EasyVista Research Finds AI Is Widely Adopted in ITSM, Scaling Its Impact Is the Next Challenge

EasyVista, a global leader in IT service management (ITSM) solutions, released new research revealing that while AI adoption in ITSM is widespread, most organizations are still building the operational maturity required to consistently scale its impact.

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While AI adoption in ITSM is widespread, most organizations are still building the operational maturity required to consistently scale its impact.

Only 12% of enterprises describe their ITSM approach as fully mature and proactive, while more than 40% operate in partially structured or ad hoc environments, limiting how consistently AI can be applied and how far its value can extend.

Based on a global survey of 1,100 IT professionals, the research shows that 95% of organizations are already using AI to improve ITSM processes. Common applications include workflow automation, reporting, and incident prevention. While a majority report that AI meets or exceeds expectations, particularly in structured use cases like task automation and incident management, leaders lack clear measures of ROI and hesitate to deploy AI without human-in-the-loop intervention.

“AI is already widely adopted in ITSM, particularly in early-stage use cases, and that is an important signal,” said Keith Andes, Head of Product Marketing, EasyVista. “However, leader sentiment is outpacing reality, as the value of global deployment outcomes are not yet proven. This research shows that weak process discipline, poor integration, and low-quality data limit what AI can consistently deliver. Scaling from here will require organizations to invest in operational maturity.”

These findings reflect a shift from isolated AI use cases toward more integrated, workflow-driven models. This is reflected in EasyVista’s continued investment in embedding AI directly within ITSM processes, including its EV Pulse AI Conversations to support more efficient, user-centric service interactions.

The research also highlights broader operational pressures. Budget constraints (38%), lack of skilled IT personnel (36%), and integration gaps (35%) are among the top challenges organizations face. These same issues are reflected in AI adoption, where integration complexity (18%), lack of expertise (13%), and cost concerns (13%) are key barriers.

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