Computer network architects

Document network design and deployment processes for future reference

AI can automatically generate documentation from configuration files, network diagrams from discovery tools, and process descriptions from deployment logs. Documentation is largely structured technical writing that AI handles well, requiring only light human review for accuracy and completeness.

BLS evidence: Network architects create documentation throughout the design and deployment process as a reference for future network enhancements or maintenance.

Analyze data traffic and system performance for future upgrades

AI can process network telemetry, identify traffic patterns, forecast capacity needs, and model performance under various scenarios. Modern ML tools already automate much of this analysis, producing actionable insights with minimal human intervention beyond validating assumptions.

BLS evidence: They analyze data traffic and system performance to determine future upgrades.

Test networks during implementation to check for performance issues

AI can execute automated test suites, analyze packet captures, measure latency/throughput metrics, and identify performance bottlenecks from monitoring data. Most routine performance testing can be scripted and AI-analyzed, with humans reviewing summary reports and handling edge cases.

BLS evidence: They test the equipment and the network during all stages of implementation to check for slowdowns, blackouts, or points of failure.

Research and recommend new technologies for network performance

AI excels at scanning technical literature, comparing vendor specifications, benchmarking performance data, and synthesizing technology trends. It can produce comprehensive research reports and recommendations, though humans still add value in understanding organizational fit and vendor relationships.

BLS evidence: Computer network architects research and recommend new technologies for network performance.

Design and create plans for data communication networks

AI can generate network topology designs, calculate bandwidth requirements, and propose architecture patterns from requirements documents. However, complex enterprise constraints, political considerations, and novel use cases still benefit from human architectural judgment and stakeholder negotiation.

BLS evidence: Computer network architects design and implement data communication networks, and create plans and layouts for data communication networks.

Manage networks and troubleshoot issues that arise

AI-driven network management tools can detect anomalies, diagnose common issues, and execute remediation scripts. However, novel problems, security incidents, and high-stakes troubleshooting still require human expertise to interpret context and make judgment calls about interventions.

BLS evidence: After deployment, they also may manage the networks and troubleshoot any issues that arise.

Present network designs to management, customers, and staff

AI can generate presentation slides, visualizations, and talking points from technical designs. However, reading the room, handling challenging questions from executives, building consensus among stakeholders, and adapting messaging in real-time requires human social intelligence and credibility.

BLS evidence: Computer network architects typically present designs to management, customers, and staff.

Coordinate with IT workers to ensure networking needs are met

AI can facilitate scheduling, track requirements, and flag conflicts, but coordination involves negotiating priorities, managing personalities, understanding unspoken organizational dynamics, and building trust across teams—social tasks where AI assists but humans remain central.

BLS evidence: Some computer network architects work with other IT workers, such as network and computer system administrators and computer and information systems managers, to ensure that an organization's networking needs are being met.

Upgrade hardware and software to support computer networks

AI can identify compatible hardware/software versions, generate upgrade procedures, and flag potential conflicts. However, executing upgrades in production environments requires physical access to equipment, real-time troubleshooting of unexpected issues, and risk judgment that organizations won't fully delegate to AI.

BLS evidence: Upgrade hardware, such as routers or adaptors, and software, such as network drivers, as needed to support computer networks.

Deploy and configure network equipment and infrastructure

Physical deployment requires hands-on installation of routers, switches, and cabling in data centers and buildings. While AI can generate configuration files and scripts, the actual racking, cabling, and physical troubleshooting requires human presence and manual dexterity in unpredictable physical environments.

BLS evidence: As part of the implementation process, network architects deploy and configure network equipment.