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IT Has Proven Rapid Digital Transformation is Possible - What's Next?

Paul Davenport
AppNeta

The pandemic effectively "shocked" enterprises into pushing the gas on tech initiatives that, on the one hand, support a more flexible, decentralized workforce, but that were by-and-large already on the roadmap, regardless of whether businesses had been planning to support widespread work-from-home or not.

Retiring legacy, hardware-based applications and workflows that committed workers to sharing an office for more flexible and scalable cloud tools, for instance, was already in progress (though relatively slowly) at many businesses well before the pandemic made cloud migration a top priority. Showing business leaders that accelerated "digital transformations" like these could even be pulled off (let alone successfully) was just one business myth that was dispelled as part of the pandemic.

The second myth (at least among wary enterprise decision makers) was that IT teams couldn't successfully deploy network infrastructures that were fit to support widespread WFH. Not only has this been dispelled (again, many of the required changes to enable WFH go hand-in-hand with long-simmering digital overhauls), but many newly-remote teams are actually performing better in their new environment.

However, just because enterprise IT have proven in many cases that they can support an almost fully remote workforce doesn't mean that this will be the enterprise standard going forward.

A study conducted by workplace chat app Blind found that among the biggest Silicon Valley tech companies, for instance, pandemic-induced WFH is leading to workers feeling 68 percent more burnt out than they did last year. While the feeling is subjective, the increase can't be ignored. That said, workers in other industries like healthcare are seeing tangible benefits in conducting work at a distance.

So while WFH will never be a fit for every worker, now that both IT and knowledge workers have debunked the misconceptions of their most skeptical enterprise leaders, it'll be hard to convince everyone to "go back" to the old way once restrictions are finally lifted.

All of this goes to show that as much as we've learned about the positives of WFH in this global "experiment" in decentralization, it's too soon to fully say goodbye to the office as we knew it before the pandemic. Instead, companies will need to adapt to support a more fluid, "anywhere operations" model for work that will allow employees to enjoy similar experiences with the job wherever they log on.

For network operations teams going forward, the biggest challenge will be keeping up with the accelerated pace of change now that they've proven to skeptical business leaders their efficiency (and efficacy) in successfully transforming the network. This will require teams to put a greater emphasis on leveraging comprehensive visibility into end-user performance wherever users are located now that the footprint for potential errors has expanded with workers at home.

Supporting this "new normal" calls for enterprise IT teams to synchronize visibility across their rapidly evolving network footprint to ensure they can monitor and manage the digital experiences of users leveraging any app, from any location, at any point in time. With users logging onto the network from all over the map and adopting new technologies to stay in sync with their times, enterprise IT teams have to seek out visibility into network environments that they don't inherently have clear insights into or control over to ensure successful deployment.

Paul Davenport is Marketing Communications Manager at AppNeta

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

IT Has Proven Rapid Digital Transformation is Possible - What's Next?

Paul Davenport
AppNeta

The pandemic effectively "shocked" enterprises into pushing the gas on tech initiatives that, on the one hand, support a more flexible, decentralized workforce, but that were by-and-large already on the roadmap, regardless of whether businesses had been planning to support widespread work-from-home or not.

Retiring legacy, hardware-based applications and workflows that committed workers to sharing an office for more flexible and scalable cloud tools, for instance, was already in progress (though relatively slowly) at many businesses well before the pandemic made cloud migration a top priority. Showing business leaders that accelerated "digital transformations" like these could even be pulled off (let alone successfully) was just one business myth that was dispelled as part of the pandemic.

The second myth (at least among wary enterprise decision makers) was that IT teams couldn't successfully deploy network infrastructures that were fit to support widespread WFH. Not only has this been dispelled (again, many of the required changes to enable WFH go hand-in-hand with long-simmering digital overhauls), but many newly-remote teams are actually performing better in their new environment.

However, just because enterprise IT have proven in many cases that they can support an almost fully remote workforce doesn't mean that this will be the enterprise standard going forward.

A study conducted by workplace chat app Blind found that among the biggest Silicon Valley tech companies, for instance, pandemic-induced WFH is leading to workers feeling 68 percent more burnt out than they did last year. While the feeling is subjective, the increase can't be ignored. That said, workers in other industries like healthcare are seeing tangible benefits in conducting work at a distance.

So while WFH will never be a fit for every worker, now that both IT and knowledge workers have debunked the misconceptions of their most skeptical enterprise leaders, it'll be hard to convince everyone to "go back" to the old way once restrictions are finally lifted.

All of this goes to show that as much as we've learned about the positives of WFH in this global "experiment" in decentralization, it's too soon to fully say goodbye to the office as we knew it before the pandemic. Instead, companies will need to adapt to support a more fluid, "anywhere operations" model for work that will allow employees to enjoy similar experiences with the job wherever they log on.

For network operations teams going forward, the biggest challenge will be keeping up with the accelerated pace of change now that they've proven to skeptical business leaders their efficiency (and efficacy) in successfully transforming the network. This will require teams to put a greater emphasis on leveraging comprehensive visibility into end-user performance wherever users are located now that the footprint for potential errors has expanded with workers at home.

Supporting this "new normal" calls for enterprise IT teams to synchronize visibility across their rapidly evolving network footprint to ensure they can monitor and manage the digital experiences of users leveraging any app, from any location, at any point in time. With users logging onto the network from all over the map and adopting new technologies to stay in sync with their times, enterprise IT teams have to seek out visibility into network environments that they don't inherently have clear insights into or control over to ensure successful deployment.

Paul Davenport is Marketing Communications Manager at AppNeta

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...