Uber has deployed 30 of its top AI engineers across departments in finance, legal, and HR, where they worked directly with employees to build AI agents that automate manual tasks. The results are substantial—financial reports that took two days now take 10 minutes, and capital allocation across 150 cities dropped from 15 hours to 30 minutes. Though the efficiency gains are clear, Uber's operations chief has expressed concern that the company's large AI spending has not yet produced equivalent increases in consumer-facing features.
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Uber's CTO embedded 30 of the company's top AI engineers with teams in finance, legal, and human resources for two-week stints. Over two months, the company ran 16 such "agentic pods" (teams pairing engineers with business units) and built AI agents to automate repetitive tasks—financial pacing reports now take 10 minutes instead of two days, and capital allocation across 150 cities now takes 30 minutes instead of 15 hours.
Why it matters
The approach bypasses documentation and process diagrams by having engineers observe actual work to understand how tasks really get done, making automation more effective. However, the company's COO acknowledged in May that the AI spending has not yet translated into proportional consumer-facing features, raising questions about return on the investment.
What to watch
Uber plans to form a dedicated team to scale the agentic pod model further and redesign business processes from the ground up using AI. The company previously maxed out its Claude Code budget this spring, signaling continued heavy investment in AI tooling.
Uber's agentic pod model addresses a core challenge in enterprise AI automation: understanding how work actually happens rather than relying on documented processes. By embedding senior engineers directly within business teams, the company discovered that traditional documentation often does not capture the messy reality of tasks involving multiple systems and manual steps. The approach has yielded concrete time savings—particularly in finance and operations, where repetitive multi-system workflows are common—suggesting that hands-on collaboration between AI engineers and domain experts can unlock practical value.
However, the news comes with a caveat. In May, Uber's chief operating officer Andrew Macdonald stated on a podcast that the company is finding it harder to justify its level of AI spending, noting that the investments have not produced a proportional increase in consumer-facing features. Uber also maxed out its Claude Code budget this spring, indicating sustained and substantial investment in AI tooling. This tension between internal operational gains and the lack of visible consumer impact frames the strategic challenge: the company has achieved measurable backend efficiencies but has not yet translated those gains into differentiated products or services that end users would notice. Scaling the pod model, as Uber intends, will require navigating this question of whether internal automation alone justifies the cost.
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