Enterprise AI oversight should not stop at whether a system is governed. It should ask whether the exposure is reasonably likely. Ergo, the AI Likelihood Test.

If an AI system is embedded in a material workflow, management should be able to answer three questions before scaling it:
· 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐠𝐨 𝐰𝐫𝐨𝐧𝐠?
· 𝐇𝐨𝐰 𝐥𝐢𝐤𝐞𝐥𝐲 𝐢𝐬 𝐢𝐭?
· 𝐖𝐡𝐚𝐭 𝐜𝐨𝐮𝐥𝐝 𝐢𝐭 𝐜𝐨𝐬𝐭?
That sounds simple, but it changes the conversation.

A technical review may confirm that the model was tested, monitored, and approved. A governance review may confirm that policies, committees, and controls exist. A procurement review may confirm that contracts, indemnities, and vendor terms were negotiated. However, none of those alone answers the CFO-level question: 𝐈𝐬 𝐭𝐡𝐢𝐬 𝐀𝐈 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚 𝐫𝐞𝐚𝐬𝐨𝐧𝐚𝐛𝐥𝐲 𝐥𝐢𝐤𝐞𝐥𝐲 𝐦𝐚𝐭𝐞𝐫𝐢𝐚𝐥 𝐞𝐱𝐩𝐨𝐬𝐮𝐫𝐞?

That exposure may come from incorrect outputs, customer harm, regulatory scrutiny, biased decisions, data leakage, contractual breach, IP claims, business interruption, financial misstatement, remediation costs, brand damage, or over-reliance on automation.

Not every AI risk requires a reserve. Not every AI issue becomes a disclosure. However, every material AI deployment should pass a likelihood test before it becomes a scaled dependency. That means estimating probability, severity, timing, mitigation cost, insurance availability, vendor recovery, contractual transfer, and residual exposure.

The point is not to slow AI adoption. The point is to stop approving AI investments with upside-only math.

AI ROI cannot only measure efficiency gained. It must include reasonably likely exposure introduced. That is how AI governance becomes financial oversight. And that is how enterprise leaders move from AI ambition to AI discipline.