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The Magnificent Seven ITSM 2.0 Challenges

Dennis Drogseth

This is my second blog targeting the next generation of IT service management, or ITSM 2.0. The first blog described the characteristics I see as defining ITSM 2.0. Here we’ll look more closely at the key challenges you might face in getting there from a more traditional ITSM background.

First of all, given this blog’s headline, you may well ask if challenges in themselves can be “magnificent.” I would argue that once challenges are viewed as a means for overcoming barriers, the answer is yes.

Here are the seven challenges I’ll be discussing specific to ITSM transformation:

1. Organizational, political, and leadership issues

2. Issues surrounding dialog across IT and between IT and business stakeholders

3. The need for enhanced levels of automation and more effectively defined processes

4. The challenges surrounding the move to cloud

5. The growing requirement to support mobile end users

6. The challenges surrounding fragmented technologies, fragmented data, and toolset complexity

7. The need to integrate a wide variety of cost, governance, and value-related metrics across all of IT

1. Organizational, political, and leadership issues

In almost all my research, whether it’s on digital and IT transformation or more specific ITSM-related initiatives, this challenge stands out as number one. It’s often identified as the single toughest challenge of them all. But the best way to approach it is by establishing a baseline for your organization — not through some linear grading system, but by talking and listening to key stakeholders about these and other issues as they perceive them. Moreover, addressing the other six challenges discussed here can go a long way toward helping you overcome challenge number one.

2. Issues surrounding dialog across IT and between IT and business stakeholders

If there’s indeed a magic bullet for addressing organizational and political issues, it’s promoting a more effective community within the ITSM team, and across IT, through enhanced communication and dialog. Here technology really can come into play, through social IT and chat groups that include ITSM teams, their customers, and IT stakeholders more broadly. Communication can also be improved through better process workflows and automation (see Challenge #3). Finally, good shared data and enhanced dashboards and visualization (see Challenge #6) can go a long way toward building better IT communities overall, with far less finger-pointing and more well-directed consensus building.

3. The need for enhanced levels of automation and more effectively defined processes

Communication is not just about talking, in person or online, although good dialog in all its forms is still key. Good communication is also about effectively sharing information and promoting better means of collaboration. Here well-designed workflows (that ideally don’t require scripting) can be evolved to support and help define a wide variety of process interactions. In parallel, ITSM automation can free up time lost to repetitious, and often isolating, tasks — such as configuration changes, patch updates, catalog-driven service provisioning, and, in some cases, triage and remediation in conjunction with Operations.

4. The challenges surrounding the move to cloud

An entire blog, an entire book, and an entire IT curriculum could be (and have been) written about challenge number four. From an ITSM perspective, cloud is not something you can or should run away from. It can be empowering, just as it can place new demands on you. The chief challenges include the need for superior approaches to security and compliance with more dynamic awareness of everything from software licenses to IT infrastructure to the Ts and Cs of managing cloud service providers. Cloud also requires approaching options for service delivery differently, with enhanced awareness of cost and relevance to business consumers. As we’ve seen in multiple research analyses, ITSM teams that are willing and able to stand in the middle of the challenge of optimizing cloud invariably fare better than those that aren’t.

5. The growing requirement to support mobile end users

My prior blog introduced some of the requirements for endpoint management overall. Mobile shares in these requirements, which include security, optimizing endpoint value across laptops and mobile, understanding and assuring effective service delivery to end users, and enabling more effective visualization capabilities that empower end users, and especially mobile service consumers, to be fully productive in their roles and responsibilities, including in interacting with IT.

6. The challenges surrounding fragmented technologies, fragmented data, and toolset complexity

While each of the three items here, fragmented technologies, fragmented data, and toolset complexity are unique problems in and of themselves, they are also closely interrelated. This challenge is of course not limited to ITSM teams, but one that reaches across all of operations and all of development. While there is no magic bullet here (indeed none of these obstacles can be overcome in a single long weekend), investing in technologies that promote assimilation of multiple data sources and do so with an eye to superior data integrity, visualization, time to value, and relevance can offer you a big step forward.

7. The need to integrate a wide variety of cost, governance, and value-related metrics across all of IT

ITSM 2.0 teams are playing a greater role in governance and planning across all of IT. This requires a willingness to go beyond the usual silos when looking at costs—from IT asset management and software asset management, to operational efficiency and governance metrics, to portfolio planning, to analytics that can support if/then insights, to costs and efficiencies associated with cloud adoption. Doing all this cohesively is easier said than done, especially when there is no defined industry market that reflects this landscape of critically interrelated components. But it is at the heart of ITSM 2.0.

Dennis Drogseth is VP at Enterprise Management Associates (EMA).

Image removed.

