Skip to main content

Why Are NetOps Teams Struggling to Deliver on Their Network Automation Strategy?

Song Pang
NetBrain Technologies

Network automation remains a top challenge for enterprise IT departments. Despite years of effort from vendors and IT professionals to develop tools to reduce manual network management, results have been mixed. A recent report by Enterprise Management Associates (EMA) reveals that nearly 95% of organizations use a combination of do-it-yourself (DIY) and vendor solutions for network automation, yet only 28% believe they have successfully implemented their automation strategy.

Why is this mixed approach so popular if many engineers feel that their overall program is not successful?

The short answer is that each type of automation has different advantages and weaknesses. DIY automation, which involves engineers writing their own scripts for specific tasks or using open-source tools like Ansible, offers customization and cost-effectiveness but is hard to manage and scale, and relies almost completely on individual engineer's skillsets. On the other hand, commercial network automation products are often expensive, but provide stability and scalability and are easier to use.

So, where's the disconnect?

Why are NetOps teams struggling to deliver on their network automation strategy?

Should teams go all in on either a DIY or vendor solution?

Let's take a closer look.

First, a quick note — a successful network automation strategy depends on many factors, for the sake of time today we will focus on DIY vs. vendor solutions and related issues.

Benefits of DIY:

Capabilities align with the organization's specific network. With homegrown solutions, tools are tailor-made to fit the unique needs of a network environment. Vendor solutions can't ever be that customized. For organizations with unusual network architectures, this can be important.

Security and compliance requirements. DIY solutions can be designed to follow the particular security and compliance requirements for the business, such as GDPR, HIPAA, and PCI-DSS.

Cost savings. With DIY tools, you get exactly what you need for little to no cost (other than your engineer's time). When this works well, it means better operational efficiency, and complex processes are more streamlined.

Benefits of Using Vendor Solutions:

Scale. Vendor solutions are built to cover an entire network, handle large data loads, and integrate with other tools and data sources.

Security and compliance requirements. Hey wait a second, wasn't this one of the key drivers for using DIY? Yes, but it's a benefit here as well. Vendor products often come already compliant with certain security standards where making a DIY tool compliant would take too much work. Network teams often manage complex environments using commercial tools for particular needs and DIY tools for other tasks.

Platform requirements. Commercial solutions are more scalable and stable than DIY tools. While a homegrown automation solution might handle a few dozen changes really well, it will likely struggle to scale to thousands of changes.

Breadth of functionality. Vendor tools generally provide a broader range of features than DIY solutions, often addressing multiple issues from the get-go.

Despite all the benefits, each solution has its drawbacks. DIY solutions often struggle to scale up larger than the initial scenario they were written for, and it will take much more time and work to do this manually. They can also be slower than commercial tools and will lack multi-vendor support (unless the creator builds it). You also need network engineers who know enough scripting to write and manage these tools. If you don't have anyone with that skillset (or they leave the company), you're out of luck.

Drawbacks for vendor solutions include high upfront costs, lack of customization, and the training expenses associated with learning a new system. Cost and budget matters; the EMA report found a strong correlation between network automation success and significant budget investments. 80% of entirely successful organizations had well-funded projects, compared to only 57% of partially successful and 29% of partially failed organizations.

Many organizations are ultimately using each type of automation where it's needed. Rather than picking one, they're using both. Commercial network automation products have room for improvement, particularly in their customizability. The more they can adapt to fit each unique customer network, the more useful they will be. But the products aren't the real problem. The more important roadblocks I see (that are keeping the percentage of successful automation programs so low) are IT leadership problems. This includes difficulties gaining buy-in, establishing direction and ensuring commitment, as well as skill gaps, staff turnover and budget constraints.

Looking ahead, the future of automation involves an ecosystem of tools and products that must integrate seamlessly to create an effective solution for each unique environment. Organizations must maintain a repository of network intent and network state data to ensure adherence to design standards and security policies.

