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Uday
Sharma
Head of GTM Analytics, Revenue Operations
Postman
Over the past 17 years, he has led data organisations across hypergrowth tech companies, most recently building and scaling Cloudflare’s 40+ person global data engineering and analytics organisation from the ground up, supporting the company’s journey from $192M to $2B in ARR. The platforms and products his teams built influenced $100M+ in ARR growth, powered decisions for 1,500+ employees, and processed billions of daily events from around 20% of global internet traffic. Uday operates at the intersection of technical depth and business impact. He is equally comfortable architecting multi-petabyte data platforms as he is presenting revenue intelligence to CROs and board-level stakeholders. He has led teams through pre-IPO investor narratives, pricing strategy, product analytics, and real-time customer intelligence. More recently, Uday has been focused on AI-powered data tooling, building LLM-powered analytics assistants, semantic data layers, and improving model outputs with the right datasets to make AI-generated insights trustworthy in production.
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22 September 2026 10:15 - 11:00
Panel: The architecture behind AI-native GTM
Most AI conversations focus on models, agents, and use cases. Far fewer focus on the systems, data, and architecture that determine whether those initiatives succeed in production. As organizations embed AI into forecasting, lead routing, seller workflows, enablement, and revenue operations, the quality of the underlying GTM architecture has become a competitive advantage. This session explores how leading teams are designing the foundations that power AI at scale - from data models and orchestration layers to governance, enrichment, and system design. Join GTM leaders responsible for data, analytics, systems, and operations as they share how they're building AI-ready GTM organizations and the lessons they've learned along the way. You'll walk away with: - The core architectural components of an AI-ready GTM organization - How leading teams are connecting data, systems, and workflows to improve execution - Common infrastructure and governance challenges that limit AI performance - Practical lessons from organizations scaling AI across revenue operations