Computer systems analysts

Prepare diagrams for programmers or engineers to use when building systems

AI can produce system diagrams, architecture charts, network topology maps, and technical specifications from requirements documents. Tools like GPT-4 with vision and code capabilities can generate UML diagrams, flowcharts, and technical drawings that programmers can directly use.

BLS evidence: Analysts prepare diagrams for programmers or engineers to use when building the system.

Write instruction manuals and train end users

AI can generate comprehensive instruction manuals from system specifications and create training materials with screenshots and step-by-step procedures. Current LLMs produce high-quality documentation requiring only light editing, and can generate training scripts from feature descriptions.

BLS evidence: Computer systems analysts write instruction manuals and train the systems' end users.

Calculate system requirements for memory, storage, and computing power

Calculating system requirements from workload specifications, user counts, and performance targets is highly algorithmic. AI can apply sizing formulas, reference vendor guidelines, and account for growth projections with minimal human oversight beyond input validation.

BLS evidence: Analysts calculate requirements for how much memory, storage, and computing power the computer system needs.

Use data modeling techniques to view processes and data flows

AI excels at creating data flow diagrams, process models, and entity-relationship diagrams from textual descriptions or existing documentation. Tools can automatically generate visualizations and identify bottlenecks, requiring human validation of business logic accuracy.

BLS evidence: Computer systems analysts use a variety of techniques, such as data modeling, to design computer systems and view processes and data flows.

Research technologies to determine if they would increase organizational efficiency

AI can rapidly scan technology landscapes, summarize emerging tools, benchmark performance claims, and map technologies to business processes. Current systems excel at research synthesis and efficiency analysis, requiring human judgment mainly for strategic prioritization.

BLS evidence: Analysts research different technologies to decide if they would increase the organization's efficiency.

Analyze costs and benefits of IT systems and upgrades

AI excels at financial modeling, TCO calculations, and comparative analysis of IT investments. Given specifications and cost data, current LLMs with code execution can produce detailed cost-benefit analyses requiring only human review of assumptions and final recommendations.

BLS evidence: Analysts analyze costs and benefits of IT systems and upgrades to help managers decide which, if any, to install.

Devise ways to add functionality to existing computer systems

AI can analyze existing system capabilities, identify integration points, and propose architectural modifications to add functionality. Code-aware LLMs can suggest specific implementation approaches, though humans validate technical debt and maintenance implications.

BLS evidence: Computer systems analysts devise ways to add functionality to existing computer systems.

Design new computer systems by configuring hardware and software

AI can generate system architecture designs, select appropriate hardware/software configurations, and produce detailed specifications from requirements. Tools like GPT-4 with technical knowledge can draft comprehensive designs, though human architects validate feasibility and organizational fit.

BLS evidence: Computer systems analysts design new systems by configuring hardware and software.

Test systems to ensure they work as expected

AI can execute automated test scripts, analyze test results, identify anomalies, and generate test cases from specifications. Current systems handle regression testing and functional validation well, though humans design test strategies and investigate complex failures.

BLS evidence: Computer systems analysts test systems to ensure that they work as expected.

Consult with managers to determine the role of IT systems in the organization

AI can synthesize information and suggest IT strategies, but determining organizational role requires nuanced understanding of business politics, culture, and stakeholder priorities that emerge through real-time dialogue. AI assists with analysis but humans drive the consultative relationship.

BLS evidence: Computer systems analysts typically consult with managers to determine the role of information technology (IT) systems in an organization.

Oversee the installation and configuration of new systems

Overseeing installation requires physical presence, real-time troubleshooting of hardware issues, coordination with multiple vendors and technicians, and judgment calls on unexpected compatibility problems. AI can provide guidance but cannot substitute for on-site supervision.

BLS evidence: Analysts oversee the installation and configuration of new systems and customize them for the organization.