Skip to main content

Transforming Operations, and IT as a Whole, with the Right Technology Investments

Dennis Drogseth

In my last blog, I expressed my opinion that IT operations teams may be about to enjoy a renaissance rather than dismally fading away — but only if they adopt new ways of working, measuring themselves and interacting with business stakeholders.

Start with Is IT Operations Going Away or Is It Enjoying a Renaissance?

In this blog, I'd like to discuss how technology investments can help smooth the way toward operational transformation with a few examples from recent interviews. More specifically, I'd like to focus on three key areas of innovation, all in some way related to Advanced IT Analytics (what some in the industry call IT Operations Analytics or ITOA):

1. How Advanced IT Analytics (AIA) can pave the way

2. How next-generation ITSM promotes AIA-relevant process improvements

3. How AIA and service modeling can become a magic combination

These three areas of innovation are, admittedly, far from a complete list. But hopefully they will offer you a provocative place to start for seeking out transformative IT technologies.

How Advanced IT Analytics Can Pave the Way

Probably one of the hottest, most diverse and most misunderstood areas of technological innovation is AIA. There are a lot of reasons for this, but the most significant reason is that AIA is not a single market but a diverse landscape of options ranging from those bordering on traditional "big data" to those that are focused on real-time predictive insights with tiered approaches to data collection that typically include leveraging third-party tools. The variances here also embrace multiple use cases ranging from performance management to change and capacity optimization to financial optimization to integrated security concerns.

Here are just a few highlights from EMA's recent AIA research focusing on performance, change and capacity management:

■ AIA analytic heuristics range from anomaly detection to predictive trending to machine learning, to rule-based analytics to data mining — as just some examples. On average, our respondents wanted at least four different heuristics.

■ On average, respondents wanted their AIA investments to support 11 different roles (and many more stakeholders), including four domain-specific roles, four cross-domain roles (including executive IT), and three non-IT business roles.

■ The top three achieved benefits were with faster time to repair problems, faster time to deliver new services and more efficient use of cloud resources.

Quoting from a conversation earlier this year:

QUOTE #1: "The move [to advanced analytics] allowed us to unify our operations team with a single-pane-of-glass view and drill down so that we could share information more effectively. In the past, we caught only 3% of our problems proactively. Now that percentage went up to 88% … In effect, we are able to see everything we need to see to focus and resolve issues far more cohesively and dynamically than before."

How Next-Generation ITSM Promotes AIA-Relevant Process Improvements

Next-generation ITSM is in my view pivotal for both IT and IT operational transformation. So what are they? Next-generation ITSM teams are more progressively integrated with operations teams, more proactive, more likely to leverage and share analytics, more likely to provide workflows and automation that unify IT as a whole (as well as support enterprise business process needs), and more likely to provide meaningful metrics for IT efficiencies and governance than the more reactive ITSM teams of the past.

Following the AIA data path, we saw that 82% of our respondents indicated strong levels of ITSM/operations integration for shared advanced analytics!

Quoting from two other conversations earlier this year highlighting next-generation ITSM values:

QUOTE #2: "First and foremost we've been able to consolidate our processes for change, incident, and problem management across our entire operation by leveraging one single platform …"

QUOTE #3: "We've also enjoyed improved visibility into the impacts of changes on service performance and availability, so we can more quickly get to the root cause of many of the issues caused by changes and begin to automate fixes more consistently."

How AIA + Service Modeling Can Result in (at least a little) Magic

The AIA research showed that 96% of respondents wanted at least some level of modeled insight on interdependencies across the application infrastructure. Among the top three priorities were application-to-infrastructure, infrastructure-to-infrastructure, and application-to-application (application ecosystem) dependency insights.

Combining analytics with service modeling is a growth area in AIA, as analytics providers are becoming more effective in not only leveraging existing application dependency mapping solutions and even CMDB data, but also in creating their own unique approach to dependency modeling.

In another very recent dialog, I found some rather striking benefits when service modeling and analytics are combined:

QUOTE #4: "We estimate that we will be saving about $500,000 in the area of toolset consolidation … We were averaging 2.5 hours for MTTR … now it's about 38 minutes … You might say we never had eyes before. Now we have eyes."

In Summary

This is, admittedly, only a taste of what I've learned from research and conversations with IT about how technology can help to transform IT operations for the "brave new world" we live in. My focus on AIA, next-generation ITSM and service modeling was deliberate, as I see these as lying at the heart of the "operations renaissance." Also important are requirements for more advanced levels of automation and integrated support for security and operations. Superior digital experience management or end user experience management is also key.

The challenge in the market today is that while all these technologies are interdependent and mutually reinforcing, the innovations are coming from many different vendor sources. And true to form, many of these vendors are seeking to redefine the world around themselves in their marketing and messaging. Sorting through the pieces, and understanding where real value lies, takes time and patience and more than a little sober skepticism. But the innovations are real. And hopefully this blog can give you at least a hint — by category — of where to begin to look for them.

I deliberately kept the quotes anonymous on all fronts. If you'd like to see more information, then please check out our EMA library:

quote 1

quotes 2 and 3

quote 4

Image removed.

