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AI’s Hidden Failure Point Is in Your Data Stack (it’s costing you millions)
Manage episode 498379717 series 3515340
Barr Moses grew up running experiments in her dad’s physics lab. Today, she’s the CEO of Monte Carlo, the leading platform for data + AI observability.
This episode goes beyond her founder story. It’s about why data trust is the new currency in AI—and how Barr built the playbook for companies that want to move fast without breaking everything.
She’ll show you:
Why clean dashboards don’t mean reliable data.
The messy middle of making data dependable at scale.
The real reasons intuition still matters when the numbers run out.
Listen if you want to bulletproof your AI stack from the inside out.
Barr Moses is CEO & Co-Founder of Monte Carlo, a data + AI observability company backed by Accel, GGV, Redpoint, and other top Silicon Valley investors. Previously, she was VP Customer Operations at Gainsight, a management consultant at Bain & Company and served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science.
🤝 Connect with Barr Moses
Barr is the author of the book ""Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines."
Barr has written many articles, including:
- https://www.montecarlodata.com/blog-the-past-present-and-future-of-data-quality-management/
- https://www.montecarlodata.com/blog-top-3-ai-problems-to-solve
- https://www.montecarlodata.com/blog-are-we-in-an-ai-bubble/
- https://www.montecarlodata.com/blog-6-things-every-cdo-needs-to-know-about-ai-readiness/
- https://www.montecarlodata.com/blog-ai-fomo-is-tearing-your-company-apart/
- https://www.montecarlodata.com/blog-2026-will-be-the-year-of-data-ai-observability/
- https://www.montecarlodata.com/blog-will-genai-replace-data-engineers
- https://www.montecarlodata.com/5-hard-truths-about-generative-ai-for-technology-leaders/
⏱ Episode Chapters
(00:00) Barr Moses’ early path from consulting to data(03:41) Lessons from building customer success at Gainsight(06:08) The pain of unreliable data and Monte Carlo’s origin story(08:43) Why data + AI observability must be end-to-end(12:58) The growing complexity of today’s data and AI supply chain(16:03) From data observability to AI observability(20:15) The four root causes of data and AI product failures(24:51) Rebuilding trust after AI incidents go wrong(29:55) When companies realize they need observability in place(33:02) The executive questions shaping AI adoption at scale(38:52) Maturity stages in data quality and observability(43:01) The tipping point for AI in production—and what’s next(45:20) How to prepare for AI experimentation failures(47:38) What keeps Barr motivated building in this space
#DataObservability #AIInfrastructure #AIDataQuality #AIObservability #MonteCarloData #BarrMoses #AIProductFailures #DataReliability #AIInsights #AIDecisions #DataInnovation #TechLeadership #AIExecution #AIStartups #AIApplications #DecisionMaking #AIRevolution #DataDriven #BusinessStrategy #AILeadership
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