BLOG 6 Practical Uses of AI in Service Management

It seems like you can’t look at a single business application without hearing about Artificial Intelligence (AI). It’s everywhere! Writing headlines for your Facebook Ads, generating images, and helping developers debug their code. Beyond the buzzword lies an incomprehensibly useful tool.. when it’s used in the right ways.

IT Service Management (ITSM) and Enterprise Service Management (ESM) platforms have long touted increased efficiency of service teams and speeding up resolution times. But what happens when you combine a service management platform with AI?

AI Service Management (AISM) offers service teams the ability to elevate their service management processes with AI and automate the repetitive tasks that AI excels at handling. But what are the best ways you can use AI in service management for the maximum impact?

1. Categorise and route tickets correctly

Correct categorisation and routing of tickets are critical in service management. Incorrect categorisation can skew performance data and misrouted tickets usually take longer to resolve, as they need to be manually reassigned to the correct team. Using AI within your service management platform removes the guesswork, or simple human error, that can occur when you’re manually categorising and routing tickets.

With AI built into your service management platform, it can analyse the data points of incoming tickets, looking for keywords and phrases, and match these against previous tickets and knowledge base articles to determine what kind of issue it is, the level of urgency, and the correct agent or team to handle it.

Tickets are received by the right team on the first try. End-users have their requests resolved rapidly. Service agents aren’t stuck filling in fields or manually reassigning tickets. Win, win, win.

2. Deliver zero-touch self-service with an AI copilot

As humans, we might have been told our whole lives that we’re all unique. BUT… this isn’t the case when it comes to the problems we encounter. With any service or business, there are a number of common issues that can be resolved without a service agent – this is where an AI chatbot or copilot comes into play.

An AI copilot built into a service management platform can draw on knowledge articles, ticket data and previous interactions to get smarter over time. With all this information at their disposal, the AI can have meaningful interactions with users and provide accurate guidance or information.

When a request is raised, the AI can share knowledge articles, policies or other documents. If the AI needs more information, the AI can ask qualifying questions to hone in on the cause of the problem or clarify what the user needs. But the real magic is AI’s ability to carry out actions – e.g. reset a user’s password, unlock a SaaS session a user has been locked out of, provision a guest Wi-Fi pass, and more.

The end-user can get an instant resolution on their request and the service agents can stay focussed on higher-value work without getting distracted.

3. Guided service through recommended remediations

Your service agents are great, but they don’t know the answer to everything. Between incidents, problems, and requests, you might have hundreds of different processes to follow and remember. Additionally, even if your team has encountered an issue before, there is no guarantee that the agent assigned to an incident has been exposed to it. So how can you draw upon all of the data available on your service management platform and service it to an agent in a timely fashion?

In AI Service Management platforms, your AI can act as a copilot whenever agents are handling a ticket. When an agent starts work on a ticket, AI can analyse the ticket and look for similar incidents or problems that have been previously closed. If it finds any matches, the AI then surfaces this information as recommended remediation steps.

This not only speeds up the time to resolve tickets but also stops guesswork and repeated investigation by your service team. The use of an AI copilot creates a guided service management process that makes it easier to get new starters up to speed and reduces the number of tickets that have to be escalated to tier 2.

4. Create a comprehensive knowledge base

When it comes to service management, the more comprehensive your knowledge base is, the fewer tickets you’ll receive. While not all customers or end-users will look at your knowledge articles before logging a ticket – a large number will. HBR found that 81% of customers will attempt to take care of an issue themselves before reaching out to support.

While a great knowledge base will have a huge impact on your support ticket volume and ability to resolve tickets via self-service, writing knowledge articles can be time-consuming. Using an AI-powered service management platform, the AI can be used to both identify recurring issues that articles should be created for, and do the heavy lifting in the writing process by creating a draft based on the information captured in previous incidents and problems. You can even analyse search queries and conversations with your AI copilot that led to a ticket creation to find gaps in your knowledge base.

This means your team will spend less time writing articles and resolving recurring tickets that a knowledge article could address. And your users can get the answers they want and the fix they need instantly, without having to log a ticket.

5. Assign risk scores to change requests

Change management is a critical function in IT Service Management. Often, simply moving from unstructured change management to an ITIL-based ITSM platform can be enough to take a lot of risk out of change requests. But when it comes to high-stakes changes, like updating critical infrastructure or deploying organisation-wide software updates, you can’t be too cautious.

AI can be used in change management to assess the potential risks of a change request and assign a risk score to each request. Analysing the complexity of the change, similar changes made previously, and the systems that the change will affect, AI can determine the potential for downtime or disruption. Depending on thresholds set for the risk score, this change request can be routed through different approval layers to ensure that the change is made in the most risk-free way.

Using a data-driven approach to change management, IT teams can prioritise low-risk requests for quick implementation, while ensuring that high-risk changes are thoroughly reviewed and planned. Using AI in this way can deliver changes with fewer disruptions and downtime, and better resource allocation.

6. Speed up email responses

Let’s face it, no one enjoys writing emails (apologies if you do). For better or worse, email communication is a critical part of service management. But your agents don’t need to write all their emails themselves – or copy/paste potentially confidential data into ChatGPT and ask it to write their email response for them.

AI can analyse a service ticket and any steps the agent has taken, and quickly draft email responses to the end-user, sharing any key data points from the ticket. Your agents can even feed the AI a prompt on what they’d like to send the end-user and the AI can combine this with the available data for a more customised email.

Your team can spend less time writing emails, and you reduce the risk of your agents creating a data breach by inputting confidential information into a 3rd party AI tool like ChatGPT.

Need help applying AI to your service management?

Are you looking to use AI in practical ways that improve your service management but not quite sure where to start?

Servicely’s AI-powered service management platform has enabled our customers to use AI in practical ways that elevate their service to both their end-users and clients. Get in touch if you’d like to talk AI with our team.