to staff in departments ranging from operations to finance and product.
ChatGPT Work, launched in July, is designed for business settings—letting teams leverage AI agents to support everyday workflows across the enterprise. Uber’s pilot saw heavy use beyond its engineering ranks: close to 80% of early users weren’t engineers. They applied the tech to tasks like parsing operational data for insights, tailoring training materials to different markets and languages, and building interactive dashboards suited to individual workflows.
How Uber Scaled
The company didn’t just drop the tool into everyone’s lap; it scaled deliberately. Early this year, Uber formed small “AI pods”—groups pairing about 30 AI engineers with other teams—for two-week sprints meant to automate time-consuming parts of jobs. These experiments revealed clear chances to boost productivity when technical know-how intersects with domain knowledge.
Uber hasn’t specified its spending on this global rollout. The strategy now is to monitor usage: seeing what workflows employees develop, how they’re actually using ChatGPT Work, and what performance gains emerge. OpenAI has been closely involved in the implementation process.
In the broader market, OpenAI continues aggressively expanding its enterprise services as it competes with other major players in AI like Anthropic. The shift marks its evolution from a consumer-focused product into a staple of business operations.
This move at Uber underscores a growing trend across tech and traditional businesses alike: serious internal adoption of AI tools, not just in research or product teams but across entire companies. It’s a signal that what was often an experiment is becoming foundational to how work happens.
What this means and what to watch: Uber’s decision to standardize ChatGPT Work across all levels suggests confidence that AI can deliver real efficiency gains beyond isolated pilots. But challenges remain—measuring impact, managing adoption, and ensuring employees retain agency. The outcomes from this rollout could set important precedents for AI governance, productivity metrics, and the role of AI knowledge in non-technical work.
