Qualify Predictive AIOps rollout element by element — set each state and watch the score update.
Updates live as you set each element below.
MEDDICC 50% · Org 30% · Value 20%
Targets from discovery: cut production incidents 50% and reduce alert noise 90%. Dollar value of prevented downtime still being baselined with Finance.
Marcus Webb (CFO) owns the reliability budget; will sign off once ROI on reduced incidents is proven.
Predictive accuracy, auto-remediation, and time-to-value are the top criteria; must beat Datadog and the build-in-house option.
Security review (Sofia Reyes) and CFO ROI sign-off not yet sequenced; procurement path unclear.
Two payment outages last quarter; ~90% of alerts are noise; root-cause analyzis takes SREs hours. Direct revenue impact from downtime.
Priya Nair (VP Engineering) is actively driving the project internally; pulling Staff SRE Elena Torres in as a proof sponsor.
Datadog is in the evaluation and the platform team floated building anomaly detection in-house (Kevin Nakamura). Our wedge: unsupervised ML, no manual thresholds, value in days.
Your qualification framework is configurable on the Methodology screen.