Skip to main content

Making Digital Transformation Work for You – Part 1

Joshua Dobies

Today we think nothing of using our smartphones to access systems and share information with colleagues, partners and customers anytime, anywhere. It may be hard to believe, but ten years ago those capabilities were just wishful thinking. The decade-long (and still on-going) maturation of technologies like cloud computing, mobile, big data and social media platforms is fueling a digital transformation in how we get work done.

But the IT organization can do more than oversee the ad hoc adoption of these technologies. IT has the opportunity to drive an executive-led, enterprise-wide strategy that leverages technology to improve business processes. That means moving away from IT's traditional role of maintaining systems and troubleshooting users' problems to offering services that improve users' productivity levels and spur revenue growth. That's a significant change, so your first question is likely along the lines of, "What are the first steps to creating this digital transformation strategy?" This three-part series will answer that question.

Imagine if you asked an IT professional in 2005 to set up several servers and storage resources, install the various applications, ensure that users at all branch offices and other remote locations have seamless access on all their devices, and to have it all up and running within 24 hours. That IT pro would have laughed out loud. After all, that would have been before the 2006 introduction of Amazon Web Services (AWS), the 2007 unveiling of the first iPhone and other developments that served as the catalysts for the digital transformation.

Today, you can spin up servers, storage resources and applications in minutes. And users don't just ask for 24/7 access on all their devices, they demand it without suffering poor performance-related issues.

That's why it's so important to understand that digital transformation is a business strategy, not simply a technology trend. There is no automatic competitive advantage in adopting what everyone else is adopting. Mobile is a given in today's digital environment. Social is a business requirement. Big data and cloud are nearly universally used by businesses, as well, if they wish to remain competitive. To achieve competitive advantage from digital transformation, companies must move beyond the mere adoption of these digital technologies.

According to Capgemini Consulting and MIT Sloan Management, less-mature digital companies tend to take a tactical, piecemeal approach as they solve discrete business problems with individual digital technologies. As a result, they don't fully integrate digital technologies with their business operations, don't solve the underlying infrastructure problems that cause frequent application performance issues across the enterprise, and fail to deliver the required technical capabilities at scale.

These companies face mounting challenges down the road. IDC predicts that:

■ By 2017, 60% of digital transformation initiatives will be unable to scale due to a lack of a strategic architecture.

■ By 2018, 70% of siloed digital transformation initiatives will ultimately fail due to insufficient collaboration, integration, sourcing, or project management.

In contrast, maturing digital companies take a strategic approach, integrating digital technologies to transform how their businesses work. As a result, they are able to:

■ Generate 26% higher profits than less digitally mature competitors.

■ Generate 9% more revenue.

■ Achieve a 12% higher market valuation ratio.

These companies embrace and absorb cloud, mobile, social, and big data technologies. They create competitive advantage with the superior ability to orchestrate these technologies enterprise-wide to create and deliver digital services. Of course, that's easier said than done.

Today's globally distributed, hybrid application environments are more complex than ever. There are so many moving parts and operational dependencies that the weak links in the chain are bound frequently to get stressed to the breaking point.

The primary culprit is the network. Even as enterprises adopt new digital technologies, network operators still remotely maintain routers and push applications over the Web to each branch office.

As a result, users must contend with more instances of poor application performance. Their productivity suffers, and IT shoulders the blame. That should raise red flags in the executive suite considering the importance of the remote and branch office (ROBO). Application performance fails to meet the needs of the business not just some of the time but most of the time.

This is why a strategic approach to managing your organization's digital transformation is necessary. In parts two and three of this series, I will more closely examine the factors behind this "performance gap," and why it's time to move on from the traditional WAN architectures built on routers and switches.

Read Making Digital Transformation Work for You – Part 2: Bridging the Performance Gap

Joshua Dobies is VP of Product Marketing, Riverbed Technology.

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 ...

Making Digital Transformation Work for You – Part 1

Joshua Dobies

Today we think nothing of using our smartphones to access systems and share information with colleagues, partners and customers anytime, anywhere. It may be hard to believe, but ten years ago those capabilities were just wishful thinking. The decade-long (and still on-going) maturation of technologies like cloud computing, mobile, big data and social media platforms is fueling a digital transformation in how we get work done.

But the IT organization can do more than oversee the ad hoc adoption of these technologies. IT has the opportunity to drive an executive-led, enterprise-wide strategy that leverages technology to improve business processes. That means moving away from IT's traditional role of maintaining systems and troubleshooting users' problems to offering services that improve users' productivity levels and spur revenue growth. That's a significant change, so your first question is likely along the lines of, "What are the first steps to creating this digital transformation strategy?" This three-part series will answer that question.

Imagine if you asked an IT professional in 2005 to set up several servers and storage resources, install the various applications, ensure that users at all branch offices and other remote locations have seamless access on all their devices, and to have it all up and running within 24 hours. That IT pro would have laughed out loud. After all, that would have been before the 2006 introduction of Amazon Web Services (AWS), the 2007 unveiling of the first iPhone and other developments that served as the catalysts for the digital transformation.

Today, you can spin up servers, storage resources and applications in minutes. And users don't just ask for 24/7 access on all their devices, they demand it without suffering poor performance-related issues.

That's why it's so important to understand that digital transformation is a business strategy, not simply a technology trend. There is no automatic competitive advantage in adopting what everyone else is adopting. Mobile is a given in today's digital environment. Social is a business requirement. Big data and cloud are nearly universally used by businesses, as well, if they wish to remain competitive. To achieve competitive advantage from digital transformation, companies must move beyond the mere adoption of these digital technologies.

According to Capgemini Consulting and MIT Sloan Management, less-mature digital companies tend to take a tactical, piecemeal approach as they solve discrete business problems with individual digital technologies. As a result, they don't fully integrate digital technologies with their business operations, don't solve the underlying infrastructure problems that cause frequent application performance issues across the enterprise, and fail to deliver the required technical capabilities at scale.

These companies face mounting challenges down the road. IDC predicts that:

■ By 2017, 60% of digital transformation initiatives will be unable to scale due to a lack of a strategic architecture.

■ By 2018, 70% of siloed digital transformation initiatives will ultimately fail due to insufficient collaboration, integration, sourcing, or project management.

In contrast, maturing digital companies take a strategic approach, integrating digital technologies to transform how their businesses work. As a result, they are able to:

■ Generate 26% higher profits than less digitally mature competitors.

■ Generate 9% more revenue.

■ Achieve a 12% higher market valuation ratio.

These companies embrace and absorb cloud, mobile, social, and big data technologies. They create competitive advantage with the superior ability to orchestrate these technologies enterprise-wide to create and deliver digital services. Of course, that's easier said than done.

Today's globally distributed, hybrid application environments are more complex than ever. There are so many moving parts and operational dependencies that the weak links in the chain are bound frequently to get stressed to the breaking point.

The primary culprit is the network. Even as enterprises adopt new digital technologies, network operators still remotely maintain routers and push applications over the Web to each branch office.

As a result, users must contend with more instances of poor application performance. Their productivity suffers, and IT shoulders the blame. That should raise red flags in the executive suite considering the importance of the remote and branch office (ROBO). Application performance fails to meet the needs of the business not just some of the time but most of the time.

This is why a strategic approach to managing your organization's digital transformation is necessary. In parts two and three of this series, I will more closely examine the factors behind this "performance gap," and why it's time to move on from the traditional WAN architectures built on routers and switches.

Read Making Digital Transformation Work for You – Part 2: Bridging the Performance Gap

Joshua Dobies is VP of Product Marketing, Riverbed Technology.

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 ...