Enterprises are racing to deploy AI services, but the teams responsible for running them in production are seeing familiar problems reemerge—most notably, silos between data scientists and operations teams, reminiscent of the old DevOps divide. In a discussion recorded at AWS re:Invent 2025, IBM’s Thanos Matzanas and Martin Fuentes argue that the challenge isn’t new technology but repeating organizational patterns. As data teams move from internal projects to revenue-critical, customer-facing applications, they face new pressures around reliability, observability, and accountability.

The speakers stress that many existing observability and governance practices still apply. Standard metrics, KPIs, SLOs, access controls, and audit logs remain essential foundations, even as AI introduces non-determinism and a heavier reliance on human feedback to assess quality. Tools like OpenTelemetry provide common ground, but culture matters more than tooling.

Both emphasize starting with business value and breaking down silos early by involving data teams in production discussions. Rather than replacing observability professionals, AI should augment human expertise, especially in critical systems where trust, safety, and compliance are paramount.

Learn more from The New Stack about enabling AI with silos:

Are Your AI Co-Pilots Trapping Data in Isolated Silos?

Break the AI Gridlock at the Intersection of Velocity and Trust

Taming AI Observability: Control Is the Key to Success

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Breaking Data Team Silos Is the Key to Getting AI to Production

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