Reinventing ITSM? It’s Not Going Away

by Dennis Drogseth, VP of Research, IT Megatrends, Analytics, and CMDB Systems

I must admit that the more I research IT service management (ITSM)—what it means, what its trends are, and what its future is—the more I feel like an explorer on a strikingly rich continent that, so far, the industry has either ignored or misunderstood. The brand-new data we just received in EMA (working with my cohort, Valerie O’Connell) is a case in point. Soon to be a webinar (on April 11), the data explores priorities in AI and analytics, automation, virtual agents, AI bots, and enterprise service management (ESM) initiatives globally, with 200 respondents in North America, 100 in Europe, and 100 in Asia.

Ignored, or misunderstood?

So what do I mean by saying that, by and large, ITSM is either ignored or misunderstood? My primary concern is the industry’s prejudice that ITSM, with its traditional center in the service desk, is a reactive blast from the past with legacy processes, legacy mindsets, and legacy career opportunities.

Our data overall shows just the opposite and points to a growing synergy across AI/analytics, automation, virtual agents, AI bots, and ESM that frankly surprised us in doing our analysis. To be clear, this synergy is most strongly reflected in a consistently progressive group that shows advancements in all these areas, with high corresponding success rates across multiple dimensions, from more effective incident handling to improved end-user and customer satisfaction, to greater IT operational efficiencies and cost savings, to accelerated levels of IT-to-business alignment, and the list goes on (actually, it’s quite long).

What is ITSM, anyway?

Well, you’d think I’d know the answer since I’ve been researching ITSM for nearly a decade. The truth is, both Valerie and I were curious to find out a number of dimensions, attributes, and qualities that reflect ITSM today. We wanted to examine it from various perspectives, including IT executives, core ITSM teams, ITSM affiliates beyond the service desk, non-IT executives, and ITSM service consumers. We also looked at obvious differences in terms of size, geography, vertical, organization (central IT versus LOB-affiliated), and even age. For the latter, we contrasted…

  • iGen (up to 4 years in their profession)
  • Millennials (5-10 years)
  • GenX (11-20 years)
  • Boomers (more than 20 years)

I personally was most intrigued by the consistent indications from prior research that ITSM teams really do stretch well beyond the service desk, and that this trend tends to amplify as ITSM teams become more progressive. A list of teams we found among our respondent base (saving the actual numbers for the webinar) includes:

  • Operations
  • App management
  • Development
  • End-user experience
  • Data science
  • Asset management
  • Architecture
  • Consultants
  • PC/mobile management
  • Automation
  • Security
  • IoT

Analytics, AI, and Automation

As some of you may know by now, one of my ongoing areas of focus is analytics, AIOps, and the intersection with AI and machine learning more broadly. Within this space, sad to say, semantic confusion surrounding just what these terms mean echoes the confusions surrounding ITSM.

We asked our respondents for a moment of “AI-free association,” with a wide list of diverse terms to choose from. Spoiler alert, just to let you know now, the top choice was machine learning—which was the most logical single equivalent. The longer list of priorities was yet more telling and more surprising, especially when you link “AI” definitions to IT and non-IT roles.

In addressing analytics and AI, we looked at the following technology initiatives, both in terms of prevalence and priority:

  • AIOps
  • Incident response analytics
  • Governance-related analytics (improving OpEx efficiencies)
  • Asset and cost optimization analysis
  • Big data
  • Analytics specific to business performance (e.g., revenue, business process efficiencies)

Then we mapped these, as well as priorities in automation (a list too long to go into here), to the following use cases:

  • Integrated operations (for superior availability, performance, and change management)
  • Integrated asset management/IT financial planning
  • Self-service capabilities for routine requests and services
  • Enterprise service management (ESM for HR, facilities, etc.)
  • DevOps/agile initiatives
  • Major incident response
  • Integrated security and operations (SecOps)
  • Internet of Things (IoT)

The patterns we saw highlighted a lot of commonalities in terms of priorities for combining analytics and automation, integration needs, benefits, and obstacles. However, we also found some striking differences as we mapped the use case-specific details across a wide range of variables from company size, to level of process and technology sophistication, to success rates, and many others.

If there was one common lesson, it was that those most progressed in use cases were also most progressed in AI, analytics, and automation. Not surprisingly, they were also more willing to let analytic insights and AI drive automation.

Virtual agents, AI bots, ESM, and wrapping up

The first three topics in this header could easily be another blog in themselves, or two blogs, or actually a whole series of blogs, but to echo what I mentioned earlier, the overarching message turned out to be surprising commonality.

Even ESM, which reaches out to enable enterprise service workflows (and we examined how and why in-depth) showed strong synergies with AI/analytics and automation investments, as well as many other factors that turned out to characterize our more progressive groups.

To learn more about how and why, please join Valerie and me on April 11 as we discuss our findings in “Automation, AI, and Analytics: Reinventing ITSM.” In the meantime, I invite you to share your questions, perspectives, areas of interest, and concerns with us at