Opening Fireside Chat Keynote 

The Data Trust Maturity Curve: The Foundation of Data + AI Reliability

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. 

Customer Story

Driving Data Product Adoption at Scale with Observability 

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. 

Customer Story

Empowering Data Teams with Self-Service Data Quality 

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. 

Customer Story

Building AI Readiness with Governance and Observability

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. 

Customer Story

Scaling AI in Production with Data + AI Observability

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. 

Monte Carlo Product Vision - Closing Keynote

From Data Reliability to AI Reliability

Join Monte Carlo co-founder and CTO Lior Gavish and Field CTO Ethan Post for a look at how enterprises can achieve reliability from data to agents using Monte Carlo's Data + AI Observability platform.

This session unveils Monte Carlo’s latest innovations in data + AI observability, including Agent Observability and the AI-powered Troubleshooting Agent, which together help teams detect, triage, and resolve reliability issues across the full data + AI estate, ensuring trusted data, scalable AI adoption, and measurable impact on business outcomes.