Will AI replace Computer and Information Research Scientists?

Some of your tasks will be automated. People who use AI will have the edge.

Task exposure to today's AI

45/100

Moderate exposure

This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.

Get your personal score

Will AI replace Computer and Information Research Scientists?

Not entirely. Today's AI tools can take on about 45% of the typical work in this occupation, mostly: analyze problems to develop solutions involving computer hardware and software. The parts that stay human include: Apply theoretical expertise and innovation to create or apply new technology, such as adapting principles for applying computers to new uses.; Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by computers.; Analyze problems to develop solutions involving computer hardware and software..

People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.

Which tasks AI can take on

The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.

TaskAutomatable

Analyze problems to develop solutions involving computer hardware and software.AI already used 65%

Evaluate project plans and proposals to assess feasibility issues. 62%

Apply theoretical expertise and innovation to create or apply new technology, such as adapting principles for applying computers to new uses. 45%

Design computers and the software that runs them. 45%

Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by computers.AI already used 35%

Participate in multidisciplinary projects in areas such as virtual reality, human-computer interaction, or robotics. 23%

+ 9 more tasks (26% of the week) in the full test.

Your human edge

Apply theoretical expertise and innovation to create or apply new technology, such as adapting principles for applying computers to new uses.
Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by computers.
Analyze problems to develop solutions involving computer hardware and software.

AI tools to try

Coding assistants (GitHub Copilot, Claude Code, Cursor)ChatGPT / Claude for design and debuggingAI test and documentation generators

Related roles

A 3-phase AI learning plan

Phase 1

Pair programming with AI

Use a coding assistant for boilerplate, tests and refactoring.
Give it context: project rules, examples, small steps.
Review AI code like a colleague's pull request.

Project

Use a coding assistant to add a unit-test suite and refactor one research prototype of yours to 80% coverage, reviewing every diff and logging hours saved versus your own estimate.

Phase 2

AI in your systems

Call an LLM API with structured output.
Retrieval over your own documents or data.
Evaluate quality, cost and latency before shipping.

Project

Build a retrieval tool over your own 200-paper reference library that returns structured JSON method summaries via an LLM API, then benchmark answer accuracy, cost and latency on 30 test queries.

Phase 3

Production-grade AI

Evals and regression tests for prompts.
Security: prompt injection, data leaks, permissions.
Monitoring and cost control.

Project

Add an eval harness with 50 regression cases, prompt-injection tests and token-spend alerts to that retrieval tool, and report pass rate and cost per query before and after hardening.

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