Knowledge-Driven Root Cause Analysis
Cut MTTR by 40-60% with AI-powered root cause analysis that auto-suggests top three root causes for 70% of incidents with 85% precision.
Challenge
SME knowledge on root causes and fixes is scattered across manuals, tickets, logs, and tribal memory. Teams repeatedly diagnose recurring issues, context switch across CRM ITSM, knowledge bases, and telemetry portals, and deliver inconsistent outcomes. MTTR stays high, knowledge attrition hurts continuity, and data only methods miss SME context that actually drives accurate diagnosis.
The objective: cut MTTR by ≥40%. Auto-suggest a top three root cause list for ≥70% of incidents with ≥85% precision. Capture new learnings from tickets and logs and publish to the knowledge base within 24 hours.
Solution: How AIP changed the operating model
Learning and setup
Powered by the Aftermarket Intelligence Platform, the agentic solution applied predictive, NLP LLM reasoning with retrieval, ontology graph reasoning, and policy models. Training data came from historical tickets and resolutions, service manuals and SOPs and FMEAs, field logs and telemetry, asset histories, parts consumption, SME guides, and annotated RCA sessions. This enabled the AI agent to recognize and interpret free text symptoms, failure codes, equipment model and serial, environment and duty cycle, prior fixes and parts used, log snippets and telemetry, site and region, and warranty status.
Workflow orchestration
The RCA agent listens on the event bus for new or updated cases in CRM ITSM, enriches context from Asset, Inventory, and Document modules via the ontology, then queries the knowledge graph. It determines the path to suggest likely causes, procedures, and parts, while writing status and notes back to CRM and the knowledge base and notifying stakeholders. If confidence is low or policy rules apply, the case routes to an SME with full context and a concise summary.
Execution and resolution
The AI agent normalizes incident text, extracts entities, retrieves similar cases, ranks likely causes, and generates a stepwise troubleshooting plan. It fetches procedures and needed parts, asks clarifying questions when data gaps exist, posts recommendations to the agent desktop, and documents outcomes. Responses are completed in under two minutes, with updates posted across connected systems. Exceptions such as novel error codes, missing telemetry, or conflicting asset history are escalated to SMEs with full evidence and rationale.
