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IT Leadership: Measuring Employee Experience in a Post-COVID World

Nik Koutsoukos

I'm an old-school advocate for employee experience. For years, my colleagues and I have urged (and cajoled, and begged) business leaders to take the subject seriously. We've argued that engaged and well-supported workers are the cornerstone of business success — and that nothing kills productivity quicker than when people can't rely on the tools they need to do their jobs. That message has been getting through. It's been gratifying to see more and more C-suites prioritize measuring employee experience in recent years. And then … COVID-19 hit.

You can understand why many employee experience initiatives got put on the back burner. It's hard enough just to keep a business running during a pandemic. But when most of your workforce suddenly shifts to work-from-home, understanding employee experience becomes more important, not less. Not to mention that, for many businesses, large portions of the workforce will continue working remotely long after the COVID-19 crisis subsides.

Bottom line, "work" means something very different than it did a year ago. If we're going to give people the support they need to thrive in this new normal, we need to rethink employee experience: what we measure, how we measure it, and what we can ultimately do about it.

Accelerating Longstanding Trends

For many of the changes we've seen — like huge growth in remote work and digital collaboration — COVID-19 didn't so much create new models as kick pre-existing trends into high gear. The fact is, work has been growing more decentralized, and IT has been steadily losing control of business infrastructure, for years. Just look at the major IT trends of the last decade:

■ Core business applications moving from on-premises data centers to cloud-based software-as-a-service (SaaS)

■ Dominant connectivity models shifting from Ethernet to Wi-Fi

■ Explosive growth in mobile and remote work, often using devices outside IT control

Even if you care about deeply about employee experience, it's much harder to measure than it was back when everybody worked in the office, and all applications and networks and devices were controlled by IT. If employees are having issues, just figuring out where to look becomes much tougher. The challenge grows exponentially when most of your workforce shifts to full-time work-from-home, practically overnight.

At this point, businesses need to put aside the deep metrics about application performance and start from ground zero: Can my remote employees actually do their jobs? How are they feeling? Do they have the bare minimum they need to be productive?

Measuring the Right Things

If you're in human resources — or really, any executive leadership role in your organization — it's important to step back and look at employees holistically. We advocate measuring well-being across five broad categories:

The basics: When companies are scrambling to react to an unexpected crisis, it's easy to get caught by surprise. All of a sudden, you need to ask different kinds of questions:
- Do my employees have the equipment they need?
- Not just a working laptop, but a good monitor and keyboard?
- A comfortable chair?
- A place they can work that's free from noise and distractions?
- A fast and reliable network connection that can support video conferencing?

These aren't the kinds of things most companies have had to worry about before. But they're essential to employee well-being, so they need to be on your radar.

User experience: Assuring a quality digital experience is relatively easy when everyone works in the corporate office. But if COVID-19 has taught us anything, it's that we need our employees to be ready to work from anywhere, anytime. You're better off thinking proactively about assuring digital experience, rather than waiting to react to the next big disruption.

What are the core applications that each employee needs to do his or her job? Can they access those tools from home and use them as effectively as in the office? You'll need more granular monitoring tools to find out. It's not enough to know, for example, that a home user can connect to the Internet. Can they access all the apps they need reliably all day long? If not, why?

Connectivity: Traditionally, most businesses treated remote work connectivity as a "best-effort" scenario. When you're remote, you do the best you can. If you need business-class connectivity — guaranteed bandwidth under a service-level agreement (SLA), managed Wi-Fi, state-of-the-art performance monitoring — you come into the office. That's no longer an option for millions of workers.

So, it's more important than ever for IT to be able to look deeply into every user's connection and troubleshoot problems all the way into home networks. For many connectivity issues, the problem lies with the user's home network or local Internet service provider (ISP).

But, even if you can't fix the problem, you can tell users what's wrong and what they can do about it. Sometimes, that might be, "Your ISP is having issues and it could be a while. Focus on offline work for now" That's a lot better than hours of frustrated users and wasted IT effort trying to diagnose a problem they can't solve.

Device: Along those lines, it's more important than ever to understand what's happening on the employee's device. For many problems, the cause is just a lack of memory or CPU power in older hardware. The good news is that those are among the easiest problems for IT to solve. But, if you're not monitoring all the way to the device, it will take a lot longer to zero in on that.

Applications and services: Businesses need to be able to look outward when measuring employee experience, as well as inward. Yes, you need to know how employees' devices and network connections are performing. But, you should also be keeping tabs on your SaaS applications and cloud services. If, for example, Microsoft Office 365 is having slow performance across the southwestern United States, it sure would be useful for IT to know that before they spend hours trying to diagnose the problem.

Eliminating the Guesswork

The truth is, the list of issues outside IT's control keeps getting longer. But, that makes visibility even more important. When you have hard data about what your people are actually experiencing, you can:

■ Hold your SaaS and Internet providers accountable

■ Empower employees (and reduce their frustration) by quickly diagnosing problems in their own devices and home networks that they can fix themselves

■ Quickly identify problems beyond IT's control, so they can focus their time on tasks where they can make a real difference

We're all still adjusting to the challenges of a post-COVID world. But, when your business relies on a remote workforce every day, just knowing what's happening out there can be hugely beneficial.

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 ...

