Intelligent Application Support: Using AI to Resolve IT Issues Faster

This changes application support from a reactive model into a more proactive process.

2. Faster Incident Triage

When an incident occurs, support teams need to understand its severity and determine which team should handle it.

This process can involve reviewing multiple monitoring dashboards, logs, tickets, and system records.

AI can assist by analyzing incoming incident information and identifying relevant patterns. It can categorize incidents, identify potentially related alerts, and provide additional context based on historical events.

For example, if a new incident resembles a problem that occurred previously, AI can surface that relationship and help the support team begin with a proven troubleshooting approach.

This can reduce the time spent on initial investigation.

3. Supporting Root Cause Analysis

Identifying the root cause is often one of the most time consuming parts of application support.

A user may report that an application is slow, but the underlying cause could be a database issue, API failure, infrastructure constraint, recent deployment, configuration change, or dependency problem.

AI can analyze relationships between different sources of operational data and help narrow down possible causes.

It can correlate application errors with infrastructure metrics, recent changes, and historical incidents to provide support engineers with a more complete picture.