Minimum Viable Data Foundation for Predictable AI Deployments

Generative AI rollouts often fail because underlying data is inconsistent, poorly classified, or hard to access—so AI amplifies noise and compliance exposure. The post breaks down common mid-market pitfalls (fragmented sources, unclear “authoritative” datasets, weak metadata, sparse logs) and the resulting failure modes: unreliable outputs, governance gaps, and engineering drag. It proposes a “minimum viable” foundation: owned repositories with lifecycle rules, basic sensitivity classification, repeatable pipelines that preserve provenance, and observability so outputs can be traced back to inputs—plus a time-boxed human-in-the-loop review loop. For CTOs, this becomes practical go/no-go gates before scaling copilots across enterprise content.