Why Uber Just Handed ChatGPT Work to All 30,000 Employees

According to TechCrunch, the ride-sharing giant Uber is scaling up its partnership with OpenAI to deploy ChatGPT Work across its entire global workforce of 30,000 employees. This follows an initial pilot involving just 1,000 workers earlier this year, moving the tool from a restricted trial to an enterprise standard.

Who is actually using enterprise AI at Uber?

The most revealing detail from the rollout isn't the scale, but who is using the tool. Sumanth Sukumar, Uber's VP of engineering, revealed to TechCrunch that 80% of the early participants in the pilot were non-engineers.

Instead of writing code, workers from finance, operations, and product departments used the platform to analyze complex operational data, localize training materials across multiple languages, and build personalized interactive dashboards for daily workflows.

How does the deployment work in practice?

Rather than imposing strict rules from day one, Uber is letting teams experiment with the technology organically. The company previously used short two-week sprints where 30 AI engineers were paired with operational teams to automate routine tasks.

• Cross-departmental access: The tool is now available to all 30,000 employees worldwide, removing silos.
• Custom workflow creation: Teams are encouraged to build interactive dashboards and analyze data without relying strictly on IT departments.
• Performance tracking: Uber is actively monitoring how employees use the platform to guide future business integration rather than enforcing rigid use cases.

Is this kind of rollout worth it for your team?

Mega-deployments like Uber's show that AI is moving past developer-only stacks. However, without disclosing exact financial terms, the real challenge for companies following this path will be measuring productivity gains against software costs.

If your team spends hours on data analysis, localization, or internal reporting, giving non-technical staff direct access to enterprise agents can remove operational bottlenecks. The key takeaway from Uber's strategy is to let workers closest to the problems experiment with automation rather than dictating use cases from the top down.