How will AI and Machine learning change service management?

Artificial Intelligence – AI – continues to be a hot topic at service management events. It’s still exciting, still full of promise for a smarter, more responsive future. But what are the advantages of AI today? And how can it help you actually improve your service management, right now? Let’s answer some common questions.

AI in Service Management

Artificial Intelligence is a broad concept. What you’d describe as AI some 20 years ago – think IBM’s chess computer Deep Blue – has long become standard technology. These days, when people talk about AI, they’re often referring to Large Language Models (LLMs) and advanced Machine Learning.

With Machine Learning, a system uses data and examples to learn how to complete a task – and in 2025, it’s no longer just hype. It’s already embedded in tools we use every day. And with the rise of conversational AI like ChatGPT, Claude, and Gemini, we’re seeing even greater integration into service management workflows. One such example can be seen in service desk chatbots.

Gartner’s updated research shows Machine Learning has moved beyond the hype cycle into what they call the "Plateau of Productivity." We’ve outgrown the inflated expectations of a full chatbot takeover, and we're now focused on what’s practical and sustainable. And that’s a good thing.

Which Machine Learning application for service management should we expect right now?

Machine Learning excels at spotting trends, and in service management, this means rapid recognition of abnormal spikes in tickets, recurring incidents, or emerging service disruptions.

Today, ML can automatically group related tickets, flag major incidents, and route them to the right agent – or even kick off automated remediation using scripts or self-healing workflows.

But it doesn’t stop there. AI is increasingly used in problem management. Where once you had to sift through countless tickets to find a pattern, ML now does that heavy lifting. It connects the dots, helping teams uncover systemic issues and recurring root causes faster than ever before.

It’s also transforming knowledge bases and management. When a new issue comes in, AI can instantly surface similar resolved cases or even summarize steps from long articles. In many cases, AI tools are now suggesting solutions to customers in real-time – or preventing them from logging a ticket at all by solving the problem as they type.

Common misconceptions about Machine Learning versus service management

A common misconception that still lingers is the fear that chatbots and virtual agents will replace service desk staff entirely. That’s not how this is playing out.

Even in 2025, people still matter. While AI has gotten a lot better – much better – at handling routine, repetitive questions, it still struggles with nuance, empathy, and the human side of support. Technical possibilities are vast, but not every implementation works well out of the box. And no AI can fully replace the experience of a skilled, empathetic support agent handling a frustrated user.

What AI does do really well now is free up time. It handles the easy stuff, so your team can focus on the tickets that need a human touch. As AI improves at retrieving and summarizing relevant information, your team has more bandwidth to focus on what matters most: the customer experience.

How do you respond to a stressed-out user? What language should you use to de-escalate tension? What can you do to truly exceed expectations? These are still human questions. And I don’t see Machine Learning solving that any time soon.