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Reinventing ITSM? It's Not Going Away - Part 2

Dennis Drogseth

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.

Start with Reinventing ITSM? It's Not Going Away - Part 1

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.

■ 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. 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.

Read Reinventing ITSM? It's Not Going Away - Part 3

In the meantime, I invite you to share your questions, perspectives, areas of interest, and concerns with us ...

Click here to email Dennis Drogseth with your comments

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Reinventing ITSM? It's Not Going Away - Part 2

Dennis Drogseth

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.

Start with Reinventing ITSM? It's Not Going Away - Part 1

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.

■ 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. 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.

Read Reinventing ITSM? It's Not Going Away - Part 3

In the meantime, I invite you to share your questions, perspectives, areas of interest, and concerns with us ...

Click here to email Dennis Drogseth with your comments

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...