Assesses where AI-driven decisions are trusted, where humans intervene or override outputs, and how those behaviors affect outcomes, risk, and accountability.
What This Dimension Examines
This dimension examines how organizations govern AI-driven decisions in practice, including when human intervention is required, when overrides occur, and where informal controls replace formal decision logic.
Trust Boundaries In AI Decisions
This dimension assesses which decisions are trusted to AI, which require human approval, and where trust boundaries are unclear or inconsistently applied.
Override Mechanisms & Escalation Paths
This dimension examines how overrides are triggered, documented, escalated, or bypassed, and whether those mechanisms are intentional or ad hoc..
Human Intervention Patterns
This dimension identifies where humans routinely adjust, correct, or ignore AI outputs, and whether those interventions signal trust gaps or design flaws.
Control Points In Execution
This dimension assesses where human control is exercised during execution, and where lack of clear intervention points creates operational or compliance risk.
Why This Matters
When trust and override behavior are poorly understood, AI decisions appear automated while humans quietly control outcomes. This creates hidden risk, inconsistent execution, and unclear accountability.
What Leadership Gains
Clear Visibility Into Human Control
Leadership gains visibility into where AI-driven decisions are trusted, where humans intervene or override outputs, and how those behaviours shape real outcomes, accountability, and risk.
Reduced Hidden Override Risk
Leadership gains clarity on how intervention and override controls operate in practice, where they are missing or informal, and how those gaps create inconsistent execution and hidden risk.
