Skip to main content

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...