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Ali Pilehvar
Ali Pilehvar

Ali Pilehvar

Director of Product Analytics and Data Science
Realtor.com
Bio

Ali Pilehvar is a Director of Product Analytics and Data Science at Realtor.com, where he leads analytics teams across pricing, experimentation, monetization, and marketplace strategy for large-scale digital products. He is also an adjunct professor of analytics at Georgetown University and George Washington University, and an entrepreneur focused on building AI-first analytics products. Ali specializes in translating advanced analytics and AI into practical operating models that drive real business outcomes.


From Bottom-Up Experimentation to AI-First Analytics: Building the Leadership Playbook for Scalable GenAI

Many analytics organizations rely on bottom-up GenAI experimentation driven by individual teams. While the intent is good, this approach leads to fragmented tools, inconsistent quality, duplicated effort, and limited business impact. Without deliberate leadership intervention, these scattered efforts rarely scale beyond isolated use cases and fail to move the needle on how an organization actually makes decisions.
This talk argues that AI-first analytics requires deliberate top-down leadership action. Drawing on real-world experience leading analytics and data science teams at scale, I will walk through what it takes to move from ad hoc GenAI adoption to a structured operating model that delivers consistently. The focus will be on three areas: establishing clear ownership and accountability for AI initiatives, building standardized platforms that reduce tool fragmentation, and designing a centralized hub operating model that embeds AI into analytics workflows and decision processes.
Attendees will learn why bottom-up efforts alone fail to scale and what leadership actions are needed to close that gap. The session will provide concrete steps to build an AI platform and operating model that consistently supports high-quality, business-critical decisions. I will also cover how to evaluate organizational AI readiness, how to structure collaboration between analytics and engineering teams, and how to measure whether AI-embedded analytics is actually improving outcomes. Whether you are just starting to explore GenAI in your analytics function or looking to mature existing efforts, this session offers a practical roadmap grounded in operational experience rather than theory.

Professional / Leadership
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