Skip to main content

Modernization Limited by Legacy Tech Despite Increasing Budgets

Jonathan Sullivan
NS1

The COVID-19 pandemic has compressed six years of modernization projects into 6 months. According to the recent report, Meeting Application and Access Network Modernization Challenges, IT leaders have accelerated projects aimed at increasing productivity and business agility, improving application performance and end-user experience, and driving additional revenue through existing channels.

In a study of more than 400 technology leaders from mid- to large-sized companies across the US, UK, and Germany, NS1 and IDG Research examined enterprise network and application modernization efforts. For the study, modernization was defined as the transformation of IT platforms of all types, applications, governance, and processes to achieve desired business outcomes. Results revealed that 80% of organizations are struggling to reach application delivery requirements on top of their existing infrastructure. With the pandemic placing additional burden on supporting infrastructure that was in many cases already running at redline, efforts to modernize networks and applications to meet demand are accelerating, with 83% reporting budget increases for related initiatives over the next three years.

According to the report, IT modernization initiatives that were expected to span 5 years are being rapidly condensed. Within the broad scope of IT modernization, companies are prioritizing transformation initiatives for:

■ mobility (70%)

■ remote data access (68%)

■ automation (65%)

■ security (61%)

■ IT resilience (60%)

Other areas where efforts are accelerating include public and private cloud deployments (58% and 57% respectively), improvements to scalability (58%), and deployment velocity (56%). Also interesting to note: private cloud was a greater priority in the US, cited by 65% of respondents vs. 44% in the UK and 54% in Germany.

And yet, even with the heightened sense of urgency and budget behind them, survey respondents reported facing a number of obstacles in their IT modernization projects. Although 4 out of 5 acknowledge some progress with modernization, only 8% report that they have achieved their initial objectives, and 28% report "significant progress" (75% or greater).

Challenges to modernization include a skill and talent skills gap along with competing priorities (both 37%), as well as aging networks (35%) and the outdated and rigid organizational structures that often come with them. Technical and operational debt was cited by 31% overall, although it was more of an obstacle in the US and Germany (37% and 32%, respectively) vs. the UK (19%).

These findings illustrate how it is crucial for organizations to examine the core technologies that enable them to deploy, connect, and deliver applications in order to ensure they can provide the user experiences required in today’s modern world. Static, legacy tech drags down modernization efforts because it lacks the flexibility and agility necessary to support dynamic, scalable applications and IT environments.

Successful digital transformation starts with modernizing the foundational components of enterprise networking and application infrastructure

Successful digital transformation starts with modernizing the foundational components of enterprise networking and application infrastructure — DNS, DHCP, and IP address management, known collectively as DDI. When a DDI platform is purpose-built for speed, reliability, and scalability, it can provide massive benefits that can be leveraged all the way up and down the stack, and horizontally and across complex, heterogeneous environments.

The study found that 45% of respondents are currently using DDI, and another 48% plan to adopt the technology within 12 months. Current adopters reported the two most common use cases to be accelerating service discovery in microservice environments (60%) and connecting cloud and on-premise applications and data (56%). Those with plans to implement DDI (59%) cited the ability to connect cloud and on-premise applications and data as a top benefit. Other major use cases for modern DDI adoption included accelerating application delivery (55%), automating network management tasks (54%), accelerating service discovery in microservices environments (42%), and controlling costs associated with application and network management (40%).

In addition to DDI, nearly all companies are adopting modern application stack solutions that are aimed directly at addressing network and application performance requirements. This includes network monitoring tools, where 96% of respondents were either already implementing or planning to implement within 12 months. Other top choices were public/private cloud and multi-cloud (94%), automation and orchestration solutions (93%), intelligent traffic management (87%), and multi-CDN (85%).

Jonathan Sullivan is CTO at NS1

The Latest

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...

Modernization Limited by Legacy Tech Despite Increasing Budgets

Jonathan Sullivan
NS1

The COVID-19 pandemic has compressed six years of modernization projects into 6 months. According to the recent report, Meeting Application and Access Network Modernization Challenges, IT leaders have accelerated projects aimed at increasing productivity and business agility, improving application performance and end-user experience, and driving additional revenue through existing channels.

In a study of more than 400 technology leaders from mid- to large-sized companies across the US, UK, and Germany, NS1 and IDG Research examined enterprise network and application modernization efforts. For the study, modernization was defined as the transformation of IT platforms of all types, applications, governance, and processes to achieve desired business outcomes. Results revealed that 80% of organizations are struggling to reach application delivery requirements on top of their existing infrastructure. With the pandemic placing additional burden on supporting infrastructure that was in many cases already running at redline, efforts to modernize networks and applications to meet demand are accelerating, with 83% reporting budget increases for related initiatives over the next three years.

According to the report, IT modernization initiatives that were expected to span 5 years are being rapidly condensed. Within the broad scope of IT modernization, companies are prioritizing transformation initiatives for:

■ mobility (70%)

■ remote data access (68%)

■ automation (65%)

■ security (61%)

■ IT resilience (60%)

Other areas where efforts are accelerating include public and private cloud deployments (58% and 57% respectively), improvements to scalability (58%), and deployment velocity (56%). Also interesting to note: private cloud was a greater priority in the US, cited by 65% of respondents vs. 44% in the UK and 54% in Germany.

And yet, even with the heightened sense of urgency and budget behind them, survey respondents reported facing a number of obstacles in their IT modernization projects. Although 4 out of 5 acknowledge some progress with modernization, only 8% report that they have achieved their initial objectives, and 28% report "significant progress" (75% or greater).

Challenges to modernization include a skill and talent skills gap along with competing priorities (both 37%), as well as aging networks (35%) and the outdated and rigid organizational structures that often come with them. Technical and operational debt was cited by 31% overall, although it was more of an obstacle in the US and Germany (37% and 32%, respectively) vs. the UK (19%).

These findings illustrate how it is crucial for organizations to examine the core technologies that enable them to deploy, connect, and deliver applications in order to ensure they can provide the user experiences required in today’s modern world. Static, legacy tech drags down modernization efforts because it lacks the flexibility and agility necessary to support dynamic, scalable applications and IT environments.

Successful digital transformation starts with modernizing the foundational components of enterprise networking and application infrastructure

Successful digital transformation starts with modernizing the foundational components of enterprise networking and application infrastructure — DNS, DHCP, and IP address management, known collectively as DDI. When a DDI platform is purpose-built for speed, reliability, and scalability, it can provide massive benefits that can be leveraged all the way up and down the stack, and horizontally and across complex, heterogeneous environments.

The study found that 45% of respondents are currently using DDI, and another 48% plan to adopt the technology within 12 months. Current adopters reported the two most common use cases to be accelerating service discovery in microservice environments (60%) and connecting cloud and on-premise applications and data (56%). Those with plans to implement DDI (59%) cited the ability to connect cloud and on-premise applications and data as a top benefit. Other major use cases for modern DDI adoption included accelerating application delivery (55%), automating network management tasks (54%), accelerating service discovery in microservices environments (42%), and controlling costs associated with application and network management (40%).

In addition to DDI, nearly all companies are adopting modern application stack solutions that are aimed directly at addressing network and application performance requirements. This includes network monitoring tools, where 96% of respondents were either already implementing or planning to implement within 12 months. Other top choices were public/private cloud and multi-cloud (94%), automation and orchestration solutions (93%), intelligent traffic management (87%), and multi-CDN (85%).

Jonathan Sullivan is CTO at NS1

The Latest

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...