AI-Based IT Issue Automation With Remote Action Sets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face IT issues that require immediate resolution but are often unable to get timely assistance from IT technicians due to their unavailability, and existing automation solutions are time-consuming and labor-intensive.
Innovation Solution
A system utilizing machine learning models to automatically identify and resolve IT issues by determining appropriate actions based on user inputs, creating entries in a database for new issues, and coordinating the execution of action sets, including remote execution when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IT technicians manually assist users with technical issues, then the quality of resolution is high, but the response time is delayed due to technician unavailability
Solution Approach 1:
The system performs preliminary actions by proactively monitoring computer systems for technical issues before users report them. The automated assistant detects incidents, diagnoses problems, and executes resolution actions without waiting for user initiation, thereby reducing response time while maintaining resolution quality through pre-configured troubleshooting protocols
Solution Approach 2:
The system enables self-service by implementing an automated IT assistant that independently monitors systems, diagnoses issues, and resolves technical problems without human intervention. The assistant uses machine learning models to autonomously determine appropriate actions and execute them, freeing technicians from routine tasks while providing immediate response to users
2Loss of time
If automation scripts are implemented to resolve routine IT issues, then response time is reduced, but the complexity of implementing and maintaining automation increases
Solution Approach 1:
The system incorporates feedback mechanisms where the automated assistant learns from each interaction and resolution outcome. Machine learning models analyze successful and unsuccessful resolution attempts, continuously improving diagnostic accuracy and action selection. This feedback loop reduces the need for manual automation configuration while enhancing system intelligence over time
Solution Approach 2:
The system dynamically adjusts operational parameters based on system state and issue characteristics. Rather than relying on fixed automation scripts, the machine learning models select and modify resolution actions in real-time based on detected conditions, making the automation adaptable to varying situations without requiring complex pre-programming for every scenario
3Measurement precision
If comprehensive monitoring and analysis systems are deployed to detect all technical issues, then detection accuracy is improved, but the computational resources and system complexity increase
Solution Approach 1:
The system applies local quality by focusing monitoring and analysis resources on specific areas where technical issues are most likely to occur or where they would have the greatest impact. Rather than uniformly monitoring all system components at maximum detail, the assistant prioritizes critical systems and adjusts monitoring intensity based on risk assessment, reducing overall computational overhead while maintaining high detection accuracy for important issues
Data Source
AI summary
A method includes receiving a first user input and automatically determining whether the first user input indicates a technical issue. The method includes automatically determining, using at least one machine learning model, whether the technical issue is associated with a database entry. The method includes, in response to a determination that the technical issue is associated with a database entry, automatically identifying at least one action set. The method includes, in response to detecting a second input, coordinating execution of the at least one action set, including remotely executing a series of one or more actions. The method includes, in response to a determination that the technical issue is not associated with the one or more entries in the database, detecting a third user input, recording the third user input, analyzing the recording of the third user input, and creating an entry in the database.