IT Service Management: How AI Will Influence Core ITSM Capabilities

Most blogs on artificial intelligence (AI) in IT service management (ITSM) focus on the technology itself, such as the various AI “flavors,” AI algorithms and Large Language Models (LLMs), AI platforms, and APIs. However, to truly understand AI’s impact on the status quo of IT operations, IT professionals must look at AI adoption through a process and practice lens.

This blog explores how AI reshapes core ITSM processes, using incident management, service request management, and problem management as examples. Taking you from the available technology to better understand its impact on how you and your colleagues work.

Common incident management issues

Most of us know the many issues or challenges IT organizations face with traditional incident management capabilities. For example, greater IT support needs (thanks to increased corporate technology use and reliance), high volumes of repetitive incidents, long wait times, underused IT service portals, reactivity rather than proactivity, or first-contact resolution rates that don’t reflect the low business perceptions of IT support.

There are many possible root causes that will differ slightly between organizations. For example, the over-reliance on manual effort (which is slow, costly, and error-prone), ineffective knowledge management practices, and misaligned performance measures. The good news is that AI-enabled capabilities can help to tackle these root causes.

How AI changes incident management

There are many opportunities to leverage AI-based capabilities in incident management. Some are end-user-facing, while others sit with the service provider. Examples include:

• Virtual agents for end-users (also called chatbots) – these can handle common incidents conversationally, using Natural Language Processing (NLP) to understand end-user intent. An end-user can simply type “I can’t access my emails,” and the virtual agent will identify the issue, offer troubleshooting steps, and escalate the issue to a human agent if needed. End-users are empowered to self-help and get quicker resolutions while the service desk’s workload is reduced. Everyone wins.
• Intelligent ticket triage – AI can analyze ticket content and automatically assign incoming tickets to the correct resolution team, calculate and set ticket priority, and suggest relevant knowledge articles to the end-user or agent based on historical data.
• Knowledge presentation – IT support staff can benefit from AI insights and recommendations while working on tickets. This could be the provision of fix details for similar issues or the availability of a subject matter expert (SME) who can help with a complicated end-user issue.
• Content creation – AI can create incident-management-related communications to send to end-users, altering the tone and level of detail to suit the need. It can create draft knowledge articles from existing incident tickets and other sources. It can also automatically generate performance reports for all levels of IT support personnel.
• Proactive incident prevention – predictive AI models can analyze monitoring data, end-user behavior, and past incidents to identify issues before they affect end-users and business operations.

Common service request management issues

Many of the common service request management issues or challenges mirror those for incident management. For example, long wait times for provisioning, underused service catalogs and IT service portals, and poor personalization. Again, AI-enabled capabilities help to address these challenges and their root causes.

How AI changes service request management

There are many opportunities to leverage AI-based capabilities in service request management. Again, some are end-user-facing, while others sit with the service provider. Examples include:

• Personalized service catalog experiences – AI enables dynamic, role-based service catalogs where end-users see only the services relevant to their role, department, and location. It also provides recommendations such as “You might also need to request…” This speeds up fulfillment and improves the service request experience.
• Automated request fulfillment – AI agents can take over easy and repetitive service request tasks and free up the service desk agent and do more complex tasks where human intervention is required.
• Conversational service requesting – instead of end-users needing to navigate complex menus, AI-powered assistants can enable natural language service requests. For example, an end-user can state, “I need access to Dynamics Sales Hub,” and the virtual agent will identify the right form, pre-fill it with known information, and submit it. This reduces the effort needed to request services and provides a better service experience.
• Compliance and policy enforcement – using anomaly detection, AI can flag service requests that violate corporate IT policies or deviate from normal behavior. For example, if an end-user repeatedly requests high-privilege access without justification, this can be flagged for human review.
• Cost-effective—AI agents are scalable, while service desk agents must be hired and trained.

Common problem management issues

Unlike with incident management and service request management, the common issues related to problem management might be enough to prevent this important ITSM capability from happening.

Many of the common problem management issues or challenges relate to having insufficient resources and time (and possibly funding) to do problem management justice. This can manifest in various ways, such as difficulties identifying patterns across incidents, slow or ineffective root-cause analysis (RCA), and no time for proactive problem detection.

How AI changes problem management

There are many opportunities to leverage AI-based capabilities in problem management. These commonly reduce the human effort required for problem management and increase proactive problem management capabilities. Examples include:

• (Problem) pattern recognition across incidents – AI can analyze large incident datasets to identify “hidden” patterns and suggest current or emerging problems. For example, AI analysis might link repetitive printer issues across locations to a firmware bug, flagging a problem before traditional manual review would.
• Automated problem record creation – AI can automatically generate a problem record when incident clustering thresholds are crossed. This is more than simply opening a ticket. For example, AI can add linked incidents, provide an initial impact assessment, and suggest probable root causes.
• SME identification for problem management participation – while people might consider themselves SMEs (or not), AI knows who they are based on the work they’ve recently conducted. AI can, therefore, suggest the most appropriate SMEs to work on specific problems.
• Root-cause hypothesis generation – AI tools can suggest likely root causes (for problems) based on historical data, log analysis, and past resolutions. For example, AI might recommend database query timeouts as the most likely cause for recurring web application slowdowns.

But remember, AI brings about more than ITSM process change

While the above examples show how AI capabilities will improve core ITSM capabilities, their adoption will bring about more than process (or practice) change. There will be people-change aspects, too, including increased customer satisfaction. Examples are provided in our blog post, The Skillsets IT Service Desk Agents and Managers Need in the Age of AI. However, a good starting point is considering your staff’s current inclination and ability to work with new AI-based ITSM capabilities. More education and training might be needed to fully benefit from AI capabilities than you previously thought.

To learn more how AI will help your IT operations and outcomes, check out these resources:

Blog Post: The Human Touch in AI Adoption

Blog Post: What’s the Difference Between Automation and AI? 

Blog Post: The Skillsets IT Service Desk Agents and Managers Need in the Age of AI

Blog Post: AI Driven ITSM: Pioneering Workload Management for the Future

Blog Post: Microsoft Copilot: The AI and Automation Opportunity for ITSM

Whitepaper: Cloud Lighthouse Crafting Your Future-Ready Enterprise AI Strategy