Model validation
Compare with ground truth.
Measure accuracy, precision, recall, F1, and confusion before deployment.
AI analytics
Validation defines the automation boundary.
An AI output can assist only when measured performance supports the decision and weak cases remain under human control.
Visual model
Measure first. Automate second.
Model confidence is not enough. Compare predictions with known labels, set a threshold, and preserve human review.
Model validationAutomation boundaryHuman review
InputSupport ticket
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ModelPredicted labels
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TestMeasured accuracy
Pass the gate
Use limited category automation and monitor results.
Decision gate
Fail the gate
Keep the decision under human review.
Threshold
Set a minimum decision rule.
Use empirical performance and an adequate sample size.
Human review
Keep control where evidence is weak.
Priority and department decisions remain manual when accuracy is low.
Monitoring
Production is not the end.
Track errors, drift, exceptions, and the effect on users after release.
Project evidence
Category accuracy.
Priority accuracy.
Department accuracy.
Decision
Only 89 Request cases qualified for limited category routing. Priority, department, and other categories remain under human review.
View AI Support evidence →