Many organizations successfully build AI proofs of concept, only to see those systems struggle once deployed in production. Models continue to run. Pipelines execute as expected. Dashboards appear healthy. Yet performance drifts, trust erodes, and teams can no longer explain what changed or why.

The root cause is rarely the model itself. More often, the problem lies in the platform and organizational architecture around data. Backfills rewrite historical datasets. Features are recomputed without clear provenance. Inference runs without traceability. Teams move quickly in isolation, and no one owns end-to-end reproducibility.

This talk reframes AI reliability as a systems and organizational design problem rather than a modeling problem. Drawing from real-world AI systems and machine telemetry pipelines, it shows how data lineage, telemetry capture, observability, and reproducibility must be treated as core platform capabilities rather than afterthoughts.

Attendees will learn how organizational structure, team boundaries, and architectural decisions interact to either preserve or undermine trust in AI systems over time. The session concludes with practical guidance on structuring teams and platforms so AI systems can scale without accumulating silent data and technical debt.