incident_management · services · workflow
Lyzr AI workflow predicts incident risk and generates remediation steps for NTT Data, saving 70,000+ hours
NTT Data's change management engineers were overwhelmed by thousands of daily change log entries that manual review could not keep pace with, leaving incident risks unpredictable and remediation guidance inconsistent and slow to surface.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Engineer submits change log
Change management engineers submit logs via a ReactJS UI.
Tools used
ReactJSQdrantGPT-4o-miniMicrosoft Azure Cosmos DBMicrosoft AzureOkta SAMLLyzr Agent Platform
Outcome
The AI workflow saved over 70,000 hours, improved work-life quality for engineers, and enhanced safety and compliance by automating risk assessment and delivering AI-generated remediation steps.
Results
Time saved70,000+
Grounding & classification
Source type: vendor customer story
28 fields verified against source quotes.
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