AI Change Management Data Agent

How Cassidy automates Change Management using AI

Step 1: Ingest change requests and operational data

The Workflow triggers when new change requests are created or updated in your ITSM platform. Cassidy pulls RFC details, CI/service mappings from your CMDB, related incidents, and assignment group history into a unified view.

Step 2: Score risk and predict outcomes

Cassidy analyzes the change against historical success data, CI dependency graphs, and conflict calendars. The agent calculates a risk score using probability and impact factors, flags scheduling conflicts, and generates a Change Success Score based on the team's track record.

Step 3: Route for governance or auto-approve

Based on configurable approval policies, Cassidy either auto-approves low-risk changes from high-performing teams or routes complex changes to the appropriate reviewers. High-risk items are queued for CAB with a complete evidence package—risk visuals, backout plans, and conflict analysis.

Step 4: Aggregate ADKAR and adoption telemetry

Cassidy pulls pulse survey responses, training completion rates, sentiment from comms channels, and usage telemetry from your digital adoption platform. The agent segments ADKAR scores by role, region, and business unit to identify readiness gaps.

Step 5: Surface insights and prescriptive actions

Cassidy delivers dashboards showing portfolio risk heatmaps, CAB pipeline health, change success rates, and ADKAR trends by audience. The agent recommends targeted interventions—increased comms for low Awareness, coaching for low Ability, or elevated governance for teams with declining success scores.

Step 6: Log decisions for audit and continuous improvement

Every automated approval, risk calculation, and routing decision is captured with timestamps, inputs, and rationale. Cassidy maintains a complete audit trail for SOX/ITGC compliance and feeds closure data back into the model to improve future predictions.