Governance Is Not Policy
Most AI governance fails because it is written as policy and enforced after outcomes. Real governance exists at decision time. It defines who can decide, under what conditions, what discretion is allowed, and how exceptions are handled. This article reframes governance as accountability in advance and explains why committees and principles do not survive operational pressure.
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Governance is not policy. It is accountability in advance
AI governance has become synonymous with documentation.
Principles are written. Committees are formed. Guidelines are published. Frameworks are approved.
Yet when something goes wrong, the same confusion appears.
Who owned the decision?
Who approved it?
Who intervened?
First principle: governance only exists at decision time
Governance is not what you write.
Governance is what constrains behavior before outcomes are known.
If accountability is assigned after the fact, governance has failed.
Why policy-based governance collapses
Policy collapses under pressure because:
- it is abstract
- it competes with incentives
- it does not specify ownership
- it does not survive urgency
When faced with real decisions, people follow incentives, not documents.
The difference between responsibility and accountability
Responsibility means contributing.
Accountability means owning the outcome.
Most AI decisions have many responsible parties and no accountable owner.
This is the governance gap.
What real governance looks like
Real governance answers four questions at decision time:
- who can decide
- under what conditions
- with what discretion
- with what obligation to explain later
If these are unclear, governance is cosmetic.
Why committees are insufficient
Committees review. They do not decide.
Decision ownership must exist outside committees, or decisions drift until risk materialises.
The executive question
For our most important AI-informed decisions, who is accountable before outcomes occur?
If that answer is unclear, governance does not exist.
