Computer Network Architects
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Design and implement computer and information networks, such as local area networks (LAN), wide area networks (WAN), intranets, extranets, and other data communications networks. Perform network modeling, analysis, and planning, including analysis of capacity needs for network infrastructures. May also design network and computer security measures. May research and recommend network and data communications hardware and software.
The occupation "Computer Network Architects" has an automation risk of 50.7%, which positions it close to the base risk value of 51.5%. This suggests that while roughly half of the tasks associated with the role could be automated with current or near-future technology, the other half require uniquely human skills that resist automation. The automation risk reflects the dual nature of this profession: it includes many repeatable, pattern-based tasks suitable for AI and software, as well as tasks that require critical thinking, judgment, and adaptability. As technology advances, many network management and routine design decisions can be handed over to AI-powered systems. However, complex problem-solving and creative decision-making remain challenging to automate completely. Among the most automatable tasks for Computer Network Architects are developing disaster recovery plans, recommending or implementing network security measures like firewalls and automated security probes, and devising solutions for network problems. These duties are increasingly supported by intelligent automation tools capable of simulating attack scenarios, conducting routine audits, and diagnosing issues using large datasets and predefined logic. As such, tools can perform these repetitive, analytically driven functions faster and more reliably than humans in many cases. Automation platforms now routinely generate and test disaster recovery plans, or routinely probe networks for vulnerabilities, making these components of the role particularly vulnerable to displacement by automation. Conversely, the tasks most resistant to automation center around maintenance coordination, project reporting, and staying current with rapid changes in technology. Specifically, maintaining network peripherals like printers often involves hands-on, situational problem-solving that automation struggles to replicate. Developing or maintaining project reporting systems, while partially automatable, often requires human judgment for interpretation and customization to unique organizational needs. Most notably, the necessity for ongoing professional development—such as attending conferences, vendor visits, and technical journal reviews—demands a level of originality (measured at 3.0–3.6%) and adaptive thinking that AI has yet to achieve. These bottleneck skills create a buffer against full automation, ensuring the continuing importance of human expertise in this evolving field.