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AI is likely to take on some repeatable Level 1 support tasks, but the available evidence does not show that it will eliminate the whole role. Routine ticket triage, standard troubleshooting guidance and documented fixes are exposed. People will still be needed for unclear incidents, user communication, escalation, accountable decisions and hands-on work. The practical outlook is a changing task mix, not a proven count of jobs that will disappear.

What “Level 1” means—and why the answer varies

“Level 1” is an employer-defined support tier, not one standardized occupation. A job with that title might focus on user support, network support or basic systems operations. The U.S. Bureau of Labor Statistics (BLS) categories for computer user support specialists, computer network support specialists, and network and computer systems administrators are useful reference points, but they do not map perfectly to every employer’s job description. BLS’s occupational utilization factors discuss pressures affecting these roles, not a universal forecast for all Level 1 jobs.

That distinction matters: evidence that AI can affect particular tasks does not establish how many positions an employer—or the industry—will remove. The available sources do not provide a directly comparable forecast isolating Level 1 system and network engineers across countries, company sizes and time horizons.

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Which Level 1 tasks are most exposed to AI?

Work is more exposed when it is frequent, predictable and can be resolved using approved instructions or a documented workflow. Examples include scripted account guidance, standard software setup instructions, common connectivity checks, repetitive ticket categorization, knowledge-base retrieval and routine status updates. These are task-level examples consistent with BLS’s identification of generative AI and routine-task automation as factors affecting some occupations; they are not a measured list of tasks already eliminated.

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BLS also identifies offshoring among factors that may reduce the share of computer user support specialist work, and routine-task automation and substitution by adjacent roles among factors affecting network and computer systems administrators. Those factors describe possible shifts in occupational utilization. They do not mean an entire occupation is going away.

What is harder to automate completely?

Support becomes less predictable when a problem crosses systems, lacks useful documentation or depends on a user’s circumstances. Diagnosing an unusual outage, coordinating permissions and approvals, deciding whether a change is safe, verifying a fix in context, and escalating with a clear account of what has been tried all require more than retrieving a standard answer. Frustrated users also need communication and expectation-setting, while some incidents require physical access or intervention.

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This is a reasoned distinction based on the breadth of skills in the IT Support Technician profile published by the AI Workforce Consortium and evidence that support responsibilities are changing; it is not a claim that each task has been tested for its automation rate. The profile includes hardware, software installation, networking, operating-system troubleshooting, documentation, communication, customer service and problem solving, alongside emerging skills such as AI diagnostics, automated troubleshooting, predictive maintenance, remote-support AI and ticket intelligence. The consortium’s 2025 report presents these as skills associated with the role, not a guarantee of employment.

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What the workforce numbers do—and do not—say

Two sets of figures illustrate why the change should be treated as a shift in work rather than a settled forecast for IT support:

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  • Gartner: In a survey of 321 service and support leaders worldwide conducted in September–October 2025, 85% said they were expanding frontline agent responsibilities, while 31% had implemented or planned AI-related frontline layoffs through the first quarter of 2027. These findings cover customer service and support leaders broadly, not specifically IT help desks or network engineering teams. Gartner reported the survey on April 28, 2026.
  • World Economic Forum: Its Future of Jobs Report 2025 estimates that AI and information-processing technology will create 11 million jobs and displace 9 million by 2030 across the labor market. This is not a forecast for IT support. The same report says 77% of surveyed employers plan to reskill or upskill existing workers by 2030. The jobs outlook and workforce strategies sections describe broad employer expectations, not measured outcomes for this job title.

These figures come from different populations and methods, so they should not be combined into a single prediction. BLS likewise cautions that its 2025–2035 projections describe expected outcomes under specified assumptions, not a certain future; actual results can differ. Its release notes that AI may also support demand for workers who develop or maintain AI systems and infrastructure. Read the BLS projections release, published August 27, 2026.

Should you still pursue entry-level IT support?

It can still be a useful entry point if you treat it as a way to build diagnostic judgment and technical range—not as a career built only around repeating scripts. A role dominated by high-volume, standardized fixes faces more task-level automation pressure than work involving broader system context, user coordination and operational responsibility. But demand depends on the employer, geography, outsourcing, cloud migration, regulation, user population and systems in use; the available evidence does not give a precise Level 1 forecast by country or employer type.

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When comparing roles or planning a next step, consider:

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  • Repeatability: How much of the work follows a stable script, and how often do problems require investigation?
  • Scope and accountability: Does the role build experience with identity, endpoints, networks, change control or other decisions requiring careful access?
  • People skills: Will you practice explaining problems, setting expectations and coordinating with users or other teams?
  • Technical depth: Can you develop networking, operating-system, cloud, security and scripting skills that support more complex work?
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How to prepare for AI-enabled support work

  1. Build a repeatable diagnostic method. Gather symptoms, reproduce the issue where possible, isolate likely causes, record what you tried, verify the result and escalate with useful evidence.
  2. Strengthen the fundamentals. Practice networking concepts, Windows and Linux basics, software installation, remote-support workflows, and account and endpoint concepts. These align with skills listed in the AI Workforce Consortium’s IT Support Technician profile.
  3. Learn to use AI without treating its answer as proof. Use approved tools to draft explanations or find relevant knowledge, then check suggestions against your organization’s procedures and system telemetry before acting.
  4. Develop communication alongside technical skill. A clear explanation, careful handoff and accurate documentation help users and the next person troubleshooting the incident.

This preparation follows the skills profile and task changes described above; no certificate, tool or individual skill guarantees a job.

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