Most AI governance frameworks are written for organizations with a dedicated AI ethics board and a compliance headcount most mid-market companies don't have. That doesn't mean governance is optional - ungoverned AI is how a well-intentioned pilot turns into a data-leak incident or a discrimination complaint. It means the framework needs to fit the team you actually have.
At minimum, four things need an owner before an AI system touches production data: what data the system can access (and can't), who can see its outputs, what happens when it's confidently wrong, and how a human overrides it. None of these require a committee - they require one accountable person and a written answer, reviewed when the system changes materially.
The failure mode we see most often isn't malicious misuse - it's scope creep. A model approved for internal draft-generation quietly starts being used for customer-facing responses because it's convenient, without anyone re-evaluating whether the accuracy bar and oversight that were fine for internal use are still fine once a customer sees the output unreviewed. Governance at mid-market scale is mostly about catching that kind of drift, not writing a 40-page policy document nobody reads.
We build a lightweight version of this into every AI engagement: a one-page data/access/oversight/override sheet per system, reviewed at each material change, logged alongside the system's own audit trail. It's not enterprise-grade governance theater - it's the minimum that lets you answer "who approved this and what data can it see" honestly when someone asks.
