Agentic AI in IT Service Management (ITSM)

With Agentic AI driving innovation in ITSM, let’s explore some key use cases where agentic AI in ITSM delivers significant value by automating processes, improving efficiency, and enhancing the user experience.

AI agents are transforming employee self-service by enabling end users to resolve incidents and process requests without involving the IT service desk. Through AI-driven virtual assistants that leverage prescriptive or generative knowledge agents, and dynamic or pre-configured action agents, employees can troubleshoot common issues, reset passwords, submit software requests and proactively address issues with their device or applications that could be affecting their work.

Business Value:

– Reduced service desk volume and cost: AI agents handle common requests and user queries, reducing the workload for IT staff.

– Improved employee experience: Faster, self-service options improve employee productivity and satisfaction for end users.

One of the most impactful applications of artificial intelligence in ITSM is incident auto-resolution. Proactive AI agents use historical data and real-time system monitoring to autonomously resolve incidents as they occur, without needing human intervention. For example, a proactive AI agent detects a server overload and automatically reallocates resources to balance the load, resolving the issue without escalating to IT staff.

Business Value:

– Decreased mean time to resolution (MTTR): Automated resolutions minimize downtime for users.

– Scalability: AI agents can simultaneously manage a high volume of incidents, increasing IT efficiency.

AI agents can proactively detect major issues before they escalate into critical problems. By continuously monitoring systems and analyzing data patterns, these agents can provide early warnings for potential failures, giving IT teams the opportunity to resolve issues before they affect end users.

Business Value:

– Reduced downtime: A proactive approach helps in early detection to avoid costly system failures.

– Higher system reliability: Early intervention minimizes the impact of major issues on business operations.

In an agentic AI system, AI agents excel at problem identification. They analyze patterns across incidents and service requests, identifying patterns and configuration items (CIs) that may require deeper investigation to resolve at the root cause level. For example, an AI agent notices that multiple users are experiencing performance issues after a recent software update and flags this as a potential root cause for further investigation by autonomously creating a Problem record (or PRB in ITIL vocabulary).

Business Value:

– Fewer recurring incidents: Addressing the root cause reduces future incidents.

– Cost savings: Resolving underlying complex issues results in long-term operational efficiency.

Unlike traditional AI systems, dynamic action agents, or hyperflows, can execute adaptive workflows based on the various actions and APIs available across ITSM and UEM (unified endpoint management) endpoints. This flexibility ensures that critical issues are prioritized and resolved efficiently, without the need for creating and maintaining workflows

Business Value:

– Increased agility: Dynamic action workflows ensure that incidents are addressed with speed and accuracy without the need to maintain manual workflows.

AI agents also enhance the efficiency of IT service desks by acting as intelligent copilots for the IT, Infrastructure, and Operations staff. These AI assistants can triage tickets, summarize tickets, recommend solutions from knowledge bases or past resolved incidents, and resolve low-complexity issues autonomously.
The Agent Assistant can take into account end-user sentiment and triage issues accordingly. Moreover, these assistants adapt to the state of the incident, helping human agents write root cause analyses and create KB articles during the incident wrap-up phase.

Business Value:

– Increased service desk efficiency: AI agents handle repetitive tasks, allowing human agents to focus on high-priority incidents.

– Improved user experience: Faster resolution times for simple issues lead to greater user satisfaction.

AI agents are instrumental in knowledge generation for IT teams, continuously learning from past incidents and conversations and updating the knowledge base (KB). This ensures that the IT team has access to the most relevant and up-to-date information and reduces the knowledge gap, improving overall incident resolution.

For example, after resolving a complex incident, the AI agent helps create a KB article from the resolution notes and update the internal knowledge base with the troubleshooting steps, ensuring other agents can resolve similar issues faster in the future.

Business Value:

– Faster incident resolution: An updated knowledge base helps IT teams resolve issues more quickly.

– Consistent service delivery: AI-generated knowledge ensures accuracy and relevance in troubleshooting.

AI agents are becoming essential for unified endpoint management (UEM), especially as organizations manage a growing number of end-user devices across distributed environments. By integrating with tools like Nexthink, Intune, JAMF, Aternity, AI agents can automate patch management, ensure compliance, and optimize device performance.

For example, by leveraging integration with Nexthink, an AI agent proactively detects that several devices are out of compliance with security protocols and automatically pushes updates to secure them, ensuring alignment with IT policies.

Business Value:

– Improved security and compliance posture: Automated patching and monitoring reduce vulnerabilities.

– Optimized device performance: Proactive management ensures devices run smoothly, reducing the need for manual intervention.

AI agents streamline IT change management by proactively assessing the risks associated with proposed changes, recommending the best times for implementation, and even automating low-risk changes. These agents help organizations implement changes efficiently while minimizing disruptions. For example:

An AI agent analyzes past change management data and recommends a maintenance window that will have the least impact on business operations, reducing the risk of failed deployments.

Business Value:

– Reduced risk of change failure: AI-driven risk assessments help avoid disruptions caused by poorly timed or executed changes.

– Faster implementation: Automating routine changes allows for quicker rollouts without sacrificing quality.