Skip to main content

The True Cost of IT Inefficiency

Song Pang
NetBrain Technologies

A recent study found that 75% of CIOs struggle to strike the right balance between business innovation and operational excellence. Finding this equilibrium can be challenging, as focusing on operational efficiency may come at the expense of innovation and growth. For all their excitement about digital transformation, AI, or cloud computing, enterprises spend a majority of their time simply keeping the lights on.

Since IT costs can consume a significant share of revenue — between 2 and 19 percent, depending on the industry — enterprises should (but often don't) pay close attention to the efficiency of IT operations at scale. Improving operational cost structures even fractionally can yield major savings for larger organizations, often in the tens of millions of dollars. Conversely, there are long-term financial consequences to not optimizing operational processes. One way to avoid these issues is through no-code network automation.

But first, it's important to understand the context of how enterprises have fallen into a pattern of ignoring the importance of operational efficiency. For decades, IT decision makers have mistakenly assumed that the capabilities gained by new technologies have been matched by advancements in the operation of those technologies. Unfortunately, this isn't the case. Not only do organizations rely on the same highly inefficient, labor-intensive operations and maintenance (O&M) plans and practices, they're growing in scale and consume an ever-larger share of IT budgets.

And that's where the problem lies; with the increasing complexity of hybrid networks and accelerating pace of technology refresh cycles, the number of service tickets is skyrocketing. But those outdated O&M plans dictate that every month thousands of tickets must be manually assessed by a help desk or level one engineer. The result is an average mean time to repair of up to two days. Moreover, in more than two thirds of cases, problems no longer exist by the time service tickets are escalated to skilled network engineers. Problems that do persist, no matter how familiar they are to the engineers working on them or how often they've been addressed in the past, tend to be resolved with brute force using basic tools.

This network operations efficiency problem also carries significant risks and costs for the entire business. These include:

  • An increased number of service disruptions, network outages and service degradations
  • Incidents that last longer, affect more users and take longer to resolve
  • Higher security risks, more "hidden" vulnerabilities and audit and compliance failures
  • Lower available computing capacity, degraded application response times
  • Higher Infrastructure carrying costs (including warranty) for devices no longer present
  • Declining customer satisfaction, retention and valuation
  • Negative reputational impacts
  • Limited available resources across geographies, higher staffing and training costs, mismatched skill sets, and increased escalations

It's fiscally and managerially indefensible for a modern enterprise to operate with these kinds of costs and risks hanging like a Sword of Damocles over their heads.

From a strategic point of view, IT leaders need to change the way they think about IT operations and service delivery. Rather than looking purely at IT products in isolation, they need to consider the fully burdened costs of owning and operating their digital infrastructure, with the primary metric being the total cost of service delivery per unit of work.

Of course, this can be a difficult transition to make as it requires IT leaders to first accept that their existing plans don't work. However, organizations that recognize the fundamental underlying problem and make improving IT operational efficiency a strategic priority do have a powerful tool at their disposal: no code network automation.

Simply put, no-code network automation empowers IT network and support teams to do more — much more — with less. It enables every existing network resource to become an automation expert by capturing and abstracting subject matter expertise, automating repetitive manual tasks (like network configuration, outage prevention, assessment and daily operations and diagnostic troubleshooting) and performing them efficiently at scale across every hybrid cloud-connected network infrastructure. Problems only need to be "solved" once and then can be corrected automatically when they reoccur.

The bottom-line impact of this kind of modern O&M approach is significant. Even small improvements can translate to significant cost savings at enterprise scale. A multi-billion-dollar enterprise, greater network operations efficiency could reduce costs by tens of millions of dollars per year.

Despite these compelling numbers, for most organizations the potential of network automation is still largely untapped. But as networks continue to grow in scale and complexity, IT leaders must pay closer attention to the structural and process inefficiencies that are eating their IT budgets. Continuing along the same path is simply untenable at a time when they are being asked by the C-suite to do more with less. Investing in network automation will not only provide financial rewards, but tangible benefits in terms of greater network reliability, improved performance and stronger security.

