Network and Computer Systems Administrators
Network and Computer Systems Administrators — AI exposure, safer roles, and a pivot plan.
Also known as: Information Analyst · Administrator (Admin) · E-Mail System Administrator · Information Systems Operator · Enterprise Systems Administrator · Application Systems Administrator
This score estimates how exposed the tasks in a role are to current and near-term AI capabilities. It does not predict whether a specific person will lose a job.
Most exposed tasks
Highest structured exposure values in this role’s task mix — the work AI systems can already do most of.
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Maintain logs related to network functions, as well as maintenance and repair records.86
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Gather data pertaining to customer needs, and use the information to identify, predict, interpret, and evaluate system and network requirements.86
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Analyze equipment performance records to determine the need for repair or replacement.84
Most durable tasks
Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.
The current data release does not distinguish durable tasks for this role.
Task exposure values and classifications come from the versioned data release — they are structured data, not model output. Bars show exposure contribution relative to this role’s task mix.
What this means
A score of 74 puts Network and Computer Systems Administrators in the most-exposed quarter of analyzed occupations. In practice, exposure this high is about the mix: 20 of 20 analyzed tasks lean automatable, 0 augmentable, and 0 durable. The useful question isn’t “will AI take this job” — it’s which tasks go first, which get faster, and where to reposition time. That’s what the personalized report maps against your actual week.
One next move: audit how much of your week sits in the exposed tasks above — then shift time toward the durable set or investigate the adjacent roles below.
Lower-exposure adjacent roles
No adjacent role in the current data release is at least 10 points lower with ≥50% skill overlap — we don’t label anything “safer” unless the data supports it.
Labor-market context
- $99,130median wage
- 314,340employed
- 14,300annual openings
- -4.2%projected growth
Context only — labor statistics are not inputs to the exposure score. See methodology.
Your week probably doesn’t match the average
This page scores the occupation. The $9 Personalized Risk & Action Report scores your task mix — paste what you actually do and get your own score, confidence level, task matrix, human moat, and a 7/30/90-day plan.
Personalize my result — $9Related roles
Adjacent by skills or family — no exposure claim implied.
FAQ — Network and Computer Systems Administrators
- What does a score of 74 mean for a Network and Computer Systems Administrators?
- It means that, weighted across the 20 tasks we analyzed for this role, the task mix sits at 74 on a 0–100 exposure scale — in the most-exposed quarter of analyzed occupations. It measures task exposure to current and near-term AI capabilities, not the probability of losing a job.
- Which tasks in this role are most exposed to AI?
- The highest-exposure tasks are: Maintain logs related to network functions, as well as maintenance and repair records; Gather data pertaining to customer needs, and use the information to identify, predict, interpret, and evaluate system and network requirements; Analyze equipment performance records to determine the need for repair or replacement. Exposure is scored per task from structured data, not generated by a language model.
- Which parts of this job are most durable?
- The current data release does not distinguish durable tasks for this role.
- Is this score personalized to me?
- No — this page shows the occupation-level baseline. Two people with the same title often do different work. The $9 personalized report recalculates the score from the tasks you actually do and builds a concrete 7/30/90-day plan around them.
Score version jr-v1 · data release 2026.07.11-r1 · updated 2026-07-11 · baseline mapping: 20 of 20 tasks carry source-level provenance · methodology