Analyze science, engineering, business, and other data processing problems to implement and improve computer systems. Analyze user requirements, procedures, and problems to automate or improve existing systems and review computer system capabilities, workflow, and scheduling limitations. May analyze or recommend commercially available software.
22 of 22 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Test, maintain, and monitor computer programs and systems, including coordinating the installation of computer programs and systems.
AI: Fully automatable - Automated testing, CI/CD pipelines, monitoring, and deployment orchestration enable AI and automation to fully handle most testing, maintenance, and installation coordination for software systems.
Troubleshoot program and system malfunctions to restore normal functioning.
AI: Fully automatable - AIOps, automated root-cause analysis, runbooks, and auto-remediation systems can resolve many program and system malfunctions end-to-end and restore normal functioning without human intervention in most routine cases.
Train staff and users to work with computer systems and programs.
AI: Fully automatable - AI can create interactive training content, tutorials, simulations, and personalized learning paths that can fully deliver training at scale for many systems.
Develop, document and revise system design procedures, test procedures, and quality standards.
AI: Fully automatable - AI can automatically generate, update, and standardize system design, test procedures, and quality standards from specifications and code, automating the bulk of the work though organizations may perform final review.
Read manuals, periodicals, and technical reports to learn how to develop programs that meet staff and user requirements.
AI: Fully automatable - AI can ingest, summarize and synthesize manuals, periodicals and technical reports to derive development guidance and implement programs that meet documented requirements.
Prepare cost-benefit and return-on-investment analyses to aid in decisions on system implementation.
AI: Fully automatable - Given financial inputs and assumptions, AI can calculate cost–benefit and ROI analyses, run scenario models and produce decision-ready analytical reports reliably.
Expand or modify system to serve new purposes or improve work flow.
AI: Partial - AI can propose and implement code and configuration changes to expand systems but typically requires human oversight for integration, deployment, and organizational context.
Use the computer in the analysis and solution of business problems, such as development of integrated production and inventory control and cost analysis systems.
AI: Partial - AI can analyze business data, design prototypes, and generate integrated production/inventory and costing solutions, but complex domain knowledge and stakeholder validation still require humans.
Use object-oriented programming languages, as well as client and server applications development processes and multimedia and Internet technology.
AI: Partial - AI can generate and modify object-oriented code and build client/server and web/multimedia components, but lacks full autonomy for end-to-end development and deployment in complex, context-rich projects.
Consult with management to ensure agreement on system principles.
AI: Partial - AI can prepare recommendations, talking points and simulate consultations, but cannot fully replace human-to-human negotiation and executive buy-in activities.
Review and analyze computer printouts and performance indicators to locate code problems, and correct errors by correcting codes.
AI: Partial - AI tools can analyze logs and performance indicators and suggest and implement fixes for many common coding errors, but they struggle with complex, environment-specific debugging without human oversight.
Supervise computer programmers or other systems analysts or serve as project leaders for particular systems projects.
AI: Partial - AI can assist with scheduling, progress tracking, code review and reporting, but cannot fully replace human supervisors for people management, accountability, and nuanced leadership decisions.
Confer with clients regarding the nature of the information processing or computation needs a computer program is to address.
AI: Partial - AI can conduct structured intake, translate client needs into specifications, and assist requirements elicitation, but nuanced client interaction and judgment remain human-led.
Coordinate and link the computer systems within an organization to increase compatibility and so information can be shared.
AI: Partial - AI can design integration architectures, generate interface code and mappings, and automate much of linking systems, but cross-team coordination, deployment orchestration and governance need human management.
Assess the usefulness of pre-developed application packages and adapt them to a user environment.
AI: Partial - AI can evaluate off-the-shelf packages against requirements and perform many adaptation/configuration tasks, but final suitability judgments and complex customization often require human oversight.
Define the goals of the system and devise flow charts and diagrams describing logical operational steps of programs.
AI: Partial - AI can translate requirements into goals and produce flowcharts and diagrams automatically, but initial goal-setting and prioritization require stakeholder input and human decision-making.
Provide staff and users with assistance solving computer related problems, such as malfunctions and program problems.
AI: Partial - AI-driven help systems can resolve many software issues and guide users through troubleshooting, but handling complex malfunctions, on-site fixes or policy decisions still needs humans.
Determine computer software or hardware needed to set up or alter system.
AI: Partial - AI can recommend hardware and software configurations from requirements and constraints, but final determination often requires human validation for site-specific, compliance, and procurement considerations.
Analyze information processing or computation needs and plan and design computer systems, using techniques such as structured analysis, data modeling and information engineering.
AI: Partial - AI can perform structured analysis, data modeling and produce system designs from specifications, yet comprehensive information-processing planning still requires human judgment and stakeholder coordination.
Interview or survey workers, observe job performance or perform the job to determine what information is processed and how it is processed.
AI: Partial - AI can run surveys, analyze logs and recorded workflows to infer information processing, but it cannot fully replicate in-person interviews and contextual observations needed to capture tacit job details.
Specify inputs accessed by the system and plan the distribution and use of the results.
AI: Partial - AI can specify inputs and design result distribution and data flows from requirements and models, but real-world integration, policy and governance choices typically need human oversight.
Recommend new equipment or software packages.
AI: Partial - AI can analyze requirements and market data to recommend equipment or software packages, but human decision-makers remain necessary for procurement decisions, vendor negotiations and contextual trade-offs.