AI Incident Response Engine for IT Ticket Resolution
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Solution Overview
Problem
Large organizations face challenges in efficiently addressing a high volume of incident tickets in IT response teams, where increasing team size is not a viable solution.
Innovation Solution
Implementing a system that uses artificial intelligence through a trained machine learning engine to predict solutions for IT incidents based on historical data, providing self-service troubleshooting guides, FAQs, solution instructions, scripts, patches, and configurations, and re-training with feedback to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If team size is increased to address more incident tickets, then incident response capacity is improved, but operational cost and management complexity increase
Solution Approach 1:
The system enables self-service incident resolution by automatically analyzing incident tickets, predicting root causes, and providing resolution recommendations without requiring human analyst intervention for every ticket. The AI engine processes incidents autonomously, reducing the need for additional human resources while maintaining or improving response capacity.
Solution Approach 2:
The patent replaces the mechanical system of human analysts manually processing incident tickets with an automated AI-based system. The machine learning engine automatically categorizes incidents, predicts root causes, and generates resolution recommendations, substituting human cognitive labor with computational processes that scale without proportional increases in operational complexity.
2Speed
If more human analysts are hired to process incident tickets, then incident resolution speed is improved, but training time and operational cost increase
Solution Approach 1:
The system performs preliminary analysis and categorization of incident tickets automatically before human analysts need to review them. The AI engine pre-processes incoming incidents, identifies patterns, and prepares initial diagnosis recommendations, so that when human analysts do intervene, they can immediately act on pre-analyzed information rather than starting from scratch, thereby reducing both resolution time and the need for extensive training.
3Measurement precision
If manual incident processing is used to ensure accurate diagnosis, then diagnostic accuracy is improved, but processing time and operational cost increase
Solution Approach 1:
The system implements feedback loops where AI-generated diagnostic recommendations are continuously refined based on outcomes and analyst corrections. The machine learning engine learns from verified resolutions and feedback from human analysts, progressively improving diagnostic accuracy over time while maintaining rapid automated processing. This allows the system to achieve high accuracy without proportionally increasing processing time.
Data Source
AI summary
Systems and methods for incident response using artificial intelligence for information technology operations are disclosed. In one embodiment, a method may include an incident response computer program executed by an electronic device: (1) receiving an incident ticket for an incident involving a computer product or a computer system within an organization from a service management platform for the computer product or the computer system; (2) providing incident information from the incident ticket to a trained incident response machine learning engine, wherein the trained incident response machine learning engine is trained to predict a solution for the incident based on historical incident data; (3) receiving, from the trained incident response machine learning engine, a predicted solution for the incident; and (4) providing the predicted solution to the service management platform. The service management platform provides the predicted solution to the computer product or the computer system.