Evidence-backed findings
Each finding ties to observed workflow behavior, artifact review, dependency mapping, or explicit stakeholder input.
AI Operational Assurance Review
Deterministic assessment for workflows that now depend on AI.
The review maps how an AI-enabled workflow is supposed to operate, where it actually depends on judgment or vendor behavior, and which failures deserve escalation before they become routine.
Review outputs
Findings written for operators
Operator narrative
A clear account of how the workflow behaves under normal, exception, and escalation conditions.
Known unknowns
Open questions are named directly so leadership can distinguish risk from missing evidence.
Escalation thresholds
Practical triggers for when an issue moves from local handling to leadership, security, legal, or vendor escalation.
Deterministic assessment
A repeatable method and bounded scope, rather than vague transformation or maturity language.
Follow-on actions
A prioritized set of fixes, controls, or monitoring points sized to operational load.
Sample use cases
Where reviews are useful
A review works best when the workflow already exists, a tool is entering production use, or governance needs to catch up with operating reality.
An internal agent summarizes customer, security, financial, or operational records before a human decision.
A vendor AI feature has become embedded in a workflow but ownership and fallback behavior remain unclear.
A team needs to understand where human review is meaningful and where it has become ceremonial.
Leadership needs an evidence-backed narrative before expanding AI usage into adjacent workflows.
A governance artifact exists, but operators are unsure whether it matches how work is actually done.