healthcarereimagined

AI governance fails when legal, risk, engineering, and business are all looking at the same system and calling it different things.

Treasury just made an uncomfortable point many AI programs are still ignoring: to build workable AI governance, we first need to solve the Tower of Babel problem inside enterprise AI.

One team says “copilot.”
Another says “agent.”
Another says “automation.”
Another says “pilot.”

This is not semantics. It is governance.

Each label implies a different approval path, different control level, different documentation burden, and different escalation route.

So Treasury did not start with a model benchmark.
It started with something more foundational: a shared AI Lexicon and a Financial Services AI Risk Management Framework.

Treasury’s stated concern was inconsistent terminology and uneven risk-management practices across the sector. 
The response was organizational before technical: align language first, then align treatment.

This is why the move matters far beyond financial services.

You cannot govern what functions cannot classify consistently.
And you cannot classify consistently if every department uses different words for the same capability, risk, or use case.

Shared language is what lets you decide:
👉 who owns the use case inventory,
👉 who classifies the system,
👉 who approves deployment into higher-risk workflows,
👉 which monitoring thresholds apply,
👉 and who has stop or escalation authority when teams disagree.

Most firms still think AI governance begins with model testing.

Treasury is signaling that real AI governance begins earlier: with semantic alignment, a common maturity lens, and a framework the whole institution can actually operate.

When your AI system crosses functions, who decides what it is, and therefore how it must be governed?

🔔 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 if you want the next two breakdowns: I’ll unpack the actual AI Lexicon first, then the Treasury

Article link: https://www.linkedin.com/posts/michelevaccaro_ai-governance-fails-when-legal-risk-engineering-activity-7436787749581606913-2mKh?