IT Leadership: Measuring Employee Experience in a Post-COVID World

Nik Koutsoukos

I'm an old-school advocate for employee experience. For years, my colleagues and I have urged (and cajoled, and begged) business leaders to take the subject seriously. We've argued that engaged and well-supported workers are the cornerstone of business success — and that nothing kills productivity quicker than when people can't rely on the tools they need to do their jobs. That message has been getting through. It's been gratifying to see more and more C-suites prioritize measuring employee experience in recent years. And then … COVID-19 hit.

You can understand why many employee experience initiatives got put on the back burner. It's hard enough just to keep a business running during a pandemic. But when most of your workforce suddenly shifts to work-from-home, understanding employee experience becomes more important, not less. Not to mention that, for many businesses, large portions of the workforce will continue working remotely long after the COVID-19 crisis subsides.

Bottom line, "work" means something very different than it did a year ago. If we're going to give people the support they need to thrive in this new normal, we need to rethink employee experience: what we measure, how we measure it, and what we can ultimately do about it.

Accelerating Longstanding Trends

For many of the changes we've seen — like huge growth in remote work and digital collaboration — COVID-19 didn't so much create new models as kick pre-existing trends into high gear. The fact is, work has been growing more decentralized, and IT has been steadily losing control of business infrastructure, for years. Just look at the major IT trends of the last decade:

■ Core business applications moving from on-premises data centers to cloud-based software-as-a-service (SaaS)

■ Dominant connectivity models shifting from Ethernet to Wi-Fi

■ Explosive growth in mobile and remote work, often using devices outside IT control

Even if you care about deeply about employee experience, it's much harder to measure than it was back when everybody worked in the office, and all applications and networks and devices were controlled by IT. If employees are having issues, just figuring out where to look becomes much tougher. The challenge grows exponentially when most of your workforce shifts to full-time work-from-home, practically overnight.

At this point, businesses need to put aside the deep metrics about application performance and start from ground zero: Can my remote employees actually do their jobs? How are they feeling? Do they have the bare minimum they need to be productive?

Measuring the Right Things

If you're in human resources — or really, any executive leadership role in your organization — it's important to step back and look at employees holistically. We advocate measuring well-being across five broad categories:

The basics: When companies are scrambling to react to an unexpected crisis, it's easy to get caught by surprise. All of a sudden, you need to ask different kinds of questions:
- Do my employees have the equipment they need?
- Not just a working laptop, but a good monitor and keyboard?
- A comfortable chair?
- A place they can work that's free from noise and distractions?
- A fast and reliable network connection that can support video conferencing?

These aren't the kinds of things most companies have had to worry about before. But they're essential to employee well-being, so they need to be on your radar.

User experience: Assuring a quality digital experience is relatively easy when everyone works in the corporate office. But if COVID-19 has taught us anything, it's that we need our employees to be ready to work from anywhere, anytime. You're better off thinking proactively about assuring digital experience, rather than waiting to react to the next big disruption.

What are the core applications that each employee needs to do his or her job? Can they access those tools from home and use them as effectively as in the office? You'll need more granular monitoring tools to find out. It's not enough to know, for example, that a home user can connect to the Internet. Can they access all the apps they need reliably all day long? If not, why?

Connectivity: Traditionally, most businesses treated remote work connectivity as a "best-effort" scenario. When you're remote, you do the best you can. If you need business-class connectivity — guaranteed bandwidth under a service-level agreement (SLA), managed Wi-Fi, state-of-the-art performance monitoring — you come into the office. That's no longer an option for millions of workers.

So, it's more important than ever for IT to be able to look deeply into every user's connection and troubleshoot problems all the way into home networks. For many connectivity issues, the problem lies with the user's home network or local Internet service provider (ISP).

But, even if you can't fix the problem, you can tell users what's wrong and what they can do about it. Sometimes, that might be, "Your ISP is having issues and it could be a while. Focus on offline work for now" That's a lot better than hours of frustrated users and wasted IT effort trying to diagnose a problem they can't solve.

Device: Along those lines, it's more important than ever to understand what's happening on the employee's device. For many problems, the cause is just a lack of memory or CPU power in older hardware. The good news is that those are among the easiest problems for IT to solve. But, if you're not monitoring all the way to the device, it will take a lot longer to zero in on that.

Applications and services: Businesses need to be able to look outward when measuring employee experience, as well as inward. Yes, you need to know how employees' devices and network connections are performing. But, you should also be keeping tabs on your SaaS applications and cloud services. If, for example, Microsoft Office 365 is having slow performance across the southwestern United States, it sure would be useful for IT to know that before they spend hours trying to diagnose the problem.

Eliminating the Guesswork

The truth is, the list of issues outside IT's control keeps getting longer. But, that makes visibility even more important. When you have hard data about what your people are actually experiencing, you can:

■ Hold your SaaS and Internet providers accountable

■ Empower employees (and reduce their frustration) by quickly diagnosing problems in their own devices and home networks that they can fix themselves

■ Quickly identify problems beyond IT's control, so they can focus their time on tasks where they can make a real difference

We're all still adjusting to the challenges of a post-COVID world. But, when your business relies on a remote workforce every day, just knowing what's happening out there can be hugely beneficial.

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 ...