Network pros are wasting half of their time on tasks that could be automated
Network automation could help free up IT professionals to waste less time on manual tasks


Network professionals lose nearly half of their week to manual tasks that could be automated, new research reveals.
Skybox Security has released a report based on a Censuswide survey that spoke to network operations managers, security architects, and infrastructure leaders.
The results showed manual tasks continue to bog down network operators, despite the fact they could be automated, preventing them from getting to more pressing work.
Nearly half (40%) of respondents said half of their workweek was being taken up with firewall management and network provisioning.
Network teams said they also spend an additional 10% of their time remediating misconfigurations each month.
The problem was even worse in larger organizations with a headcount of over 10,000 employees, Skybox found, rising to 12% of their time each month spent remediating misconfigurations.
This heavy workload doesn’t just impact efficiency, but produces a number of other negative outcomes such as higher turnover among networking professionals tired of highly manual and unfulfiling tasks dominating their workload.
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On top of this, 90% of network managers reported they have serious fears around failing both internal and external compliance audits as a result of preventable misconfigurations.
Skybox said these fears look to be well-founded, with 69% of respondents indicating they had two or more compliance issues identified during external audits.
In contrast, just 4% of network and security teams reporting they had zero failures picked up in their audits.
In addition to risk of incurring penalties, workers said they were worried about the potential attacks these misconfigurations are leaving their organization exposed to.
Half of the respondents said they fear network misconfigurations could introduce vulnerabilities that threat actors could exploit, a further 44% said they were afraid network downtime caused by misconfigurations could result in significant financial losses.
Enterprise network automation expected to grow, but business challenges are limiting success
In its latest Hype Cycle for I&O Automation report, Gartner said it expects network automation to take off among enterprises within the next two years, but at the moment adoption rates remain relatively low.
In mid-2023, Gartner found less than 10% of enterprises were automating more than half of their network activities.
The consultancy said it expects this figure to triple over the next two years, predicting 30% of enterprises will automate over 50% of their networking tasks by 2026.
Chris Saunderson, senior director analyst at Gartner said technology leaders’ appetite for automation solutions is growing as they contend with rapidly expanding workflows.
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“Infrastructure and operations (I&O) leaders are increasingly looking to AI-based analytics and augmented decision making, including intelligent automation (IA), to improve operational resilience and responsiveness, address complexity and process increasingly large amounts of data through automation.”
Research conducted by IT research firm EMAfound the success rate for network automation projects is still very low.
Just 18% of respondents said their network automation initiative was a complete success. Key business challenges identified in the report included:
- IT leadership (31%)
- Staffing issues (27%)
- Budget constraints (25%)
- Collaboration problems (25%)
- Security policy constraints (25%)
Network professionals also cited technical problems such as integration issues (25%), network complexity (25%), legacy infrastructure hurdles (24%), as key challenges they face when trying to automate network activities.

Solomon Klappholz is a former staff writer for ITPro and ChannelPro. He has experience writing about the technologies that facilitate industrial manufacturing, which led to him developing a particular interest in cybersecurity, IT regulation, industrial infrastructure applications, and machine learning.
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