How do you think about AI governance?
AI governance starts before the model. Most teams treat it as an output problem, which is the wrong layer: a guardrail bolted onto a generation step. The platform decisions made upstream — how data is labeled, how lineage is tracked, who owns the enrichment pipeline — determine whether the AI is governable at all.
The pattern I look for is whether the platform can answer three questions for any AI-derived value: where did it come from, who is accountable for it, and what happens when it is wrong. If those answers are clear, the model is operationally governed. If not, no amount of policy layered on top will fix it.