AI’s potential is limitless—but only as strong as the data that fuels it. In this opening keynote fireside chat, Barr Moses, CEO and Co-Founder of Monte Carlo, and Sol Rashidi, former Chief Analytics Officer and enterprise AI leader, unpack the evolution of data trust: from early data quality initiatives, to modern data observability, and now toward true AI reliability. Drawing from their experiences across industries, Barr and Sol will explore the Data Trust Maturity Curve—a framework for understanding how organizations evolve from reactive data management to proactive, automated, and AI-ready systems. They’ll discuss what separates companies that experiment with AI from those that scale it responsibly, and why reliability must come before scale. Attendees will walk away with a clear vision of what “trusted AI” really means, the organizational and cultural shifts required to achieve it, and the practical steps to begin moving up the maturity curve today.

Manual data quality checks can’t keep up with the pace and complexity of modern data pipelines. In this session, Andrew Foster, Chief Data Officer at M&T Bank, shares how governance and engineering partnered to move beyond manual testing to a scalable, automated approach to reliability. He’ll walk through the frameworks that prioritize critical data products, attach SLAs, and standardize ownership; how automation and lineage accelerate detection and triage; and the operating model that enables trust at scale across analytics, regulatory reporting, and AI use cases. Learn a pragmatic blueprint to reduce mean time to detect and resolve issues, improve stakeholder confidence, and embed data reliability into day-to-day workflows—without slowing delivery.

How do you turn observability from an engineering safeguard into a catalyst for business adoption? At Warner Bros. Discovery (WBD), the Data: Insights, Collection & Engineering team faced a familiar challenge: as streaming, sports, and advertising data grew more complex, so did the need for trust, transparency, and speed across the company’s analytics ecosystem.

In this session, Pamela Zirpoli, Director of DICE: Data: Insights, Collection & Engineering at WBD, shares how her team built an enterprise-wide observability program that empowered thousands of data consumers, from executives to analysts, to confidently rely on trusted insights. By embedding Data + AI observability across platforms and aligning reliability metrics to business goals, WBD elevated the visibility, accountability, and perceived value of its data products.

As data volumes and business demands grow, central data teams can’t keep pace with every quality issue or pipeline dependency. At T. Rowe Price, the solution isn’t just more automation—it’s empowerment.

In this session, Srikumar Kanthadai, Head of Data – Global Distribution Technology, shares how T. Rowe Price is rolling out a self-service data quality framework to boost productivity and collaboration across analytics teams. He’ll walk through how the initiative is scaling data ownership through self-serve controls, standardized SLAs, and observability-driven metrics—allowing engineers and analysts to proactively monitor and manage the health of their own data products.

How do you scale AI responsibly in a federated, fast-moving data environment? At Roche, this challenge sits at the heart of enabling innovation while safeguarding trust. In this session, Pierre Fischer, Product Line Lead – Data Platforms at Roche, shares how the company combined a pragmatic data mesh architecture with governance and observability to deliver AI at scale—without compromising accountability. By defining clear ownership, policies, and SLAs across domains, and operationalizing them through end-to-end observability, Roche built the guardrails that make responsible AI a reality in production.

Pierre will walk through lessons learned in balancing autonomy and control—how Roche enabled self-serve data domains while maintaining centralized visibility and trust.

Organizations push more AI projects into production, trust is often the first casualty. At Pilot, maintaining confidence in model outcomes meant ensuring reliability across every layer of the data and AI stack.

In this session, Travis Lawrence, Data Leader at Pilot, shares how his team paired data and AI observability to scale responsibly and accelerate delivery. He’ll walk through the frameworks that connect upstream data quality to downstream model performance — including lineage-driven impact analysis, freshness and accuracy SLAs, drift and anomaly detection, and incident-response playbooks that span both structured and unstructured data.

Attendees will learn how Pilot standardized reliability signals across platforms, reduced time-to-detection and resolution, and embedded trust directly into the AI release process. The result: faster deployment cycles, fewer surprises, and greater confidence in production AI.

More details coming soon!