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The Magnificent Seven ITSM 2.0 Challenges

Dennis Drogseth

This is my second blog targeting the next generation of IT service management, or ITSM 2.0. The first blog described the characteristics I see as defining ITSM 2.0. Here we’ll look more closely at the key challenges you might face in getting there from a more traditional ITSM background.

First of all, given this blog’s headline, you may well ask if challenges in themselves can be “magnificent.” I would argue that once challenges are viewed as a means for overcoming barriers, the answer is yes.

Here are the seven challenges I’ll be discussing specific to ITSM transformation:

1. Organizational, political, and leadership issues

2. Issues surrounding dialog across IT and between IT and business stakeholders

3. The need for enhanced levels of automation and more effectively defined processes

4. The challenges surrounding the move to cloud

5. The growing requirement to support mobile end users

6. The challenges surrounding fragmented technologies, fragmented data, and toolset complexity

7. The need to integrate a wide variety of cost, governance, and value-related metrics across all of IT

1. Organizational, political, and leadership issues

In almost all my research, whether it’s on digital and IT transformation or more specific ITSM-related initiatives, this challenge stands out as number one. It’s often identified as the single toughest challenge of them all. But the best way to approach it is by establishing a baseline for your organization — not through some linear grading system, but by talking and listening to key stakeholders about these and other issues as they perceive them. Moreover, addressing the other six challenges discussed here can go a long way toward helping you overcome challenge number one.

2. Issues surrounding dialog across IT and between IT and business stakeholders

If there’s indeed a magic bullet for addressing organizational and political issues, it’s promoting a more effective community within the ITSM team, and across IT, through enhanced communication and dialog. Here technology really can come into play, through social IT and chat groups that include ITSM teams, their customers, and IT stakeholders more broadly. Communication can also be improved through better process workflows and automation (see Challenge #3). Finally, good shared data and enhanced dashboards and visualization (see Challenge #6) can go a long way toward building better IT communities overall, with far less finger-pointing and more well-directed consensus building.

3. The need for enhanced levels of automation and more effectively defined processes

Communication is not just about talking, in person or online, although good dialog in all its forms is still key. Good communication is also about effectively sharing information and promoting better means of collaboration. Here well-designed workflows (that ideally don’t require scripting) can be evolved to support and help define a wide variety of process interactions. In parallel, ITSM automation can free up time lost to repetitious, and often isolating, tasks — such as configuration changes, patch updates, catalog-driven service provisioning, and, in some cases, triage and remediation in conjunction with Operations.

4. The challenges surrounding the move to cloud

An entire blog, an entire book, and an entire IT curriculum could be (and have been) written about challenge number four. From an ITSM perspective, cloud is not something you can or should run away from. It can be empowering, just as it can place new demands on you. The chief challenges include the need for superior approaches to security and compliance with more dynamic awareness of everything from software licenses to IT infrastructure to the Ts and Cs of managing cloud service providers. Cloud also requires approaching options for service delivery differently, with enhanced awareness of cost and relevance to business consumers. As we’ve seen in multiple research analyses, ITSM teams that are willing and able to stand in the middle of the challenge of optimizing cloud invariably fare better than those that aren’t.

5. The growing requirement to support mobile end users

My prior blog introduced some of the requirements for endpoint management overall. Mobile shares in these requirements, which include security, optimizing endpoint value across laptops and mobile, understanding and assuring effective service delivery to end users, and enabling more effective visualization capabilities that empower end users, and especially mobile service consumers, to be fully productive in their roles and responsibilities, including in interacting with IT.

6. The challenges surrounding fragmented technologies, fragmented data, and toolset complexity

While each of the three items here, fragmented technologies, fragmented data, and toolset complexity are unique problems in and of themselves, they are also closely interrelated. This challenge is of course not limited to ITSM teams, but one that reaches across all of operations and all of development. While there is no magic bullet here (indeed none of these obstacles can be overcome in a single long weekend), investing in technologies that promote assimilation of multiple data sources and do so with an eye to superior data integrity, visualization, time to value, and relevance can offer you a big step forward.

7. The need to integrate a wide variety of cost, governance, and value-related metrics across all of IT

ITSM 2.0 teams are playing a greater role in governance and planning across all of IT. This requires a willingness to go beyond the usual silos when looking at costs—from IT asset management and software asset management, to operational efficiency and governance metrics, to portfolio planning, to analytics that can support if/then insights, to costs and efficiencies associated with cloud adoption. Doing all this cohesively is easier said than done, especially when there is no defined industry market that reflects this landscape of critically interrelated components. But it is at the heart of ITSM 2.0.

Dennis Drogseth is VP at Enterprise Management Associates (EMA).

Image removed.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...