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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:
83%
of tickets are still handled manually
25%
increase in ticket handling costs from 2019-2023
$20
average cost per agent-handled ticket
Service Desk Agents save time on first responses with AI
"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
Efecte
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).
Algorithm
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.
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.
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.
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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
The key to success with AI is to find an area where it will deliver the most business value in the shortest space of time, with the least amount of business disruption. You don’t have to transform the whole Service Desk at once—it’s much better to start with a quick but valuable win.
This includes mapping your current support processes and case lifecycle and identifying where the biggest bottlenecks and inefficiencies lie today. We see most case lifecycles as having five key stages:
- Ticket created: The user has a problem that they can’t self-solve, so they raise a ticket with the Service Desk.
- Classify and route: The ticket is classified based on the type of problem that has been raised, and routed to an appropriate agent for handling.
- Investigate: The agent investigates the ticket, either looking for a solution in the knowledge base, asking the user for further information, or escalating to a specialist.
- Communicate: The agent communicates the solution to the user, whether by phone, email, or another channel. This may result in further rounds of investigation and communication.
- Document: The ticket is closed and the resolution is documented and added to the knowledge base.
It’s also important to analyze what kind of data you’re collecting during your support activities. Understanding your data has multiple benefits. Firstly, your chosen AI solution might need to be trained on high-quality, relevant and balanced data in order to come up with helpful answers. Analyzing your data will help you plan resources for storage and processing, and avoid any privacy or ethical issues. It will also help you identify key features for the AI model, improving its accuracy and efficiency.
Step 2: Identify the biggest bottlenecks and inefficiencies
Somewhere within the case lifecycle there will be bottlenecks that slow your agents down and frustrate your users. In a typical organization, those bottlenecks might include:
- Tickets wait in a queue for classification, slowing down the time it takes to get a suitable agent assigned.
- Agents are assigned too many tickets in a day, creating stress, delaying resolutions for users, and potentially reducing the quality of agent-user communications.
- Agents spend a lot of time writing emails to users, either asking for clarifications or providing solutions. Finding the right language for non-technical users can slow agents down further.
- Time spent documenting ticket resolutions takes agents away from solving users’ problems. While it’s necessary for the knowledge base, it can affect the user experience.
Identifying these bottlenecks in your own Service Desk will give you a good idea of where to focus your first AI implementation. They can be found by examining reports and by interviewing agents to collect qualitative data around their day-to-day experience. Talking to agents can also help to reassure them that AI won’t put their job at risk, but will just help them to work smarter.
Step 3: Pick a first use case for an AI pilot project
From the bottlenecks you’ve identified, pick one area where you think the introduction of AI will deliver the most business value with the least disruption to Service Desk operations. This will become the first ‘quick win’ to prove the value of AI and get agents used to having a digital assistant.
The area you choose will depend on your business, but here at Efecte we’ve found that email can be a really good place to start. For a start, the business value is high: in our experience, around 40% of manually-handled tickets involve time-consuming use of email, so an AI assistant can unlock efficiencies very quickly. In fact, since we implemented our own Effie AI Email assistant in our Efecte customer Service Desk, we’ve found that agents can answer 30% more emails per month.
Step 4. Iterate and Improve
Based on performance metrics, user feedback, and evolving business needs, iterate and improve the AI solution over time. Incorporate new use-cases, and optimizations to maximize the value of AI in your service desk and ensure ongoing success.
Introducing AI gradually into the Service Desk means you can score quick wins in terms of efficiency and productivity without overwhelming agents with new functionality.
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.
