Computer Network Professionals

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Computer Network Professionals (ISCO-08 2523) score an average of 0.52 on a 0–1 exposure scale — more exposed than about 89% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 3 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Analysing, developing, interpreting and evaluating complex system design and architecture specifications, data models and diagrams in the development, configuration and integration of computer systems;”

Scores 0.58 on the 2025 scale. The task involves analyzing, developing, interpreting, and evaluating complex system designs and architecture specifications along with data models, which require both high-level technical skills and nuanced interpretation—areas where Generative AI can provide significant assistance. Similar tasks incorporating design, implementation, and maintenance, such as "Designing, implementing and maintaining teleinformatics technology recovery plans" (adjusted score 0.55), reflect the potential for AI to aid in structured and technical processes but not completely automate them due to the necessity for human oversight and contextual understanding. Additionally, "Developing technical specifications for road traffic safety" (0.475) and "Developing protection plans for national parks" (0.475) illustrate the balance between automation potential and required human insight in complexities that Generative AI cannot fully replicate. Given the highly structured nature of architectural specifications and designs and comparing with similar data-driven tasks like "Designing forms, reports, and surveys for statistical research" which had an adjusted score of 0.68 and "Analyzing and evaluating strategies of entities financially related to the organization" (0.625), this task inherently allows for more AI-driven assistance. Considering the work environment in a high-income country like Poland, where technological tools are widely available, an adjusted automation score of 0.62 reflects AI's capacity to facilitate analysis and development processes while still acknowledging the need for human expertise in interpreting and evaluating intricate system designs and architectures.

Moving fastest, 2023 → 2025

“Assessing and recommending improvements to network operations and integrated hardware, software, communications and operating systems;”

Model capability on this task changed by +0.26 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2523, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

No U.S. role resolves through the crosswalk for this occupation. Search the encyclopedia for the closest match →

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Computer Network Professionals sit at the 89th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Computer Network Professionals rank in the 89th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.13 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Analysing, developing, interpreting and evaluating complex system design and architecture specifications, data models and diagrams in the development, configuration and integration of computer systems;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Computer Network Professionals". https://singulariki.com/gradient/2523-computer-network-professionals.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.