Generative AI use cases for operation and maintenance
After software is deployed, the focus shifts to operation and maintenance. Generative AI can enhance traditional approaches by providing more proactive and efficient system management. AI-powered operations tools continuously monitor system performance and predict potential issues before they affect users. They perform automated root cause analysis when problems occur, which significantly reduces the mean time to resolution. AI also optimizes system performance in near real time. It automatically adjusts configurations based on changing load patterns and user behaviors. For example, an operations team might use an AI assistant to generate predictive maintenance schedules, automatically identify components that are likely to fail, and suggest preemptive actions. The AI could also help with capacity planning by analyzing usage trends and predicting future resource needs with high accuracy.
The following table shows operation and maintenance use cases that you can enhance with generative AI and the persona responsible for those use cases.
| Subcapability: Use case | Persona |
|---|---|
| Incident management: Manage incidents in near real time by integrating monitoring tools with chat platforms so that teams can detect, discuss, and resolve issues directly within the chat environment | Site reliability engineer |
| Incident management: Allow teams to initiate deployments, run scripts, and run commands directly from the chat interface, which streamlines operations | DevOps engineer |
| Code upgrades: Upgrade code dependencies and libraries to reduce manual effort and make sure that the codebase stays up to date with the latest versions | Software developer |
| Code optimization: Review code for optimization opportunities | Software developer |
| Code optimization: Identify bottlenecks in the code and refactor or optimize the code to enhance performance | Software developer |
| Technical debt management: Log technical debt as part of the development process | Product manager |
| Technical debt management: Prioritize and address technical debt based on impact, risk, and cost, and integrate it into the regular sprint planning process | Software developer |
| Technical debt management: Reduce technical debt in existing application code | Software developer |
| Change management: Implement a change approval process that makes sure that all code changes are reviewed, tested, and approved by the necessary stakeholders before deployment | Change manager |
| Change management: Perform impact analysis of proposed changes | DevOps engineer |
| Reverse engineering: Analyze and understand the structure and behavior of legacy code | Solutions architect |
| Reverse engineering: Explain existing code and generate documentation | Software developer |
| Code modernization: Translate code from one programming language to another | Software developer |
| Code modernization: Modernize legacy code into the latest programming language | Software developer |
| Performance optimization: Continuously monitor and tune system performance by optimizing resource allocation, load balancing, and reconfiguring the application | Site reliability engineer |
| Performance optimization: Identify and refactor code that is causing performance degradation in order to improve speed and system responsiveness | Software developer |