The Latest

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

Transforming Operations, and IT as a Whole, with the Right Technology Investments

Dennis Drogseth

In my last blog, I expressed my opinion that IT operations teams may be about to enjoy a renaissance rather than dismally fading away — but only if they adopt new ways of working, measuring themselves and interacting with business stakeholders.

Start with Is IT Operations Going Away or Is It Enjoying a Renaissance?

In this blog, I'd like to discuss how technology investments can help smooth the way toward operational transformation with a few examples from recent interviews. More specifically, I'd like to focus on three key areas of innovation, all in some way related to Advanced IT Analytics (what some in the industry call IT Operations Analytics or ITOA):

1. How Advanced IT Analytics (AIA) can pave the way

2. How next-generation ITSM promotes AIA-relevant process improvements

3. How AIA and service modeling can become a magic combination

These three areas of innovation are, admittedly, far from a complete list. But hopefully they will offer you a provocative place to start for seeking out transformative IT technologies.

How Advanced IT Analytics Can Pave the Way

Probably one of the hottest, most diverse and most misunderstood areas of technological innovation is AIA. There are a lot of reasons for this, but the most significant reason is that AIA is not a single market but a diverse landscape of options ranging from those bordering on traditional "big data" to those that are focused on real-time predictive insights with tiered approaches to data collection that typically include leveraging third-party tools. The variances here also embrace multiple use cases ranging from performance management to change and capacity optimization to financial optimization to integrated security concerns.

Here are just a few highlights from EMA's recent AIA research focusing on performance, change and capacity management:

■ AIA analytic heuristics range from anomaly detection to predictive trending to machine learning, to rule-based analytics to data mining — as just some examples. On average, our respondents wanted at least four different heuristics.

■ On average, respondents wanted their AIA investments to support 11 different roles (and many more stakeholders), including four domain-specific roles, four cross-domain roles (including executive IT), and three non-IT business roles.

■ The top three achieved benefits were with faster time to repair problems, faster time to deliver new services and more efficient use of cloud resources.

Quoting from a conversation earlier this year:

QUOTE #1: "The move [to advanced analytics] allowed us to unify our operations team with a single-pane-of-glass view and drill down so that we could share information more effectively. In the past, we caught only 3% of our problems proactively. Now that percentage went up to 88% … In effect, we are able to see everything we need to see to focus and resolve issues far more cohesively and dynamically than before."

How Next-Generation ITSM Promotes AIA-Relevant Process Improvements

Next-generation ITSM is in my view pivotal for both IT and IT operational transformation. So what are they? Next-generation ITSM teams are more progressively integrated with operations teams, more proactive, more likely to leverage and share analytics, more likely to provide workflows and automation that unify IT as a whole (as well as support enterprise business process needs), and more likely to provide meaningful metrics for IT efficiencies and governance than the more reactive ITSM teams of the past.

Following the AIA data path, we saw that 82% of our respondents indicated strong levels of ITSM/operations integration for shared advanced analytics!

Quoting from two other conversations earlier this year highlighting next-generation ITSM values:

QUOTE #2: "First and foremost we've been able to consolidate our processes for change, incident, and problem management across our entire operation by leveraging one single platform …"

QUOTE #3: "We've also enjoyed improved visibility into the impacts of changes on service performance and availability, so we can more quickly get to the root cause of many of the issues caused by changes and begin to automate fixes more consistently."

How AIA + Service Modeling Can Result in (at least a little) Magic

The AIA research showed that 96% of respondents wanted at least some level of modeled insight on interdependencies across the application infrastructure. Among the top three priorities were application-to-infrastructure, infrastructure-to-infrastructure, and application-to-application (application ecosystem) dependency insights.

Combining analytics with service modeling is a growth area in AIA, as analytics providers are becoming more effective in not only leveraging existing application dependency mapping solutions and even CMDB data, but also in creating their own unique approach to dependency modeling.

In another very recent dialog, I found some rather striking benefits when service modeling and analytics are combined:

QUOTE #4: "We estimate that we will be saving about $500,000 in the area of toolset consolidation … We were averaging 2.5 hours for MTTR … now it's about 38 minutes … You might say we never had eyes before. Now we have eyes."

In Summary

This is, admittedly, only a taste of what I've learned from research and conversations with IT about how technology can help to transform IT operations for the "brave new world" we live in. My focus on AIA, next-generation ITSM and service modeling was deliberate, as I see these as lying at the heart of the "operations renaissance." Also important are requirements for more advanced levels of automation and integrated support for security and operations. Superior digital experience management or end user experience management is also key.

The challenge in the market today is that while all these technologies are interdependent and mutually reinforcing, the innovations are coming from many different vendor sources. And true to form, many of these vendors are seeking to redefine the world around themselves in their marketing and messaging. Sorting through the pieces, and understanding where real value lies, takes time and patience and more than a little sober skepticism. But the innovations are real. And hopefully this blog can give you at least a hint — by category — of where to begin to look for them.

I deliberately kept the quotes anonymous on all fronts. If you'd like to see more information, then please check out our EMA library:

quote 1

quotes 2 and 3

quote 4

Image removed.

The Latest

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...