Nearly every growth-stage company has a slightly different AI governance stack — and almost none of them have a governance problem that more tooling will fix. What most of them have is nobody with clear authority to say no.

17%

of organizations report no clear owner of AI strategy at all, per Cynozure's 2026 State of the Industry Report

28%

of CEOs take direct responsibility for AI governance, per McKinsey's 2026 research

2 in 3

CIOs are accountable for AI systems they don't control, per IBM's June 2026 study

The pattern shows up the same way almost everywhere. Someone in engineering ships an AI feature because it was fast and the model made a good demo. Legal finds out three weeks later. Security finds out when the vendor questionnaire comes back. The executive who's supposed to own "AI strategy" finds out when a board member asks about it.

None of that is a tools problem. A model registry doesn't fix an org chart where nobody has the authority — or the obligation — to say "not like this."

A model registry doesn't fix an org chart. It just documents what the org chart already got wrong.

The governance programs that hold up under pressure share one trait, and it isn't a platform:

That distinction is why the ownership numbers above matter more than any platform comparison. A model registry can tell you what's deployed. It can't tell you who decided it should be, or who's accountable when it goes wrong. Two-thirds of CIOs already know the difference — they're the ones holding accountability for systems somebody else greenlit.

If an organization's AI governance conversation keeps circling back to "which platform should we buy," that conversation is happening at the wrong layer of the problem — and no amount of tooling budget will fix a decision-rights gap.

Naming that owner — and building the evaluation and audit trail behind them — is the work. We help growth-stage companies do it without hiring a full-time CIO to run it.