Song Pang is SVP of Engineering at NetBrain Technologies

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

The True Cost of IT Inefficiency

Song Pang
NetBrain Technologies

A recent study found that 75% of CIOs struggle to strike the right balance between business innovation and operational excellence. Finding this equilibrium can be challenging, as focusing on operational efficiency may come at the expense of innovation and growth. For all their excitement about digital transformation, AI, or cloud computing, enterprises spend a majority of their time simply keeping the lights on.

Since IT costs can consume a significant share of revenue — between 2 and 19 percent, depending on the industry — enterprises should (but often don't) pay close attention to the efficiency of IT operations at scale. Improving operational cost structures even fractionally can yield major savings for larger organizations, often in the tens of millions of dollars. Conversely, there are long-term financial consequences to not optimizing operational processes. One way to avoid these issues is through no-code network automation.

But first, it's important to understand the context of how enterprises have fallen into a pattern of ignoring the importance of operational efficiency. For decades, IT decision makers have mistakenly assumed that the capabilities gained by new technologies have been matched by advancements in the operation of those technologies. Unfortunately, this isn't the case. Not only do organizations rely on the same highly inefficient, labor-intensive operations and maintenance (O&M) plans and practices, they're growing in scale and consume an ever-larger share of IT budgets.

And that's where the problem lies; with the increasing complexity of hybrid networks and accelerating pace of technology refresh cycles, the number of service tickets is skyrocketing. But those outdated O&M plans dictate that every month thousands of tickets must be manually assessed by a help desk or level one engineer. The result is an average mean time to repair of up to two days. Moreover, in more than two thirds of cases, problems no longer exist by the time service tickets are escalated to skilled network engineers. Problems that do persist, no matter how familiar they are to the engineers working on them or how often they've been addressed in the past, tend to be resolved with brute force using basic tools.

This network operations efficiency problem also carries significant risks and costs for the entire business. These include:

  • An increased number of service disruptions, network outages and service degradations
  • Incidents that last longer, affect more users and take longer to resolve
  • Higher security risks, more "hidden" vulnerabilities and audit and compliance failures
  • Lower available computing capacity, degraded application response times
  • Higher Infrastructure carrying costs (including warranty) for devices no longer present
  • Declining customer satisfaction, retention and valuation
  • Negative reputational impacts
  • Limited available resources across geographies, higher staffing and training costs, mismatched skill sets, and increased escalations

It's fiscally and managerially indefensible for a modern enterprise to operate with these kinds of costs and risks hanging like a Sword of Damocles over their heads.

From a strategic point of view, IT leaders need to change the way they think about IT operations and service delivery. Rather than looking purely at IT products in isolation, they need to consider the fully burdened costs of owning and operating their digital infrastructure, with the primary metric being the total cost of service delivery per unit of work.

Of course, this can be a difficult transition to make as it requires IT leaders to first accept that their existing plans don't work. However, organizations that recognize the fundamental underlying problem and make improving IT operational efficiency a strategic priority do have a powerful tool at their disposal: no code network automation.

Simply put, no-code network automation empowers IT network and support teams to do more — much more — with less. It enables every existing network resource to become an automation expert by capturing and abstracting subject matter expertise, automating repetitive manual tasks (like network configuration, outage prevention, assessment and daily operations and diagnostic troubleshooting) and performing them efficiently at scale across every hybrid cloud-connected network infrastructure. Problems only need to be "solved" once and then can be corrected automatically when they reoccur.

The bottom-line impact of this kind of modern O&M approach is significant. Even small improvements can translate to significant cost savings at enterprise scale. A multi-billion-dollar enterprise, greater network operations efficiency could reduce costs by tens of millions of dollars per year.

Despite these compelling numbers, for most organizations the potential of network automation is still largely untapped. But as networks continue to grow in scale and complexity, IT leaders must pay closer attention to the structural and process inefficiencies that are eating their IT budgets. Continuing along the same path is simply untenable at a time when they are being asked by the C-suite to do more with less. Investing in network automation will not only provide financial rewards, but tangible benefits in terms of greater network reliability, improved performance and stronger security.

Song Pang is SVP of Engineering at NetBrain Technologies

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...