Opportunities, challenges, and success factors—AI in ITSM is transforming how companies deliver and optimize their IT services. More and more organizations are adopting AI-powered solutions to improve service desk processes, reduce costs, and enhance the user experience. However, the path to successful implementation is not without obstacles.
The market research and consulting firm Gartner has conducted an in-depth analysis of the opportunities, challenges, and success factors of AI in ITSM, identifying the key prerequisites for the successful deployment of artificial intelligence in IT Service Management. You can find Gartner’s full analysis here.
Challenges: Costs, data quality, and change management
Expectations for AI in IT Service Management are high, but not all projects deliver the anticipated value. According to Gartner, by 2027, around 50% of AI projects for service desks will fail due to unforeseen costs, risks, or an inability to achieve the expected ROI. The three biggest challenges are:
- Cost and pricing models: AI solutions are typically subscription-based, with costs depending on licensing fees, transaction fees, or additional services. Without a clear cost strategy, expenses can quickly spiral out of control.
- Data quality: AI systems require high-quality, up-to-date data to make well-informed decisions. Outdated, incomplete, or inaccurate data can lead to poor results and erode trust in the technology.
- Employee acceptance: The introduction of AI changes workflows and demands new skill sets. IT teams must be involved early in the transformation to alleviate concerns and redefine roles and responsibilities.
Success factors for AI in ITSM
Despite these challenges, artificial intelligence offers significant advantages in IT Service Management, provided that companies take the right approach. Gartner recommends the following steps:
- Define business-driven objectives: AI should not be implemented just because it is technologically feasible. Companies should identify concrete use cases that provide measurable value, such as virtual support agents, intelligent ticket categorization, or automated case summarization.
- Develop a clear cost model: In addition to direct costs (licenses, implementation), companies should also consider indirect costs such as employee training, data optimization, and ongoing maintenance.
- Adapt ITSM processes: AI cannot simply mask inefficiencies in existing workflows. A maturity analysis of ITSM practices can help identify weaknesses and enable targeted automation.
- Ensure data quality: A clean and structured data foundation is essential for any successful AI implementation. IT teams should regularly review data sources to ensure that relevant information is correctly captured and updated.
- Engage employees early: Change management is crucial for the acceptance of new technologies. Transparent communication, training, and hands-on workshops help overcome skepticism and introduce new ways of working.
AI in ITSM: Leveraging potential, mitigating risks
Opportunities, challenges, and success factors in AI for ITSM show that artificial intelligence has enormous potential for IT Service Management—from more efficient service desk processes to improved incident diagnosis and greater service availability. However, implementation is not automatic. Companies must address cost models, data quality, and change management early on to achieve the desired success.
