Loading page…
Non-engineers made up about 80% of early adopters. Uber will track the workflows employees develop and use those results to guide its AI deployment.
Listen to this story
Uber is moving ChatGPT Work from a cross-functional test into company-wide access, making enterprise AI available beyond technical teams at a scale that could reveal which workflows employees actually adopt. The earlier 1,000-person pilot included finance, operations and product, and Uber says its findings—not a disclosed productivity benchmark—will shape future deployments. The companies announced the expansion October 22; Uber did not disclose rollout costs, leaving adoption and measurable impact as key indicators to watch.
About 80% of pilot users were non-engineers, according to Uber engineering vice president Sumanth Sukumar.
Employees used the tool to analyze operations data, localize training materials and build interactive daily dashboards.
Uber previously deployed 30 AI engineers in two-week “AI pods” to identify job-specific automation opportunities.
Uber says its entire global workforce of about 30,000 employees will use ChatGPT Work, taking the workplace AI tool beyond a 1,000-person pilot. Uber and OpenAI announced the partnership expansion on October 22, moving from a test across several departments to a rollout across the company.
The companies are extending an existing partnership, not introducing a new product. ChatGPT Work arrived in July as an enterprise-focused AI agent—a tool corporate teams can deploy for different workflows. Uber says OpenAI worked closely with it on integrating the tool, which it had already tested with employees earlier this year.
The earlier pilot included workers in finance, operations and product. About 80% of early adopters were non-engineers, Uber vice president of engineering Sumanth Sukumar told TechCrunch. He described three uses:
Our approach is to make powerful AI tools available across Uber while giving teams room to experiment,
Sumanth Sukumar, Uber vice president of engineering, speaking to TechCrunch
Uber has also tried a more hands-on approach to finding automation opportunities. Earlier this year, it paired 30 AI engineers with teams across the company and gave them two weeks to automate key parts of their jobs. Sukumar called these groups “AI pods” and said they showed the opportunity in combining technical expertise with a deep understanding of a particular job.
For the wider deployment, Uber will examine employee use, the workflows people develop and how the tool helps them work more effectively. Sukumar said those outcomes will guide future AI deployment—a plan to evaluate results, rather than a quantified productivity finding.
Uber did not disclose how much it is spending on the rollout.
Loading discussion...
Join the conversation
Explain where team experimentation should give way to company-wide direction.
Be the first to share a perspective or an experience.
Reader comments
Newest comments first. Replies stay oldest first.