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ITIL 5 brings AI into the heart of service management guidance. With six core publications already released and more guidance on AI governance and practices coming in 2026, ITIL now addresses how organizations can use, manage, and govern AI across the digital product and service lifecycle.

One of the biggest concerns surrounding AI in IT services is the fear of losing control over operational decisions. ITSM confidence scoring addresses this by measuring how certain AI is about its recommendations, enabling it to act autonomously only when confidence is high and involving human operators when needed.

ITSM data quality is no longer just operational hygiene, but a strategic prerequisite for AI. Successful AI-driven automation relies on three foundations: an accurate CMDB, a structured workflow history, and consistent operational records that capture incidents, changes, and service requests across the IT landscape.

For years, IT has measured success through technical metrics such as response times, availability, and SLA compliance. While useful, these indicators don't reveal whether users can work effectively. Experience Management fills this gap by combining operational metrics with what matters most: user productivity and satisfaction.

AI is transforming ITSM, but without strong processes, reliable data, and clear governance, it can amplify inefficiencies instead of solving them. This article explores the four biggest risks of AI in ITSM and explains why organizations must strengthen their operational foundations before scaling AI initiatives.

AI knowledge management is transforming IT service management by accelerating incident resolution, improving self-service, and enhancing operational efficiency. But without strong governance, structured content, and frameworks like KCS, AI can also amplify errors and security risks. Discover the key benefits, challenges, and best practices for implementing AI-driven knowledge management successfully.

Cyber-Resilient Service Management goes beyond traditional cybersecurity by focusing on operational continuity before, during, and after incidents. Instead of asking “if” an attack will happen, it prepares organizations for “when” it does – combining visibility, orchestration, and ITSM processes to minimize disruption and accelerate recovery.

Many companies fail in their cybersecurity efforts because of implementation issues. ITSM bridges this gap by translating security events into structured, traceable processes. From alert normalization and prioritization to automation and change management, ITSM becomes a critical factor in enabling rapid, coordinated, and measurable responses to cyber incidents.

As IT environments become more fragmented and AI adds new governance demands, simplifying ITSM requires more than another tool. This article breaks down the causes of ITSM complexity, the criteria that matter when evaluating platforms, and the practical steps organizations can take to streamline service delivery.

In today’s hybrid and highly automated IT environments, cyber resilience is essential. As complexity and speed increase, organizations must shift from prevention alone to a broader strategy that absorbs impact, enables rapid response, and drives continuous improvement.

Modern cybersecurity demands full CMDB visibility, integrated ITSM, and real-time asset intelligence to reduce risk.

SLAs, XLAs, and OLAs are evolving from static agreements into dynamic frameworks for managing service quality in modern IT. With AI and automation transforming IT governance, they must function like a real-time GPS, continuously adapting to change. The article explores how AI is reshaping these agreements within the IT ecosystem.

Most IT organizations today are eager to chase Artificial Intelligence (AI). On the other hand, while the pressure to be “AI first” continues to grow, IT leaders are still struggling to answer a simple question: Where do we actually start? We’re using it, generally. But how do we get value?

The ITSM market is shifting beyond ticket management toward delivering better service continuity, faster response and improved user experience. Service Experience Platforms (SXPs) address this change, as demonstrated by solutions like EasyVista.

In this article, we will explore the five fundamental metrics to include in an XLA strategy oriented toward 2026

ITSM demands agentic AI to autonomously resolve incidents, prioritize intelligently, and orchestrate fragmented tools for scalable service delivery.

Effective ITSM in 2026 requires robust cybersecurity, well-governed AI automation, accurate CMDBs, and continuous team training to mitigate emerging risks.

Mean Time to Repair (MTTR) is a critical KPI for measuring how quickly organizations can restore systems and services after an incident. This article explains what MTTR is, why it plays a central role in operational efficiency and customer satisfaction, and the key factors that influence it. It also explores practical strategies, tools, and technologies to help organizations effectively reduce MTTR and minimize downtime.

AI-driven ITSM enables predictive maintenance, detecting anomalies early and automating interventions to keep production lines running.

EasyVista Service Manager brings IT and OT teams together on a single platform to reduce downtime and accelerate incident resolution.