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The Gartner Magic Quadrant can help narrow your ITSM vendor shortlist, but it should not be treated as a product ranking. Learn how to evaluate and choose an ITSM solution that delivers value today and supports future growth.

Most AI pilots succeed, but few make it to production. Discover why AI deployments fail at scale and what your organization needs to successfully transition from pilot to production in ITSM.

The introduction of the EU AI Act has made clear to IT leaders that AI cannot be considered merely as an ITSM platform feature, but it must be governed through processes and accountability.

In 2026, most IT departments find themselves managing a multitude of tools. In this article we'll explore some paths to reduce tool proliferation without sacrificing the functionality an organization needs.

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.

Shift-left aims to resolve issues earlier in the support chain to reduce costs. But it only works when supported by a strong knowledge base, effective self-service, and mature service desk automation.

ITSM modernization is hard to fund when budgets are tight. Learn how to quantify hidden costs, reduce risk, leverage AI budgets, and build a credible ROI case.

Many IT issues never become tickets. Employees simply work around slow systems, login problems, and other daily frustrations, creating hidden digital friction that reduces productivity. Because traditional ITSM metrics rarely capture these issues, IT leaders risk overlooking growing costs and the true business impact of IT.

Many organizations pay for ITSM features they never use. According to Gartner, I&O leaders are expected to spend around $2 billion on unused ITSM capabilities by the end of 2026. This article explains how to reduce overspending through smarter licensing, feature audits, and flexible contracts.

AI is reshaping ITSM, but new AI features deliver value only when supported by high-quality data, well-defined processes, and mature ITSM practices. The smartest investment is not simply adopting more AI, but building the ITSM foundations that make AI successful.

Virtual assistants, agentic AI, and generative models are rapidly transforming IT Service Management. But their success depends on more than just the technology itself: without well-defined processes, consistent data, clear categorization, and established SLAs, AI cannot reach its full potential. This article explores the six ITSM fundamentals that are essential for successful AI adoption.

AI depends on ITSM best practices. Discover why documented workflows, ITIL maturity, clear ownership, and structured processes are the foundation for successful AI adoption in ITSM, and why organizations must return to the basics before scaling AI initiatives.

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.