What Is Problem Management in ITSM?

Problem management in IT Service Management (ITSM) focuses on identifying the root cause of recurring incidents and reducing their impact on business operations. Unlike incident management, which resolves immediate issues, problem management aims to prevent future disruptions by fixing the underlying causes.

IT problem management plays a key role in improving system stability, maintaining uptime, and supporting service quality. It involves detection, root cause analysis, workaround documentation, and permanent resolution tracking.

With agentic AI and advanced automation, problem management moves from manual investigation to predictive intelligence. AI agents can scan patterns, detect issues before they escalate, and suggest fixes based on past cases and real-time data.

Key Features and Benefits of AI-Powered Problem Management

Root Cause Analysis with AI

The platform reviews data from logs, alerts, tickets, and monitoring tools to identify patterns. It uses machine learning to detect connections that may signal an underlying cause. Teams can use these insights to resolve the actual source of recurring issues.

Incident Correlation and Problem Linking

When the system detects related tickets, it groups them and flags the issue as part of a broader problem. The platform uses natural language processing and structured data to connect incidents that share a common cause.

Proactive Problem Detection

AI agents continuously scan for anomalies in systems and behavior. When patterns appear that could lead to future incidents, the platform automatically creates a problem record. This helps IT teams act early, before users experience disruption.

Workarounds and Knowledge Sharing

When a permanent resolution isn't available, the system offers workarounds to reduce impact. These are shared in the knowledge base and surfaced to service agents and users when relevant. Knowledge is updated as more information becomes available.

Automated Remediation Suggestions

The platform draws from prior cases, known errors, and existing fixes to offer possible solutions. If a match is found, it presents these recommendations to agents or automates resolution based on business rules and confidence scores.

Feedback-Driven Learning

The platform monitors which actions succeed and which escalate. With each closed case, it adjusts its detection models and decision logic, improving future accuracy and response time.

AI virtual agent

Resolve incidents with zero touch, minimising manual intervention and improving efficiency.

Integrated knowledge base

Access relevant solutions instantly from a comprehensive knowledge base.

Integrated knowledge base

Access relevant solutions instantly from a comprehensive knowledge base.

Time Tracking

Track service desk resource time and performance to improve team efficiency.

Email automation

Communicate updates immediately with automated email notifications on incident status.

Omnichannel incident creation

Create incidents across multiple channels like portal, Teams, or Slack for seamless support.

AI in the Problem Management Lifecycle

Monitor and Detect

AI agents continuously collect data across IT systems. They look for patterns in incidents, logs, system behavior, and alerts. When something deviates from normal, the platform begins analysis.
Customer service reports and dashboards within Servicely's service management platform.

Analyze and Link Cases

The system correlates the current issue with past incidents and known problems. It identifies whether the issue is new or already being addressed. If linked, all associated incidents are tracked under one record.

Prioritize and Assign

Problem records are scored based on urgency, business impact, and affected users. Teams are notified, and tasks are assigned according to expertise, availability, and service agreements.

Recommend and Remediate

The system offers possible fixes or next steps. If confidence is high, the action can be automated. Otherwise, the information helps agents decide on the best path forward.

Capture and Reuse

Once resolved, the platform logs the outcome, updates knowledge content, and uses that data to improve the next round of detection and resolution.

Why Use AI for Problem Management in ITSM

  • Prevent repeat incidents through early problem detection
  • Shorten root cause analysis by automating data correlation
  • Improve team efficiency with AI-driven prioritization and suggestions
  • Surface known issues and workarounds at the point of need
  • Automate lower-risk fixes where confidence is high
  • Keep service knowledge accurate and searchable
  • Support uptime and SLA compliance through faster response and fewer incidents

FAQs

What is AI problem management and how does it differ from traditional methods?

AI problem management uses machine learning and real-time analytics to detect and resolve root causes. Traditional approaches rely on manual investigation and static rules, which can be slower and less consistent.

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How does your platform assist with root cause analysis?

It reviews system behavior, ticket history, logs, and prior resolutions. When a pattern is found, it highlights the most likely cause and suggests how to fix it, based on similar resolved problems.

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Can it predict problems before incidents occur?

Yes. The system tracks signals like repeated errors, performance dips, or application instability. When early signs appear, it creates a problem record before users start reporting issues.

How is NLP used to identify recurring issues?

The system reads ticket text and user input to find repeated descriptions or keywords. This helps identify incidents that relate to the same underlying issue, even if they’re worded differently.

What’s the role of RPA vs AI in this platform?

AI is used for detection, analysis, and recommendations. RPA executes rule-based actions like updating records, triggering workflows, or closing duplicates.

Can the system generate and maintain knowledge articles automatically?

It drafts knowledge content based on resolved problems. Articles can be reviewed by staff before publishing, or set to auto-publish for internal use. Additionally, Servicely can be used to identify knowledge gaps based on user interactions with the knowledge base and AI virtual agent to determine what content needs to be created.

How does the AI continuously improve its detection capabilities?

The platform learns from each case, using success and failure outcomes to adjust how it analyzes new data. This includes how it weights signals, groups issues, and suggests resolutions.

Is the data analyzed securely and in compliance with our policies?

Yes. The platform encrypts data at rest and in transit. It applies access controls and logs all actions. Data can be hosted in specific regions based on compliance needs.

What’s the typical implementation time and onboarding process?

Deployments of Servicely depend on the level of complexity, with simple implementations able to be completed in as little as 6 weeks. More complex implementations will take more time depending on the level of configuration required. Activating AI in your problem management process will require a data gathering stage where you build up learning data to train the AI on how it should behave. This can be aided through the addtion of knowledge assets that can speed up the learning time.

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