Governance
AI Governance and Risk
Why AI execution fails without clear ownership, oversight, and decision authority.
Governance is not paperwork. It is how an organization assigns responsibility before AI decisions scale.
Context
Why governance is not optional
AI introduces operational, legal, and reputational risk that looks different from traditional software. Without governance, organizations create decisions that cannot be explained, outputs that cannot be traced, and accountability gaps that surface under pressure.
Most AI failures are not technical. They are governance failures: unclear ownership, missing escalation paths, and decisions made without oversight.
Governance is decision rights. It defines who can approve deployment, who owns outcomes, and what happens when something goes wrong.
Patterns
Failure patterns seen in production
No clear model ownership
No accountable owner for model behavior in production.
No accountability for outputs
AI is used in decisions without responsibility for consequences.
Shadow AI usage
Tools adopted outside sanctioned procurement and oversight.
Human review undefined
No clarity on when human oversight is required.
Inconsistent approval thresholds
Risk tolerance varies by team without coordination.
Framework
Governance as a readiness pillar
Governance is a core pillar in the AI Readiness Framework. It directly influences whether an initiative should Stop, Test, or Go.
Stratify evaluates governance relative to impact, scale, and decision risk, not generic maturity checklists.
Strong governance enables speed. Clear decision authority reduces late-stage debates and accelerates execution.
Approach
How Stratify approaches governance
Governance must match the use case. A customer-facing recommendation engine needs different controls than internal automation. High-stakes decision support requires more oversight than summarization.
The goal is proportionate governance: enough structure to manage risk without blocking legitimate delivery.
Surfacing differences in risk tolerance early across legal, compliance, product, and engineering prevents surprises during launch.
Sequencing
How governance affects Stop, Test, Go
The AI Readiness Assessment evaluates governance alongside other pillars to produce a sequencing recommendation.
Pause initiatives until governance constraints are resolved.
Stop
- Ownership not assigned
- Legal or compliance exposure unresolved
- Escalation path missing
- Decision authority unclear
Validate governance assumptions in a bounded pilot before scaling.
Test
- Ownership defined but untested
- Controls exist but not exercised
- Pilot validates approvals and oversight
Proceed when governance supports confident execution.
Go
- Clear ownership and accountability
- Escalation paths established
- Oversight operating as designed
Audience
Who this is for
- Executives accountable for AI outcomes and enterprise risk
- Legal, compliance, risk, and audit leaders
- Transformation and AI sponsors managing cross-functional delivery
This is not a technical implementation guide. It is a decision framework for leaders responsible for governance posture.
Questions
Common questions
What is AI governance?
AI governance defines who can approve AI deployment, who owns outcomes, and what happens when something goes wrong. It is decision rights, not paperwork.
Why do AI initiatives fail without governance?
Most AI failures are not technical. They stem from unclear ownership, missing escalation paths, and decisions made without appropriate oversight.
How does governance affect whether we should Stop, Test, or Go?
Governance is a core readiness pillar. Unresolved ownership triggers Stop. Untested controls trigger Test. Clear accountability and oversight enable Go.
Governance decisions should be made before AI initiatives scale.
Stratify Insights supports executive teams responsible for delivery, governance, and enterprise outcomes.