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.
Analytics, AI and Automation
So 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. But 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.
■ 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. But 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, among many others.
If there was one common lesson, it was that those most progressed in use cases, were also most progressed in AI and analytics and most progressed in automation. Not surprisingly, they were also more willing to let automation be driven by analytic insights and AI.
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 ...
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