· the problem
AI governance is not what IT puts in place alone.
· insufficient governance
AI usage charter
IT internal policy
regulatory response
after-the-fact governance
· board-level governance
authority framework at executive level
responsibilities explicitly designated
effective human oversight
governance before dependencies form
Governance built after the fact is defensive.
It frames practices already installed
instead of structuring decisions before they are taken.
· our reading
Questions the executive committee must be able to resolve.
IT, legal, compliance, data and business units each have an essential role. But the authority framework must be clear at executive level — particularly for decisions that engage the organisation's accountability.
which AI uses are authorised, limited or prohibited?
which AI systems are critical?
which AI decisions must be supervised by humans?
who validates structural AI vendors?
who controls data transmitted to third parties?
who is responsible in case of error, bias or contested decision?
what is the escalation procedure?
which indicators reach the board?
· ai agents — a new governance category
With AI agents, governance no longer only covers recommendations. It covers rights of action: what the system can do, when it must escalate, who can stop it and who answers for its actions. Human accountability must be effective — not merely declarative.
· what good governance produces
Not a brake on innovation. A strategic asset.
Good AI governance does not slow innovation. It prevents innovation from creating uncontrolled dependencies, floating responsibilities and invisible risks.
→ decisions made at the right level, not too low
→ reduction of shadow AI through doctrine, not prohibition
→ responsibilities clearly designated and documented
→ effective human oversight on critical systems
→ EU AI Act compliance structured, not undergone
→ governance of AI agents before the first incident
→ AI mastery as a trust and credibility asset
· d-c-a-r applied to ai governance
The four dimensions of solid AI governance.
D
Dependency
Which critical systems are controlled by third parties? Which AI agents have rights of action on your systems?
C
Control
Who supervises, validates, can stop an AI usage? Is supervision real or merely nominal?
A
Advantage
Does governance create a credibility and trust asset, or only a compliance cost?
R
Responsibility
Is accountability explicitly designated? Can it be proven before a regulator, a court or a shareholder?