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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

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 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

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 ...