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Challenges of DIY Network Automation

Shamus McGillicuddy

Unlike other areas of IT operations where commercial tools dominate, network teams continue to rely heavily on homegrown scripts and open-source tools — despite the costs.

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest Enterprise Management Associates (EMA™) report.

Network engineers often talk about the 80/20 rule with network automation. Most vendors can fulfill 80% of network automation use cases with commercial tools, but there always remains that 20% that only a DIY approach can address. The report explores a possible hybrid approach, where engineers can merge their DIY automation with commercial platforms to hit 100% of use cases more efficiently and effectively.

Based on 12 in-depth interviews with enterprise network automation experts, this new report examines why do-it-yourself (DIY) network automation persists, the value it offers, and the hidden costs and risks it introduces. It also explores how network automation vendors can enhance or replace these tools without disrupting existing operations.

Some of the key findings from the report include:

  • The drivers of DIY network automation include budget issues and the need to have full control over an automation roadmap.
  • DIY automation strategies typically struggle with skills gaps in the network automation team, tool complexity, usability issues.
  • Network automation pros would like vendors to deliver value around tool governance, modularity and extensibility, and logging and reporting.

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Challenges of DIY Network Automation

Shamus McGillicuddy

Unlike other areas of IT operations where commercial tools dominate, network teams continue to rely heavily on homegrown scripts and open-source tools — despite the costs.

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest Enterprise Management Associates (EMA™) report.

Network engineers often talk about the 80/20 rule with network automation. Most vendors can fulfill 80% of network automation use cases with commercial tools, but there always remains that 20% that only a DIY approach can address. The report explores a possible hybrid approach, where engineers can merge their DIY automation with commercial platforms to hit 100% of use cases more efficiently and effectively.

Based on 12 in-depth interviews with enterprise network automation experts, this new report examines why do-it-yourself (DIY) network automation persists, the value it offers, and the hidden costs and risks it introduces. It also explores how network automation vendors can enhance or replace these tools without disrupting existing operations.

Some of the key findings from the report include:

  • The drivers of DIY network automation include budget issues and the need to have full control over an automation roadmap.
  • DIY automation strategies typically struggle with skills gaps in the network automation team, tool complexity, usability issues.
  • Network automation pros would like vendors to deliver value around tool governance, modularity and extensibility, and logging and reporting.

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In today's enterprise landscape, two seismic shifts are converging: the mainstreaming of hybrid work and the rapid adoption of AI-enhanced applications. While both promise productivity gains and competitive advantage, they also expose a hidden Achilles' heel, application performance ...

To examine the growing gap between how software is built and how secure it is, Security Journey brought together a panel of seasoned developers, security leaders, and AI experts for a roundtable discussion on Closing the Security Gap in AI ... Together, they explored the real-world challenges organizations are grappling with when it comes to software development leveraging AI, from fragile governance frameworks and inconsistent policy enforcement to the growing over-reliance on AI generated code ...

A new study by the IBM Institute for Business Value reveals that enterprises are expected to significantly scale AI-enabled workflows, many driven by agentic AI, relying on them for improved decision making and automation. The AI Projects to Profits study revealed that respondents expect AI-enabled workflows to grow from 3% today to 25% by the end of 2025. With 70% of surveyed executives indicating that agentic AI is important to their organization's future, the research suggests that many organizations are actively encouraging experimentation ...

Respondents predict that agentic AI will play an increasingly prominent role in their interactions with technology vendors over the coming years and are positive about the benefits it will bring, according to The Race to an Agentic Future: How Agentic AI Will Transform Customer Experience, a report from Cisco ...

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As artificial intelligence (AI) adoption gains momentum, network readiness is emerging as a critical success factor. AI workloads generate unpredictable bursts of traffic, demanding high-speed connectivity that is low latency and lossless. AI adoption will require upgrades and optimizations in data center networks and wide-area networks (WANs). This is prompting enterprise IT teams to rethink, re-architect, and upgrade their data center and WANs to support AI-driven operations ...

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

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