{"slug": "uber-says-agentic-pods-reworked-workflows-across-16-business-functions", "title": "Uber Says Agentic Pods Reworked Workflows Across 16 Business Functions", "summary": "Uber Technologies Inc. CTO Praveen Neppalli Naga reported that the company ran 16 two-week 'Agentic Pods' across 16 business functions, pairing about 30 AI-proficient engineers with domain experts, and cut several workflows from hours or days to minutes, including capital-allocation work spanning 150 cities from 15 hours to 30 minutes. The results, disclosed in a July 7 post, are company-reported and not independently audited, and Uber is forming a dedicated team to scale the model.", "body_md": "# Uber Says Agentic Pods Reworked Workflows Across 16 Business Functions\n\nUber CTO Praveen Neppalli Naga said the company paired about 30 AI-proficient engineers with domain experts and ran 16 two-week “Agentic Pods” across 16 business functions. In a July 7 post, he reported cutting several workflows from hours or days to minutes and said Uber is forming a dedicated team to expand the model; the figures are company-reported and not independently audited.\n\nUber has outlined an internal operating model for moving AI agents beyond software engineering and into business functions such as finance, legal, operations, marketing, customer support, human resources, and procurement. In a July 7 post, CTO Praveen Neppalli Naga said the company paired about 30 engineers who were proficient with AI tools with domain experts who understood the workflows being changed.\n\nThe disclosure is a retrospective account of work completed over the prior two months, not a new product launch. Naga said Uber ran 16 Agentic Pods across 16 functions and is now forming a dedicated team to scale the approach.\n\n### A two-week build-and-validate cycle\n\nEach pod received two weeks. According to Naga's description, engineers spent the first two days shadowing a domain expert and documenting the existing workflow. They then prioritized opportunities by scale, repetition, business impact, and data availability; built a working agent alongside the expert; validated it with other people performing the same work; and aimed to ship on day 10.\n\nThe design matters because it treats access to domain knowledge as part of the engineering problem. Rather than asking a central AI team to automate a process from documentation alone, Uber placed builders next to the people who could explain exceptions, handoffs, and the systems involved.\n\n### Uber reports large time reductions\n\nNaga reported that capital-allocation work spanning 150 cities fell from 15 hours to 30 minutes, financial pacing reports went from two days to 10 minutes, and marketing web quality assurance dropped from two weeks to 50 minutes. He also said support workflow creation shifted from 9,000 manual workflows toward self-service automation.\n\nThose numbers are company-reported results. The post does not disclose error rates, review requirements, implementation costs, security controls, or how the before-and-after timings were measured. Independent reporting from The Times of India corroborates that Uber announced the pod structure and the same headline results, but it does not provide an external evaluation of their durability or return on investment.\n\n### What practitioners can take from the experiment\n\nThe strongest lesson is the operating method, not any single speed claim: pair technical and domain expertise, time-box discovery and building, validate with multiple users, and measure the whole workflow rather than isolated tasks. Teams considering a similar model would still need quality, risk, cost, and adoption metrics before treating faster completion as proof of business value.\n\n## Key Points\n\n- 1Uber says about 30 AI-proficient engineers worked with domain experts in 16 two-week Agentic Pods across 16 business functions.\n- 2The company reported several large workflow-time reductions, including capital-allocation work falling from 15 hours to 30 minutes.\n- 3The results are company-reported and omit quality, cost, risk, and measurement details needed to establish durable ROI.\n\n## Scoring Rationale\n\nUber disclosed a concrete cross-functional operating model and several before-and-after workflow timings that are useful to enterprise AI practitioners. The impact is notable rather than major because the results are self-reported and lack independent quality, cost, and ROI validation.\n\n## Sources\n\nPrimary source and supporting public references used for this report.\n\nPractice with real Ad Tech data\n\n90 SQL & Python problems · 15 industry datasets\n\n[Active Search Campaigns by BudgetEasy](/problems/sql/active-search-campaigns-by-budget)\n\n[High CPC Clicks & Poor Landing PagesMedium](/problems/sql/high-cpc-clicks-poor-landing-page)\n\n[Campaign ROAS by Attribution ModelHard](/problems/sql/campaign-roas-by-attribution-model)\n\n250 free problems · No credit card\n\n[See all Ad Tech problems](/problems/datasets/adtech)", "url": "https://wpnews.pro/news/uber-says-agentic-pods-reworked-workflows-across-16-business-functions", "canonical_source": "https://letsdatascience.com/news/uber-says-agentic-pods-reworked-workflows-across-16-business-b91b3f30", "published_at": "2026-08-02 13:48:52+00:00", "updated_at": "2026-08-02 15:57:45.833048+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-products"], "entities": ["Uber Technologies Inc.", "Praveen Neppalli Naga", "The Times of India"], "alternates": {"html": "https://wpnews.pro/news/uber-says-agentic-pods-reworked-workflows-across-16-business-functions", "markdown": "https://wpnews.pro/news/uber-says-agentic-pods-reworked-workflows-across-16-business-functions.md", "text": "https://wpnews.pro/news/uber-says-agentic-pods-reworked-workflows-across-16-business-functions.txt", "jsonld": "https://wpnews.pro/news/uber-says-agentic-pods-reworked-workflows-across-16-business-functions.jsonld"}}