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What is Service Experience and Why Does It Matter? - Part 1

Nancy Van Elsacker Louisnord

What's the difference between user satisfaction and user loyalty? How can you measure whether your users are satisfied and will keep buying from you? How much effort should you make to offer your users the ultimate experience? If you're a service provider, what matters in the end is whether users will keep coming back to you and will stay loyal. We often think that the best way to measure loyalty is through satisfaction figures. After all, a satisfied user will keep coming back, right?

But if you want to accurately predict whether your users will come back, try looking at how much effort users have to put in to do business with you. According to the Service Desk Institute, service experience is something that has more than one meaning based on the potential outcomes. The definition of "customers" changes what the "service experience" is. For example, in enterprise service management, a "customer" is usually another term for "user." A user of services provided. These users are consumers of a service they are neither choosing nor paying for. However, regardless of the need for the service, they all rely on it. They use computers provided by the organization (usually), for example. Thus, they need the service, but they don't choose the machine or model.

In "service management" you're typically in the business of running or making sure services are provided. The service experience then is the experience of that service by users; the experience of the user in regard to the service provided. Simple, but not.
 
Breaking down this a little further, services provided by the organization in the workplace generally are the things people require to do their jobs effectively, as stated in the previous example. The services provided in combination with each other – computer and printer, access card and meeting room, automobile fleet reservation site and car check out – should, for the most part, be seamless. They should work together flawlessly. If they do not, the service provided is in need of, well, service. These service inconsistencies can interrupt the service experience for the user. 

A service desk agent may be able to improve some aspects of the service experience. If the service is not working seamlessly as required then an unequipped service desk agent who cannot fix the problem only serves to make the poor experience worse and more stressful for the user.

A Variety of Service Management Definitions

Research firm Forrester provides three distinct definitions of "service management." The reason for this is that it is a "byproduct of the fact that we all interpret information sources, such as ITIL, differently. For instance, asking 10 different people to define what a ‘service' is will result in nearly as many definitions."

Starting with the ITIL (the ITSM best practice framework)-espoused definition, according to Forrester:

"The implementation and management of quality IT services that meet the needs of the business. IT service management is performed by IT service providers through an appropriate mix of people, process and information technology. See also service management." Where service management is defined as: "A set of specialized organizational capabilities for providing value to customers in the form of services."

Forrester agrees that just delivering IT services via the best practices espoused by ITIL is not enough if the IT infrastructure and operations (I&O) organization is still focused on the creation, rather than the consumption, of IT services. Another scenario or definition is where I&O organizations continue to be supply-centric (focused on costs and volumes) rather than demand-centric (focused on business needs and delivered-business-value) IT services.

A third definition, again according to Forrester, moves ITSM closer to the customer, dropping the "IT" from "ITSM" to talk in terms of "service management." This is provided from the Universal Service Management Body of Knowledge (USMBOK) – a "companion piece" that supplements existing resources such as ITIL on both strategic and operational levels.

"Also termed service management thinking, service management is a systematic method for managing the offering, contracting and provisioning of services to customers, at a known quality, cost and designed experience. Service management ensures the desired results and customer satisfaction levels are achieved cost effectively, and is a means by which the customer experience and interaction with products, services, and the service provider organization is designed and managed. Service management is also a transformation method for any organization that wishes to operate as a service provider organization."

Read What is Service Experience and Why Does It Matter? - Part 2

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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 ... 

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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 ...

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.

What is Service Experience and Why Does It Matter? - Part 1

Nancy Van Elsacker Louisnord

What's the difference between user satisfaction and user loyalty? How can you measure whether your users are satisfied and will keep buying from you? How much effort should you make to offer your users the ultimate experience? If you're a service provider, what matters in the end is whether users will keep coming back to you and will stay loyal. We often think that the best way to measure loyalty is through satisfaction figures. After all, a satisfied user will keep coming back, right?

But if you want to accurately predict whether your users will come back, try looking at how much effort users have to put in to do business with you. According to the Service Desk Institute, service experience is something that has more than one meaning based on the potential outcomes. The definition of "customers" changes what the "service experience" is. For example, in enterprise service management, a "customer" is usually another term for "user." A user of services provided. These users are consumers of a service they are neither choosing nor paying for. However, regardless of the need for the service, they all rely on it. They use computers provided by the organization (usually), for example. Thus, they need the service, but they don't choose the machine or model.

In "service management" you're typically in the business of running or making sure services are provided. The service experience then is the experience of that service by users; the experience of the user in regard to the service provided. Simple, but not.
 
Breaking down this a little further, services provided by the organization in the workplace generally are the things people require to do their jobs effectively, as stated in the previous example. The services provided in combination with each other – computer and printer, access card and meeting room, automobile fleet reservation site and car check out – should, for the most part, be seamless. They should work together flawlessly. If they do not, the service provided is in need of, well, service. These service inconsistencies can interrupt the service experience for the user. 

A service desk agent may be able to improve some aspects of the service experience. If the service is not working seamlessly as required then an unequipped service desk agent who cannot fix the problem only serves to make the poor experience worse and more stressful for the user.

A Variety of Service Management Definitions

Research firm Forrester provides three distinct definitions of "service management." The reason for this is that it is a "byproduct of the fact that we all interpret information sources, such as ITIL, differently. For instance, asking 10 different people to define what a ‘service' is will result in nearly as many definitions."

Starting with the ITIL (the ITSM best practice framework)-espoused definition, according to Forrester:

"The implementation and management of quality IT services that meet the needs of the business. IT service management is performed by IT service providers through an appropriate mix of people, process and information technology. See also service management." Where service management is defined as: "A set of specialized organizational capabilities for providing value to customers in the form of services."

Forrester agrees that just delivering IT services via the best practices espoused by ITIL is not enough if the IT infrastructure and operations (I&O) organization is still focused on the creation, rather than the consumption, of IT services. Another scenario or definition is where I&O organizations continue to be supply-centric (focused on costs and volumes) rather than demand-centric (focused on business needs and delivered-business-value) IT services.

A third definition, again according to Forrester, moves ITSM closer to the customer, dropping the "IT" from "ITSM" to talk in terms of "service management." This is provided from the Universal Service Management Body of Knowledge (USMBOK) – a "companion piece" that supplements existing resources such as ITIL on both strategic and operational levels.

"Also termed service management thinking, service management is a systematic method for managing the offering, contracting and provisioning of services to customers, at a known quality, cost and designed experience. Service management ensures the desired results and customer satisfaction levels are achieved cost effectively, and is a means by which the customer experience and interaction with products, services, and the service provider organization is designed and managed. Service management is also a transformation method for any organization that wishes to operate as a service provider organization."

Read What is Service Experience and Why Does It Matter? - Part 2

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.