Uber is advancing its internal AI integration strategy by embedding top engineering talent directly into core business operations. Under the direction of CTO Praveen Neppalli Naga, the company has deployed Agentic Pods, specialized two-week teams tasked with overhauling daily workflows across finance, legal, marketing, customer support, human resources, and procurement. Rather than building isolated software tools, these engineers shadow employees to map existing processes, identify bottlenecks, and rapidly prototype AI solutions alongside department staff. The strategy prioritizes comprehensive workflow redesign over narrow task optimization. By observing real-world operations, engineers quickly develop systems that remove redundant approvals, migrate legacy software, and accelerate decision-making. Early deployments have delivered significant efficiency gains. A financial planning cycle originally requiring 15 hours was reduced to 30 minutes, while comprehensive financial reports that previously took two days to finalize now generate in 10 minutes. Marketing quality assurance processes, once spanning two weeks, have been compressed to under an hour. Industry analysts observe that this initiative adapts the widely recognized forward-deployed engineer model for internal use. While traditional forward-deployed roles focus on customizing software for external clients, Uber applies the methodology to internal infrastructure. Peter Wilczynski, chief product officer at Vantortech, describes the approach as the Rearward Deployed Engineer model, noting that embedding technical expertise directly within corporate operations enables deeper structural transformation rather than superficial speed improvements. The program reflects a maturation in corporate AI adoption, shifting focus from experimental pilot projects to systematic operational integration. By restructuring processes around AI capabilities rather than simply automating legacy tasks, Uber aims to establish a repeatable framework for sustained enterprise productivity. The initiative underscores a broader industry pivot toward workflow-native AI, where technology is treated as a foundational operational layer rather than an add-on utility. This methodology positions Uber to scale efficiency gains across complex business functions while maintaining alignment with actual employee workflows.