AI Incident Management for KPI Impact Prioritization
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Solution Overview
Problem
Current incident management systems face challenges in dynamically identifying and prioritizing incidents based on their impact on critical organizational key performance indicators (KPIs), often relying on static parameters like user impact and severity, which do not effectively link incidents to critical KPIs.
Innovation Solution
An artificial intelligence and machine learning-based apparatus that classifies incidents using tokenization and knowledge graphs to identify organizational operations and KPIs, determining the impact of incidents on KPIs and prioritizing them accordingly, while also utilizing service level agreements to ensure high-priority incidents receive appropriate attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static parameters like user impact and severity are used to prioritize incidents, then the prioritization process is simple and fast, but the linkage between incidents and critical KPIs is ineffective
Solution Approach 1:
The patent replaces manual/static incident prioritization methods with an AI-based natural language processing system that automatically analyzes incident descriptions, identifies affected KPIs, and determines impact levels. This substitution enables dynamic, accurate KPI linkage while maintaining operational efficiency through automated processing.
Solution Approach 2:
The patent introduces an AI-based NLP model as an intermediary between incident data and KPI assessment. This intermediary automatically extracts meaningful information from unstructured incident descriptions, maps them to relevant KPIs, and prioritizes incidents based on their actual impact, bridging the gap between simple prioritization and accurate measurement.
2Measurement precision
If AI and machine learning models are used to identify and prioritize incidents based on KPI impact, then the accuracy of KPI linkage is improved, but the system complexity increases
Solution Approach 1:
The patent employs a multi-functional AI system that performs multiple tasks within a unified architecture: natural language processing, entity recognition, KPI identification, impact assessment, and incident prioritization. This universal approach consolidates what could be separate complex systems into one integrated solution, managing complexity through functional consolidation.
Solution Approach 2:
The AI-based system operates autonomously, automatically processing incident descriptions, identifying affected KPIs, and generating prioritization recommendations without requiring manual configuration or intervention. This self-service capability reduces operational complexity while maintaining high measurement precision.
3Reliability
If dynamic incident prioritization based on KPI impact is implemented, then the relevance of incident management to organizational goals is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of incident descriptions using pre-trained NLP models, quickly extracting key entities and potential KPI impacts before full processing. This preliminary action enables rapid initial prioritization while maintaining reliable KPI linkage through subsequent detailed analysis when needed.
Solution Approach 2:
The AI system applies varying levels of analysis depth based on incident characteristics - using partial analysis for straightforward incidents that require less processing time, and excessive (comprehensive) analysis for complex incidents where high reliability is critical. This adaptive approach balances processing time with linkage reliability.
Data Source
AI summary
In some examples, artificial intelligence and machine learning based incident management may include analyzing incident data related to a plurality of incidents associated with organization operations of an organization to train and test a machine learning classification model. Based on mapping of the organization operations to associated organizational key performance indicators, a corpus may be generated and used to determine an organizational key performance indicator that is impacted by each incident. New incident data related to a further plurality of incidents may be ascertained, and specified organizational key performance indicators associated with further organizational operations may be determined. Based on the corpus and the trained machine learning classification model, an output that includes an organization operation impacted by an incident, and a specified organizational key performance indicator associated with the organizational operation may be determined, and used to control an operation of a system associated with the organization.