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Sushmitha
Nannuru
Principal Product Lead
Microsoft
Sushmitha Nannuru is a Principal Product Lead at Microsoft, where she leads platform and infrastructure initiatives powering Microsoft 365 Copilot, enterprise search, and agentic AI experiences. With deep expertise across AI systems, retrieval and grounding architecture, cloud platforms, and enterprise-scale product development, she focuses on turning complex infrastructure investments into scalable customer-facing AI products used by millions globally. Prior to Microsoft, Sushmitha led platform product initiatives at Amazon Web Services, including data protection and commerce infrastructure, and also worked with Apple during her MBA at Stanford Graduate School of Business. Across more than 14 years in product and engineering leadership, she has specialized in building large-scale, customer-centric platforms that balance technical complexity, usability, and measurable business outcomes. Passionate about the future of AI-enabled productivity and enterprise systems, Sushmitha brings a practitioner’s perspective on operationalising AI products at scale — from retrieval quality and enterprise data systems to platform strategy, experimentation, and AI adoption inside large organizations.
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22 September 2026 11:15 - 11:45
From AI infrastructure to outcomes: Turning cloud & data platforms into product wins
As more companies rush to launch AI-powered experiences, many product teams are discovering that great AI products are built on far more than just models and interfaces. Behind every successful copilot, assistant, or intelligent workflow sits a complex foundation of data systems, retrieval architecture, permissions, governance, and platform decisions that directly shape customer outcomes. In this session, Sushmitha Nannuru draws on her experience building infrastructure and platform capabilities across Microsoft, AWS, and large-scale enterprise AI systems to explore how product leaders can better connect AI infrastructure investments to real product impact. The session will unpack practical lessons from building and scaling AI-powered platforms that support millions of users — including how teams can think about grounding and retrieval systems, align infrastructure roadmaps with product strategy, and avoid common mistakes when operationalising AI at scale.