The New Stack Podcast Breaking Data Team Silos Is the Key to Getting AI to Production
Dec 17, 2025
Thanos Matzanas is an IBM observability expert focused on AI production, while Martin Fuentes is an AI ops practitioner specializing in product management. They delve into how organizational silos hinder AI deployment, emphasizing the importance of breaking down barriers between data scientists and operations. They discuss using OpenTelemetry for observability, the challenges of measuring AI performance, and the critical role of human feedback. Both guests stress that AI should enhance human expertise, particularly in high-stakes environments, while advocating for business-focused AI applications.
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Data Teams Face Silo Breakpoints
- Data teams become siloed when models move from experiments to revenue-facing production.
- Thanos Matzanas says that integration with ops is essential once stakes and accountability rise.
Don’t Skip Observability Fundamentals
- Start with observability basics: metrics, KPIs, SLOs, access controls, and audit logs.
- Thanos Matzanas urges building trust in infrastructure before trusting AI models in production.
OpenTelemetry As Common Ground
- OpenTelemetry adoption in major AI services gives a common telemetry foundation.
- Thanos Matzanas highlights that built-in telemetry accelerates integration between platforms and observability tools.