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Introducing ITSM 2.0: A Cornerstone for Digital and IT Transformation

Dennis Drogseth

Over the course of numerous deployment dialogs and multiple research projects starting with last year’s work on ITSM futures, I have been tracking a still largely unheralded phenomenon: ITSM teams in many organizations are evolving to take a leadership role in helping all of IT become more efficient, more business aligned, and ever more relevant to business outcomes. Indeed, an ITSM 2.0 is emerging that’s radically different from its inherited, reactive past in ways that are sometimes predictable but more often surprising.

ITSM 1.0

OK, let’s start with what still seems to be the industry’s most common caricature of the “reactive service desk.” ITSM is potentially a great deal more than this, but frayed nerves on both sides of the IT/service-consumer divide have hardened suspicions, frustrations, and hands-up-in-the-air impatience levels with service desk operations.

When we first looked at progressive versus reactive ITSM in our ITSM futures research, we saw that those ITSM teams that were struggling the most had a number of predictable characteristics. These included:

■ Failure of credibility in supporting business requirements, which was directly correlated with being outsourced, as well as with losing staffing and other resources to Operations.

■ Inability to grapple with emerging (and in some cases, already well-established) requirements in adapting to cloud, agile process requirements, mobile, and endpoint awareness overall.

■ Failure to invest in more strategic and potentially transformative technologies ranging from classic ITSM investments, such as configuration management databases (CMDBs) and service catalogs, to broader shared investments, such as analytics, application discovery and dependency mapping (ADDM), and more advanced levels of automation for diagnostics and managing change.

■ Similar failure to invest in best practices, including, but in no way limited to, the IT Infrastructure Library (ITIL).

ITSM 2.0

The first thing to do here is take the ITSM 1.0 list and turn it on its head. We see then that, just for starters, ITSM 2.0 is:

■ More effective in supporting business requirements, and hence more likely to experience greater investment in terms of staffing and other resources.

■ More likely to play a role in shaping and optimizing IT operations efficiencies by helping to promote far more effective cross-silo (network/systems/applications) interaction and dialog.

■ Far more likely to participate in cloud, mobile, and even agile/DevOps initiatives.

■ Far more invested in strategic technologies ranging from CMDBs, service catalogs, and automation to even more advanced and shared levels of analytics, with often dramatic improvements in endpoint optimization for mobile and non-mobile devices, including lifecycle management and more effective customer/consumer experience.

■ Far more likely to address security requirements ranging from proactive support for incident and problem management (often through integrated technologies shared with operations), to endpoint compliance in patch and configuration management, to change management more broadly across the infrastructure.

■ Far more likely to play a role in promoting process efficiencies with best practices across all of IT.

In addition to this, ITSM 2.0 is beginning to take a growing role in supporting enterprise process efficiencies (for facilities, HR, etc.), as well as both Green IT and its successor, the Internet of Things (IoT).

Two Key ITSM 2.0 Differentiators: Integrated IT Operations and Endpoint Optimization

While each of these areas of differentiation deserves a more extensive discussion, in this blog I’d like to highlight two: integrated IT operations and endpoint optimization.

Integrated IT Operations

Bringing IT operations together with ITSM is one of the most poorly documented and yet most critical areas of advancement in the industry.

Here are some of the attributes of integrated IT operations that stand out in ongoing research and dialogs:

■ Sharing data for a far more integrated approach to availability and performance management, as combined with incident and problem management – This data can include event and time-series data, more advanced analytics including support for security, service modeling (CMDB, ADDM), shared knowledgebase access, and a growing role for social media and business data. Common mobile access can make this sharing of information even more compelling.

■ Sharing data for change management, and even agile or DevOps needs – This often requires increased insight into service modeling and automation in particular.

■ Improved workflow automation across IT operations and ITSM teams – As I mentioned, in many conversations I’m finding that it’s ITSM that is becoming the creative force in breaking through operations silos.

■ Project management governance.

■ Documented OpEx efficiencies to help IT operations and ITSM continue to improve in how they work, both collectively and individually.

■ Far more effective user experience management that places all the resources of ITSM teams and IT operations together on a common footing.

Endpoint Optimization

EMA is just concluding research on “Optimizing IT for Financial Performance.” And in that research ITSM once again plays a central role. Given the ascendant requirements to support mobile stakeholders, optimizing endpoints in terms of cost and value is a leading feature of ITSM 2.0. The top prioritized functional areas were the following:

■ Security

■ Software usage

■ License management

■ Software distribution

■ Power management

■ Hardware lifecycle management

■ Endpoint hardware usage

Endpoint optimization can also be greatly enhanced through service catalogs and app stores that integrate cost, SLAs, and usage insights into how end consumers access IT services.

In Conclusion

By implication at least, I hope you can see why I view ITSM 2.0 as a cornerstone of both IT and digital transformation, as it can be a unifier for IT, as well as for IT-to-business efficiencies and relevance. This unification stretches across process, data, technology, and dialog, with ITSM teams often forming a hub for all of these factors to come together.

But this isn’t actually the end of my discussion on ITSM 2.0. I’ll be following up with one more blog: ITSM 2.0 challenges. So stay tuned for more.

Image removed.

