Uber is deploying an internal artificial intelligence strategy termed agentic pods, aimed at embedding highly skilled AI engineers directly within corporate departments to streamline operational workflows. Praveen Neppalli Naga, Uber’s chief technology officer, announced that thirty AI-proficient engineers have been integrated across finance, legal, and human resources divisions. Over a two-month period, the company has established sixteen such pods, with each team observing day-to-day tasks to develop custom automation tools. Naga emphasized that effective automation requires direct engagement with operational realities rather than reliance on process documentation. The initiative has yielded measurable efficiency gains: financial pacing reports previously requiring two days of manual coordination are now generated in ten minutes, while capital allocation across Uber’s 150 operational markets has been reduced from fifteen hours to thirty minutes. The agentic pod model reflects a strategic pivot toward targeted AI integration amid broader industry scrutiny over artificial intelligence expenditures. Despite previously maxing out its Anthropic Claude Code budget this spring, Uber’s leadership has questioned the return on investment for generalized AI spending. Chief Operating Officer Andrew Macdonald recently noted mounting challenges in justifying large-scale AI capital outlays without corresponding improvements in consumer-facing features. In response, Naga confirmed that Uber will expand the pod framework by establishing a dedicated scaling team tasked with fundamentally redesigning internal workflows from the ground up. This forward-deployed engineering approach aligns with a growing industry trend of deploying specialized AI personnel directly into client or internal operational units to ensure practical, high-impact automation. Uber intends to maintain this focused methodology to drive sustained efficiency improvements across its enterprise functions.