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The AI Resilience Report helps you understand how AI is likely to impact your current or future career. Drawing on data from over 1,500 occupations, it provides a clear snapshot to support informed career decisions.
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The AI Resilience Report is a project from CareerVillage®, a registered 501(c)(3) nonprofit.
Mostly Resilient
Last Update: 4/23/2026
Your role’s AI Resilience Score is
57.8%
Median Score
Meaningful human contribution
Measures the parts of the occupation that still require a human touch. This score averages data from up to four AI exposure datasets, focusing on the role’s resilience against automation.
Low
Long-term employer demand
Predicts the health of the job market for this role through 2034. Using Bureau of Labor Statistics data, it balances projected annual job openings (60%) with overall employment growth (40%).
High
Sustained economic opportunity
Measures future earning potential and career flexibility. This score is a blend of total projected labor income (67%) and the role’s inherent ability to adapt to economic and technological shifts (33%).
High
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
Most data sources align, with only minor variation. This is a well-supported result.
Contributing sources
AI Resilience Report forComputer Systems Analysts
Computer Systems Analysts are somewhat more resilient to AI impacts than most occupations, according to our analysis of 7 sources.
This career is labeled as "Mostly Resilient" because while AI tools help with routine tasks like monitoring and bug detection, they can't replace the creativity and judgment needed for designing systems, choosing technology, and leading projects. AI can assist analysts by speeding up problem-solving and testing, but human insight is crucial for making complex decisions and ensuring quality.
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View analysisChat with CoachLatest newsMore career infoAnalysisChatNewsMoreThis role is mostly resilient
This career is labeled as "Mostly Resilient" because while AI tools help with routine tasks like monitoring and bug detection, they can't replace the creativity and judgment needed for designing systems, choosing technology, and leading projects. AI can assist analysts by speeding up problem-solving and testing, but human insight is crucial for making complex decisions and ensuring quality.
Read full analysisAnalysis of Current AI Resilience
Computer Systems Analysts
Updated Quarterly • Last Update: 2/17/2026
How is AI changing Computer Systems Analysts jobs?
In practice, many technical parts of a systems analyst’s job are now aided by AI tools. For example, so-called “AIOps” platforms use machine learning to watch system performance and spot problems automatically [1] [1]. This means routine monitoring, testing and alerting can happen faster – the AI sifts through logs and metrics to flag issues like slowdowns or security threats.
New AI helpers also find bugs in code. For instance, OpenAI’s Aardvark can scan code for vulnerabilities and suggest fixes (though a person still reviews them) [2], and DeepMind’s CodeMender can even patch security bugs in code [1]. In testing work, generative AI can draft test scripts from simple prompts [1].
Early studies report that many tech teams already use AI in testing, but experts stress that humans must still “keep context and integrity front and center” [1] [1]. In one view, “AI should augment testers, not replace them” [1].
Other tasks remain mostly human jobs. Choosing new software or hardware, designing whole systems, training people, and leading projects involve creativity and judgment. We didn’t find examples of AI fully doing those tasks.
For now, analysts still do the research, planning and face-to-face teaching that machines can’t easily replicate. In short, simple data checking and problem-spotting are increasingly automated by AI tools, while strategy, design and supervision still rely on human insight.
Sources
How fast is AI adoption growing for Computer Systems Analysts?
AI tools for IT work are growing more available, but adoption will depend on costs, benefits and trust. On one hand, cloud AI services and open-source models make it easier to try AI for things like monitoring and testing. Many companies plan to invest more in AI soon – one report found over 60% of organizations intend to boost AI use in projects by 2025 [3].
This is because AI can improve efficiency (for example, fixing issues faster [1]) and help where skilled analysts are in short supply.
On the other hand, AI brings challenges. It can be expensive to set up good AI systems and to train people to use them. Some tech workers still worry about accuracy.
A recent survey noted that while most developers use AI tools daily, about one-third distrust AI-generated results [1]. In fields like systems work, mistakes can be costly, so teams are cautious. Finally, there are few big social or legal barriers in IT (unlike in medicine or law), so using AI is generally accepted, as long as human experts stay involved.
Overall, routine tasks are being automated quickly, but firms tend to roll out new AI tools carefully, keeping skilled analysts “in the loop” to handle complex decisions [1] [2].
Sources
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More Career Info
Career: Computer Systems Analysts
They improve how companies use computers by studying their systems, finding problems, and suggesting solutions to make everything work better.
Parent Careers
Similar Careers
Employment & Wage Data
Median Wage
$103,790
Jobs (2024)
521,100
Growth (2024-34)
+8.7%
Annual Openings
34,200
Education
Bachelor's degree
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
Task-Level AI Resilience Scores
AI-generated estimates of task resilience over the next 3 years
1
Use object-oriented programming languages, as well as client and server applications development processes and multimedia and Internet technology.
2
Use the computer in the analysis and solution of business problems, such as development of integrated production and inventory control and cost analysis systems.
3
Analyze information processing or computation needs and plan and design computer systems, using techniques such as structured analysis, data modeling and information engineering.
4
Expand or modify system to serve new purposes or improve work flow.
5
Train staff and users to work with computer systems and programs.
6
Test, maintain, and monitor computer programs and systems, including coordinating the installation of computer programs and systems.
7
Provide staff and users with assistance solving computer related problems, such as malfunctions and program problems.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.
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