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Preparing for the Future of ITSM

Sean Sebring
SolarWinds

The global IT skills shortage will persist, and perhaps worsen, over the next few years, carrying a collective price tag of more than $5 trillion. Organizations must search for ways to streamline their IT service management (ITSM) workflows in addition to, or even apart from, hiring more staff. Those who don't find alternative methods of ITSM efficiency will be left behind by their competitors. Fortunately, there are best practices any organization can embrace to empower clients and create a path to a future-proof ITSM framework.

To find these best practices, SolarWinds analyzed over 2,000 ITSM data systems and 60,000 points of anonymized and aggregated customer data. The data found three proven techniques — service level agreements (SLAs), automation rules, and self-service portals — that save time, conserve resources, and lessen the workload on ITSM staff.

1. Service Level Agreements Create Accountability and Direction

The first step to building a mature and future-proof ITSM function is developing SLAs for each type of ticket that comes through the ITSM help desk. This creates a level of accountability the entire team can refer to. Per data in the report, SLAs help resolve tickets an average of 2 hours faster than organizations that don't employ them. If a team can maintain fast and effective ticket resolutions, it helps build customer trust, optimize resources, and lead to greater ROI.

In addition to accountability, SLAs help create benchmarks that ITSM leaders can use to decide whether to increase or decrease the scale of their operations. Teams should develop a regular cadence for checking in on SLA completion rates. If service desks continually meet their SLAs, it may be time to set new goals and benchmarks to measure efficiency. If the opposite is happening, the team can use the percentage of SLAs to potentially scale back operations or pinpoint potential adjustments.

2. Automation Rules Speed up Workflows

The report suggests a relationship between automation and an organization's ability to meet its ITSM SLAs. Data from the report shows that when ITSM desks embed automation rules/artificial intelligence (AI) within their workflows, they have a lower percentage of SLA misses — or the percentage of total tickets the desk could not complete. Teams that use methods such as automated ticket routing, incident identification and categorization, and smart notifications will be able to cut their SLA misses in half while simultaneously expediting their resolution times.

The best way to install automation throughout various workflows is through a comprehensive ITSM platform and an implementation plan that grows as the organization does. The right ITSM platform will allow teams to leverage AI technology to improve service catalog searches, offer service suggestions, and ensure teams don't spend time working on tickets irrelevant to a client's root problem.

3. Self-Service Portals and Knowledge Base Articles Empower Clients

Self-service portals play a vital role in an organization's automation workflow. A complete self-service portal can simultaneously relieve staff workload while allowing clients to partner with ITSM staff as they solve their own issues. According to the report, self-service portals can reduce ticket resolution times by as much as two hours.

Knowledge base (KB) articles provide clients with the information they need for a successful self-service portal experience. To be most effective, KB articles must be both comprehensive and easy to understand. As the client takes charge of resolving their own ticket, the information in each article must also be easy to find. When self-service portals and KB articles work correctly, they result in more free time for customers and ITSM agents to focus on the most important tasks that require a human in the loop.

The Future of ITSM is More About Workflows Than Staff

A popular English proverb from the 1300s states, "Many hands make light work." While conventional wisdom would agree, data from the report found no direct correlation between the number of ITSM staff members and the time it takes to resolve tickets. It should be noted that ITSM leaders will, and should, try to address the IT hiring shortage through non-traditional hiring means — training non-IT staff and implementing skills-based hiring, for example — but that shouldn't be the only priority. It would behoove today's IT decision-makers to focus much of their efforts on the tools and workflows that are proven to make IT teams run smoother and more efficiently. In other words, a future-proof ITSM framework is one that shuns conventional thinking and embraces unconventional practices to adapt alongside modern enterprises. 

Sean Sebring is Solutions Engineering Manager at SolarWinds

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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 ...

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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 ...

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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 ...

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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.

Preparing for the Future of ITSM

Sean Sebring
SolarWinds

The global IT skills shortage will persist, and perhaps worsen, over the next few years, carrying a collective price tag of more than $5 trillion. Organizations must search for ways to streamline their IT service management (ITSM) workflows in addition to, or even apart from, hiring more staff. Those who don't find alternative methods of ITSM efficiency will be left behind by their competitors. Fortunately, there are best practices any organization can embrace to empower clients and create a path to a future-proof ITSM framework.

To find these best practices, SolarWinds analyzed over 2,000 ITSM data systems and 60,000 points of anonymized and aggregated customer data. The data found three proven techniques — service level agreements (SLAs), automation rules, and self-service portals — that save time, conserve resources, and lessen the workload on ITSM staff.

1. Service Level Agreements Create Accountability and Direction

The first step to building a mature and future-proof ITSM function is developing SLAs for each type of ticket that comes through the ITSM help desk. This creates a level of accountability the entire team can refer to. Per data in the report, SLAs help resolve tickets an average of 2 hours faster than organizations that don't employ them. If a team can maintain fast and effective ticket resolutions, it helps build customer trust, optimize resources, and lead to greater ROI.

In addition to accountability, SLAs help create benchmarks that ITSM leaders can use to decide whether to increase or decrease the scale of their operations. Teams should develop a regular cadence for checking in on SLA completion rates. If service desks continually meet their SLAs, it may be time to set new goals and benchmarks to measure efficiency. If the opposite is happening, the team can use the percentage of SLAs to potentially scale back operations or pinpoint potential adjustments.

2. Automation Rules Speed up Workflows

The report suggests a relationship between automation and an organization's ability to meet its ITSM SLAs. Data from the report shows that when ITSM desks embed automation rules/artificial intelligence (AI) within their workflows, they have a lower percentage of SLA misses — or the percentage of total tickets the desk could not complete. Teams that use methods such as automated ticket routing, incident identification and categorization, and smart notifications will be able to cut their SLA misses in half while simultaneously expediting their resolution times.

The best way to install automation throughout various workflows is through a comprehensive ITSM platform and an implementation plan that grows as the organization does. The right ITSM platform will allow teams to leverage AI technology to improve service catalog searches, offer service suggestions, and ensure teams don't spend time working on tickets irrelevant to a client's root problem.

3. Self-Service Portals and Knowledge Base Articles Empower Clients

Self-service portals play a vital role in an organization's automation workflow. A complete self-service portal can simultaneously relieve staff workload while allowing clients to partner with ITSM staff as they solve their own issues. According to the report, self-service portals can reduce ticket resolution times by as much as two hours.

Knowledge base (KB) articles provide clients with the information they need for a successful self-service portal experience. To be most effective, KB articles must be both comprehensive and easy to understand. As the client takes charge of resolving their own ticket, the information in each article must also be easy to find. When self-service portals and KB articles work correctly, they result in more free time for customers and ITSM agents to focus on the most important tasks that require a human in the loop.

The Future of ITSM is More About Workflows Than Staff

A popular English proverb from the 1300s states, "Many hands make light work." While conventional wisdom would agree, data from the report found no direct correlation between the number of ITSM staff members and the time it takes to resolve tickets. It should be noted that ITSM leaders will, and should, try to address the IT hiring shortage through non-traditional hiring means — training non-IT staff and implementing skills-based hiring, for example — but that shouldn't be the only priority. It would behoove today's IT decision-makers to focus much of their efforts on the tools and workflows that are proven to make IT teams run smoother and more efficiently. In other words, a future-proof ITSM framework is one that shuns conventional thinking and embraces unconventional practices to adapt alongside modern enterprises. 

Sean Sebring is Solutions Engineering Manager at SolarWinds

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.