Uber's AI engineers shadow finance, legal, and marketing teams before building tools. One task dropped from two weeks to under an hour.
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Uber CTO Praveen Neppalli Naga has a different take on AI than the one that made headlines earlier this year.
Remember when he said Uber had already burned through its 2026 budget for Anthropic's Claude Code? That sparked the tokenmaxxing panic. His latest lesson is quieter, and maybe more useful.
Naga says Uber has started sending its best AI engineers into departments like finance, legal, marketing, customer support, HR, and procurement. The mission is to study how people actually work before building AI tools for them.
He calls these two-week teams "Agentic Pods." Engineers shadow employees for the first few days, learning every step of a workflow. Then they build and test software to improve it.
The results speak in minutes and hours. A financial planning process dropped from 15 hours to 30 minutes. Financial reports fell from two days to 10. Marketing quality checks shrank from two weeks to less than an hour.
Naga said the biggest gains rarely come from speeding up one task. They come from redesigning an entire workflow around AI, eliminating unnecessary approvals, replacing old software, and helping people decide faster.
Several tech executives compared the approach to Silicon Valley's "forward-deployed engineer" role. The twist is that instead of sending these AI experts to customers, Uber is deploying them inside its own business.
Peter Wilczynski, chief product officer at Vantortech, jokingly dubbed the role the "Rearward Deployed Engineer" -- engineers embedded deep inside corporate operations to redesign workflows around AI rather than simply speed up tasks.
Naga's approach mirrors a pattern visible across enterprise AI adoption. The companies getting the most out of the technology are not plugging it into existing processes. They are rebuilding those processes from the ground up, starting with a blank slate.
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