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Why IT Consulting Can Be a Vicious Triangle - and 5 Steps to Escape the Pain

Dennis Drogseth

Enterprise Management Associates (EMA) is looking to extend the reach of its consulting practice, and we'll be soliciting your inputs on your priorities. (The URL for participating in our 5-minute survey is at the end of this blog.) But before you do, I'd like to share some of what we've learned from our work in the past.

Lesson 1: Try to avoid the vicious triangle of IT consulting by learning how to stand in the middle

Just about everyone's heard of the Bermuda Triangle. But the IT Consulting Triangle, though arguably far less elusive, is not nearly as well known. This triangle has three clear corners, each of which can generate its own mini hurricanes.

Let's call the first one process consulting or best practices. This can be very valuable, as it can lead to support from best practices ranging from the IT Infrastructure Library (ITIL) to Six Sigma to the IT Balanced Scorecard to fill in the blank.

The second corner of the triangle, which often comes with a premium price, is organizational consulting. This, too, can be of value, especially as IT often needs to reshape itself in the face of shifting business priorities.

And the third corner is systems integration in all its variations — where actual software and other solutions for managing and optimizing IT are selected, configured and deployed. There's no question that this is often essential.

So what's wrong with this picture?

The problem comes when investments are made across all these areas without a common awareness of interdependencies. Organization, process and technology are indeed not separate discussions in IT, but closely interrelated. This is ever more the case given the dynamic options associated with cloud and the pressures for agile and digital transformation. Investing in advice in each of these areas can be essential. But doing so without common oversight of how they interrelate can lead to a lot of expensive wheel-spinning and sometimes destructive decisions that contradict each other.

Lesson 2: Embrace the need for documenting what's true and what's not

My favorite example here, and one I frequently cite, is a case where EMA required 20 stakeholder interviews in support of a strategic, cross-domain technology initiative. At first the CIO tried to dismiss this. "I've sent out an email," he said. But we insisted and did the interviews. Afterwards that same CIO not only accepted the value of what he'd learned, but wanted us to do 20 more.

The lesson here is that what's really going on within anything more than a mom-pop IT organization in terms of priorities, issues, favored toolsets, and processes (or lack of them) is often full of surprises. And it's rarely consistent across stakeholders and roles. Building a strategy to support all of operations, or all of ITSM, or all of IT (how often do development, security and operations see eye to eye?) requires understanding the human dimensions of what's going on, as well as the technology deficits that are keeping you from going forward.

Lesson 3: Find your true maturity level(s)

I put this in the plural because your IT maturity level can vary across organizations within IT, sometimes in surprising ways. For instance, I once interviewed a development team that pushed a configuration management system with associated automation into development using SCRUM, because development, not operations, was too siloed. Finding out which IT teams relevant to your initiative are ready to fly and which aren't is one of the key ingredients to success. And of course, doing this, depends in large part on honoring Lesson 2.

Lesson 4: Only invest in generic technology winners if your IT organization is also generic

Adopting the right technologies, especially when it comes to managing and optimizing IT business services, is rarely a simple, linear scorecard decision. Generic "winners" are only right for generic IT organizations. But then, happily, I've never encountered a generic IT organization or a generic IT professional for that matter.

Try to find what fits your environment, your skill sets, and your unique needs — which isn't always necessarily what just scored the highest on "Dancing with the Stars."

Lesson 5: Invest in a staged approach to a strategic initiative, both in selecting your technologies, and in integrating them into your environment

EMA, and I'm sure we're not alone, has a ladder with clearly defined steps for going forward with major strategic initiatives — one that can apply to everything from operational and even digital transformation, to analytics, to DevOps, to ITSM-centric initiatives in service modeling and dependency mapping. But whatever staged approach you take, be sure to include dialog, process, technology, communication (a lot of communication!) and listening (a lot of that as well) as you go forward and evolve. Strategic change is not likely to make everyone happy. But needless alienation can not only cause individual pain, it can bring down the effectiveness of the entire organization — leaving digital transformation up to Penn & Teller and not up to you.

I'd like to practice what I just preached and learn from you!

To participate in our 5-minute survey just click here.

