The Olympics demonstrate that success depends on teamwork, preparation, governance, and seamless coordination – not individual talent alone. Discover how these same principles help IT organizations deploy Agentic AI successfully through better context sharing, AI governance, continuous validation, and clearly defined AI responsibilities.
AI is transforming IT service desks, but not in the way many expect. The real limitation isn’t the intelligence of AI models, it’s the lack of usable, high-quality context within most IT environments. From CMDB accuracy and knowledge articles to service catalogs and tacit expertise, AI is only as effective as the information it can access. Without strong contextual foundations, even the most advanced AI simply scales existing issues. The real opportunity lies in building richer, more reliable ITSM context so AI can deliver consistent, meaningful operational value.
Many IT teams are juggling tight budgets, security gaps, compliance demands, and the growing pressure of AI — yet one discipline that could ease all of these challenges continues to be overlooked. IT asset management (ITAM) has been around for years, but for too many organisations it still means a spreadsheet and good intentions. This article explores why that needs to change, with real-world examples showing how a mature ITAM approach transforms everything from onboarding and budgeting to security and compliance.
AI adoption in ITSM is nearly universal, but many organizations are discovering that AI can only be as effective as the processes, data, and governance supporting it. New research shows a significant gap between widespread AI implementation and the maturity of underlying ITSM practices. While AI is delivering value through automation, predictive insights, and improved user experiences, it cannot compensate for fragmented workflows, disconnected tools, or inconsistent data. As organizations pursue agentic AI and larger-scale automation, strengthening ITSM foundations (an ITSM reset) may be the most important step toward achieving sustainable AI success.
As AI agents gain autonomy and become embedded in critical business processes, organizations face a growing governance challenge. This article explains how established ITSM capabilities – including risk classification, service ownership, CMDB visibility, change management, incident response, and access governance – can provide a practical framework for AI agent governance. Rather than creating entirely new governance models, organizations can extend existing service management practices to ensure accountability, oversight, and operational resilience in the age of agentic AI.
Many ITSM tool renewals are treated as procurement exercises when they should be strategic business decisions. With AI reshaping service management, increasing pressure to reduce costs, and rapidly evolving vendor capabilities, renewing the status quo is no longer the default choice. This guide outlines a practical 90–180 day framework for evaluating your current platform, assessing alternatives, and building stakeholder alignment before contract renewal. It also covers the seven critical questions every IT leader should answer to determine whether to renew, expand, or replace their ITSM solution with confidence.
AI doesn’t fix broken processes – it scales them. While many ITSM teams focus on automation and autonomous IT service desks, the real determinant of AI success is operational maturity. Learn why knowledge quality, workflow consistency, governance, and data hygiene are the foundations of sustainable AI adoption in IT service management.
The best support tickets are the ones never submitted. And with the emergence of AI, a ticketless enterprise is becoming a reality, allowing Enterprise operations teams to become more effective and less expensive.
Provance ServiceTeam ITSM Enterprise 3.0 continues the platform’s evolution as a Microsoft-centric service management solution built natively on Microsoft Power Platform. Designed for organizations looking to maximize existing Microsoft investments, the solution combines ITIL-aligned service management capabilities with low-code flexibility, workflow automation, AI-driven innovation, and deep integration across Microsoft 365, Azure, Power BI, Power Automate, and related technologies. ServiceTeam ITSM Enterprise 3.0 offers organizations a modern alternative to traditional ITSM tools while leveraging the scalability, security, and extensibility of the Microsoft ecosystem.
Strategic roadmaps are filling up with AI agents, command towers, and intelligent automation. Yet both Agentic IT and third-party risk programs share a hidden constraint that rarely makes it into keynotes: neither can succeed without a trusted, runtime view of what exists, how it is connected, and which services it supports.
Lynda Cooper has edited ISO 20000 for over a decade. Her view of why the standard works is also her clearest answer to the question everyone gets wrong about it: standards tell you what to do, not how to do it. The how is what frameworks are for.
A quarter of UK enterprise AI agent deployments aren’t paying back. New research from KTSL and BMC Helix shows the reasons aren’t about the technology.
A shift is beginning, including for self-healing. AI agents are increasingly automating not only operational IT service tasks but also parts of ITSM platform implementation, configuration, and ongoing maintenance. As these capabilities mature, organizations can expect lower costs, less manual intervention, and systems that increasingly manage and optimize themselves.
More investment, more automation, more AI, and yet ticket volumes refuse to fall. Akshaya argues the reason is that none of it touches the layer where most tickets actually start: the endpoint. A look at how unified endpoint management shifts IT from cleaning up failures to preventing them, and why the organizations doing it run their service desks at a fraction of what their peers spend.
Mathies Wähner on why Agentic AI fails in ITSM. Drop it into an operating model that isn’t ready and it doesn’t make you smarter, just faster at being wrong, while removing the people who used to catch the mistakes. The real question isn’t how to implement it, but whether your operating model can survive it.