Automated IT Incident Resolution Workflows for Lower MTTR
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Solution Overview
Problem
Conventional IT systems face challenges in efficiently managing and resolving anomalous events across different levels of support, particularly at L2 and L3, due to reliance on manual processes, prolonged downtimes, human error, and lack of contextual understanding, leading to inefficient resource utilization and delayed resolutions.
Innovation Solution
An automated system and method utilizing predefined resolution workflows and bots to identify and resolve anomalous events, integrating automated technologies into IT support frameworks to streamline incident management, automate routine tasks, and enhance operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used for incident management at L2 and L3 levels, then human technicians can handle complex problems requiring in-depth technical knowledge, but the mean time to resolve (MTTR) increases and IT resources are strained
Solution Approach 1:
The system enables automated self-service for incident management through AI bots that autonomously diagnose and resolve issues without human intervention. The bot performs tasks such as analyzing incident data, identifying root causes, and executing resolutions automatically, allowing the system to serve itself and reducing dependency on human technicians for routine operations.
Solution Approach 2:
The patent replaces manual mechanical processes with automated digital systems. Human technicians manually sifting through logs and performing analyses are substituted with an AI-powered bot that automatically processes incident data, correlates information from multiple sources, and executes resolution workflows, thereby eliminating the time-consuming manual aspect while maintaining problem-solving capability.
2Difficulty of detecting and measuring
If conventional IT systems focus on Mean Time to Find Problems (MTFP), then issue detection and reporting is improved, but resolution efficiency deteriorates due to lack of comprehensive solutions and prolonged downtimes
Solution Approach 1:
The system merges detection and resolution functions into a single integrated automated workflow. Instead of separate processes where issues are detected by monitoring tools and then manually resolved by technicians, the AI bot combines both functions by automatically detecting anomalies, analyzing them, and executing resolutions in one continuous automated process, thereby improving both detection and resolution efficiency simultaneously.
Solution Approach 2:
The system implements continuous feedback loops where the bot monitors system performance, detects anomalies, executes resolutions, and learns from outcomes to improve future detection and resolution. The feedback mechanism allows the system to adjust its detection thresholds and resolution strategies based on historical data and actual results, enhancing both detection accuracy and resolution efficiency over time.
3Extent of automation
If L1 support handles routine issues such as password resets and basic troubleshooting, then automated automation can be effectively applied, but escalation to L2 and L3 delays attention to critical issues and increases overall time to resolve
Solution Approach 1:
The AI bot is designed with universal capabilities to handle incidents across all support levels (L1, L2, and L3) rather than being limited to a single level. The bot can perform routine L1 tasks, analyze complex L2 issues, and assist with L3 problems, making it a multi-functional system that eliminates the need for manual escalation and reduces overall resolution time by addressing critical issues directly.
Solution Approach 2:
The system performs preliminary actions by proactively monitoring system health and detecting potential issues before they escalate. The bot continuously analyzes system data, identifies anomalies early, and executes preventive resolutions, thereby addressing critical issues before they require manual intervention and reducing the need for escalation to higher support levels.
Data Source
AI summary
The present subject matter relates to a system (100) and a method (300) for providing automated resolution to one or more anomalous events in an enterprise information technology (IT) environment. The system (100) integrates a processor (201) and a memory (202) that stores instructions to execute various tasks. The system (100) monitors activities within the enterprise IT environment, identifies one or more anomalous events, and correlates the identified anomalous events with one or more predefined resolution workflows. Each workflow includes specific operating instructions tailored to address the identified anomalies. Upon detecting an anomaly, the system (100) extracts the relevant operating instructions from the corresponding workflow and executes them to resolve the issue. Thus, the system (100) significantly reduces the mean time to resolve by automating the detection and resolution of anomalies, thereby improving operational efficiency and minimizing downtime in the enterprise IT environment.