Image removed.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Why IT Consulting Can Be a Vicious Triangle - and 5 Steps to Escape the Pain

Dennis Drogseth

Enterprise Management Associates (EMA) is looking to extend the reach of its consulting practice, and we'll be soliciting your inputs on your priorities. (The URL for participating in our 5-minute survey is at the end of this blog.) But before you do, I'd like to share some of what we've learned from our work in the past.

Lesson 1: Try to avoid the vicious triangle of IT consulting by learning how to stand in the middle

Just about everyone's heard of the Bermuda Triangle. But the IT Consulting Triangle, though arguably far less elusive, is not nearly as well known. This triangle has three clear corners, each of which can generate its own mini hurricanes.

Let's call the first one process consulting or best practices. This can be very valuable, as it can lead to support from best practices ranging from the IT Infrastructure Library (ITIL) to Six Sigma to the IT Balanced Scorecard to fill in the blank.

The second corner of the triangle, which often comes with a premium price, is organizational consulting. This, too, can be of value, especially as IT often needs to reshape itself in the face of shifting business priorities.

And the third corner is systems integration in all its variations — where actual software and other solutions for managing and optimizing IT are selected, configured and deployed. There's no question that this is often essential.

So what's wrong with this picture?

The problem comes when investments are made across all these areas without a common awareness of interdependencies. Organization, process and technology are indeed not separate discussions in IT, but closely interrelated. This is ever more the case given the dynamic options associated with cloud and the pressures for agile and digital transformation. Investing in advice in each of these areas can be essential. But doing so without common oversight of how they interrelate can lead to a lot of expensive wheel-spinning and sometimes destructive decisions that contradict each other.

Lesson 2: Embrace the need for documenting what's true and what's not

My favorite example here, and one I frequently cite, is a case where EMA required 20 stakeholder interviews in support of a strategic, cross-domain technology initiative. At first the CIO tried to dismiss this. "I've sent out an email," he said. But we insisted and did the interviews. Afterwards that same CIO not only accepted the value of what he'd learned, but wanted us to do 20 more.

The lesson here is that what's really going on within anything more than a mom-pop IT organization in terms of priorities, issues, favored toolsets, and processes (or lack of them) is often full of surprises. And it's rarely consistent across stakeholders and roles. Building a strategy to support all of operations, or all of ITSM, or all of IT (how often do development, security and operations see eye to eye?) requires understanding the human dimensions of what's going on, as well as the technology deficits that are keeping you from going forward.

Lesson 3: Find your true maturity level(s)

I put this in the plural because your IT maturity level can vary across organizations within IT, sometimes in surprising ways. For instance, I once interviewed a development team that pushed a configuration management system with associated automation into development using SCRUM, because development, not operations, was too siloed. Finding out which IT teams relevant to your initiative are ready to fly and which aren't is one of the key ingredients to success. And of course, doing this, depends in large part on honoring Lesson 2.

Lesson 4: Only invest in generic technology winners if your IT organization is also generic

Adopting the right technologies, especially when it comes to managing and optimizing IT business services, is rarely a simple, linear scorecard decision. Generic "winners" are only right for generic IT organizations. But then, happily, I've never encountered a generic IT organization or a generic IT professional for that matter.

Try to find what fits your environment, your skill sets, and your unique needs — which isn't always necessarily what just scored the highest on "Dancing with the Stars."

Lesson 5: Invest in a staged approach to a strategic initiative, both in selecting your technologies, and in integrating them into your environment

EMA, and I'm sure we're not alone, has a ladder with clearly defined steps for going forward with major strategic initiatives — one that can apply to everything from operational and even digital transformation, to analytics, to DevOps, to ITSM-centric initiatives in service modeling and dependency mapping. But whatever staged approach you take, be sure to include dialog, process, technology, communication (a lot of communication!) and listening (a lot of that as well) as you go forward and evolve. Strategic change is not likely to make everyone happy. But needless alienation can not only cause individual pain, it can bring down the effectiveness of the entire organization — leaving digital transformation up to Penn & Teller and not up to you.

I'd like to practice what I just preached and learn from you!

To participate in our 5-minute survey just click here.

Image removed.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.