Song Pang is CTO at NetBrain Technologies

Hot Topics

The Latest

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

Why Are NetOps Teams Struggling to Deliver on Their Network Automation Strategy?

Song Pang
NetBrain Technologies

Network automation remains a top challenge for enterprise IT departments. Despite years of effort from vendors and IT professionals to develop tools to reduce manual network management, results have been mixed. A recent report by Enterprise Management Associates (EMA) reveals that nearly 95% of organizations use a combination of do-it-yourself (DIY) and vendor solutions for network automation, yet only 28% believe they have successfully implemented their automation strategy.

Why is this mixed approach so popular if many engineers feel that their overall program is not successful?

The short answer is that each type of automation has different advantages and weaknesses. DIY automation, which involves engineers writing their own scripts for specific tasks or using open-source tools like Ansible, offers customization and cost-effectiveness but is hard to manage and scale, and relies almost completely on individual engineer's skillsets. On the other hand, commercial network automation products are often expensive, but provide stability and scalability and are easier to use.

So, where's the disconnect?

Why are NetOps teams struggling to deliver on their network automation strategy?

Should teams go all in on either a DIY or vendor solution?

Let's take a closer look.

First, a quick note — a successful network automation strategy depends on many factors, for the sake of time today we will focus on DIY vs. vendor solutions and related issues.

Benefits of DIY:

Capabilities align with the organization's specific network. With homegrown solutions, tools are tailor-made to fit the unique needs of a network environment. Vendor solutions can't ever be that customized. For organizations with unusual network architectures, this can be important.

Security and compliance requirements. DIY solutions can be designed to follow the particular security and compliance requirements for the business, such as GDPR, HIPAA, and PCI-DSS.

Cost savings. With DIY tools, you get exactly what you need for little to no cost (other than your engineer's time). When this works well, it means better operational efficiency, and complex processes are more streamlined.

Benefits of Using Vendor Solutions:

Scale. Vendor solutions are built to cover an entire network, handle large data loads, and integrate with other tools and data sources.

Security and compliance requirements. Hey wait a second, wasn't this one of the key drivers for using DIY? Yes, but it's a benefit here as well. Vendor products often come already compliant with certain security standards where making a DIY tool compliant would take too much work. Network teams often manage complex environments using commercial tools for particular needs and DIY tools for other tasks.

Platform requirements. Commercial solutions are more scalable and stable than DIY tools. While a homegrown automation solution might handle a few dozen changes really well, it will likely struggle to scale to thousands of changes.

Breadth of functionality. Vendor tools generally provide a broader range of features than DIY solutions, often addressing multiple issues from the get-go.

Despite all the benefits, each solution has its drawbacks. DIY solutions often struggle to scale up larger than the initial scenario they were written for, and it will take much more time and work to do this manually. They can also be slower than commercial tools and will lack multi-vendor support (unless the creator builds it). You also need network engineers who know enough scripting to write and manage these tools. If you don't have anyone with that skillset (or they leave the company), you're out of luck.

Drawbacks for vendor solutions include high upfront costs, lack of customization, and the training expenses associated with learning a new system. Cost and budget matters; the EMA report found a strong correlation between network automation success and significant budget investments. 80% of entirely successful organizations had well-funded projects, compared to only 57% of partially successful and 29% of partially failed organizations.

Many organizations are ultimately using each type of automation where it's needed. Rather than picking one, they're using both. Commercial network automation products have room for improvement, particularly in their customizability. The more they can adapt to fit each unique customer network, the more useful they will be. But the products aren't the real problem. The more important roadblocks I see (that are keeping the percentage of successful automation programs so low) are IT leadership problems. This includes difficulties gaining buy-in, establishing direction and ensuring commitment, as well as skill gaps, staff turnover and budget constraints.

Looking ahead, the future of automation involves an ecosystem of tools and products that must integrate seamlessly to create an effective solution for each unique environment. Organizations must maintain a repository of network intent and network state data to ensure adherence to design standards and security policies.

Song Pang is CTO at NetBrain Technologies

Hot Topics

The Latest

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...