Artificial Intelligence in Service Management

You are already familiar with Generative AI, but what does it mean for Service Management?


    What does AI mean for Service Management?

    The adoption of Artificial Intelligence (AI) in Service Management has been a topic for various years. However, with the rise of Generative AI technologies, the use of AI has gained the potential of revolutionizing the work of Service Desks independently of size or sector, especially in areas where human-computer interaction and language understanding are critical.

    In the context of service management, AI can help either the end-users directly or by helping support teams work more efficiently.

    Everyone from vendors to organizations is scrambling to add AI to their ITSM, IAM, and other enterprise service systems. This is often easier said than done, but not impossible.

    When we meet different Service Desk and IT teams, they are consistently tackling similar problems:

    • IT support workloads are increasing.
    • The complexity of contacts to the Service Desk is rising.
    • Infrastructure and the number of services are growing, along with the dependencies between them.

    So the job of the IT Support team is getting harder by the day. Work at the Service Desk is largely communication-based, and years of automation haven't significantly changed this. Based on Gartner, over 80 percent of all requests and tickets are still handled 'manually' by agents. Furthermore, based on our experience in European markets, 40 percent of these are managed via more labor-intensive channels such as phone calls and emails. So the teams are looking for automation, but the issues are complex.


    "Generative AI technologies can generate new derived versions of content, strategies, designs, and methods by learning from large repositories of original source content. Generative AI has profound business impacts, including on content discovery, creation, authenticity, and regulations; automation of human work; and customer and employee experiences"

    Source: Gartner

    As Service Desk teams struggle with increasing workloads, increasingly complex IT infrastructures and digital services, the need for intelligent assistance, and automation is increasingly important. Service management, a field traditionally reliant on reactive and manual processes, is ripe for the new innovations:

    Service Desk Agents save time on first responses with AI

    Watch the video to see how AI has helped Efecte's service desk to work more efficiently.

    "The initial response to incoming tickets can now be done much faster and it only took a few days to start being more productive with AI"

    Aleksi Koli, Support Specialist

    AI Terms to Know

    Artificial Intelligence (AI)

    AI is an often overused and misunderstood term. Artificial intelligence is simply a program/system that mimics human-like-intelligence. A simple example of this would be summoning a voice assistant on your phone. The phone listens for a specific phrase, and when it hears it, it launches the program.

    Generative AI

    Generative AI is a type of artificial intelligence that creates new content, such as text, images, music, or videos, based on what it has learned. Instead of just following set rules, it uses advanced computer models to make original content. It can for example generate responses to common issues or create troubleshooting guides.

    Natural Language Processing (NLP)

    Natural Language Processing (NLP) is the technology used to understand and interpret human language. When NLP is used with AI, it allows the two technologies to work in harmony to identify keywords or phrases using NLP and take the appropriate action (rule) or categorize the data source (depending on the form of AI).


    AI is all about the algorithm. An algorithm is the formula or commands used to set the rules of the system. AI systems can use a single algorithm or multiple algorithms, and the training can be singular or periodic. The important thing to remember is that the algorithm makes AI, AI even if it doesn't always get the recognition it should.

    Machine Learning (ML)

    Machine learning (ML) is a subset of AI which often causes confusion and misuse of the term. AI will typically give answers based on the “rules” defined by the algorithm. Machine learning is when the AI tries to learn what action it should take based on its data. Hence the learning in machine learning.

    Supervised Learning

    Every AI system will need training, and your goals and the data define your option. Supervised learning is when you feed the algorithm known questions and answers. The algorithm will learn based on this information and provide answers for future questions based on the previous data.

    Unsupervised Learning

    AI can be a powerful tool for identifying patterns when there seem to be none. This form of AI is referred to as unsupervised learning and is the practice of feeding huge amounts of data into an algorithm with no answers, questions, or direction. The system will learn all it can and attempt to identify partners and solutions.

    Starting with AI in Service Management?


    AI is one of the hottest topics in IT Service Management. Everyone is looking to add a virtual agent, coach, or other AI form to their systems. 

    So why has it been so difficult for so many to reap the benefits?

    Our guide will present four of the most common areas of contention when it comes to AI and ITSM and gives some tips on how to make your adoption smoother.

    AI use-cases for Service Management

    Organizations have a wide range of possibilities to benefit from AI. Where will you start?

    AI support for end-users

    Intelligent self-service options can reduce the reliance on manual agent interaction for routine queries. Building on the foundation set by integrating AI to enhance agent efficiency, extending AI's benefits directly to end-users is another use-case in the AI journey. This shift not only addresses the increasing complexity and volume of service requests but also aligns with the growing user preference for self-service solutions. The introduction of AI-powered tools for end-users marks a critical step in transforming service management into a more autonomous, user-friendly ecosystem.

    Virtual Agents

    Virtual agents or bots are designed to simulate real service desk agents. When discussing bots in the realm of AI, it is important to understand the difference between a bot powered by automation and one powered by AI. A chatbot that has canned responses based on predefined choices does not constitute AI. This type of bot is classified as a rule-based chatbot. For a chatbot to be considered AI-powered, it must have some form of algorithm to set the rules used. 

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    AI-amplified service desk

    Integrating AI deeper into the Service Desk work done by agents can increase productivity even for the more complex issues without compromising service quality for the end-user. In practice this means empowering agents with tools that streamline ticket handling via e.g. intelligent categorization, suggestions, and summarization; which all help reduce manual work.

    Agent assistants

    AI-powered agent assistants are often very similar to virtual agents except in one key characteristic; they interact with agents, not end-users. Agent assistants will use algorithms to identify similar incidents or interactions based on a scoring mechanism using natural language processing (NLP) and potentially supervised training. To allow the algorithm to move past basic AI into machine learning (ML), the support persons can score the suggestions based on their usefulness. 

    Chat / Email automation

    Using Artificial Intelligence to automate responses can lead to quick efficiency gains and frees up resources for complex tasks. At it's best, AI can maintain high user satisfaction by generating accurate, contextual and personalized responses. By focusing on chat / email automation first, organizations can quickly enhance operational efficiency, gain immediate insights into AI's impact, and set the stage for the next steps in their AI journey in service management.

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    AI Workflows

    Today, processes are automated based on rules using workflow automation. The workflow engines of today have workflow activities that make decisions based on AI and machine learning (ML). Complex combinations of If/Then conditions in workflows will be replaced with dynamically adjusted, score-based decisions that continuously learn.



    How to start using AI in your Service Desk?

    Launching AI within a service management ecosystem is a strategic process, rooted in careful analysis and planning. The process begins by understanding the current service management related needs and identifying how AI can best be leveraged to address them. Here are key steps we have seen in successful AI deployments:

    Step 1: Analyze your current support processes

    Step 2: Identify the biggest bottlenecks and inefficiencies 

    Step 3: Pick a first use case for an AI pilot project

    Step 4. Iterate and Improve

    Efecte Effie AI - The AI for Service Desk that keeps your data local

    Helping Service Desk teams and end-users to work smarter and solve issues quicker at a lower cost of service.