AI in Service Management: Real-World Examples and Applications

Written by Coursera Staff • Updated on

Discover how you can use artificial intelligence (AI) in service management, including real-world examples and the types of artificial intelligence behind them.

[Featured Image] A customer service representative uses AI in service management to complete their job. They are sitting at a desk, looking at a computer that is barely in the picture.

Key takeaways

AI in service management improves operational efficiency by automating tasks like ticket routing and customer support.

  • AI in service management automates processes like ticket routing, customer support, and incident detection to improve operational efficiency.

  • Service management tools rely on machine learning, natural language processing, generative AI (GenAI), and agentic AI.

  • Implementing AI in service management can reduce costs, improve employee performance, and optimize resource allocation.

Explore how AI in service management is revolutionizing the industry. Ready to start building AI skills? The AI Foundations for Everyone Specialization from IBM can help you grow your understanding of artificial intelligence and how you can use AI across industries.

How is AI being used in the service industry? 

AI in service management helps support various processes through task automation, so you can focus your efforts on more strategic projects. By analyzing data and responding to user requests, these tools not only detect incidents but also provide solutions. You can utilize different forms of artificial intelligence based on the task, such as natural language processing, machine learning, generative AI, and agentic AI. As a result, you can experience a wide range of benefits, including increased productivity, internal support, and problem-solving capabilities.

What is the 30 percent rule in AI?

The 30 percent rule in AI states that automation through artificial intelligence should account for approximately 30 percent of a task, while humans complete the other 70 percent. Following this rule helps ensure AI is used as a support tool rather than taking over roles completely. You can still draw on its advantages while preserving the human strengths that AI can’t replicate, like emotional intelligence, critical thinking, empathy, and creativity.

What are real-world examples of AI in service management?

Whether for supporting efficient service management operations for employees or providing better support to your customers, you can find numerous ways to use AI. Businesses are already experiencing a boost to their operations as a result, and you can too.

AI-powered chatbots for customer support

Using natural language processing, AI-powered chatbots can have conversations with customers by understanding audio and text inputs before generating useful responses, rather than simply following a script. As chatbots gain experience responding to user inputs, they become increasingly effective at solving problems. This supports scalable customer support and provides valuable support to customer service employees. 

Intelligent ticket routing and prioritization

With intelligent ticket routing and prioritization, AI analyzes tickets to first classify the issue and then, based on its impact on your operations, address them according to their priority level. Intelligent ticket routing also relies on natural language processing, machine learning, and agentic AI for some tasks.

Virtual assistants for IT service desks

AI virtual assistants directly support IT employees with tasks like troubleshooting, resetting passwords, and suggesting potential solutions to user concerns. As with chatbots, virtual assistants learn as they gain experience, so their ability to accurately diagnose and solve problems only grows over time.

Predictive maintenance in field service operations

AI can assess when your equipment may fail and strategically schedule maintenance so it doesn’t disrupt operations, while prioritizing the most critical machines. This is possible by analyzing historical data that shows when machinery failed in the past, as well as data highlighting current performance.

What technologies power AI in service management?

The core AI technologies in service management include machine learning, generative AI, agentic AI, and natural language processing. Each of these forms of artificial intelligence powers service management in its own way:

  • Machine learning: By analyzing data, machine learning algorithms learn to identify patterns and make accurate predictions. For service management, this could mean forecasting your business's needs or identifying and diagnosing IT threats. 

  • Generative AI: With the ability to create new content such as images, videos, and text, generative AI is a useful tool in service management for code development, or drafting knowledge base articles, which you can simply review instead of writing yourself.

  • Agentic AI: Since it’s capable of autonomous decision-making, you can use agentic AI to solve problems on its own, making it a valuable tool for routine maintenance and strategic scheduling to avoid interfering with other business processes. 

  • Natural language processing: Enabling computers to understand human language, natural language processing allows chatbots to comprehend user inputs and provide responses. Chatbots can retain information from their interactions to continue learning and improve response accuracy in the future.

What are the benefits and challenges of AI in service management?

Implementing AI in service operations creates a range of benefits for both employees and the business itself, as employees can spend less time troubleshooting issues, giving them more time to focus on normal tasks. Meanwhile, your business is experiencing efficient resource optimization, lower costs, and improved incident response, resulting in a more stable working environment.

However, AI implementation isn’t immune to challenges. Having sufficient data is necessary for generating high-quality results, so before you can fully implement AI, you will need to ensure your data is clean. Given all the data you will be handling, security also becomes an important factor, as you must ensure your AI platform meets regulatory standards and adequately protects sensitive data. Additionally, organizational buy-in is crucial for successful AI adoption. This is possible through effective communication of AI’s advantages and investing in employee training so they can use the technology effectively.

What is the 10-20-70 rule for AI?

The 10-20-70 rule for AI suggests that for optimal results, you should direct 10 percent of your efforts towards building an effective machine learning model, 20 percent toward technology  and high-quality data implementation, while the largest portion, 70 percent, focuses on establishing ways you can improve business processes.

How can you implement AI in service management?

To start incorporating AI in your service management operations, it's crucial to first establish clear goals and identify the problems you wish to solve. You will want to identify metrics you can track to assess whether you’re achieving the desired results based on your goals. 

Although various AI tools exist, make sure that you’re choosing the one that provides the most financial value based on your circumstances. With an understanding of what you want to accomplish by implementing AI, you can explore the different tools available to identify what best fits your needs. Make sure you select AI tools that can grow with you as your operations scale. 

Read more: 53 Artificial Intelligence Use Cases + How to Get Started

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