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Introducing ITSM 2.0: A Cornerstone for Digital and IT Transformation

Dennis Drogseth

Over the course of numerous deployment dialogs and multiple research projects starting with last year’s work on ITSM futures, I have been tracking a still largely unheralded phenomenon: ITSM teams in many organizations are evolving to take a leadership role in helping all of IT become more efficient, more business aligned, and ever more relevant to business outcomes. Indeed, an ITSM 2.0 is emerging that’s radically different from its inherited, reactive past in ways that are sometimes predictable but more often surprising.

ITSM 1.0

OK, let’s start with what still seems to be the industry’s most common caricature of the “reactive service desk.” ITSM is potentially a great deal more than this, but frayed nerves on both sides of the IT/service-consumer divide have hardened suspicions, frustrations, and hands-up-in-the-air impatience levels with service desk operations.

When we first looked at progressive versus reactive ITSM in our ITSM futures research, we saw that those ITSM teams that were struggling the most had a number of predictable characteristics. These included:

■ Failure of credibility in supporting business requirements, which was directly correlated with being outsourced, as well as with losing staffing and other resources to Operations.

■ Inability to grapple with emerging (and in some cases, already well-established) requirements in adapting to cloud, agile process requirements, mobile, and endpoint awareness overall.

■ Failure to invest in more strategic and potentially transformative technologies ranging from classic ITSM investments, such as configuration management databases (CMDBs) and service catalogs, to broader shared investments, such as analytics, application discovery and dependency mapping (ADDM), and more advanced levels of automation for diagnostics and managing change.

■ Similar failure to invest in best practices, including, but in no way limited to, the IT Infrastructure Library (ITIL).

ITSM 2.0

The first thing to do here is take the ITSM 1.0 list and turn it on its head. We see then that, just for starters, ITSM 2.0 is:

■ More effective in supporting business requirements, and hence more likely to experience greater investment in terms of staffing and other resources.

■ More likely to play a role in shaping and optimizing IT operations efficiencies by helping to promote far more effective cross-silo (network/systems/applications) interaction and dialog.

■ Far more likely to participate in cloud, mobile, and even agile/DevOps initiatives.

■ Far more invested in strategic technologies ranging from CMDBs, service catalogs, and automation to even more advanced and shared levels of analytics, with often dramatic improvements in endpoint optimization for mobile and non-mobile devices, including lifecycle management and more effective customer/consumer experience.

■ Far more likely to address security requirements ranging from proactive support for incident and problem management (often through integrated technologies shared with operations), to endpoint compliance in patch and configuration management, to change management more broadly across the infrastructure.

■ Far more likely to play a role in promoting process efficiencies with best practices across all of IT.

In addition to this, ITSM 2.0 is beginning to take a growing role in supporting enterprise process efficiencies (for facilities, HR, etc.), as well as both Green IT and its successor, the Internet of Things (IoT).

Two Key ITSM 2.0 Differentiators: Integrated IT Operations and Endpoint Optimization

While each of these areas of differentiation deserves a more extensive discussion, in this blog I’d like to highlight two: integrated IT operations and endpoint optimization.

Integrated IT Operations

Bringing IT operations together with ITSM is one of the most poorly documented and yet most critical areas of advancement in the industry.

Here are some of the attributes of integrated IT operations that stand out in ongoing research and dialogs:

■ Sharing data for a far more integrated approach to availability and performance management, as combined with incident and problem management – This data can include event and time-series data, more advanced analytics including support for security, service modeling (CMDB, ADDM), shared knowledgebase access, and a growing role for social media and business data. Common mobile access can make this sharing of information even more compelling.

■ Sharing data for change management, and even agile or DevOps needs – This often requires increased insight into service modeling and automation in particular.

■ Improved workflow automation across IT operations and ITSM teams – As I mentioned, in many conversations I’m finding that it’s ITSM that is becoming the creative force in breaking through operations silos.

■ Project management governance.

■ Documented OpEx efficiencies to help IT operations and ITSM continue to improve in how they work, both collectively and individually.

■ Far more effective user experience management that places all the resources of ITSM teams and IT operations together on a common footing.

Endpoint Optimization

EMA is just concluding research on “Optimizing IT for Financial Performance.” And in that research ITSM once again plays a central role. Given the ascendant requirements to support mobile stakeholders, optimizing endpoints in terms of cost and value is a leading feature of ITSM 2.0. The top prioritized functional areas were the following:

■ Security

■ Software usage

■ License management

■ Software distribution

■ Power management

■ Hardware lifecycle management

■ Endpoint hardware usage

Endpoint optimization can also be greatly enhanced through service catalogs and app stores that integrate cost, SLAs, and usage insights into how end consumers access IT services.

In Conclusion

By implication at least, I hope you can see why I view ITSM 2.0 as a cornerstone of both IT and digital transformation, as it can be a unifier for IT, as well as for IT-to-business efficiencies and relevance. This unification stretches across process, data, technology, and dialog, with ITSM teams often forming a hub for all of these factors to come together.

But this isn’t actually the end of my discussion on ITSM 2.0. I’ll be following up with one more blog: ITSM 2.0 challenges. So stay tuned for more.

Image removed.

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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.